Implementation of Lean Manufacturing in aLow-Volume Production Environment

By

Garret J. Caterino

B.S. Mechanical Engineering, Worcester Polytechnic Institute, 1993

Submitted to the Sloan School of Management and the Department of Mechanical Engineeringin Partial Fulfillment of the Requirements for the Degrees of

Master of Science in ManagementAnd

Master of Science in Mechanical Engineering BARKER

In conjunction with the Leaders for Manufacturing Program at the F sMassachusetts Institute of Technology Jut

June 2001 -

@ 2001 Massachusetts Institute of Technology. All rights reserved.

Signature of Author

I/1

Sloan School of ManagementDepartment of Mechanical Engineering

May 11, 2001

Certified by

Certified by

Accepted by

Accepted by

James M. Utterback, Thesis SupervisorProfessor of Management and Engineering

David E. Hardt, Thesis SupervisorProfessor of Mechanical Engineering

Margaret AndreWs, Exdcutive Director of Masters ProgramSloan School of Management

Ain Sonin, Chairman, Department Committee on Graduate StudiesDepartment of Mechanical Engineering

* - ~C~rr

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Implementation of Lean Manufacturing in aLow-Volume Production Environment

By

Garret J. Caterino

Submitted to the Sloan School of Management and the Department of Mechanical Engineeringon May 11, 2001 in partial fulfillment of the requirements for the Masters of Science in

Management and the Masters of Science in Mechanical Engineering.

AbstractLean Manufacturing is a powerful method to improve a manufacturing environment. Movingbeyond the more traditional Lean settings where high manufacturing volumes and "part"production are often common elements, the use of Lean techniques for a low-volume final-assembly application was explored in this work.

Instron Corporation was utilized as a research setting to develop and demonstrate theimplementation of these Lean techniques to their final assembly operations. Challenges for thisproject included 1) reducing the production throughput time of Instron's Electro-Mechanical andHardness material testing products and 2) providing greater assembly flexibility to handlevariations in customer orders. A framework of Lean Manufacturing techniques was specificallyoutlined for a low-volume environment. Both the physical assembly environment and workprocesses were analyzed as a system. Revised assembly area layouts, standardized workprocedures, point of use (POU) inventory, worker cross-training, organized kanban card-driveninventory re-supply policies and kanban-driven assembly procedures were proposed andimplemented.

Improvements were realized through reductions in assembly throughput time and variationreductions in these times. In addition, greater visibility and control of the assembly processes forboth assemblers and management on a day-to-day basis was achieved. Beyond improving theassembly process, the research demonstrated the importance of integrating inventorymanagement with the defined assembly process. Results from a revised inventory policyrevealed potential reductions in inventory and improved vendor coordination. Overall, resultsfrom this research effort proved that Lean Manufacturing techniques can successfully be adaptedto low-volume assembly environments. Further, the methods outlined in this project can be usedas a process roadmap to achieve similar improvements in other low-volume assembly areas.

Thesis Supervisor: David E. HardtTitle: Professor of Mechanical Engineering

Thesis Supervisor: James M. UtterbackTitle: Professor of Management and Engineering

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Acknowledgements

I would first like to acknowledge the support and resources provided by the Leaders forManufacturing (LFM) Program. The past two years have been an incredible experience, and Iwould like to thank everyone involved in creating this unique program and the education itprovides.

I would also like to thank Dave Hardt and Jim Utterback, my LFM project advisors, for theirsupport and guidance through the internship process. During numerous visits to Instron, theygreatly helped in analyzing the needs of the company and making suggestions to implementlasting changes within Instron's environment. They also provided clear direction and insight intomaking this thesis a worthwhile reference for implementing Lean methods in similar low-volumeenvironments.

At Instron, I would like to thank Bill Milliken, Vice President of Manufacturing, for sponsoringthe project and providing the funding to make the project a success. I would also like to thankKerry Rosado for his time in supervising the project and setting its direction and objectives.

The Instron process improvement management team members also deserve thanks for theirefforts and willingness to explore new production and inventory management methods. Teammembers include Marc Montlack, Paul Meroski, Paul Carmichael, Len Travers, Scott MacEwen,and Peter Paska. Additional thanks must also be given to all of the technicians on the factoryfloor who provided insight into the proposed work process changes and who took an active partin implementing the new processes.

Outstanding administrative and purchasing support was always available during the project, forwhich thanks must be given to Jan Masterson, Ron Mills and Phil Hood. Last, I greatlyappreciated the time for numerous conversations with and recommendations from Brad Monroe,Vice President of Purchasing, and Jud Broome, Director of Parts and Service. Such candidconversations provided much insight into the work conducted during the project term andbeyond.

Finally, I would like to dedicate this work to my wife Debby, for her unending support andcommitment through the past two years. Her love and companionship make all of these effortsworthwhile.

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Table of Contents

Title Page I

Abstract 3

Acknowledgements 5

Table of Contents 7

1. Introduction 91.1 Thesis Objective 91.2 Lean Transformation: Prepare the Environment then Implement Process Changes 91.3 Instron Corporation as the Research Environment 10

1.3.1 Electromechanical and Hardness Testers - Examples of Low Volume Products 111.3.2 Original Project Perspective 121.3.3 Resulting Project Goals for Instron 121.3.4 Pilot Process to Exemplify New System 13

1.4 Summary of Thesis Chapters 13

2. Lean Manufacturing and its Application in a Low-Volume Environment 152.1 Lean Manufacturing Introduction 152.2 Key Concepts of Lean Manufacturing 16

2.2.1 Adding Value and Removing Waste 162.2.2 Implementing Flow in a Production Process 172.2.3 Implementing Pull in a Production Process 19

2.3 System Implementation and Management Influence 192.4 Review of Prior LFM Lean Manufacturing Thesis Research 202.5 First Look at Instron - Identifying Opportunities for Improvement in a Cyclical

Low-Volume Environment 222.6 Cost of Non-Optimized Process 252.7 Lean Manufacturing for a Low-Volume Manufacturer 27

3. Process Selection and Layout Design of a Manufacturing Environment 293.1 Identification of Manufacturing Process 293.2 Decision Parameters to Design the Factory Layout 313.3 Instron Electromechanical/Hardness Assembly Process 32

3.3.1 Classification of Instron's Manufacturing Process 333.3.2 Process Proposal for Instron 343.3.3 Instron's Physical Factory Arrangement 353.3.4 Final Layout Proposal 36

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4. Component Inventory Stocking and Material Handling 394.1 Point of Use Inventory Placement 394.2 Failure Modes to Consider for Point of Use Inventory 39

4.2.1 Multiple Use Inventory - Optimized Stocking Locations 404.2.2 Material Handling Ownership and Control 40

4.3 Integrating Point of Use Inventory with the External Supply Chain 414.4 How Point of Use Inventory is Managed at Instron 414.5 Materials Resource Planning vs. Pull Inventory Policies 424.6 Kanban Inventory Management at Instron 434.7 Combining Kanban and MRP Processes - Mixed Model Solution for Instron 44

5. Implementation of a Single Piece Flow Assembly Process 475.1 Process Flow Definitions 475.2 Process Implementation at Instron 48

5.2. 1Capacity Analysis 485.2.2 Level Loading the Assembly Schedule 505.2.3 Pull Production, Assembly Kanbans and Strategically Placed WIP 51

5.2.3. 1Kanban Quantity 535.2.3.2 Kanban Locations for Strategic WIP Placement 54

5.2.4 Decision Rules Govern Work Process 56

6. Alignment of Inventory and Manufacturing Processes 596.1 Setting Proper Inventory Control Measures-The Hidden Costs of Independent Metrics 596.2 Inventory Management Calculations 62

6.2.1 Frequency of Inventory Review 626.2.2 Determining the Minimum Reorder Points (ROP) 626.2.3 Lot Size Order Quantities: Should EOQ Theory Be Used? 63

6.3 Proper Inventory Level for Instron Electromechanical 666.3.1 Inventory Classified According to Distribution By Value Calculations 666.3.2 Example Minimum Level Calculations for Class "A" Part 67

6.4 Linearized Assembly Output Enables Inventory Reductions 69

7. Results and Recommendations 737.1 Results at Instron - Flow Time Decreased by 40% in Electromechanical Production 737.2 Additional Improvements at Instron 747.3 Sustaining the Process Improvements 757.4 New Models Arrive in Manufacturing 767.5 Comparison of the Low-Volume vs. the Original Lean Manufacturing Process Goal 777.6 Future Recommendations for Continuous Improvement 79

Appendix A: Data Timesheets 81

Appendix B: Labor Capacity Model 83

Appendix C: Inventory Analysis Model and Spreadsheets 85

Annotated Bibliography 91

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/ INTRODUCTION

Lean Manufacturing is a powerful method used to make lasting improvements in a productionenvironment. Moving beyond the more traditional Lean settings where high manufacturingvolumes and "part" production are often common elements, the use of Lean techniques in a low-volume final-assembly application was explored in this work. Instron Corporation was utilizedas a research setting to develop and demonstrate the implementation of numerous Leantechniques. Results from this research effort proved that Lean Manufacturing techniques can besuccessfully adapted to such low-volume environments to provide improvements in throughputtime, product output flexibility, and coordination of inventory requirements.

1.1 Thesis Objective:The objective of this thesis was to develop a practical methodology to improve theresponsiveness and flexibility of a low-volume assembly process that experiences an inherentlycyclical demand pattern. Using the elements of Lean Manufacturing as a basis for improvement,a framework of selected Lean techniques was proposed for such a low-volume process thatwould specifically provide:

1. Reductions in assembly throughput times to allow manufacturing to become a strategicmethod in improving customer order responsiveness

2. Increased production flexibility to allow multiple product variants to be produced usingone standardized production process

3. Increased consistency of output quantity per unit of time4. Increased coordination of inventory levels to both statistically satisfy manufacturing

demands and maximize inventory metrics

The reader is encouraged to use the framework in similar environments to achieve comparableprocess improvements. Numerous functional examples and descriptions from Instron'simplementation are outlined in detail to provide direction in applying this process. Criticalanalyses of Lean methods and the problems encountered during the pilot development andimplementation process are also explained to minimize similar encounters in future LeanManufacturing implementations.

1.2 Lean Transformation: Prepare the Environment then Implement Process ChangesIn embracing Lean methods in an assembly process, both the physical manufacturingenvironment and work processes must be considered as a complementary system. Often theexisting physical manufacturing environment must be modified first to more fully accommodatea new planned process. Both the process and environment of a low volume manufacturer wereanalyzed to reengineer them as a system in this project (Hammer, 1992). To better explain theprocess in detail, the general methodology was organized into four phases as outlined below.

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Step One: Identify an optimal work processThe existing work methods were analyzed to determine how well they satisfied the desiredmanufacturing process performance metrics. For example, in this research example, metricsincluded assembly throughput time and the consistent "heartbeat" of production output. Oncethe gap between desired and actual performance was measured, process improvements using thebuilding blocks of Lean Manufacturing were outlined to prepare an assembly processmethodology that would provide improvements to the chosen metrics.

Step Two: Reorganize the physical plant floor layout and supporting structuresThe layout of the factory floor was physically rearranged to reflect the chosen assembly process.Decision parameters were derived to guide the arrangement, again placing emphasis on thetargeted process output metrics as well as with consideration to the physical design of theproducts being produced. For manual assembly operations, actions included aligning assemblystations to facilitate production flow, moving inventory locations adjacent to the factory floor,standardizing material handling, and removing physical barriers that reduce teamwork andcommunication within product lines.

Step Three: Implement the desired work processHaving organized the physical environment, the revised work process using the chosen leanmanufacturing structure was implemented. Techniques such as pull-based demand production,strategic kanban placement, development of production decision rules to govern the workprocess, and worker coordination and training were initiated. Further, a combination of temporalassembly strategy, increased labor flexibility, and the creation of a more visually controlledenvironment were additional action items implemented.

Step Four: Align inventory management with assembly processOnce production demands were established as part of the process, inventory management wasalso restructured to provide quantities that statistically fulfills such production demands. Theinternal demands and external suppliers were then coordinated based on these statistical needs.

1.3 Instron Corporation as the Research Environment:Instron Corporation, headquartered in Canton, MA, is a manufacturer of materials testingequipment, software and accessories used to evaluate the mechanical properties of variousmaterials such as plastics, metals, textiles, composites, rubber, asphalt, microelectronicsmaterials, and ceramics. The company is viewed as the industry leader in materials testingequipment.

Instron's primary product offering is ElectroMechanical (EM) tensile testing machines. Instronhas also been adding to its original ElectroMechanical group through acquisition of additionalmaterial testing equipment companies. Many of these smaller acquisitions have been moved in-house to Instron's Canton, MA facility. The Wilson Hardness testing equipment group is oneexample of a recent acquisition. Manufacturing integration of acquired products with theirexisting production methods has become an issue for Instron, requiring a manufacturing processframework to apply across multiple product lines.

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Instron's manufacturing responsiveness to customer demands has become an important aspect oftheir competitive advantage. The creation of manufacturing Centers Of Excellence (COEs)throughout its worldwide facilities has elevated the importance of such responsiveness to theirworldwide demand. Creating these COEs has been a process to consolidate assembly of eachspecific product family to one of Instron's worldwide locations. Given a product is produced ina COE, that center supplies the respective worldwide demand. Canton, MA has been designatedas the COE for Electromechanical and Hardness testing machines, requiring productioncapabilities that are dedicated to providing fast and increasingly accurate order response for allworldwide customer orders.

1.3.1 EM and Hardness Testers - Examples of Low Volume Products:Three Instron product families that are assembled in the Canton location are used as examples todemonstrate the assembly process framework developed in this project. Electromechanical (EM)(single- and double-column) and Model 2000 hardness testers are the foundations for three ofInstron's complete testing systems. Material gripping devices and accessories are added to theseframes to create total system solutions for customers' material testing needs. They are thecompany's highest profit-generating products.

The Electromechanical products are used for tensile and compression materials testing. Productvariants differ according to testing capacity, ranging from 2KN to over 50KN, with eight modelsin this range included in this project. A typical double-column Electromechanical machine isshown in Figure 1. Main subassemblies include the base tray module containing the system'selectronics, vertical columns providing motion through electric motor-driven lead screws, amoving crosshead mounted between the columns that carries the load measurement cell,electronic controller interface, and accessories. Each machine is highly configured to customerspecifications, including load capacity, working height and width, accessories, and software.

Hardness models are used to analyze material surface hardness through the application of surfacecompression forces. Model 2000, shown in Figure 2, is configured from three options of verticalsize and various load ranges. Similar in design to the Electromechanical products, the mainsubassemblies for this model include the base tray which houses the electronics, vertical actuatorwith leadscrew design, frame, loadcell, and controller interface.

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Figure 1: Model 2000 Hardness Tester Figure 2: Double Column ElectromechanicalTensile/Compression Tester

1.3.2 Original Project Perspective:Instron's original project objective can be stated as "Create a manufacturing environment thatcan produce any model of Electromechanical or Hardness tester on demand with little or nodelay time." In response to this goal, Instron originally conceptualized a single assembly line tocombine all production of Electromechanical and Hardness products, with testers simplycompleted in the order of customer demand. Although the initial approach was broad, it did setthe expectations of creating an environment that would fulfill customer demands in a moretimely manner relative to current methods.

1.3.3 Resulting Project Goals for Instron:The analysis and active change of Instron's physical manufacturing environment and assemblyprocesses was used in this thesis to demonstrate implementation of the Lean Manufacturingframework.

There were three project goals established specific to Instron's process. First, reduce assemblythroughput times in the final assembly operations. Second, transform the physical assemblyenvironment and work process to better leverage the commonality between EM and Hardnessassembly platforms and common parts usage to increase flexibility of output. Third, establish aninventory policy to better coordinate in-house inventory levels with manufacturing demands,including revising internal inventory management and improving the coordination with externalsuppliers' processes.

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The framework of Lean Manufacturing techniques, including the use of Kanban control inassembly, daily production schedules based on the demand rate, and decision rules to guide thework process, enabled these goals to be accomplished. Managers and employees wereencouraged to sustain the process by using these techniques once the project term wascompleted. This enabled such techniques to be used long-term to improve manufacturing'scustomer order responsiveness, aligning with the corporation's "On-Time" metric that measuresthe timely performance of product shipped to the customer.

1.3.4 Pilot Process to Exemplify New System:The work completed during this project focused on improving the performance of three selectproduct families at Instron. The project's focus was limited to these products to prove out thenew concepts with the expectation of expanding the learning and general process frameworkfrom this project to the other assembled products within Instron.

1.4 Summary of Thesis Chapters:This chapter provides an overview of the thesis objective and Instron project goals. A four-partprocess using Lean Manufacturing is outlined to realize process improvements in a low-volumeenvironment. Instron and its products are then briefly described as the research environmentused to demonstrate the implementation process.

Chapter 2 discusses the principles of Lean Manufacturing, including a brief history of its origins,its evolution and its current applications. Further, prior LFM research in Lean Manufacturing isoutlined with a description of the extension of such research into Instron's unique low-volumeenvironment. Instron's manufacturing process issues are identified and explained. Finally, aproposal to implement Lean Manufacturing techniques to improve Instron's manufacturingprocess is diagrammed.

Chapter 3 describes the details of assembly process identification and selection. Instron'smanufacturing environment and process are analyzed, and the most appropriate process isidentified. Physical changes required to the work environment to support a chosen leanmanufacturing process are described. Decision parameters are then provided to assist in creatingthe desired environment.

Chapter 4 outlines improvements to materials inventory coordination, including changes in thephysical production environment and material handling methodology. Chapter 4 furtherdiscusses problems in each respective area and how each require close coordination withmanufacturing's metrics.

Chapter 5 outlines the process used to implement assembly process improvements. Demandcapacity analysis, single piece product flow analysis using assembly kanbans, level loading thedemand schedule, and using a decision rule framework to govern the daily work structure areincluded

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Chapter 6 provides a model for inventory analysis. Inventory policy alignment with both theparent manufacturer and its suppliers is outlined. Further, Chapter 6 outlines the benefit ininventory reductions that can be realized by implementing a linear, lean production process.

Chapter 7 provides results and conclusions of the work performed during the project term. Thesimilarities and deviations of this low-volume Lean implementation vs. a more traditional LeanManufacturing environment are outlined. Solutions to sustain the process and recommendationsfor future work are also summarized.

Appendix A includes a sample data timesheet used to collect factory process data.

Appendix B outlines a labor capacity model generated from process data and output demands.

Appendix C outlines an inventory analysis model and required spreadsheets to determineinventory quantities required to support a given output demand level.

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LEAN MANUFACTURING AND IT'S APPLICATION IN A2 LOW- VOLUME ENVIRONMENT

An overview of Lean Manufacturing, including a brief history of its evolution, an explanation ofthe methods through demonstrating the Toyota Production System, and a review of relevant priorLeaders for Manufacturing research is provided in this chapter. Process limitations in a low-volume cyclical environment, using Instron's original assembly process as the research example,are also described. The costs of such limitations are explained to show just how much theselimitations are reducing the potential gain from production's output. Finally, a process overviewto implement numerous Lean Manufacturing techniques in Instron's low-volume environment isoutlined.

Lean techniques can and should be extended to many different functions of an organizationbeyond manufacturing, including marketing and sales, product development, and purchasing.However, for purposes of brevity and clarity, only those processes involving manufacturing arecovered within this chapter.

2.1 Lean Manufacturing IntroductionThroughout the 1990's and into the current decade, there has been great effort in makingsignificant improvements to the processes used in manufacturing. Lean Manufacturing, asoutlined by the Toyota Production System and described in such leading books as Lean Thinkingand The Machine That Changed the World, provides the manufacturing world with better waysto produce products. These methods lead to incredible reductions in human effort, inventorylevels, manufacturing floor space, and overall complexity. Lean production techniques are thebasis for improvement efforts conducted at Instron Corporation, and provide the background forwork described in this writing.

Where did Lean Manufacturing originate? The Toyota Production System (TPS), in essence theoriginal Lean Manufacturing method, was born in Japan out of necessity. In the Post-WWII era,Japan was in a financial and economic recovery mode that did not allow them to replicate thecapital-intensive automotive production methods of the western world. Nor was theirproductivity in line with the western world - it was 1/8 of that in the United States. However,Japan had growing needs for low cost transportation of diverse vehicle types, from small cars tolarge trucks. Furthermore, Japan's post-war workforce was controlled by American-installedlabor laws. These laws strengthened the position of the Japanese workers and called foremployers to acknowledge these increased rights in employment positions, removing the abilityto continue placing workers in low-paying, low-skill jobs. Working within these bounds, Toyotaset out under the direction of Taiichi Ohno to create a production system that used workers totheir fullest potential and minimized capital investment requirements. (Ohno, 1988)

Ohno created a system that removed all wasteful actions and uses multi-skilled workers toproduce varieties of products on demand. Further, this system was designed around quality, with

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quality designed and assembled into the product instead of adding additional actions to ensure itspresence. These main tenets of Lean Manufacturing and TPS are further explained in thefollowing summary.

2.2 Key Concepts of Lean Manufacturing:Although there are numerous definitions of Lean Manufacturing, there are three major conceptswithin the implementation of Lean Manufacturing Processes (Suri, 1998):

Elimination of waste, including wasteful non-value added actions and process stepsImplementation offlow to smooth production processesImplementation ofpull to produce only when product is needed

These lean production concepts are combined into a manufacturing environment that uses ahighly trained workforce to produce products in wide varieties when demanded. Each concept isnow explained in more detail.

2.2.1 Adding Value and Removing Waste:Lean Manufacturing begins by identifying which efforts and actions in a given process definevalue in the end product. Value, in this context, is defined in terms of customer value, whetherthis is the end use customer or the next activity in a given process. Lean Manufacturing then setsout to redefine a given process to only include those steps that add value. Any additional stepsthat are classified as non-value added are considered waste and must be removed. LeanManufacturing techniques systematically eliminate or at least reduce waste, leading to reducedcycle times and reduced costs (Jones and Womack, 1996).

When mapping the value of production steps, each step can be classified into three categories.The first is full value-added, meaning the step creates value in the final product. The secondcategory is a step deemed necessary to complete the process but which does not directly createvalue in the product (termed Type I Waste). Type I waste must be analyzed withrecommendations to minimize their financial and temporal costs. Finally, there is the step(s) thatcreates no value at all and should be removed immediately from the process (Type I Waste).

Within the Toyota Production System methods, wasteful actions and methods are grouped intoseven major categories, as outlined below (Suri, 1998):" Overproduction - Producing quantities that are not needed, visible as undemanded finished

goods." Inventory - Producing semi-finished parts between process steps (WIP) that remain unused

for extended periods of time. Purchased components that are held in inventory for extendedtime periods are also forms of waste.

" Transportation - Moving parts within and outside of the factory, including moving materialbetween factories and to different functional processing areas within the bounds of onefactory.

" Processing - Unnecessary machining/assembly/test steps within a manufacturing sequence.

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" Defects - Parts produced that need additional rework or are scrapped due to excessivepresence of defects. Manufacturing quality product by actively preventing defects is moreeffective in time and cost than defect repair.

" Motions - Unnecessary worker movement on the assembly line, unnecessary roboticmachine motion, or unnecessary transportation are included in wasted actions.

" Waiting - Workers with excess time, waiting for either machines to complete their operationsor parts to be completed. Workers should not have to wait for the machine, rather theutilization of the workers should be maximized - the machine is considered to be free.

In addition to the seven types of tangible wastes listed above, several attributes of waste inproduction systems cannot be so easily quantified. Job complexity, shop floor andinterdepartmental confusion, lack of engineering support for new product introductions, orderexpediting, rework and repair of nonconforming parts, and worker motivation are examples.Some, such as shop floor confusion and order expediting, are actually effects of more traditionalmanufacturing practices due to extended lead times and multiple jobs waiting in any given workcenter. Improvements must be considered for these qualitative measures as well.

2.2.2 Implementing Flow in a Production Process:Once waste is eliminated from a process (or waste-reduction goals are established), theremaining production steps are arranged in such a way to focus on the specific product'smanufacturing requirements as a system. In contrast, traditional arrangements focus on thefunction of each process step, and tend to group such similar process functions together. Refer toFigure 3 for visual comparison of these two methods. In a product focused arrangement, all ofthe required resources to assemble the product are physically arranged adjacent to each other in asingle area (loop, line, or cell), allowing each step to be processed in required order with limitedbackflows or stoppages between process steps. This mentality calls for disregard of the previousboundaries between functional processes. Physical re-arrangement of the production processfrom grouped functional equipment to lines that include elements of each functional category ismost often required. Results allow a product to be produced with a continuous "flow" ofactivity.

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Product IFunction Process1 Function Process2 FlowLine

Product 2Flowline

-0 Product 3FlowLine

-- Product 4 -Function Process3 Function Process4 FlowLine

Functional Work Area Flow Line

Figure 3: Schematic of Functional vs. Flow Line Configurations

In the flow line configuration, product orders are introduced into the manufacturing process oneat a time and completed at a constant rate. Each process step or subassembly is completed forthat product in order, with little or no WIP stored between steps. Capacity for the process iscalculated based on the total takt time. Takt time is the time per unit per process step based oncustomers' demand rate. It is the inverse of cycle time. The pace of production is therefore setbased on the pace of sales. Work is evenly distributed in each process step to allow product toenter and exit the flow process based on consistent takt time increments.

The workforce must become increasingly flexible within a flow process. Since dedicatedfunctional departments are removed, the people who operated under the old functional realmmust be retrained to gain knowledge of all steps in a production process. Any member of theworkforce will then be able to be moved to any stage of the production line when needed.Personnel performance measures must also be realigned with the flow process, rewardingadditional training and the broad knowledge of multiple areas as opposed to rewarding functionalexpertise in narrowly defined manufacturing categories.

Finally, production "flow" is based on a constant rate of average production. In environmentswhere the assembly time varies with product type and manufacturing complexity, the demandsplaced on the process must be actively managed to maintain average production rates. Levelingthe production schedule helps to accomplishes such average rates. Product variants should bestaggered in the production queue based on required assembly times, smoothing perturbations inthe order queue to achieve consistent average output times.

Leveling production is best explained through an example. Assume three configurations of aproduct, X, Y, and Z that are demanded in equal amounts and require 2, 4 and 6 hoursrespectively for assembly. If these products were assembled by batch building in lots of threeper day, such as XXX YYY ZZZ, the assembly process would take only 6 hours the first dayassembling the X configurations (leaving excess capacity) but would require 18 hours on the

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third day assembling the Z configurations (requiring greater capacity). Assembling in such amanner causes delays in producing configurations Y or Z and also places nonlinear demands oncomponent inventory levels. Delayed shipments (assuming demanded in equal amounts) andincreased levels of inventories to cover cyclical part demands result. Leveling calls for theassembly order XYZ XYZ XYZ, linearizing labor requirements each day, smoothing inventorydemands, and allowing earlier product shipments of each variant.

Tactics, such as setting "flex ranges" of acceptable order variations to which sales and marketingmust agree, are used to maintain consistent flow by putting constraints on the expectations of themanufacturing system. Such flex ranges limit the extent of leveling required. Level productionalso smoothes upstream production and decreases inventory pile-up throughout themanufacturing facility, shortening the overall average lead-time, throughput time, and reducingnon-value added activity.

2.2.3 Implementing Pull in a Production Process:Simply put, the concept of a pull process is "Ship one, build one" (Jones and Womack, 1996). Intheory, units are produced only when demanded, in effect using customer orders that removefinished units from the end of the process to initiate a "pull" of another unit into the finishedproduct area. In chain-like action all previous subassemblies are pulled through the assemblyprocess from end to beginning. By comparison, a traditional "push" process calls for schedulingproduction and building inventories at each production step. Using pull, each upstream stepproduces parts or subassemblies only when the downstream step demands additional parts orassemblies. At each step, supplied material is also pulled into the production process whenneeded, coordinated with vendors to deliver only in the quantities demanded at the times needed."Pulled" material is also known as just-in-time (JIT) delivery.

Information to initiate such pull-based manufacturing actions flows in the opposite direction ofthe material flow, often through the use of Kanbans. Kanban is the Japanese word for "sign" or"card". These cards are used on the factory floor to physically convey information aboutproduction flow. They signal what to produce, when to produce it, and what quantity of it toproduce. Overall, the goal of the pull system is to remove speculative production (that oftenresults in overcapacity or unfulfilled demand) and provide the ability to produce to actualdemands while reducing WIP levels and cycle times of each production step.

2.3 System Implementation and Management Influence:The above describes an ideal world within Lean Manufacturing of stable flow of product that ispulled by demand through multiple production processes. The key word here is "ideal."Although the concept is far superior to that of mass production, it must be understood that LeanManufacturing is a systems solution of continuous improvement that takes time to implementand refine. Further, it is very people-focused, and changing people's methods and attitudes to"see"l new solutions is often difficult to do. Implementation requires management to supportthis system as internal coaches, becoming the catalysts for change. Without high level support, itis difficult to develop a strong human infrastructure, potentially leading to functional areasembracing changes independently and realizing sub-optimized results.

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Traditional performance metrics are also modified using this process. These new metrics alsoneed to be embraced by the top management. Indicators of plant performance under a LeanProduction system are often measured by the following metrics, with lean plants being able toachieve high levels of all four metrics simultaneously:

1. Customer order responsiveness (reduced lead times)2. Productivity increase with cost reductions3. Flexibility in model output4. Quality improvements

Although stated briefly above, examples of each metric and their interactions will be displayedthroughout this writing.

2.4 Review of Prior LFM Lean Manufacturing Thesis Research:A number of Leaders for Manufacturing theses have been developed on the theory andimplementation of Lean Manufacturing processes. Through the past ten years, increasingcorporate awareness and desire to transform processes using Lean techniques has promptedmuch LFM research. Theses most relevant to the topic have been briefly outlined below.

Arthur Raymond studied the applicability of Lean Manufacturing to a low-volume fabricationfacility at the Boeing Company in 1992 with his thesis, "Applicability of Toyota ProductionSystem to Commercial Airplane Manufacturing." The work provided both a general overview ofTPS application as well as a more specific set of recommendations to apply TPS to partfabrication shop environments. It was concluded that TPS is indeed applicable in such low-volume settings; however, it was deemed more applicable to apply it to fabrication processesrather than assembly processes. His findings further concluded that it is more difficult toimplement lean manufacturing in a complex environment such as Boeing. For instance, a Just InTime supply system may not work efficiently due to Boeings tremendous product complexityand distant supplier network. Use of kanbans in Boeing's environment is also limited tocontrolling internal production flow, manufacture of small parts, and only signaling delivery (notproduction) of complex assemblies. In Raymond's view, production of complex assembliesrequired too much lead-time to make the use of kanbans effective. Results further explained thepossible savings from decreasing lot sizes, removing intermediate quality inspections, andcreating more standardized work practices.

Dennis Hager researched lean manufacturing implementation in a low-volume industry in 1992with his work "Applying Continuous Flow Manufacturing Principles to a Low VolumeElectronics Manufacturer." His work analyzed the causes of poor manufacturing performance ina turbine engine controls assembly work cell and provided solutions for understanding thegeneral manufacturing process. Metrics targeted include cycle times, capacity restraints, andproper scheduling practices. Results showed that capacity within a work center needs to beclearly understood, and that exceeding capacity leads to detrimental performance includingshipment delays and excess WIP. Further, capacity and scheduling must be coordinated withcontrolled variations in demands between time periods. Finally, Hager recommended eliminating

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schedule revisions after material has been released into manufacturing, reducing the need foradditional expediting time and lowering overall inventory levels. In summary, this was afunctional thesis that described distinct problems and specific solutions to capacity issues in anexisting work center, with recommendations that can be applied by the reader to othermanufacturing applications.

Paul Dul analyzed the "Application of Cellular Manufacturing to Low-Volume Industries" in histhesis based on research in manufacturing aircraft doors at a major aircraft manufacturer in 1994.This work compared low-volume production that is historically process-centered with a revisedproduction system that is product-centered. Justification was provided to argue that traditionalprocess layouts are outdated and many low volume producers can increase efficiencies byorganizing operations by product. To prove this point, the door assembly cell at an aircraftmanufacturer was transformed into a "product-centered" pilot cell to allow the new process ideasto be established, with the expectation that adoption in other cells would follow. Bothproduction cost and lead-time were metrics in this example, and both were reduced whenproduction was moved to the product-centered system. The work outlined two key principlesused to overcome the limitation that low-volume products do not have enough work to supportdedicated cells. First, products should be designed with common parts to leverage parts inmultiple assemblies. Second, manufacturing cells should be designed to be flexible toaccommodate variations within a part family. The feasibility of product-centered work cells in alow-volume environment was proven and cost savings justification through Net Present Valuefinancial analyses was provided.

Mark MacLean summarized "Implementing Lean Manufacturing in an Automobile Plant PilotProject" in 1996. This was an example of implementing intermediate lean methods on theproduction floor of a large existing auto plant. Methods outlined include revised assembly linedesigns, material handling methods, and assembly error reduction methods. Abrupt changes toan existing union-run mass-production plant result in system shock, and MacLean proposedtaking intermediate steps as preparation to implement a full Lean process was a better approach.Actions were implemented on a pilot assembly line that was ramped up for a new auto modelintroduction, where the risk in implementing a new process was minimized and the ability tomonitor the performance of a system was increased. MacLean concludes his work by explainingthat full transition to Lean Manufacturing must be driven by teamwork and organizationalchange, and until management and union leadership promoted such changes, Lean transitionscould not be fully realized.

Barrett Crane also analyzed a low-volume environment in 1996 in his thesis "Cycle Time andCost Reduction in a Low Volume Manufacturing Environment." This work outlined theimplementation of a kanban-controlled assembly process specifically designed for a low-volumeapplication. The work also analyzed cycle time and found that for low-volume applications it ismore feasible to track the overall cycle time as opposed to the cycle time of individual steps.Here again, a pilot production area was established for one product line to experiment with thenew process, thereby proving out the concepts with minimal negative impact on all production.Results showed that a kanban process could be successfully implemented in a low volumeenvironment, providing cost and cycle timesavings as well as a basis to provide feedback forongoing continuous improvements.

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Steve Harman researched "Implementation of Lean Manufacturing and One-Piece Flow at AlliedSignal" in 1997. This work outlined the implementation actions of a one-piece flow productionsystem in a traditional low-volume sheetmetal production work center. Numerous lean topicswere covered, including material flow, production scheduling using a pull system, and workcenter capacity modeling. To improve material flow, a focus was placed on creating dedicatedproduct-centered "flow-loops" sized for capacity needs. This showed improvements in bothwork in process (WIP) and production lead times. A production pull system was also created topromote linear production, using signal boards and kanban cards as production controlindicators. Finally, a rough-cut capacity model was created to analyze flow loop utilization,bottlenecks, and one-piece flow. The model was also proposed as a capacity planning tool forfuture expansions. Overall, this thesis provided a clear systems view of implementing a leanproduction environment. It warned to implement lean practices fully and not in isolatedsegments to realize the full benefits. Further it recommended using employee training andincentives along with fact-based data-driven decision processes for long term leanimprovements.

Jamie Flinchbaugh analyzed the interrelationships between lean manufacturing and factorydesign in his 1998 writing "Implementing Lean Manufacturing Through Factory Design." Heexplained the difficulties in diffusing lean manufacturing principles as a new technologicalsystem, and that proper factory design initially would alleviate many transition difficulties. Twotools were demonstrated to better understand and explain factory design and the factory'soperating systems. The first, Axiomatic Design, was used to derive the physical designparameters of a factory from functional requirements. The second, Queuing Theory, was used tocalculate production throughput performance and variation reduction. It concluded withreviewing the requirements of starting a new factory and how to minimize the associated risks.Results showed that design must include establishing independent production areas,decentralizing manufacturing support activities, and creating modular, scalable processes andfacilities. Further, the greatest throughput improvements were realized through variationreductions and continuous learning within the production environment.

2.5 First Look at Instron - Identifying Opportunities for Improvement in a CyclicalLow-Volume Environment:Previous LFM work has shown that Lean Manufacturing techniques can provide significantimprovements in manufacturing processes in short amounts of time. Continuing this effort, thework presented in this writing further supports the application of lean manufacturing in the low-volume environment at Instron. However, before applying lean principles, one must firstidentify the specific issues to be addressed within the existing low-volume environment.

"You will not know where you are going unless you know where you came from."

As a capital equipment provider, Instron operates within an inherently difficult salesenvironment. The nature of capital equipment sales forces the majority of capital purchases to betransacted near the end of each quarter, creating quarterly cyclical demands on manufacturing.

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Instron's own corporate sales metrics further support this difficult environment by measuring theInstron sales team on quarterly results. Given this internal metric, the sales division sells productat non-linear rates through each quarter, giving less effort to sales at the beginning of the quarterand ramping up sales by the end of the quarter (Refer to Figure 4). Great amounts of stress areplaced on the manufacturing process to satisfy such cyclical demands.

Monthly Demand for EM Product-Year 2000

8070 -4-single60 column

EME 0

40 -- DoubleW Column

30 EM

S20

Month

Figure 4: Electromechanical Demand Volume Analysis Showing Cyclical Demand Patterns

In reviewing Instron's internal operations, the original assembly process required attention toincrease standard work methods and output consistency. Production was driven by customerorders, which were retrieved from the Instron Business System (IBS) database that links sales,manufacturing and procurement. Once per week, a list of customer orders sorted by the orderpromise date were retrieved from the database and posted in the manufacturing area. Allequipment orders for the upcoming weeks, including machine type, custom specifications, anddue date, were included. Based on order data, operators were instructed to build machines tofulfill those orders, with success measured on achieving monthly/quarterly quotas and achievingthe promised "On Time" delivery dates.

Restrictions in Instron's system structure were numerous. Methods needed to be clearly outlinedto help consistently achieve the "on-time" dates. All of the steps to produce a machine wereinherently "known" due to the long tenure most employees possessed working in Instronassembly. It was true that all operators were technically knowledgeable on the assemblyrequirements, but process steps were not strictly followed. Therefore, there was a lack ofconsistency in method among employees that resulted in limited control of output and limitationsin transferring processes to new employees.

Output was measured on weekly, monthly, and quarterly segments. These time increments wereconsiderably longer than the time required to complete one unit. Therefore, total assembly

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output times often varied per order when measured against the extended time increments. Thiscreated non-linear production and shipping patterns that magnified the traditional hockey-stickeffect created by cyclical sales patterns. Partly due to human nature, assembly was always tryingto play "catch up" to the planned number of machines by week's end. Production rates wereslow at the beginning of each respective period and then "ramped up" to compensate at the endof the period. Once demand per time period overtook actual output per time, it was difficult forproduction to catch up. Figure 5 shows an example of how output lagged planned productionduring the quarter. Order demand was near exponential, but planned production was linearthrough the quarter, offset by pulling orders forward in the production schedule when possible toaccommodate the difference. However, since production was measured in long time increments,output linearity on a day-to-day basis was not often achieved.

C.2 350

3000 250 -+-Planned

CL 200 ProductionSC 1 Quantity

li~ 10 eActual_ 10050 ProductionE Quantity:: 0

C.)1415161718192021 2223242526

Week # in Quarter

Figure 5: Planned vs. Actual Units Production for Electromechanical ProductSecond Quarter of 2000

Machines were often built in small batches. The desired number of units was completed at theend of most weeks, but production output each day was not consistent. Some operators viewedthis batch production as the most efficient way to produce. However, batch production onlyprovided a local optimum at each workstation, with delays between stations a direct result ofbatch building. Process output was inconsistent with the desired metric of achieving low system-wide throughput times. To demonstrate this effect, delay effects from batch production arepresented in Figure 6, showing the extended total process time from building a small batch size(n=2) vs. building a single unit at a time (n=1) (Mahoney, 1997).

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MULTIPLE PIECE FLOW - LOT SIZE OF TWO

1

2

3

4

5CLwU 6

CO)Cl)ILl SINGLE PIECE FLOW - LOT SIZE OF ONE

0

0. 1

2

3

4

5

6

TIME

Figure 6: Lot Sizing Illustration to Demonstrate Effect of Single Piece Flow

Figure 6 illustrates the time difference required for completing assemblies when single andmultiple piece flow are considered. Each process step is shown on the vertical axis. Thecompletion time of each unit is represented on the horizontal axis. One box represents one unitof production at each stage of assembly. Each stage in the top half of the diagram is completedin batches of two; a step is not initiated until both units have completed the previous step.Compare this to the time reductions illustrated in single piece flow in the bottom half of thediagram. Increases in number of units produced per lot show both a resultant increase in timerequired to get all machines completed and an increase of work in process between assembly andtest operations. Increases in lot sizes therefore decrease order responsiveness since more time isrequired to complete a single unit.

2.6 Cost of Non-Optimized Process:The above scenarios each contributed to extended production flow time. What were the costs?Revenue opportunity costs were evident due to shipping product late in each week and month,therefore delaying revenue inflows. Inventory carrying cost increased for both excess WIP that

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was located within the production stations and inventory was carried to satisfy the resulting non-linear "hockey-stick" increases in output at the end of each period. Long flow times alsoinhibited the ability of the company to respond to order changes imposed by customers. Theorder remained in the "process" longer; therefore, to achieve the scheduled due date, assemblyhad to be started earlier, leaving less time for customer changes. Such changes ultimately costthe company in time to make adjustments to orders in process as well as disrupted themanufacturing process. A downward spiral of longer lead times, more potential changes tocustomer orders, and increased frequency of missed schedule dates due to changes and reworkoften resulted.

Intangible costs of long flow times were also considered. The discovery and feedback onproduction and/or part quality issues was prolonged. Complexity, additional scheduling support,worker confusion, and order expediting were all factors that were difficult to quantify but yetwere increased with longer lead times. However, all had to be considered when implementingsystem operational improvements.

Flow time had to be carefully considered, and its associated costs had to be included along withlabor, materials and overhead for financial management. Manufacturing had to change itsmetrics and analysis methods to account for all relevant costs, more than just focusing on laborefficiency and capital investments. (Graves et al, 1992). Traditional labor-based cost accountingdid not favor flow time reduction since it may have increased the labor cost per job and therequired capital equipment. However, looking at the labor element in a typical Instron product, itwas a small percentage of the overall cost (Figure 7).

Breakdown of Costs forRepresentative EM Assembled

Product

Material Labor84/l 6%

Figure 7: Material and Labor Cost Breakdown for Electromechanical Product

2.7 Lean Manufacturing for a Low-Volume Manufacturer:Having identified the most prominent manufacturing issues, a proposal was made to analyze andimprove Instron's product throughput flow times. Embracing the principles of LeanManufacturing, this project provided a framework to guide such improvements in a low-volumesetting. Using this lean approach, the project at Instron was directed by the building processshown in Figure 8 (Diagram modified from Monden, 1993). The elements shown in thisframework are described in detail throughout the following chapters.

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Notice that both the physical factory environment and the work process were included in thebuilding process. Within such a low-volume environment, flexibility was key to achieving flowtime reductions given the inherent variations. This was developed as a system for both thephysical set-up and more standardized work practice. To begin, the required process is definedand the physical factory environment is modified to accept the newly defined process. Thesefirst phases were developed in Chapter 3.

Physical Floor AssemblyLayout Personnel

Point of Use Physical Flow FlexibleInventory Line Workforce through

Rearrangement Cross Training-

Increased Worker Productivity

RestructuredInventory

LevelsFlexibility

Output Quar

VendorRelationships

Work Process Identification andImprovement Implementation

Standard Reduced ProductionWork Lot Sizes Leveling

Procedures

I ' T4-Single Piece Flow

Assembly Production

Lead Time Improved SalesReduction in and MarketingFlow Days Relationships

]inntity

Cost Reductions

Figure 8: Proposed Framework Using Lean Manufacturing Principles for Instron's Low-Volume Production Process

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Initiation of Lean Improvements by Team Actions

Increased Customer Responsiveness

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PROCESS SELECTION AND LAYOUT DESIGN3 OF A MANUFACTURING ENVIRONMENT

The selection and design of a factory layout must reflect the desired manufacturing process. Amethod for process identification is outlined in Chapter 3 followed by an analysis to optimize thephysical factory layout to complement the identified process. Instron's manufacturingenvironment was then used as an example to demonstrate such process identification and designlayout adaptation to the desired process.

3.1 Identification of Manufacturing Process:Numerous factors must be considered in identifying a manufacturing process. Five factors thathave the greatest influence are:

1. Annual product volumes2. Product variants under consideration3. Manufacturing's internal and external metrics in relation to customer needs (such as order

response time)4. The level of vertical integration (final assembly, parts production in addition to final

assembly, or full integration from raw material processing to finished product)5. Process flexibility to react to volume changes and product substitutions/additions

Using these factors, one can refer to Hayes and Wheelwright's widely acknowledged product-process matrix that is provided in Figure 9. It relates the manufacturing production process tothe product type and overall corporate strategy (Hayes and Wheelwright, 1979). The matrixoutlines a range of processes from lower volume, highly customized products requiring morejob-shop type manufacturing, to higher volume products with limited options allowing forsmoother line- and continuous-flow processes to be utilized. A product/manufacturing divisionwithin a company can be characterized as occupying a region of the matrix. The distinctionsbetween each segment are further described below. Although the segments are listed separately,overall the matrix should be considered a continuum often exhibiting overlapping characteristics.

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PRODUCT ___STRUCTURE

ROCESS LOW VOLUMESTRUCTURE LOW STANDARDIZATION

CUSTOMIZED PRODUCT

v

MULTIPLE PRODUCTSHIGHER STANDARDIZATION

LOW VOLUMESFEW MAJOR PRODUCTS

HIGHER VOLUMES

COMMODITY PRODUCTHIGH STANDARDIZATION

HIGHER VOLUMES

JOB SHOP

DISCONNECTEDLINE FLOW

CONNECTEDLINE FLOW

(ASSEMBLY LINE)

CONTINUOUSFLOW

INSTRONMFG

Figure 9: Product Structure is Related to Process StructureAnd Varies by Industry and Sales Volumes

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FE

Job Shop Process: Numerous unique tasks are required to complete a unit of product output,often resulting from widely dispersed product offerings or specialized product manufacturing.Volumes of each product are low. Processes incorporate flexible equipment and jobs are oftenlabor intensive.

Disconnected Line Flow Process: Product variations can be offered through this process, manycustomized with numerous options assembled in a single production area. A process flowpattern is established, even though discontinuous, with a set of distinct operations lined up inorder. Although each process step should be calculated to balance production times, often stepsresult in variations in time; thereby creating a situation where work in process can accumulatebetween process steps.

Connected Line Flow Process: The assembly line is one example of a connected flow lineprocess, characterized by higher product volumes with limited variety. Higher standardization isevident in the included products and the production method is time-paced throughout theprocess. The process is less flexible in accommodating changes over time, often due to highcapital costs of dedicated line equipment.

Continuous Flow Process: High product volumes with little to no flexibility are produced in acontinuous process. Product variation is very limited (often to a single product). Product movesin continuous motion through all process steps. Examples include chemical and food production.

The matrix in Figure 9 forces a product to be viewed in two dimensions, showings that BOTHproduct and process are important elements of a company's strategy. A great new product couldbe matched with an incompatible process that requires an excessive set up time or capital,leading to failure. On the other hand, a product with a stable design and long term productionschedule could be hampered by a non-standardized production process. Therefore, both theproduct and process should be considered as part of a company's competitive advantage.

3.2 Decision Parameters to Design the Factory Layout:Once the optimum process has been identified for producing a product or product family, thephysical factory layout of the manufacturing area must be arranged to support the process.Parameters to incorporate in the layout include:

* Production capacity requirements* Equipment layout to optimize manufacturing's throughput time metric0 Number of assembly stations required based on production time requirements and

breakdown of assembly procedures according to the product's inherent design* Commonality between products' designs to combine product variants into a given

manufacturing area* Ability to allow changes in production quantities over time* Ability to expand the layout to incorporate new product introductions* Location of parts inventory with respect to the assembly process* Efficiency of floor space utilization* Ability to enable close worker communication within and between assembly areas

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Once defined, these concepts form the main components to identify and plan the physicalarrangement of an assembly facility. As demonstration of this identification and physical layout,Instron's manufacturing environment was analyzed. To make it clear what Instron's processincluded, the required assembly steps for the product and process under analysis were firstoutlined.

3.3 Instron EM/Hardness Assembly Process:Figure 10 illustrates the typical assembly process steps used to assemble Instronelectromechanical products. In optimizing the process, it was determined that the currentphysical actions directly related to assembling each product (as outlined in Figure 10) wereappropriate to transfer to the new process. However, the flow process, the timing of assemblystarts, and the combination of products built per line were further redefined.

ElectronicsAssembly

Base Load CellsTray/Electronics and

Assembly Accessories

Tray and Frame Complete SystemTop End Run-in Frame Load Cell Assembly Audit and Ship

Integration Cycle Test Calibration External Inventory ProductAssembly Housings Update

Top EndColumn

Assembly Figure 10: Assembly Process Mapfor Instron's Electromechanical Products

Description of Process Steps:

Base tray assembly: Electronic system controller cards and cables are mechanically fastened intoa pre-formed sheet metal tray. The tray also acts as the product's structural base.

Top end column assembly: Vertical guide rods, milled lead screws, and milled structural beamsare bolted together to form the vertical frame, providing a structure to support and translate thesystem's load cell.

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Integration assembly of the tray and top end: The tray and top end are aligned to form thecomplete structural frame. Additional items such as an electric drive motor, drive belts, columncovers, and top stabilizer plate are added to complete the assembly process of the functionalframe.

Frame run-in cycle: The machine is cycled without load during an overnight time period.Cycling provides the frame a break-in time and is also the first of a series of frame tests.

System Test: Following frame cycling, compliance testing is conducted on the frame andaccessories to ensure system calibration.

Frame test: Tests the integrity of the frame itself, including linearity for its full range ofmotion and proper function of all components.Calibration: Calibrates the load cells that are purchased with the machine. The cells aretested on the frame to determine overall system-level performance.System audit: Tests all included accessories, again to ensure overall system performance.

External housing assembly: External protective covers are assembled to the frame. The coversprovide aesthetics and protection to the internal electronic components.

Audit and Inventory Adjustment: The product is audited for completeness and order tracking forthe customer. Inventory utilized in the machine is "backflushed" from the inventory database toremove it from the on-hand inventory balances.

3.3.1 Classification of Instron's Manufacturing Process:To classify Instron's processes, it was necessary to first review the key functional attributes ofthe production output. As listed earlier, these included production volumes, customer-relatedmanufacturing metrics, the level of vertical integration, the bounds of products variants includedin the analysis, and the desired level of process flexibility.

Production Volume: Both historical sales and future forecasted sales were used to obtainexpected production volumes. Trends in historical sales provided the baseline demand. Inaddition, forecasted regional sales goals provided a more realistic view of the future needs.Increased demands must also be factored in for any new planned product introductions, with theexpectation that new products typically exhibit higher demand variability during the ramp upphase. For the three major product categories analyzed in this work, the following demandswere used for the year 2001. Demands equated to approximately 40 machines per week, placingInstron into a relatively low volume manufacturing category.

Model Type Year Demand Volume ForecastTabletop EM 1000

Single Column EM 900Model 2000 Hardness 600

Table 1: Future Yearly Demand by Model Type

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Customer-Related Manufacturing Metrics: Manufacturing must satisfy the ongoing customerdemand of lower order lead times through the reduction of throughput assembly time. Inaddition, they must satisfy the corporate "On-time" target to satisfy the quoted delivery datepromised to each customer. Assembly and test times, along with availability of parts andincluded accessories, drive a large part of this value. For this analysis, the time for assembly/testwas the primary target.

Vertical Integration: At Instron in Canton, the current process included only final assembly usingparts and subassemblies supplied by outside vendors. Vertical integration into componentmanufacturing was not feasible in the short term due to floor space and capital equipmentlimitations. These limitations bounded the analysis, with the potential for increased verticalintegration being outside the scope of the project. However, this does not mean that verticalintegration for future needs should be discarded as an option, only that the strategy would requireadditional analysis with a longer-term planning horizon.

Product Variety: Were all products similar in size and complexity? Did they require similarassembly techniques and equipment? How were they be divided into product families? Theproposed process at Instron included three of Instron's major product families - Single andDouble Column Electromechanical and Model 2000 Hardness - to initiate this project as a pilotprogram in one division. These three product families had:

- Similar assembly requirements- Similar functional test requirements* Relatively similar physical size and assembly complexity

Flexibility: Instron's demand varied throughout each quarter, requiring output flexibility in eachassembly area to adjust quantities in relatively short monthly and quarterly periods. Further, newproduct introductions had to be able to be integrated into the production area with minimalrearrangement. Additional product introductions required longer-term flexibility to rearrangeand expand the process with minimal capital requirements.

3.3.2 Process Proposal for Instron:Instron's EM final assembly process was positioned in the Product-Process Matrix as shownearlier in Figure 9. Based on the products historical sales and future marketing forecasts ofrelatively low quantities (-2500 units/yr), the discrete assembly operations required, five majorproduct offerings each with numerous configurations, and ease of rearrangement, themanufacturing requirements were best met using a disconnected flow process with manualassembly / test operations. The decision parameters outlined earlier then formed the basis todetermine the specific physical layout to achieve an optimum disconnected flow process atInstron.

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3.3.3 Instron's Physical Factory Arrangement:Once the process was identified, arranging the physical environment required carefulconsideration of the specific products being produced. Each decision parameter had to beconsidered in context of the unique physical product. For Instron's products, some additionalconsiderations included:

" The products analyzed in this project have a physical size ranging from 18"x18"x36" to24"x60"x72", requiring substantial floor space to complete all assembly steps and largetransport carts to transfer the product between assembly stages.

" Parts inventory consumes large volumes of storage space (up to pallet-sized space perpart) with some parts requiring the assistance of overhead cranes to lift and position them.

" Demand volumes are similar for all three product families - no product heavily outweighsthe others in volume.

" Although all three product families are similar, they each have several distinct assemblyrequirements leading to numerous specific parts inventory requirements and assemblystations.

" Each product family has numerous unique test requirements in addition to common frametesting, therefore equipment requirements vary between product families.

Given the context of assembling the specific product in this analysis, the physical environmentwas proposed to have one flow line for each product family. In theory one line could incorporateall three product families based on similar assembly and test requirements. However, given thesize of the product and the volume of space required to store part inventories adjacent toassembly, it was not realistic to assume that all products could be produced from one line. Thiswould have resulted in a line of extensive length, making product movement more difficultbetween assembly operations. Further, one long line would limit how parts could be optimallyplaced next to each assembly location due to their size and the space limitations. The variationsin assembly time per station per product (outlined in Chapter 5) would also have causedexcessive delays between stations and increased complexity in moving product between stations.

Discrete assembly stations with dedicated assembly/test equipment were aligned along eachrespective product flow line. Providing short transfer distances between stations, such alignmentminimized non-value added motion and transportation effort. Dedicated assembly and testequipment allowed each line the capability to completely assemble and test a given product.Continuous flow was more easily achieved with this dedicated equipment, avoiding queuingproduct to wait for assembly/test equipment to be available. Dedicating equipment to each linewent against the metric of maximizing equipment utilization to minimize capital costs, sinceequipment may not always be in use for every assembly operation. However, equipment waspositioned to be available when needed to support increased product throughput.

The number of assembly/test stations within each line was determined from two parameters. Thefirst parameter was takt time, or the time for assembly based on the customer order rate. Thesecond parameter is the design-based assembly breakdown. In Instron's low-volume example,products were designed with numerous subassemblies that each required assembly at onetime/one station. As later shown during discussion on process and capacity in Chapter 5, these

35

breaking points in the designs were combined with takt time calculations to finalize the numberof assembly stations within each product family.

Commonality of subassemblies between products was also leveraged in the layout design, withcommon assemblies being assembled in one area to supply multiple assembly flow lines. Basetrays containing shared components and electronic card cages common to multiple models wereassembled in a common location to feed into each respective flow assembly line.

To further reduce manufacturing delays, point of use (POU) parts inventory used in productionwas located on the factory floor. Material was then available on demand and within reach fromeach production station. However, locating the point of use inventory could not hinder theproduction process itself. Implementation of this point of use inventory method is furtherdiscussed in Chapter 4.

Flexibility in the layout was required for both short-term variations in monthly demand and thelong-term introduction/removal of products. Relocating workers between lines to reflect demandchanges accomplished short-term flexibility. Long-term flexibility was achieved both by varyingthe number of workers and maintaining the ability to reconfigure the assembly equipment withlittle effort to allow expansion or contraction. Instron's layout provided flexibility by storingpart inventory on wheeled racks and creating mobile assembly workstations and test equipment.

Last, the layout incorporated the physical attributes of a lean environment. These included clearvisual indications of assembly sequence actions, specific areas for process control mechanismssuch as in-process subassembly kanbans, and efficient utilization of space with no areas to store"waste" including excess work in process and obsolete parts/equipment. Further, physicalbarriers creating distance between the production areas that inhibited workers fromcommunicating and collectively solving problems were removed (Schonberger, 1986).

3.3.4 Final Layout Proposal:The redesigned layout proposal is shown in Figure 11. Proposed results included three mainflow lines divided by major product family, supported with a common base tray and electronicsassembly area. This layout best supported single piece production flow for the includedproducts. Designing adjacent flow lines also supported close worker communication. The floorspace can also be easily reconfigured, providing flexibility and expandability in both the longand short term.

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POU INVENTORY

Top End g System System System Coverp Assembl Assembly Run-In Test and0 Tnteorntinn Finish

v Common K

E Base Tray A Cover

Nand Top End System + System . System - andlrn B Assembl AssembI Run-In Test Fins

T Elsens A POU INVENTORY Exit toTn Fro R lArea Shipping

Receiving LPOU INVENTORY

LseTop End System - System System plaCoverAssembl Assembly Run-In Test andsp[etm eete r . t inetn Finish

POU INVE NTORY _

Figure 11: Schematic of Final Instron Floor Layout Configuration

The proposed layout can be broken into three main elements - assembly workstations, point ofuse inventory, and kanbans. Workstations were simply benches and test stations placed whereneeded at each assembly station. Not every station required workstations, as shown in the layoutdiagram. Most assembly was conducted directly on wheeled carts, only requiring open floorspace to move between work areas. Point of use inventory was located on the assembly flooralong each flow line. Last, strategically placed kanbans to buffer against variations in assemblyand test were placed in the layout. Both the full point of use inventory and kanban usage werenew elements to this production facility, and required careful implementation. Explanation ofpoint of use inventory control is fully outlined in Chapter 4. Calculations for kanban andworkstation quantities for each line are further discussed in Chapter 5.

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38

COMPONENT INVENTORY STOCKING AND MATERIAL4 HANDLING

Materials coordination is one of the most important supporting factors for a lean factory. Amethod to implement and control point of use inventory on the factory floor has been outlined inthis chapter to assist in improving materials coordination within a final assembly factory. Insightinto optimal placement of inventory, failure modes that can occur when implementing point ofuse inventory, control mechanisms to keep momentum in the point of use system, and theintegration of point of use inventory with Materials Resource Planning has been provided.

4.1 Point of Use Inventory Placement:Point Of Use (POU) inventory placement is a complementary element of a Lean assemblyenvironment. The physical process of obtaining parts to use during the assembly process is aType I waste, meaning it is a necessary action but it does not provide direct benefit to the endcustomer. Therefore, the time required to perform these tasks must be minimized. Locatinginventory stock directly in the assembly environment removes wasted time associated withhaving assemblers retrieve parts from various storage locations.

The benefits of creating point of use inventory are far reaching. First, parts are readily availableto the assemblers on demand for use in assembly. Second, point of use placement provides aclear visual indication of what parts are in stock and what parts have been ordered in excessquantities. Third, it creates a visual awareness of parts can often run below minimum level dueto high utilization. This visual control is effective for both the operators as well as materialplanners. Although in theory inventory levels are calculated, reality shows that nonlineardemand patterns often result in utilizing all available inventories. Visual indications direct fromthe factory floor can help show how much variation exists between inventory levels listed in theinventory database and the actual levels stocked, minimizing the time required to find suchdiscrepancies that often lead to part shortages and line stoppages. Worker communication withthe material planners also provides earlier warnings of upcoming material shortages, bothformally through inventory Kanban replacement strategies (quantity calculations discussed inChapter 6) and informally through open communications.

4.2 Failure Modes to Consider for Point of Use Inventory:Although point of use inventory has been deemed an improvement over central stockroomcontrol, numerous failure modes must be overcome when placing inventory on the assemblyfloor:. Parts utilized in multiple assembly locations for "Platform" products* Ownership and control of new stocking methodology and materials handling

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4.2.1 Multiple Use Inventory - Optimized Stocking Locations:Where should parts be located when used in multiple assemblies? Conceptually, products thatshare parts under a "platform" structure are superior, saving engineering design time andinventory carrying costs by limiting part proliferation. However, control of inventory levels forsuch parts becomes difficult when demanded in multiple plant assembly locations. This problemis magnified when point of use inventory is utilized. Ideally, each assembly cell using a specificpart should have its own parts supply. This potentially leads to two scenarios:

. Stocking a greater than required level of inventory (if multiple locations are stocked withthe same part)

- Wasted operator motion if one has to retrieve parts from a central bin location.The problem of multiple use locations needs to be coordinated with all products involved. It canbecome a chaotic situation in which multiple groups feed from a common part supply. Resultsfrom improper part inventory planning from one group can easily affect the requirements ofanother group using the same part - poor planning leads to part shortages which leads tounaccounted use of the parts purchased for another group.

To deal with multiple-use parts, the following guidelines should be used to partition point of useinventory usage (Suri, 1998):

- For parts used in a single, dedicated work area, stock one location.* For high volume parts that are utilized in more than one assembly, two options must be

considered. One option is to have one point of use parts bins in a shared location whereeach line using the parts is compromised by having the operators visit the central POUlocation to obtain parts. The second option is to create multiple parts bins locations asneeded in each line. This adds both additional materials handling complexity andincreased database accounting as a trade-off to increased part access. Having one "master"part bin that feeds the other "slave" part locations provides control in this scenario. Themaster bin's inventory level triggers additional supply orders.

* For parts that are only used sporadically by multiple locations, it is best to keep them incentral stock locations and allocate them when needed.

4.2.2 Material Handling Ownership and Control:Control of point of use inventory must be clearly outlined. It transforms a once strictlyfunctional operation (independent of assembly process) to one that is integral to flow-basedassembly. Ownership of this process must now be directed into a position that is measured aspart of the overall assembly operations success.

Historically, stock room operations are a functional category with similar divisional problems asengineering or marketing - their actions are measured based on fulfilling their own department'sobjectives. Stockroom operations provide a service to the rest of the facility - providing receiptand delivery of parts to assembly personnel to be used in the assembly processes. However, thisfunctional operation may not be in alignment with the objectives of making manufacturing aresponsive system. For instance, time restraints placed on stockroom personnel may not allowadequate time for them to unpack and stock inventory in point of use parts locations. Once partsarrive on the factory floor, stocking may be left up to the assembly operators themselves whoseactions are measured on building assemblies and not stocking parts. Therefore, if no one owns

40

the complete delivery process, it can fail to provide the required responsiveness. Rather, itcreates confusion as to who completes the stocking process and when the stocking processactually is completed throughout any day or week.

It is suggested that the inventory stocking process be positioned as an integral part ofmanufacturing. Ownership of the process should be under manufacturing's direct control, withindividuals whom report to manufacturing positioned to be fully responsible for itsimplementation. This would provide fuller integration of the requirements for manufacturing,with individuals working to consistent time-reducing metrics as manufacturing. However,ownership must not be contained to a single person. A single person represents a single point offailure. This potential failure mode must be eliminated by cross-training multiple individuals ora team to ensure continuous inventory management coverage.

4.3 Integrating Point of Use Inventory with the External Supply Chain:The point of use inventory process only improves internal material handling operations. Greatermaterial control is possible by extending these boundaries to include external suppliers andhaving such suppliers directly control point of use inventory replenishment. This would beparticularly useful for high-volume, low-value parts that are not cost effective for the company tocontrol through materials planning and ordering.

Direct supplier control would allow the supplier to enter the plant and have direct responsibilityfor replenishing, tracking and ordering inventory. Point of use inventory is then used directly bythe supplier as a visual indicator of the replenishment needs. This becomes more of a servicefrom the supplier to the factory; however, it is a win-win for both sides. From the supplier'sview, he has direct control of what is ordered and when - there are less rush orders or ordersinappropriate to the plant's needs. From the plant's view, they no longer need resources tocontrol the ordering and stocking of such items.

For this supplier management system to be successful, certain criteria must be met. First, thesuppliers must be geographically located in close proximity to the plant. Second, themanufacturer supports the mentality of sharing inventory data and part demand patterns withtheir vendors. Third, the suppliers are required to adjust their deliveries dynamically to keepinventories at a minimal level for the parent manufacturing company.

4.4 Point of Use Inventory Management at Instron:An inventory strategy was implemented at Instron based on 100% Point of Use inventoryplacement for Electromechanical and Hardness products. All major parts were relocated fromstockroom locations directly into bins, racks and/or pallets at the perimeter of each assemblyflow line. Each rack location on the floor required marking for inventory tracking and each binrequired labeling to identify parts' numbers and minimum inventory quantities. Workers' inputwas critical to determine optimal locations to stock inventory. The final inventory locations onthe floor were determined directly by the assembly operators. Figure 12 shows an example ofpoint of use inventory placement at Instron.

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Figure 12: Point of Use Inventory Placement on Instron's Factory Floor

Ownership of the point of use process was established by creating a materials-handler positionthat reports directly into the manufacturing division manager. This aligned the incentives of thematerial handling personnel with the assembly process to achieve a common goal of assemblingand shipping machines in a timely manner, including the provision of parts as an integral part ofthe process. The stocking responsibilities were now known and better managed. Assemblershave been able to obtain parts from receiving more quickly, reducing the number of linestoppages. Further, a specific contact person was now available within the department forsolving materials problems, including shortages, rejected parts and parts delivered frombackorder status. This opened the communication channels between assemblers and materialhandling to further reduce delays.

Direct vendor control of select inventory was also established. Assembly hardware (nut&bolts)was set up to be delivered and stocked by a local outside supplier. This removed the requirementto order and control over 250 hardware items. This vendor arrangement further provided directfeedback to the vendor and real-time control of inventory levels.

4.5 Materials Resource Planning vs. Pull Inventory Policies:Traditional control of inventory was accomplished by using Material Resource Planning (MRP)techniques. This was considered an inventory "Push" system in which material was ordered inadvance of need based on demand forecasts. As the future demand was forecasted, MRPinventory control adjusted order quantities based on the quantity forecasted and vendor leadtimes. Inventory deliveries followed, whether or not actual demand warranted material delivery.This led to potential overloads of inventory if forecasts were greater than actual demand, or partshortages if forecasts were below actual demand. In either case, there were costs associated withpushing inventory - in both lost sales and inflated inventory holdings.

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Revising inventory supply policy to reflect levels to satisfy actual demand was bestaccomplished by pulling inventory into production when needed. The physical set-up of point ofuse inventory clearly showed the levels of inventory in real-time. The issue then became how touse this visual display to better control inventory levels. Creating a control system directly at thepoint of stock further reduced information delays as physical inventory was consumed.Feedback on this consumption was derived from inventory replenishment using a kanban cardprocess to control inventory replenishment.

How was this accomplished? Each SKU (stock keeping unit) had an associated physical cardattached to its point of use stock location. As shown in Figure 13, the card indicated the partnumber and the minimum level of inventory that the stock must be reduced to for triggering asupply order for that part (Calculating such levels are covered in Chapter 6). Once the minimumlevel of inventory in the bin was reached, the card was pulled from the bin and provided to thematerials manager, indicating the need to order another lot of parts.

INVENTORY REORDER CARD

Order Dates:Part NumberPart NameOrder QuantitySupplierStock Location

MIN BIN OIJANTITY

Figure 13: Example Kanban Inventory Card

The importance of proper card system operation cannot be understated. Timely "pulls" of thecards from the parts bins must be considered as important as the assembly process itself; withoutparts assembly operations are not possible. Again, this was a workforce discipline issue thatrequired clearly stated objectives and training for those using the process on a daily basis.

4.6 Kanban Inventory Management at Instron:Consistency in approach was very important in inventory control. The team at Instron set out tocreate a consistent and visible inventory ordering and control policy. However, with multiplemethods in existence for various parts based on lead times, vendor requirements, and part type, itwas determined that the best approach would be to start with one part category to create a pilotinventory ordering process that could eventually be extended to all part categories.

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The pilot was initiated by targeting one of the largest parts suppliers of wire harnesses, whichwas originally controlled by Materials Resource Planning. Wire harness products weretransformed to kanban-card control by calculating minimum levels and order quantities(explained in Chapter 6), and creating cards for each respective point of use parts bin. In casesthat parts were small enough to place in containers, minimum levels for each part weresegregated by bagging the quantity separately as part of the supplier's process. Upon openingthat particular bag of parts, the kanban card inside facilitated visual indication that the minimuminventory level had been reached.

Physical set-up of kanban inventory control was easily accomplished; sustaining the inventoryprocess control was more difficult. The pilot process allowed for learning and controllingproblems with using this method. Once the cards were in place, orders would only be initiatedonce the cards were pulled out. Training was required for assemblers and the material handler tounderstand the process and to take the time to view the levels when accessing the parts.Acknowledgement from the assemblers and the material handler that they were in control of thisprocess as part of their daily routine was necessary.

Kanban inventory control was not foolproof. One problem was the dependence on the ordercards. They are physical objects that control the order process. Over time, instances occurredwhen a card(s) was misplaced or ignored, leaving inventory short. Another problem was theingrained feeling of security tied into material resource planning. These failure points had to berecognized and driven out over time by commitment to the kanban method by material plannersand the assembly operators.

4.7 Combining Kanban and Material Resource Planning Processes-Mixed Model Solution:Although it was originally proposed that Instron would move all Electromechanical partsinventory to full kanban control, some parts did not lend themselves to this demand control.Kanbans worked well for items that have reasonable lead times - zero to four weeks weregenerally acceptable. This was evidenced from the Toyota Production System, which used theprinciple of retaining local suppliers that can deliver frequently and in short time. However,local suppliers and short lead times were not always achievable in the short term. If lead timeswere longer than approximately four weeks, kanbans did not work so well given the quantity ofdemand in this environment. First, they required large amounts of inventory coverage for theextended lead-time. Second, long lead-time parts were often special orders (such as foreign-supplied or custom processed), from suppliers who currently did not build to short order andsmall lot sizes. Third, the effects of demand variations increased with longer lead times, callingfor greater amounts of safety stock inventory.

This led to the question; can Materials Resource Planning and Kanban processes be combinedeffectively? The answer was yes, but with great caution. Operating with two methods wentagainst having a purely consistent pull system. It sometimes resulted in confusion on theassembly floor when all parts were not brought in on demand. Kanban would be the dominantinventory methodology utilized, with MRP-driven inventory used for select components. Thesupporting MRP system did provide a superior planning tool for long lead times, and it didprovide accurate tracking of needs for future forecasted demands. However, since it could not

44

control future demands, its use was restricted to those parts requiring its long range planningcapabilities to keep inventory levels at the correct levels.

To remain in line with as much kanban process as possible, kanban cards could be placed in thepoint of use locations for material resource planned parts. Although these specific cards wouldnot drive inventory orders directly, they would emulate the process for assemblers to monitorinventory levels on the floor and they would provide material planners additional data of actualdemand vs. planned use, given a minimum level is set for these parts. Further, long term processplanning should include reducing lead times and selecting more local vendors, creating anenvironment that would move such MRP-controlled parts to the intended kanban process.

With inventory placement reorganized and inventory kanban control established for the majorityof parts, the physical environment was in place to implement the proposed assembly process. Itwas now possible to make the whole process "flow". These most critical aspects ofimplementing this flow process and inventory management methods are explained in thechapters that follow.

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46

IMPLEMENTATION OF A SINGLE PIECE FLOW~5 ASSEMBLY PROCESS

As stated earlier, a manufacturing process and its physical environment must complement eachother. Given that the parameters to establish the physical environment have been completed, thischapter described the challenges faced when actually implementing a flow process within a lowvolume assembly environment.

Womack and Jones have elegantly phrased flow implementation:

"Once value has been precisely specified, the value stream for a specific product fullymapped by the lean enterprise, and obviously wasteful steps eliminated, it's time for the nextstep in lean thinking - a truly breathtaking one: make the remaining, value-creating stepsflow." (Womack and Jones, 1996)

5.1 Process Flow Definitions:A major goal of this process improvement was to shorten the required assembly flow time, withreductions in such time translating to the opportunity for increased order responsiveness. Beforegoing further into examining the process calculations and implementation, it was important todefine flow time, cycle time and takt time (Schonberger, 1986 and Suri, 1998).

Production flow time was defined as the total elapsed that it took to produce one unit, from thestart of the first subassembly to the time the completed unit was shipped. This included thelength of active time for each operation plus the amount of waiting or inactive time between eachactivity. A synonym used for flow time was throughput time.

Production cycle time was defined as the elapsed time between consecutive product completions.This was considered the heartbeat of production. It controlled the timing for the entire workcenter and was thought of as the time between start of assembly or the time between shipments,in units/time. This led to the calculation of the system's required takt time.

Takt Time, in time/unit, was defined as the time required to perform each operation (time perstation) to achieve the desired cycle time based on the customer demand rate:

Takt Time = (Available time per shift * Uptime factor)/Average demand per shift

Average demand per shift = average monthly demand/((# days per month)*(# shifts per day)

Uptime factor = % of time during shift that work is actively performed

Average monthly demand = Average number of parts/products required each month

For example, with 8 hours per shift, 80% uptime per shift, 100 parts per month demand, and 1shift operation, the takt time for each part was calculated as:

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Takt Time = 8hrs/shift * .80 / (100 parts/month)/ ((20 days/1 month)* 1 shift/day)) = 1.3 hrs/part

For each elapsed duration of 1.3 hours, one part was completed and each process step thereforefinished one task within 1.3 hours to supply to the next respective step.

5.2 Process Implementation at Instron:Implementing the process as proposed earlier required clear definition and structure, accuratecapacity calculations, and involvement from the entire workforce. The operators particularlyrequired the process knowledge, leading to understanding what quantity and type of assembliesto build at any time with the ability to do so in an increasingly self-directed manner. For Instron,the process that was proposed resulted in single piece flow of assembled product with a specifieddaily output quantity to match customer demand. This can be contrasted to the previous outputthat was measured in weeks and months. Implementation structure included four main elements:calculating demand quantities, level-loading production, creating strategically placed kanbans,and establishing decision rules that governed the daily work practices.

5.2.1 Capacity AnalysisCapacity requirements for each major product were used to calculate the required number ofstations per line and number of assembly operators. These requirements were established bycombining demand with the required assembly/test times. Establishing these times was nottrivial. Although standard times were utilized elsewhere in labor reporting, it was not clear ifthey were accurate; they had not been recently updated to reflect learning cycles that couldpotentially reduce times over those originally recorded, nor have they been updated to reflectproduct design refinements. Therefore the methodology to establish accurate assembly and testtimes was to collect data directly from the assembly operations.

Data was collected directly from the assembly operators using prepared time sheets that wereattached to each product assembled for a six-month period. Each operator provided informationincluding initial start date of the product, assembly and test time durations for each process step,the additional time required due to non-assignable problems, a short description of theseproblems, and the completion date. Refer to Appendix A for an example of this time sheet.Information available from this data included:

1. Number of average throughput days from assembly start to final product2. Variation in number of throughput days3. Active operator time (in hours) required at each assembly and test process step4. Variation in active time required for each assembly and test process step5. Percentage of time required for non-assignable problems

These values were combined with the required weekly customer demand quantities to determinethe staffing needs for each assembly process. In addition, calculation of each subassembly timeallowed bottlenecks in the operation to be identified that led to optimizing the subassemblykanban placement strategy. Last, the data was used to demonstrate the baseline value-added time

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for each machine during the process. The results of the data collection were organized into aspreadsheet-based planning tool for dynamically calculating the cells' staffing requirementsdepending on the output demand per week to be used for present and future line capacitycalculations. This spreadsheet has been outlined in Appendix B.

System time requirements for one example Electromechanical product line were summarized inTable 2. Using these time requirements, an example capacity calculation from the planning toolhas been presented in Table 3. Inputs included number of product demanded, time per product,available number of shifts and hours per shift, the line uptime factor, and the amount of overtimeauthorized per time period per worker. Output included takt time, number of operators requiredper product line, and the minimum number of stations needed to support takt time (equal tonumber of operators assuming one operator controls one station at minimum.)

Procedure Tray Top End Integration Test Finish Audit Total TimePer Machine

Mean (hrs) 2.20 1.44 1.00 3.48 0.93 1.45 10.50

Deviation (hrs) 0.26 0.50 0.10 0.80 0.19 0.28 1.99

StandardTime (hrs) 2.00 1.50 0.70 3.30 0.50 1.50 9.50

Table 2: Example System Assembly/Test Times From The Electromechanical Product Line

Table 3: Example Calculation Results for Takt Time and # OperatorsFor Sample Electromechanical Product Line

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Total Hours Per Machine 10.50Average Week Demand 10Total Hours Required 105.00Available Hours Per Shift 8Number of Hours Authorized for 0Overtime per PersonUptime Factor .875Number of Shifts per Day 1Number of Days Per Week 5Calculated Takt Time 3.50(hrs/unit/station)Number of Operators(Minimum # Stations) 3.0

These calculations established the baseline capacity requirements based on average test andassembly times. However, a problem arose in using these average numbers. Variations withinassembly and test times was inherent in the given product line and its various models. Thesevariations were attributed to both "assignable" and "non-assignable" causes. Machine modelsand accompanying accessories that require additional time for both assembly and testing due tospecific model complexity were "assignable" causes of variation. "Non-assignable" causesincluded problems encountered during assembly and test that require additional time to diagnoseand correct. Combined, these variations often resulted in wide distributions of total requiredtime. The distribution of test times realized for one Electromechanical model has been outlinedin Figure 14 as one example of the variation that existed in the process

Figure 14: Test Times Distribution for Sample Electromechanical Product Line

This test time data shown in Figure 14 included both assignable and non-assignable causes. Itwas true that the problems resulting from non-assignable causes have to be addressed andcorrected over time. However, the assignable portion of variation cannot totally be removed,which required the process to be designed with flexibility to account for limited variations. Thenext implementation segments, including level-loading the assembly schedule, controlling WIPand output through kanban placement, and establishing decision rules, provided control whileaccounting for variation.

5.2.2 Level Loading the Assembly ScheduleTo maintain overall average production times (excluding non-assignable problem times) in theprocess with assembly time variability between models, the weekly production schedule wasleveled by sequencing the order of models built by total required assembly/test time. This wasbest demonstrated through an example (using sample time variations):

50

0.E- 15-~

010-

5

40N b '1' <0q , ~(

Hours to Test

Model A: Test Time 3 hoursModel B: Test Time 4 hoursModel C: Test Time 5 hours

Given a demand of three for each model, there were three most likely assembly scenarios thatwould have resulted. First, machines were built in ascending test time requirements(AAABBBCCC). Second, machines were built in descending test time requirements(CCCBBBAAA). Third, machines were built in a leveled manner (ABCABCABC). The thirdapproach was most appropriate to reinforce our process of smoothing production flow. Itprovided an average time requirement of 4 hours that is repeated 3 times. Output to shippingwas consistent per each set of three machines.

A similar system was implemented at Instron. The list of weekly orders for each respectiveproduct family was first sorted from highest to lowest total dollar value. The order dollar valueexhibited high correlation with the model complexity within a machine family and with thenumber of accessories ordered, both which required additional system assembly and test time.These orders were then ranked in alternating order, assembling one high dollar value system thenone lower dollar value system. This provided a more leveled production process duringassembly over multiple orders.

5.2.3 Pull Production, Assembly Kanbans and Strategically Placed Work In ProcessA description of the production flow technique that was established can be simply described as"Build to the Hole." Assembly was triggered from the end of the line forward, to create a "pull"activity starting with the end operation. As product was completed in any one station, the actionsignaled the preceding workstation to complete another assembly for that station to "fill" the holethat was created by removing finished product. This utilized the material from upstreamKanbans setting the chain of production activity in place. This process continued through allother upstream stations - when subassemblies were removed from the area, they werereplenished from material in the upstream station. Actions (categorized as either assembly ortesting) were triggered by demand from the next downstream workstation, where demand fromthe end of the line drove the actions through all earlier stations.

This "pull" production technique has been demonstrated in Figure 15. The process started at theend of the line (Step 1) with material flowing out to shipping. For each material flow there was acorresponding information flow that was opposite in direction, which led back to earlier stationsto signal where material was needed.

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Parts

Material11 Flow

MaterialFlow

STEP 3

InformationFlow =

AssembleSubs to Fillthe Holes

MaterialFlow

STEP 2

InformationFlow =

AssembleUnit to Fillthe Hole

Figure 15: Pull Production Technique Which Shows Process Steps andOpposing Flows of Material and Information

What controlled this type of system within Instron? The system was triggered by a system ofsubassembly Kanbans. Kanban by definition means "production card." Cards for this lowvolume application were made from 4"x3" plastic-coated clip-on tags. They were attached to thefront shelves of wire racks strategically located in the physical production area as subassemblywork in process (WIP) staging locations. A tag represented a kanban location on the wire shelfto be filled with an identified subassembly - the number of tags present indicated the number ofsubassemblies required to fill the work in process staging to a desired level. Using removablecards allowed easy modification of the amount of subassemblies in WIP as demands and learningchange. Refer to Figure 16 for an example of a tray subassembly Kanban rack.

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SubassemblyKanbans

~El..

.......

Final AssemblyRun-in Kanbans

Final SystemTestand

Finish

.~ ~ [ ~i

ShipUnit

ad

MaterialFlow

STEP 1

InformationFlow =

Test andFinish

AnotherMachine

Figure 16: Electromechanical Kanban Rack Showing Work In Process Staging inQuantities that Correspond to the Number of Kanban Tags

5.2.3.1 Kanban Quantity Calculation:The minimum quantity of kanban tickets to display per staging location was calculated asfollows based on demand requirements (adapted from Nahmius, 1997):

KB = ROUNDUP [D * TT * (1+SS)]

where: KB = Number of kanbansD = Average demand of kanban stock (parts/unit time)TT = Takt time of process stage (hrs)SS = Safety stock fraction (dimensionless)

For a low volume environment, it is most appropriate to use parameters that are measured inweekly demands and hours or days of throughput time since many processes in low volumeenvironments require hours or days to complete. As an example, the number of kanban ticketsrequired for EM base trays is calculated as:

D = 20 units per weekTT = 2.7 hoursSS = 10%

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KB = ROUNDUP [10 units/week * 1 week/35 hrs * 2.7 hours/unit * (1+. 10)] = 1 kanban unit

Initially, kanban levels were set higher than calculated to ensure subassemblies' availabilitywhile the process was introduced. Operators needed to be given time to learn control within the

process and control the resulting work in process. After such learning had occurred, the numberof assemblies staged in kanbans would be reduced over time to the calculated number bychanging the quantity of tags presented on the wire racks.

5.2.3.2 Kanban Locations for Strategic Work in Process (WIP) Placement:It also had to be determined where these Kanban staging racks were to be placed within theprocess. In low volume multistage serial flow assembly environments where variation is reduced

but is still inevitably present, Kanbans are used for three reasons:

1. To create strategic locations of WIP to buffer against production time variations2. To reduce the frequency of starvation of downstream stages of assembly3. To limit the amount of WIP that is built up between process steps

Although a principle lean manufacturing technique was to remove interruptions in the steadystate process, a certain level of variation will always exists in this scenario as described earlier.It was established that a controlled volume of strategically placed WIP buffers would increaseoverall flexibility of this production system to better maintain a consistent flow quantity(Burman et al, 1998). At Instron, this required critical review of the process steps to determinethe optimum kanban placement. It was not optimal to assume Kanban placement at everyprocess step. This would have resulted in excess WIP and too many control points. Kanbanplacement was chosen for three strategic locations, as shown in Figure 17:

1. Frame Run-In2. Base Tray Assembly3. Electronics Assembly

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ElectronicAssembly A

MKBase ATray N

Assembly

N

Tray and FrameTop End Run-in

Integration CycleAssembly

Top EndColumn

Assembly

Load Cellsand

Accessories

Complete System

Frame Load Cell Assembly Audit and ShipCalibration External Inventory Pr etT Housings Update

Figure 17: Process Showing Kanban Placement Locations

System Run-In: Kanbans at run-in provided a visual indication to the number of machinesrequired from assembly each day to be staged for test and ship the following day. To provideflow, testing required consistent product volumes to be processed through the overnight run-incycle every day. If machines were not prepared for run-in one day, the limitation would carry tothe next day since testing machines could not be completed. The run-in kanban provided visualindication and limitation to that daily requirement.

Base Tray Assembly: Kanbans at base tray assembly were used as a buffer against variations intest time. Part of the new process included having operators become responsible for bothassembling and testing complete systems, creating a more flexible workforce where operatorswould flex between assembly and test stations. However, with flexibility came coordinationproblems. For instance, potential testing difficulties led to additional test hours for a givenmachine downstream of assembly, utilizing capacity that would have otherwise been rotated tothe front of the line to complete assemblies for the next day. Base tray kanbans were filled whenoperations were on schedule and time during normal operations was available. When extendedtime was needed further down the line to test product, time to build trays for the next day wasabsorbed by temporarily depleting the kanban. This prevented stalling the front of the line.Kanbans therefore provided both a time buffer to allow output to remain stable and a visual limitmechanism to control subassembly WIP during normal operations.

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Common Assembly: Kanbans for small common assemblies used in multiple products providedadditional support from each individual line. The assemblies were simply assembled by flexibleworkers to capacities calculated based on total demands. Again, this increased the flexibility ofeach line by not having to use time to build these small assemblies, and further controlled theamount of work in process for common assemblies between the three lines.

Assembling to kanbans in this environment also required coordination to the specific productorders. Custom system configurations existed, again due to model variations, which influencedthe required subassembly configurations. Because of these configurations, generic assembliescould not always be built to stage in Kanbans. Sequencing the building of subassemblies withinthe Kanbans was therefore required according to specific orders in the leveled build schedule.The operators began the assembly process for any machine by extracting the customer order dataon a printed sheet, which included the configuration information. The sheet remained with theassembly throughout its time in manufacturing, including when staged in Kanban locations. Thisclarified the model of subassembly in the kanban and provided visual indication as to whatassemblies to produce in downstream stations.

5.2.4 Decision Rules Govern Work Process:Kanban indicators showed the type and quantity of assemblies to build, but they did not conveythe daily work structure. Along with the visual Kanban indicators, complementary decision ruleswere created and applied to the process to guide the operators in making daily decisions on whatto complete throughout a given shift. Indeed, the Kanban "holes" showed the need, but thoseneeds also had to be prioritized when workers were expected to service multiple process steps.Having a limited number of rules to govern the process allowed the operators to decide thespecific activities at any given time but still remain within bounds of a process that ensured thatdaily quantity requirements were consistently met. As an example, the following decision ruleswere set for the EM and Hardness assembly process at Instron:

1. Each day, first test the required # of machines to ship that day. Complete testing andfinishing to allow product shipment.

2. Use subassemblies staged in kanbans to complete fully assembled systems to refill therun-in cycling kanban.

3. Use remaining time to refill subassembly kanbans.

Rule number one ensured that the first actions of the day were focused on the shipment ofproduct. This also worked to deplete the Kanban of machines that were run-in the previous nightand reset the visual indicator that forced assembly to "Fill the Hole" at the run-in Kanbanlocation upstream of testing by the day's end. Rules number two and three created orderlybackfilling of kanbans in order of importance to get the next product completed. These decisionrules were closely integrated with the concept of pull production and kanban control, again tomove material down the line and information back up the line.

Starting at the end of an assembly process was found to be counter-intuitive to some operatorsand managers. The original daily routine was often started by building subassemblies, followed

56

by testing the product at undefined times during the day. Often, machines were queued waitingto be tested; other times no machines were available to be tested on any given day because ofassembly difficulties earlier in the process, leading to zero shipments that day. In the originalprocess, operators often complained that there was not enough time to complete the work andthere was not enough floor space to handle the work in process. This further created chaos onthe floor since it became difficult to control many random stages of production.

Completing the testing and finishing first, followed by assembly to refill the assembly kanbanlocations, ensured a consistent quantity of product shipped every day. This also set the daily linepace and allows management to more easily visualize the production status at any given time ofeach day.

The question arose, why complete the testing first? Why not finish, audit, or assemble first?Referring to Table 4 for system time requirements, the system bottleneck was the testingoperation (Goldratt, 1992). This was both the most time-consuming sub-process and the onewith the greatest variation. Testing first every day ensured time to satisfy the process bottleneck.

Tray Top Integration Test Finish Audit TotalEnd

Mean Time (hr) 2.20 1.55 1.00 3.48 0.93 1.45 10.50% Time 21 15 10 33 9 14 100

St. Dev. (hr) 0.26 0.50 0.10 0.80 0.19 0.28 NA

Table 4: Time Requirements per Process Step forElectromechanical System Assembly/Test

From Table 4, the testing time requires the greatest concentration of labor capacity, and alsoshowed the greatest labor capacity variation. Given this variation and the process in whichworkers moved from station to station between assembly and test, testing any one model oftenconsumed labor capacity that would normally be used within assembly. However, onceproblems arose in testing, the tray kanbans acted as time buffers so that assembly did notimmediately have to rely on the labor capacity from those workers who were testing.

Table 5 further exemplified the need for WIP buffers in assembly. Given a capacity to producetwo machines per day on one line and labor equal to the average time requirements, it required 3operators on average. What happened if the "average" was extended due to additional testing?

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Average Times for Assembly/Test per Day Hrs# Machines/day 2Total average time required 21Time/day/operator 7# Operators 3Total time available 21

Extended Times - Nonassignable ProblemsTotal average time required 212*St Devs of test time on each machine 1.2Total time required with one machine over average 22.2Time available 21Time to buffer in building trays for kanbans 1.2 minimum

Table 5: Example of Extended Time Requirements Given Nonassignable Problems

As shown, an additional 1.2 hours was required within the daily assembly to maintain output oftwo machines. This 1.2 hours was buffered into the kanban WIP by having a calculated numberof base trays and small assemblies ready to be consumed.

In summary, all four concepts of capacity management, level scheduling, kanban creation &placement, and daily work decision rules were designed to work as one complementary system.With this assembly process in place, the next concern was inventory management to ensureconsistent part supply into the production process. Inventory stock levels required alignmentwith the process demands. The next chapter outlined a methodology for inventory control toalign inventory levels with this newly improved manufacturing "pull" process.

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ALIGNMENT OF INVENTORY AND MANUFACTURING PROCESSES

A chosen manufacturing strategy strongly influences the quantity and type of inventory amanufacturer carries. The selected manufacturing process (assemble to stock, assemble to order,build to order) as well as the assembly methods utilized (manual or automated processes) andprocess metrics, each have to be identified before calculating inventory quantities. Inventorypolicy for an assemble-to-order system that carries purchased parts and subassemblies asinventory was reviewed and improved through the analysis outlined in this chapter.

The uniqueness of cyclical production demands and how such demands influenced the supplychain of incoming material was strongly considered in this analysis. Two phases of inventorycontrol were developed. The first phase established a process to achieve minimum stockquantities and lot sizes to adequately supply the existing assemble-to-order process with itscyclical demands. The second demonstrated how inventory levels could be reduced wheninventory management is coordinated with lean manufacturing process management.

6.1 Setting Proper Inventory Control Measures - The Hidden Costs of IndependentMetrics:Effects of proper inventory control extend beyond internal company boundaries. How acompany controls its inventory affects its ability to satisfy customer demands as well as itsvendor relations. If based on the wrong metric, inventory policy can work against leanoperations in unforeseen ways.

Traditionally, the trend in inventory policy has been to continually reduce inventory levels,constantly monitored by measuring the number of inventory turns realized per year. It is truethat increasing turns leads to improved cash flow. However, continuing to drive down inventorylevels to achieve higher turns without regard to determining the appropriate level of inventory tofulfill manufacturing demand often leads to process problems. Unforeseen chaos can occurwhen increasing inventory turns are not coordinated with suppliers, who are unable to supplywith increasing shipment frequency given their own capacities and shop metrics. Thus, thequestion arises, "How much inventory should be carried?" "As little as possible" is not alwaysthe right answer.

Alignment of inventory levels with manufacturing's assembly metrics is first required.Manufacturing was primarily measured by "On-time" customer shipments with a secondarymeasurement of product throughput time to shipping. Parts to complete assembly must bereadily available in the factory; otherwise flow times increases as assembly is stalled waiting forparts to arrive. Part shortages are therefore a major concern. One reason for the occurrence ofshortages is that inventory is often "leaned out" too far to support the ongoing assembly processand its given cyclical variations. It is true that these variations ultimately need to be reduced, butthey exist in the short term, and must be carefully managed to provide desired output.

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/ IU /

Minimum inventory threshold levels must be established. They must be aligned and coordinatedwith both the internal manufacturing requirements and the external suppliers' capacities. Thesupplier must be considered an extension of the parent manufacturer, with consideration of thesuppliers' capacities in setting supply lot sizes and inventory delivery frequencies. Otherwise,the supplier is forced to either hold large amounts of inventory or continuously try to "catch up"with the needs of the manufacturer while falling further and further behind in his own productionschedule.

A vendor supply is bounded by the agreed upon delivery lead time. Once a lot is pulled from thevendor's supply, that vendor must be given the full replenishment lead time before another lot ispulled. If material is demanded before the vendor replenishes his own supply, chaos at both thevendor and manufacturer can ensue. On the supply side, an "inventory pull" before its time setsup an "effective" longer lead time felt by the manufacturer, since the lead time for the new orderwill include the time until the existing order is completed plus the full lead time for the neworder. At the parent manufacturing site, stockout situations will likely occur since any safetystock may be inadequate to cover this longer effective lead time. This often follows by forcingabrupt manual intervention from material planners to try to shorten the supplier's lead times forthat order.

The problems with "pulling" inventory from suppliers more frequently than they can provide toachieve a high number of turns is demonstrated in Figure 18. This shows inventory stock levelsat the parent manufacturer over six time periods. For simplicity, it assumes lead time (LT) is theagreed upon lead-time of the supplier, equal to one time period, and the duration of time for onecyclical demand cycle to be completed is equal to 3 lead time periods. Further, the supplier caninstantly replenish at the end of any lead time period. It also assumes that lot sizes cannot bechanged on every order and suppliers cannot provide partial shipments. Given existing lowreplenishment inventory lot sizes to provide high number of turns and low minimum inventorylevels, periods at the end of a time quarter with higher manufacturing demands start toexperience stockout conditions. Since lot sizes cannot be instantly increased (a reasonableassumption since suppliers need time to react to changes), the manufacturer falls further andfurther behind through the quarter. These stock outs can occur at the end of every quarter,leading to missed shipments and lost revenues for the parent manufacturer. Further, they alsolead to panic ordering, ordering from other vendors, scavenging for parts, and resultant firefighting.

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INVENTORYQUANTITY INVENTORY

DEMANDPATTERN

LEAD TIMEDEMAND+SS

QUANTITY ---

STOCKOUTS STOCKOUTS

EXISTINGLOTSIZE

EXISTING -- -- -- - ---....- - . .- -- .MIN

QUANTITY -0-

LT1 LT2 LT3 LT4 LT5 LT6

LT=LEAD TIME PER LOT OF INVENTORY

Figure 18: Original "Low as Possible" Inventory Levels Showing the Potential of Stockouts

Figure 19 shows how alignment of the parent manufacturer's demand and vendor supply lead-time, through calculating lot sizes and minimum inventory quantities, leads to a more stableprocess with little or no stock-outs occurring. The inventory is now carried to satisfy demandsand be within bounds of the suppliers' lead time. As seen, because demand does not utilize thefull lot sizes every time period, there are some times in which lots are not pulled as frequently asone LT period, shown as excess time periods beyond the LT duration. However, when thehigher demand months arrive, the calculated lot sizes statistically satisfy the demand.

INVENTORY INVENTORYQUANTITY DEMAND

LEAD TIME PA RNDEMAND+SS

QUANTITY

CALCULATEDLOT-SIZE -

CALCULATED - -------- ------- - -- -MIN

QUANTITY

0 LT1 LT2 LT3 LT4 L LT6

TIME BEYOND LEAD TIME THATINVENTORY IS AVAILABLE

LT=LEAD TIME PER LOT OF INVENTORY

Figure 19: Inventory Levels Calculated To Statistically Satisfy Demandand Remove Stockout Conditions

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Incorporating the qualitative concepts of inventory management discussed above, the appropriatelot sizes and minimum levels to achieve balanced inventory control can now be calculated.These calculations have been outlined in the next section.

6.2 Inventory Management Calculations:When setting inventory levels, three values have to be determined:

1. The frequency of reviewing inventory levels2. The minimum level of inventory at which time replenishment inventory is ordered3. The quantity of individual inventory items to order

6.2.1 Frequency of Inventory Review:Inventory control is most responsive when reviewed on a continuous basis. This removes alldelays between the time inventory reaches a minimum level and the time that level is reviewed.Responsibility for this review needs to be established with those who are in contact with theinventory most frequently, namely manufacturing operators and materials handling personnel.Further, the importance of proper and timely review needs to be enforced to establish aprocedure that is clearly understood and routinely performed by all personnel. The consequencesof not regularly reviewing inventory levels results in potential inventory stock outs. Thereforethe importance of inventory reviews must be clearly understood.

How should reviews be completed? Each stock location contains a segregated minimum amountof part inventory and a corresponding stock "pull" Kanban card on which is written theminimum bin reserve quantity for that part. The Kanban cards act as trigger mechanisms forstock replenishment. As the segregated minimum inventory quantity is reached, the card ispulled from the stock location, triggering the purchasing department to order another lot of parts.

Continuously reviewing and ordering inventory when needed versus periodic reviews ofinventory levels minimizes stockout conditions where minimum levels are exceeded due to timelags between review periods. Further, shorter review times leads to less required safety stock (ascalculated below) because there is less effective "lead time," leading to overall lower requiredinventory levels.

6.2.2 Determining the Minimum Reorder Points (ROP):Bin minimum level is determined by the parts' average usage demand, variations in demand, andvendor resupply lead times. This minimum reorder point (ROP) is the sum of average demandover the lead-time (DOLT) plus a level of safety stock (SS) to protect against stockouts thatoccur from demand variations.

ROP=DOLT + SS

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The two components of the ROP need to be calculated separately. Average demand over leadtime (DOLT) is calculated by multiplying the demand per given time unit (m) times the supplierlead time (LT):

DOLT = (i) * (LT)

DOLT is the average manufacturing demand over the vendor's resupply lead time. Demand pergiven time can be either required forecasted demand or historically calculated demand. The leadtime is the time it takes a vendor to supply the manufacturer with a new lot of materials. If avendor builds inventory to stock, then LT is simply the time required to order and ship productfrom the vendor to the parent manufacturer. If a vendor manufactures the lot during this leadtime, then LT is defined as the time it takes the vendor to manufacturer the product plus the timefor ordering and shipping.

The DOLT calculation does not consider variations in demand patterns. Such variation couldincrease demand to a point that outstrips the supply. To cover such increases in demand througha given lead time period, safety stock must be added to the average inventory level to bufferagainst variations.

Safety Stock (SS) is based on the statistical probability that demand could be higher thanaverage. It incorporates the standard deviation of demand over the chosen time period and thechosen probability that the part will remain in stock over the lead time. The stocking probabilityis typically between values of 95% and 99% depending on product. This probability is thentranslated into a z-statistic value corresponding to a normal distribution at the given probabilitylevel (Vining, 1998). Caution must be used when setting this probability. Setting it too high(100%) guarantees greater material availability but also increases inventory levels dramaticallybased the extreme tails of a normal distribution curve.

The safety stock is calculated from the square root of demand variance (i.e. standard deviation T)times the square root of the given lead time as a number (z) of standard deviations of demand.

SS = a * z * (LT) 2

The resulting Reorder Point (ROP) value provides a calculated amount of buffered inventory toensure stock is available to cover the full probabilistic demand over the supplier's lead time.

6.2.3 Lot Size Order Quantities: Should EOQ Theory Be Used?The final number to calculate is the order quantity. Once the minimum inventory quantity isreached, what quantity should be reordered? In theory, this quantity can be determined by usingthe Economic Order Quantity (EOQ) method. EOQ balances individual order costs withinventory holding costs to determine the optimum lot order size that minimizes aggregatecorporate costs of buying and holding inventory.

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EOQ = (2 * C *D / h)"

In the EOQ formula, "C" is the order cost, "D" is the annual demand, and "h" is the holding cost(calculated by multiplying standard cost of an inventory item by a holding cost percentage).

What are the realities of using this calculation? For Instron, multiple functional departmentsagreed that inventory holding cost per year is 25%. However, a single value for order cost wasnot so easy to establish. Ranges of values with up to 100% variation ($40 to $80) were providedby various Instron departments. The value of order cost has a significant impact on the quantityof inventory ordered at one time, leading to variations in the number of inventory turns anddollar value of average inventory held.

Conducting a sensitivity analysis on representative inventory clarifies the variations in inventorylevels that result from changes in the order cost. Using the highest cost and volume SKU from arepresentative Instron product Bill of Materials, Figure 20 shows the order quantity variationsthat result from using an EOQ calculation when the order cost is varied from $40 to $80 perorder.

Figure 20: EOQ Inventory Sensitivity Analysis Demonstrating Change inPart Order Quantity When Order Cost is Varied

For this single part, the increase in order cost relates directly to increases in inventory valties. Inthis example, inventory would be increased on average by almost $1500 for a part valued atnearly $500/unit when order costs are increased from $40 to $80.

Combined with an alternate method of adding overhead order costs as burden rates to everypiece of material, choosing one value for individual order costs is not often realistic. This leadsone to question the use of the EOQ formula as a method for calculating lot sizes. Further, EOQ

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24.00

E 22.00

20.00

18.00

0 1600

C 14.00

I 12.00

40 50 60 70 80

Order Cost ($)

theory does not consider the dynamics of a responsive assembly process nor does it consider thequalitative measures of varying inventory levels. EOQ-generated lot sizes fail to quantify thefollowing metrics (Suri, 1998):

1. Cost of poor quality. Large order quantities that save on order cost may increase thenumber of quality defects purchased in each lot per given unit of time. For instance, if amachine operation causes part flaws and the lot of flawed parts is shipped within a largeorder quantity, time is wasted to both produce that large lot of flawed parts and to work downstored inventory to discover those flaws.

2. Cost of obsolescence. Design changes often call for changes to ordered parts to comply withupdated designs. Parts ordered in large quantities could remain on the shelf long enough tobecome obsolete in design or standard, calling for additional rework or scrap costs.

3. Cost of long order lead times. Placing large orders to save order costs may cost more intime since large orders may have extended lead times, potentially causing greater variationsand order fulfillment problems. This can result in an upward lead time spiral by having evenlarger quantities ordered in the future to satisfy the demands over ever-longer lead times.

4. Market value of responsiveness. Sales may be connected to when a customer can receivethe finished product. Long lead times may therefore deter customers. Short lead timesmeans the product is more readily available and can be attractive to customers. Smaller lotsizes may help in obtaining parts in less time to fulfill such orders.

Overall, the Economic Order Quantity (EOQ) theory does not incorporate critical considerationsthat can lead to higher overall costs than those considered in the initial order process. Due tothese issues, EOQ is not recommended to determine order lot sizes. The question then arises asto how lot sizes should be generated. Based on experiences at Instron with the desire to maintainthe inventory turns metric yet establish acceptable limits based on demand patterns, it wasdetermined that material planners' intervention and vendor involvement, combined withminimum ROP calculations, were the best sources of knowledge to arrive at acceptable lot sizes.

Assuming vendors do not hold finished goods inventory, bounds of lot size calculation are firstestablished from the minimum reorder quantity based on the suppliers full manufacturing lead-time. For instance, if a supplier requires four weeks to produce a lot of parts plus one week forshipping, then the minimum order quantity is the DOLT + SS based on five full weeks of leadtime. The vendors' capacities and agreements to hold inventory further influence lot sizes.Shorter lead times, and therefore smaller lot sizes, are possible if vendor agreements include risksharing to hold some inventory for immediate delivery. Further, it may be desired to produceextremely small lots very frequently. This removes the cost of holding inventory from both theparent company and vendor. However, the vendors' internal capacities may call for larger thandesired lot sized to be produced, leading to a need for either party to hold the inventory. Last,internal manufacturing and purchasing time capacities to place, track, receive, and stockquantities of orders also influence the actual order quantities. It may be true that parts can beordered very frequently in low quantities from the vendor, but a given receiving capacity at theparent manufacturer may not be able to handle the high frequency of incoming small-quantityorders.

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6.3 Proper Inventory Level for Instron Electromechanical Production:For Instron EM, setting proper minimum levels and order quantities was initiated by a divide andconquer technique between part categories. To demonstrate the inventory management pullprocess, one category of wire harness assemblies from a single supplier was used. As part of thisinitial work, an inventory model was created to assist in completing the calculations forstatistical demand quantities and reorder points outlined earlier. The model has been included inAppendix C.

6.3.1 Inventory Classified According to Distribution By Value Calculations:Every part of Instron's assemblies did not have to be controlled with equal effort to provideoverall inventory management. Using the collective inventory of wire harnesses as a singleexample, there were over 400 wire harness inventory items to consider. Controlling such largenumbers of individual items became unmanageable. Distribution By Value (DBV) was themethod used to rank the highest value inventory items to be managed with the greatest scrutinyand highest frequency, which provided the greatest overall cost savings.

The Distribution by Value method was used on the sample wire harness inventory by firstmultiplying the standard cost of each inventory item under analysis with its annual usage. ACost-Volume (CV) value resulted from this calculation for each inventory item. The list ofinventory items was then sorted in descending order of the Cost-Volume value. Graphing thecumulative total of Cost-Volume values vs. the cumulative total number of items led to theresults shown in Figure 21. Refer to Appendix C for the representative spreadsheet calculationsthat demonstrated the Distribution by Value method in greater detail.

A B C1.10-

E 1 00 -0.90

S0.40S0.30

0.200 010

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0Cumulative Percentage of Analyzed Inventory Items

Figure 21: Distribution by Value Results for Wire Harness Inventory Items That ShowsThree Groups of Items A,B,C Distinguished by Cost-Volume Values

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As shown in Figure 21, the first 20% of wire harness parts contributed 65% to the total Cost-Volume value, with 50% of the parts contributing over 90% of the total Cost-Volume. (Notethese percentage cutoff values were subjectively chosen by the author and can be chosen torepresent any similar range of inventory). After the parts were analyzed, each part was classifiedinto three categories, A, B, and C. "A" parts were those with the highest Cost-Volume valuesand required the most active management on an individual basis. At the other extreme were "C"parts, typically low cost items such as hardware which would be purchased in higher volumesand did not require careful inventory value analysis. In between were "B" parts. These wouldbe managed using the inventory model calculations for minimum quantities and reorder points,but did not require the individual in-depth scrutiny as that of "A" parts.

After reviewing the Cost-Volume results for all wire harness parts (Appendix C), the first tenparts were classified "A", the next fifteen parts were classified as "B" and the last 50% of allitems were classified as "C." The "A" classified parts' minimum bin and reorder quantities werefirst calculated using the inventory model. Since these were the highest CV parts, eachcalculated minimum quantity required further scrutiny and possible minor adjustment bymaterial planners who used future forecasted data to achieve adequate coverage with the highestpossible inventory turns. Class "B" minimum inventory quantities were simply calculated withno additional management interaction. Last, for class "C" parts, it was proposed to order suchparts in larger bulk quantities to cover multiple months to avoid actively management of thoseparts on a regular basis.

6.3.2 Example Minimum Level Calculations for Class "A" Part:Using the highest ranked Class A part from the wire harness list, calculations of minimum orderquantity and bin reserve quantity have been outlined below. For more in depth inventory modelreview, the model created to calculate individual order quantities based on required demand anddesired statistical coverage has been outlined in Appendix D.

Calculation of minimums began with analysis of the part's monthly usage data (only 11 monthswere available from the Instron data system) as shown in Table 6.

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INVENTORY DEMANDMonth Usage

Jan 11Feb 21Mar 25Apr 15May 17Jun 20Jul 16

Aug 8Sept 19Oct 15Nov 15

INVENTORY PARAMETERSMonth Demand Average 17Month Demand St Dev 5Manufacturing Lead Time 28 daysResupply Shipping Lead Time 7 daysStocking Probability 97.5%Z statistic 1.96

Table 6: Inventory Data for Sample Wire Harness Component

The average demand over the vendor's lead time (DOLT) was equal to the average monthlyvalue times the lead time converted to monthly units:

DOLT = l7units per month*35days*l2months/365days = 19.5 units

The required safety stock corresponding to the 97.5% in-stock probability was equal to thestandard deviation times the (z) variable times the square root of the lead-time adjusted formonths:

SS = 5*1.96*(35days*lmonth/30days) 1/2 = 10 units

The total of the average demand and safety stock over the lead-time was the minimum lot ordersize (rounded to whole number):

Minimum Lot Size = 19.5 + 10 = 30 units

Again, this was the minimum lot size to satisfy 97.5% probability of variations in demand atfinal assembly during the vendor's lead time. This was also the minimum bin quantity thatwould trigger an order for another lot of parts. It was expected that during the lead time, theaverage number of units would be consumed, leaving the safety stock quantity remaining oncethe new lot was delivered. In cases where demand exceeded average, some safety stock wouldhave been consumed.

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From these calculations, it was proven that two quantities really drove the levels of additionalsafety stock inventory. First was the month-to-month variation in demand (from which standarddeviation was derived). Second was the vendor lead time. If either or both of these values wasreduced, the overall amount of inventory was also reduced, which saved on holding costs andallowing higher inventory turns.

The above numbers showed the importance of well-maintained vendor relationships. In thisexample, the original lead-time for wire assemblies was 49 days. This included time for apurchase order to be initiated and certified by the wire vendor. To shorten lead times to 35 days,arrangements with the vendor were made to pre-certify purchase orders so that the time delaysdue to financial review were removed from lead time values. This simple arrangement allowed areduction in inventory safety stock levels of almost 25%.

The inventory analysis outlined in this work considered one subset of a single vendor's parts forone product division at Instron. It was meant to provide a model for inventory analysis thatshould be used to analyze the remaining parts with the Electromechanical and Hardness divisionsand across inventory for other product divisions within Instron.

As demonstrated above, variation in manufacturing demand drove inventory levels. AlthoughInstron experienced cyclical demand from its customers, reducing this cyclical variation inmanufacturing could significantly reduce required inventory levels. This potential inventorystrategy has been outlined in the next section as a significant means to demonstrate the savingsfrom using a Lean, linear production method.

6.4 Linearized Assembly Output Enables Inventory Reductions:The progression towards assembling at a constant rate (described earlier in Chapter 5) brought atremendous opportunity for reductions in corresponding inventory levels. The traditional end ofquarter ramp up in sales requires parts inventory that was available to satisfy higher than averagedemands. However, carrying inventory at this level throughout the whole year increased holdingcosts. If the linear production method was implemented, inventory levels could also besignificantly reduced, leading to increased number of inventory turns per year.

Taking another part that is used consistently in the Electromechanical product line at Instron asexample inventory, cost and volume differences were derived between inventory levels tosupport the traditional variations of monthly usage vs. inventory levels to support a linearproduction demand pattern. Table 7 summarized the inventory data for this representative partbased on the historical usage of the part for the past 12 months.

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INVENTORY DEMANDMonth Usage

Jan 12Feb 23Mar 39Apr 15May 20Jun 38Jul 16

Aug 24Sept 40Oct 18Nov 26Dec 42

INVENTORY PARAMETERSMonth Demand Average 26Month Demand St Dev 11Manufacturing Lead Time 28 daysResupply Shipping Lead Time 7 daysStocking Probability 97.5%Z statistic 1.96

Table 7: Inventory Data for Sample Inventory Item Used toDemonstrate Inventory Savings from Linear Assembly Methods

Using the inventory calculations presented earlier with a vendor manufacturing lead time of 28days and a resupply time to Instron of 7 days (assuming the vendor holds one lot of material onthe shelf that was ready to ship) and 97.5% stocking, the following inventory levels werederived:

Minimum Lot Size Analysis (Based on Full Lead Time of One Lot):Demand Over Full Lead Time = 26 units/month*35days*month/30days = 30.4 unitsSafety Stock Over Lead Time = 1.96*11 *(35days*month/30days)1' = 22.9 unitsMinimum Reorder Quantity = 53.6 = 54 units

Minimum Bin Reserve Level (Based on Resupplv Time from Vendor Stock):Demand Over Resupply Time = 26units/month*7days*month/30days = 6.1 unitsSafety Stock Over Resupply Time = 1.96*11 *(7*month/30days)1 = 10.4 unitsMinimum Bin Level Reorder Point = 16.5 = 17 units

These values were compared to those that resulted using linear demand at final assembly. Asdescribed earlier, linear demand was based on both prior historical aggregate demand and futureforecasted demands. The production schedule would be set to produce 6.0 units per week withan allowed demand variance in any week of 1.0 machine with the same 97.5% in-stockprobability. Based on this demand pattern with such controlled variation, the following revisedvalues of minimum lot size and minimum bin reserve quantity were calculated.

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Minimum Lot Size Analysis (Based on Full Lead Time of One Lot):Demand Over Full Lead Time = 6 units per week*35days*52weeks/365days = 30.0 unitsSafety Stock Over Full Lead Time = 1.96*(1.0*35days*52weeks/365days)/2 = 4.4 unitsMinimum Reorder Quantity = 30.0 + 4.4 = 34.4 = 35 units

Minimum Bin Reserve Level (Based on Resupplv Time from Vendor Stock):Demand Over Resupply Time = 6 units per week*7days*52weeks/365days = 6.0 unitsSafety Stock Over Resupply Time = 1.96*(1.0*7days*52weeks/365days) 2 = 1.9 unitsBin Minimum Level = 6.0 + 1.9 = 7.9 = 8 units

This analysis has shown that reduced production demand variations translated into significantinventory lot size and minimum bin level reductions. Translated into Instron's metric ofinventory turns, this one part demonstrated a potential for a 53% increase in number of inventoryturns per year.

Cyclical Demand Constant DemandAverage Yearly Demand 312 312

Reorder Quantity 54 35Minimum Bin Quantity 17 8

Potential # Turns per Year 5.8 8.9

How was leveled control in inventory initiated? The production side was already discussed,requiring involvement from manufacturing, marketing and sales to provide consistent salestactics and understand limitations of demand increases. The supply side had additionalrequirements for creating such a consistent system. The demands on the supplier needed to bereasonably stable within a defined time period. This stability was accomplished in agreement byboth vendor and parent manufacturer on an acceptable range of demand variance (increases ordecreases) over a given time horizon. In the example above, the variance was limited to plus orminus 1/6 the level of parts normally ordered in any one week. The orders that resulted from anincreased or decreased demand had to be fulfilled without affecting the supplier's lead-time. Ifincreases greater than the agreed upon amount were necessary, the time for the supplier to rampup inventory levels was provided with an agreed upon time horizon.

This chapter provided an inventory analysis method to align inventory levels with productionoutput demand, variations in demand, supplier lead times, and statistical stock-out occurrences.In addition, the inventory analysis was used to demonstrate the potential reductions in inventoryand corresponding increases in inventory turns that were realized when the Lean linearproduction method was implemented. The reduction in inventory was another tangible benefitthat also clearly demonstrated the importance of an integrated Lean Manufacturing systemimplementation.

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77 RESULTS AND RECOMMENDATIONS

A Lean Manufacturing process was successfully developed and implemented for a low-volumeassembly manufacturing operation. Numerous improvements were realized through theapplication of Lean Manufacturing in the experimental setting of Instron's assembly operation,and have been outlined in this chapter to share the success. A great deal of learning alsooccurred during this project on the implications of implementing Lean techniques in a low-volume cyclical environment. This knowledge has been outlined to further the advance of Leanpractices into other low-volume environments, with the similarities and differences between puretheoretical Lean Manufacturing and the process developed in this work clearly distinguished.Sustaining process improvements beyond the six-month period was also a crucial aspect ofproject success, and the methods to ensure such success have been outlined. Last,recommendations for future continuous improvement opportunities at Instron have beenprovided to complement the improvements completed during the project period.

It must be noted again that it was important to closely integrate the three principles of theproject's focus to achieve the final results:

1. Production process improvements2. Changes to the physical production environment to support the process3. Inventory management methods

All three principles were strongly co-dependent, and process improvement would have been sub-optimized if they were not completed together.

7.1 Results at Instron - Flow Time Decreased by 40% in Electromechanical Production:The pull based production process that was implemented using kanbans and POU inventoryplacement showed significant flow time savings. To clearly quantify the savings, flow time foreach machine was limited to the time in manufacturing operations. It assumed that orders hadbeen approved for manufacturing and that the process was completed once a machine was sent toshipping. Using one product line to quantify improvements - Single Column Electromechanicalproducts - manufacturing flow time was reduced by an average of 40%. Just as important, thevariation in this flow time was also reduced by over 18%. These results were calculated fromproduction over the last two quarters of year 2000. The third quarter data was derived from theold production process before changes were implemented. The fourth quarter data was derivedafter both point of use inventory placement and the kanban production process were Iimplemented. In both quarters, outliers were removed from the data after identification ofassignable causes that resulted in extended flow time for those particular units (Devor, 1992).

Reductions in assembly flow throughput time per individual unit were clearly identified over thissix-month project term. Figure 22 shows the summarized data for flow throughput days permachine produced during this time. The improved production process was started at the

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beginning of quarter four in 2000. Dramatic shifts were seen even at the start of the new processperiod. This was reasonable because the start of each quarter generally showed less demand witha ramp up expected through the quarter, which allowed the new process to most easily beimplemented and tracked starting at the beginning of a quarter.

Individual machines built in order through quarter

Figure 22: Flow Time in Days For an Example Electromechanical ProductQuarter 3 vs. Quarter 4 in Year 2000

7.2 Additional Improvements at Instron:The amount of physical floor space utilized for the new process was also reduced. 1200 sq. ft. ofunderutilized space was removed from the original assembly area, resulting in a 15% reductionin required floor space for an equivalent manufacturing output.

A more qualitative savings occurred in production scheduling and worker task prioritizationthrough the use of pull production. The system alleviated many of the problems facingmanufacturing planners in coordinating the sequence of machines into assemble and determiningwhich orders were waiting to be either started or were already started within process. Limitingthe number of subassemblies per kanban and operating to a daily production output, the newsystem allowed greater visual indication of expected WIP and units ready to ship. Further, theoperators were provided with a straightforward method to prioritize their own daily actions ofassembling and testing. This led to reducing the confusion in coordinating daily activities on thefactory floor and the confusion between production planners and assemblers in determiningwhich orders to schedule and work on.

Sources of variability were identified and controlled more easily using the lean processes. Thereasons for such variations were numerous, but three most important considerations werevariations in assembly times of each model produced, variation in test times of each model, andavailability of in-stock parts inventory.

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U)

cc

Using the pull production assembly method with a daily output schedule, single piece flow ofproduct through each assembly line, kanbans to stage subassemblies, leveled order flow intoproduction and daily decision rules to govern the work process all greatly assisted in reducingthe effects of variation in test and assembly time. The particular focus on a daily productionschedule elevated the importance of achieving a consistent output every day to prevent having tosatisfy large demands at the end of each quarter.

Inventory availability became critical to maintain consistent assembly flow. Such necessitydrove the need for close coordination of production demands with inventory supply availability& supplier lead times. In response to managing these inventory requirements, this thesisprovided a methodology to calculate inventory levels aligned with production demands.Inventory was first classified according to the Cost-Volume value of each inventory item, withthe highest Cost-Volume items receiving the greatest attention for inventory level maintenance.Inventory levels were then derived from average demands, variations in demand, supplier leadtimes, and statistical service levels. After the baseline inventory quantities for the existingdemand were calculated, it was demonstrated that inventory levels could be further reducedthrough reducing demand variations and vendor lead times, the two major contributors to highinventory requirements.

Lean Manufacturing processes also contributed to potential financial gains. Improved orderresponsiveness of the production process led to two potential unit sales increases. First, givenlower lead times, customers may be more inclined to make first time purchases of Instronproduct since capital funding is often available to customers for only short periods of time.Second, a significant business is developing in the market for replacement Electromechanicaland Hardness machines. In this scenario, customers own older Instron equipment in need ofrepair. Instead of repairing such machines, Instron offers a replacement program with a newmodel. From the customers' view, this would often be an optimal solution if new equipment isavailable with short lead time to ensure the customer maintains testing functionality withminimal down time. Therefore, flow time reduction in production can directly impact increasedsales and the company's bottom line financial results.

7.3 Sustaining the Process Improvements:This project initiated the beginning of an ongoing improvement effort. The current and futureprocess developments must be "owned" by management and the workers who will be using thesemethods every day. Actions to ensure this ownership were intentionally started at the beginningof the project. Two internal employee teams were created - one including management and oneincluding all of the operators, to not only allow for learning and buy-in of a new process, but toend up with a group of people with the knowledge to sustain the work after the project termended.

Internal ownership of the top level work structure and integration between manufacturing and thesuppliers was accomplished through continuation of the management team formed at thebeginning of the project. The manufacturing managers were trained in the strategic use ofkanbans as well as the inventory management tools created during the project. Further, theywere trained how to visualize and monitor the ongoing process to ensure consistent daily output.

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Last, they were educated on how to maintain the process when consistent assembly could not becompleted due to inventory or quality issues - for which fallback plans were created to use in theshort term that would complement the steady state process once the problems were resolved. Forinstance, one fallback plan outlined how to begin multiple orders by assembling ahead ofschedule if the testing function cannot be completed due to equipment or part quality issues.Once the problem was resolved, extended time was to be provided for completing test cycles toregulate production and the kanban levels back down to their steady state calculated capacities.

Ownership of the work process itself was transferred directly to the workers within theElectromechanical/Hardness department. The use of the subassembly kanbans and decision rulesprovided a framework for the operators to control daily output, as well as a reference fordiscussions of output with management. It also provided a forum to suggest modifications to thework structure. Last, it provided a standardized method that they have already learned that couldbe applied to the assembly of new products when introduced into manufacturing.

Sustaining the process also called for maintaining consistency of actions within the department.Daily morning communication meetings on the factory floor were initiated during the project.These meetings were used to ensure that the workers realize the importance of the new processas well as have them experience continued involvement from management. Output was alsomonitored daily to ensure consistent output with the plan's expectations as well as ensure that theprocess did not revert back to "fire fighting" the steep incline of demands at the end of quarter.Linearity proved its purpose at the end of the fourth quarter of 2000, reducing overtime and flowdays through the line.

Finally, to sustain the momentum gained during the project, any problems that inhibitedconsistent output had to be resolved quickly. Lack of parts' availability on the floor was oneinstance experienced repeatedly. If parts were not available for linear production, it was seen asa failure of the system since workers could not obtain their daily quotas. Therefore, resolutionwas required quickly to ensure the process did not break down over time.

7.4 New Models Arrive in Manufacturing:The production process was structured to sustain variations in type and quantity of productsproduced. Three main elements were incorporated at Instron to ensure process flexibility as newproducts are introduced in the future:

1. The physical parameters were able to be modified and/or expanded with little cost or timepenalty.

2. The workflow process provided common baseline parameters through kanbans and dailyproduction decision rules, yet allow for modification as needed to suit specific products.

3. Workers were trained with multifunctional skills to allow labor flexibility for new rolesor tasks as product needs changed.

The modularity in the process and layout in this study allowed for product variations to beabsorbed with minimal disruption. Physical arrangement of part locations could be modified andexpanded since all inventory was now staged on wheeled racks and pallets. Floor space was not

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initially filled to capacity on each flow line - allowing future expansion within reasonable limitsfor expected new product introductions. Further, the process of single piece flow driven bykanban locations and decision rules was a generic process structure that could be applied tomany manufacturing applications. In this example, the decision rules and kanbanlocation/quantities were specifically chosen. These choices could be adapted as needed for aparticular production environment, which allowed translation to new product types and variedoutput volumes. For instance, reasonable volume increases were possible by adding labor to thesame process since the number of stations that have been set up exceeds the number of workersby 100% (Electromechanical required 3 operators and had 6 major work stations). Finally, theworkforce was being trained for cross-functional tasks, allowing each employee to use skillsfrom assembling existing products on new production models.

7.5 Comparison of the Low-Volume vs. the Original Lean Manufacturing Process Goals:The methods proposed in this project were targeted to a low volume environment to create aleaner production system. Some of the theoretical elements of Lean Manufacturing have beenadapted to fit the low volume environment, and some elements were only partially used. As areference to directly compare the this lean implementation to theoretical Lean Manufacturingprinciples, Table 6 has listed the major characteristics of this low-volume process, which werethen categorized according to how completely they fulfill the standards in a theoretical LeanManufacturing System. SIMILAR implied the low volume process directly incorporated theLean Manufacturing priciple, PARTIAL PROCESS suggested that the low-volume process wasmoving toward becoming a Lean process, and VARIANT explained that the process uses in thelow volume environment deviated from the precise definition of Lean Manufacturing, but wasused to best accommodate the unique low-volume production environment.

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Table 8: Comparison of Theoretical Lean Manufacturing Techniquesand the Low-Volume Lean Process Outlined in this Project

Theoretical Lean Manufacturing Low-Volume Lean ManufacturingProcess Principles Process Principles

Process layout Product-focused using flow-based SIMILAR: Product-focused usingproduction/assembly with physical flow-based assembly with physicalalignment of each process step alignment of each process step per

product familyLot sizing Produce only to demand with small SIMILAR: Produce to customer

lot quantities orders with single lot quantitiesPull production Only produce to fill kanbans when SIMILAR: Produce to fill kanbans

methods downstream stages demand product when downstream stages removeproduct based on daily demands anddecision rules

Ability to vary Ability to complete quick SIMILAR: Ability to produce anymodel production changeovers between models on one model within one product family in

line one lineProcess rules Standardized process SIMILAR: Standard process with

accompanying daily decision rulesProduction Using Heijunka (production SIMILAR: Using Heijunka methodplanning evenness) to balance daily to level load customer orders to

production requirements with balance daily and weekly productiondemand requirements

Supplier interface Cooperative partnerships established PARTIAL PROCESS: Increasedmutual understanding of Instron'sand suppliers' needs and processes

Cost reduction Waste reduce in overproduction, PARTIAL PROCESS: Wastethrough throughput time waiting, internal reduction concentrated on

elimination of plant product transportation, throughput time, productwaste processing, inventory, worker transportation between stations,

movement, and defective products assembly actions, inventory, andworker movement

Inventory Just In Time delivery using local VARIANT: Inventory ordermanagement suppliers quantities calculated to coordinate

with production demands usingsuppliers in their existing locations

Production rate Line pace set with strict adherence VARIANT: Flexibility toto takt time accommodate product variations

with line pace set around an averagetakt time

WIP inventory on Minimal to No WIP allowed: Ideally VARIANT: Minimized andfloor no buffers between stations, buffers strategically placed WIP in

removed when possible to reduce calculated amounts to buffer againstinventory to minimum possible assembly time variation betweenlevels products

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7.6 Future Recommendations for Continuous Improvement:1. Worker Training & Involvement:Worker cross-training ensures greater process sustainability. The flexibility of the operators is akey element of process success. During the project term, limited cross training was initiated.However, cross-functional workers within a product line allowed complete flexibility for eachperson to "Build, test, and ship" a product. This training must be formalized as part of theongoing process, with operators required to cross train as part of their yearly successmanagement goals. No longer are specialists needed in the production process. However, this"specialist" mentality can remain in the absence of formal cross-functional training, both out offear of job loss and lack of understanding that ability in multiple tasks is more desirable from aflexible manufacturing management standpoint.

Worker involvement is also critical. Incentives to train and become accountable for the processmust be put into place to make it personally desirable for each worker to learn and to motivatethe process changes. Increasing the diversity and challenge of each assembly position, withincentives to match those challenges, would also aid in retaining the best workers. Given thetight labor economy, it is best to ensure a challenged workforce that is well compensated to deterworkforce migration.

Further, involvement should extend beyond the manufacturing department. Who better toprovide manufacturability input to newly designed models than the operators who will later beresponsible for assembling those new products? Allowing operators time away from the line toreview and provide input to new designs would allow faster ramp up for new products once theyarrive in manufacturing. This would allow the removal of assembly-related flaws in the designbefore it is released. Early design involvement would also allow the operators to becomefamiliar with the product before it gets moved to manufacturing, creating "experts" for the newmodels to train others on the line. Such proactive planning is needed to provide even fastercustomer response time for future new products.

2. Determine the Validity of System Testing:Variation exists in the entire production system. The activity that shows the greatest variability istesting. On average, testing requires 33% of total system production time and accounts for 40%of its variation. Further, testing is a legacy of the process, completed to ensure quality ofassembly and proper operation of internal electronics. Given today's higher standards ofelectronics assembly and workers' ability to self-check assembly quality, it is questionablewhether testing, as a separate function, is still necessary. Therefore, it is recommended that testdata be reviewed statistically using process run charts for each test function to determine thefrequency of problems solved through testing, and to determine where process is in control andnot in need of the current testing function. Using this data, quality management can be employedto select test functions to remove. At the extreme, removing all testing would allows for furtherflow time decreases by 33%. In addition, output capacity would increase by up to 33%, giventhe existing level of labor, allowing for product line expansions without incurring additionallabor costs.

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3. Extension of Manufacturing Process Concepts to Other Divisions:Instron should consider expanding the concepts of pull production and inventory management totheir other product divisions. These concepts have been proven within the low volumeenvironment. The company can therefore further amplify its manufacturing strength bymaintaining consistency in process control and inventory management across all divisions.Replicating the common process framework would allow the creation of corporate wideproduction metrics, greater flexibility of workers who have common process knowledge betweendivisions, and greater coordination between division planners and outside suppliers.

The inventory analysis presented in this thesis modeled one group of parts fromElectromechanical production to demonstrate the inventory management methods used. Thismodel can further be used as a template for continued inventory management in all other partgroups within EM and the other divisions. To limit the extent of this analysis, ABCclassification should first be applied to each product line to determine the highest value inventoryitems for each. Management focus should then be placed on controlling these high value items.

In theory, the production "pull" process should extend throughout the value chain, from rawmaterial to finished product. This project was used as a pilot program to initiate the process onlywithin manufacturing. It is important to recognize that further optimization can be realized ifefforts within Instron's pull process are also coordinate with major suppliers. Significantamounts of lead time could be removed if vendors align their own production cycles withInstron's manufacturing demand patterns. This requires close coordination and mutualunderstanding of each process.

It must also be acknowledged that manufacturing time is only a fraction of the total timecurrently required to fulfill a product order within Instron. The time to initiate an order andapprove the order for manufacturing requires an additional time through sales and order entry.Applying lean initiatives to remove wasteful actions in the sales functions would eliminatepotentially greater amounts of time for order fulfillment, including excessive waiting forcustomers' credit approvals and time to route orders from sales to manufacturing. These pre-manufacturing functions must also be viewed as "waste" in improving overall customerresponsiveness, and customer order fulfillment must be analyzed and "leaned out" as one systemif optimum results are to be achieved.

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/ / DATA TIMESHEETS

The following sample datasheet is representative of those used to collect data from the assemblyprocess for each unit produced throughout the project period.

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ELECTROMECHANICAL / HARDNESSPROCESS IMPROVEMENT DATA TIME SHEET

Model Number Customer

Assembly Start Date Order Due Date

Procedure ie Duration Problk

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Date

Assembly End Date

til A / LABOR CAPACITY MODEL

An example spreadsheet is provided that outlines the calculations for labor capacityrequirements. It includes input of weekly production quantities and the amount of overtimelabor allowed for increasing capacity to the desired level. Output provides the number of laborhours required per unit of time as well as the number of operators required to complete thedesired production quantity.

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Tabletop EM ProductAssembly/Test Time Data Tray Top End Integration Test+Calibrate Finish Audit Total

Mean 2.4 2.6 1.9 4.1 2.2 0.5 13.7Stdev 0.4 0.5 1.2 1.2 0.7 0.1 1.9

Standard Times 3.0 1.4 1.0 3.5 1.3 2.5 12.7

Labor Capacity CalculationsWeekly Demand Quantity 10.0 INPUT

# Hrs Authorized for OT/Week/Person 0.0 INPUT

Hrs per Machine 13.7 CALCULATED FROM ASSM DATATotal Hrs Required / Week 137.3 CALCULATED FROM INPUT

# Operators Required 3.9 CALCULATED FROM INPUT

Single Column EM ProductAssembly/Test Time Data Tray Top End Integration Test+Calibrate Finish Audit Total

Mean 2.2 1.4 1.0 3.5 0.9 1.5 10.5

Stdev 0.3 0.6 0.1 0.7 0.2 1.3 2.0

Standard Times 2.0 1.7 0.7 2.3 0.5 2.5 9.7

Labor Capacity CalculationsWeekly Demand Quantity 10.0 INPUT

# Hrs Authorized for OT/Week/Person 0.0 INPUT

Hrs per Machine 10.5 CALCULATED FROM ASSM DATATotal Hrs Required / Week 105.0 CALCULATED FROM INPUT

# Operators Required 3.0 CALCULATED FROM INPUT

Hardness Model 2000 ProductAssembly/Test Time Data Tray Actuator Integration Test+Calibrate Finish Total Time

Mean 1.4 1.7 1.0 2.4 1.4 8.0

Stdev 0.4 0.5 0.2 0.5 0.2 1.0Standard Times 1.2 3.0 1.9 2.1 1.0 9.1

Labor Capacity CalculationsWeekly Demand Quantity INPUT

# Hrs Authorized for OT/Week/Person f.J INPUT

Hrs per Machine 8.0 CALCULATED FROM ASSM DATATotal Hrs Required / Week 47.7 CALCULATED FROM INPUT

# Operators Required 1.4 CALCULATED FROM INPUT

Comments:Spreadsheet used to calculate labor requirements for each product family assembly processCapacity based on test time data collected directly from assembly processMean times used to establish labor assuming leveled production7 hour work day time basisOvertime used as additional capacity when required

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APPENDIX C 7 INVENTORY ANALYSIS MODELZ~~~ ANQ 4 PREb1AD HEETS

Two example spreadsheets are outlined in the following pages. The first is a sample template forcalculating the monthly demands and Reorder Points for a single inventory item based ondemand and statistical safety stock requirements. It is separated into two sections:

1. User inputs based on historical demands and desired stocking probabilities.2. Resulting calculated outputs for ROP and Min levels

The second set of four pages is one spreadsheet that outlines one set of parts purchased from oneselect vendor. Data includes historical usage, cost-volume analysis for ABC classification,statistical demands calculated from historical usage and stocking probabilities, reorder point lotsize results for the supplier, and internal reorder point values.

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Inventory Analysis Worksheet

SPREADSHEET INPUTSPART INFORMATIONPART # #

NAME NAMESTANDARD COST $ 154VENDOR MFG LEAD TIME (DAYS) 28RESUPPLY LEAD TIME (DAYS) 7

LEAD TIME OPTIONSELECT "1" FOR SEPARATE RESUPPLY AND MFG LEAD TIMES OR "o" IF ONLY USING fMFG LEAD TIME

HISTORICAL DEMANDfMonthly Demand for Last 12 Months

JAN FEB MAR APR MAY JUNE JULY AUG SEPT OCT NOV DEC12 18 2 19 21 24 13 11 10 9 15 18

PREVIOUS YEAR TOTAL USAGE 150

STATISTICAL VALUESDESIRED PROBABILITY OF BEING STOCKED 0.9750

SPREADSHEET CALCULATED OUTPUT RESULTS

DEMANDS OVER THE PAST 12 MONTHSHIGHEST MONTH DEMAND - PAST 12 MONTHS 24.0AVERAGE MONTH DEMAND - PAST 12 MONTHS 14.3TOTAL USAGE - PAST 12 MONTHS 172.0STANDARD DEVIATION OF DEMAND - PAST 12 MONTHS 6.1

DEMAND OVER PAST 2 YEARSAVERAGE MONTH DEMAND - PAST 2 YEARS 13.4

STATISTICSSTATISTICAL Z VALUE BASED ON DESIRED PROBABILITY 1.96

DEMANDS OVER LEAD TIME TO GET MINIMUM REORDER QUANTITIESHIGHEST DOLT - WITHIN PAST 12 MONTHS 28.0AVERAGE DOLT - WITHIN PAST 12 MONTHS 16.7AVERAGE DOLT - PAST 2 YEARS 15.7REQUIRED SAFETY STOCK FOR AVERAGE DEMAND OVER LEAD TIME 12.8TOTAL DOLT+SS = MINIMUM REORDER QUANTITY 29.0

MINIMUM BIN LEVEL TO TRIGGER NEW ORDERAVERAGE DEMAND OVER RESUPPLY LEAD TIME 3.9

SAFETY STOCK FOR RESUPPLY LEAD TIME 3.2INSTRON INTERNAL MIN REORDER POINT 8.0

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COST VOLUME CALCULATIONS

Line 1999 Cost Cumulative CumulativeNumber Volume Cost Part

Amount Volume Percentage1 39163 0.20 0.0202 25461 0.33 0.0393 12603 0.39 0.0594 8955 0.44 0.0785 7939 0.48 0.0986 7832 0.52 0.1187 6915 0.55 0.1378 5951 0.58 0.1579 5478 0.61 0.176

10 5131 0.64 0.19611 5012 0.66 0.21612 4248 0.68 0.23513 4098 0.70 0.25514 3668 0.72 0.27515 3288 0.74 0.29416 3233 0.76 0.31417 3137 0.77 0.33318 3102 0.79 0.35319 3094 0.80 0.37320 2880 0.82 0.39221 2796 0.83 0.41222 2598 0.84 0.43123 2578 0.86 0.45124 2319 0.87 0.47125 2272 0.88 0.49026 2091 0.89 0.51027 2019 0.90 0.52928 1986 0.91 0.54929 1724 0.92 0.56930 1527 0.93 0.58831 1440 0.94 0.60832 1430 0.94 0.62733 1361 0.95 0.64734 1357 0.96 0.66735 1313 0.96 0.68636 1291 0.97 0.70637 1084 0.98 0.72538 969 0.98 0.74539 602 0.98 0.76540 559 0.99 0.78441 498 0.99 0.80442 432 0.99 0.82443 412 0.99 0.84344 338 1.00 0.86345 322 1.00 0.88246 291 1.00 0.90247 237 1.00 0.92248 61 1.00 0.94149 24 1.00 0.96150 24 1.00 0.98051 19 1.00 1.000

STATISTICAL VALUES BASED ON USAGE

Highest Average StdevMonth Month Month ProbabilityUsage Usage Usage Stocked Z Value

25 17 5 0.975 1.96028 20 8 0.975 1.96014 6 5 0.975 1.960

535 339 112 0.975 1.96064 37 18 0.975 1.96017 8 5 0.975 1.96059 43 12 0.975 1.960

109 71 34 0.975 1.9602290 1585 455 0.975 1.96097 67 24 0.975 1.96049 29 12 0.975 1.96029 14 9 0.975 1.96014 6 5 0.975 1.96059 41 12 0.975 1.96032 15 10 0.975 1.96011 7 2 0.975 1.96032 19 10 0.975 1.96017 8 5 0.975 1.960

127 75 33 0.975 1.96049 30 11 0.975 1.96088 50 22 0.975 1.96049 30 11 0.975 1.96029 14 9 0.975 1.96063 30 14 0.975 1.96034 15 9 0.975 1.96029 14 7 0.975 1.96017 10 6 0.975 1.96031 16 7 0.975 1.96034 16 10 0.975 1.9609 3 2 0.975 1.96044 27 11 0.975 1.96032 17 8 0.975 1.96045 25 11 0.975 1.9604 2 1 0.975 1.96044 27 11 0.975 1.96032 15 10 0.975 1.96017 8 5 0.975 1.960212 103 47 0.975 1.96014 6 5 0.975 1.96015 6 4 0.975 1.96057 39 12 0.975 1.96017 8 5 0.975 1.96017 8 5 0.975 1.96034 16 10 0.975 1.96017 8 5 0.975 1.9603 2 1 0.975 1.9604 2 1 0.975 1.9603 1 1 0.975 1.9602 1 1 0.975 1.9605 2 2 0.975 1.9605 2 2 0.975 1.960

Total197160

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ESTiMATED REORDER QUANlMES BASED ON DEMAND AM) SUPPUER LEAD TIME

Line Supplier MIg Resupply Average Average Average Safety Stock TotalNUmber Lead Time Lead Time DOLT DOLT DOLT For Ave DOLT+SS

Days Days Year 2000 Year 1999 Past 2 Yrs Demand IVIn Reorder1 28 7 19.8 23.0 21.4 9.9 322 28 7 23.3 16.8 20.1 17.4 383 28 7 7.0 7.2 7.1 9.8 174 28 7 395.5 318.2 356.9 237.4 5955 28 7 43.2 47.3 45.2 38.6 846 28 7 9.3 9.7 9.5 11.0 217 28 7 50.2 32.8 41.5 26.0 688 28 7 82.8 112.8 97.8 72.2 1709 28 7 1849.2 1868.8 1859.0 963.2 282310 28 7 78.2 62.5 70.3 49.9 12111 28 7 33.8 39.8 36.8 24.7 6212 28 7 16.3 11.7 14.0 19.8 3413 28 7 7.0 6.8 6.9 9.8 1714 28 7 47.8 31.6 39.7 26.3 6715 28 7 17.5 1a4 15.5 20.7 3716 28 7 8.2 7.7 7.9 5.2 1417 28 7 22.2 21.5 21.8 20.8 4318 28 7 9.3 8.8 9.1 10.7 2019 28 7 87.5 96.0 91.7 70.6 16320 28 7 35.0 40.9 38.0 23.2 6221 28 7 58.3 65.3 61.8 46.8 10922 28 7 35.0 41.0 38.0 22.8 6123 28 7 16.3 11.6 14.0 19.7 3424 28 7 35.0 43.8 39.4 29.0 6925 28 7 17.5 18.3 17.9 19.9 3826 28 7 16.3 21.5 18.9 14.2 3427 28 7 11.7 8.9 10.3 11.8 2328 28 7 18.7 23.0 20.9 14.2 3629 28 7 18.7 12.3 15.5 20.9 3730 28 7 3.5 4.0 3.7 5.2 931 28 7 31.5 39.0 35.2 22.8 5932 28 7 19.8 22.2 21.0 16.2 3833 28 7 29.2 27.3 28.2 23.3 5234 28 7 2.3 2.6 2.5 2.2 535 28 7 31.5 38.9 35.2 22.5 5836 28 7 17.5 12.4 15.0 20.6 3637 28 7 9.3 9.4 9.4 11.0 2138 28 7 120.2 125.2 122.7 99.7 22339 28 7 7.0 6.8 6.9 9.8 1740 28 7 7.0 5.5 6.3 8.1 1541 28 7 45.5 28.3 36.9 25.5 6342 28 7 9.3 8.8 9.0 10.7 2043 28 7 9.3 9.2 9.3 11.0 2144 28 7 18.7 18.9 18.8 22.1 4145 28 7 9.3 8.8 9.1 10.7 2046 28 7 2.3 0.5 1.4 2.7 547 28 7 2.3 2.6 2.5 2.2 548 28 7 1.2 0.7 0.9 2.1 449 28 7 12 0.1 0.6 1.4 350 28 7 2.3 0.6 1.5 3.4 551 28 7 2.3 0.6 1.5 3.4 5

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INTERNAL MINIMUM QUANTITY ROP

Line Resupply Averaged Safety StockNumber Lead Time DOLT For Ave Internal

Days Past 2 Yrs Demand Min ROP1 7 4.29 1.99 72 7 4.02 3.49 83 7 1.42 1.95 44 7 71.37 47.49 1195 7 9.04 7.72 176 7 1.91 2.21 57 7 8.29 5.20 148 7 19.56 14.43 349 7 371.80 192.63 56510 7 14.07 9.98 2511 7 7.36 4.95 1312 7 2.80 3.97 713 7 1.38 1.95 414 7 7.94 5.27 1415 7 3.09 4.14 816 7 1.58 1.04 317 7 4.37 4.16 918 7 1.82 2.15 419 7 18.35 14.12 3320 7 7.59 4.64 1321 7 12.37 9.36 2222 7 7.60 4.56 1323 7 2.79 3.94 724 7 7.88 5.80 1425 7 3.58 3.98 826 7 3.78 2.84 727 7 2.06 2.36 528 7 4.17 2.83 829 7 3.09 4.17 830 7 0.75 1.03 231 7 7.05 4.57 1232 7 4.20 3.24 833 7 5.65 4.66 1134 7 0.50 0.44 135 7 7.04 4.50 1236 7 2.99 4.13 837 7 1.88 2.20 538 7 24.54 19.93 4539 7 1.38 1.95 440 7 1.25 1.62 341 7 7.38 5.10 1342 7 1.81 2.15 443 7 1.86 2.20 544 7 3.75 4.42 945 7 1.82 2.15 446 7 0.28 0.54 147 7 0.50 0.44 148 7 0.18 0.42 149 7 0.13 0.29 150 7 0.29 0.69 151 7 0.29 0.69 1

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Annotated Bibliography

Burman, M., S. Gershwin, and C. Suyematsu, "HP Uses Operations Research to Improve theDesign of a Printer Production Line," Interfaces, Vol. 28, Jan. Feb. 1998. This article describesthe use of buffers in a production line to increase productivity by preventing blocking andstarvation of any one segment in a production line. It also describes how this method deviatesfrom a traditional Lean/JIT system where no buffers are used.

Cochran, David S., Class notes and selected papers from Course 2.812 "Design and Control ofManufacturing Systems," 1999. Selected topics on lean manufacturing with examples of theoryapplied to manufacturing processes with some actual implementation results.

Crane, Barrett, "Cycle Time and Cost Reduction in a Low Volume ManufacturingEnvironment," Masters thesis, MIT Leaders for Manufacturing program, 1996.

Devor, Richard, Tsong Chang, and John Sutherland, Statistical Quality Design and Control,Macmillan Publishing Company, 1992. This text provides in depth methods of statistical dataanalysis. Its methods were used to analyze assembly and test data taken from Instron's assemblyprocess. It also was used to explain "assignable" vs. "unassignable" causes of variation in data.

Dul, Paul, "Application of Cellular Manufacturing to Low-Volume Industries," Masters thesis,MIT Leaders for Manufacturing program, 1994.

Fine, Charles and Hax, Arnoldo, "Manufacturing Strategy: A Methodology and an Illustration,"Interfaces 15:6, Nov-Dec 1985. Article that provides examples of reviewing a manufacturingenvironment as a system and the importance of using that as part of a corporate strategy.

Flinchbaugh, Jamie, "Implementing Lean Manufacturing Through Factory Design," Mastersthesis, MIT Leaders for Manufacturing program, 1998.

Goldratt, E.M., The Goal, North River Press, 1992. This book outlines the Theory ofConstraints, used to analyze capacity and distinguish between constraints and non-constraintswithin a production environment. This theory was used at Instron to identify the processbottleneck and how this needs to be managed to reduce overall product flow time.

Graves, Stephen and Jackson Chao, "Reducing Flow Time in Aircraft Manufacturing," Workingpaper as part of MIT Leaders for Manufacturing program, 1992. This paper analyzes the fullcosts of extended assembly flow times in the low-volume aircraft manufacturing environment. Itdescribes the cost impacts for the various stages of total flow time and provides regressionanalysis to rank the major factors.

Hager, Dennis, "Applying Continuous Flow Manufacturing Principles to a Low VolumeElectronics Manufacturer," Masters thesis, MIT Leaders for Manufacturing program, 1992.

91

Hammer, Michael, "Reengineering Work: Don't Automate, Obliterate," Harvard BusinessReview, July-August 1990. Short article describing how an environment should be reengineeredas a system and not looked at as an existing system that needs improvement.

Harman, Steve, "Implementation of Lean Manufacturing and One-Piece Flow at Allied Signal,"Masters thesis, MIT Leaders for Manufacturing program, 1997.

Hayes, Robert H. and Wheelwright, Steven C., "Link Manufacturing Process and Product LifeCycles," Harvard Business Review, January-February, 1979. This article outlines Hayes' andWheelwright's product-process matrix and describes how a company should position itself basedon different processes.

Jones, D.T. and Womack, J.P., Lean Thinking, Simon and Schuster, 1996. This is a world classbook on the teachings of implementing lean operations. It provides many real corporateexamples of how the principles of lean manufacturing have been successfully implemented, aswell as provides a basis to show how lean principles can be applied throughout all of anorganization's functions. Copies of this book were provided to all of Instron's manufacturingmanagement staff to allow the potentials of lean processes to be better understood. The resultsand interest in the book's methods was incredible, providing a strong start at Instron to changingpeople's mentality of how they could make improvements in their own processes.

Krafcik, John, "Triumph of the Lean Production System," Sloan Management Review, 1988.Article that describes the success of the TPS and Lean Systems.

MacLean, Mark, "Implementing Lean Manufacturing in an Automobile Plant Pilot Project,"Masters thesis, MIT Leaders for Manufacturing program, 1996.

Mahoney, Michael R., High-Mix Low-Volume Manufacturing, Prentice-Hall, Inc., 1997. This isan industrys-sponsored book from Hewlett Packard that explains real world experiences of theauthor through many engineering and manufacturing projects that he has completed. Throughoutthese experiences, underlying principles of low volume industries are discussed along with thealignment of manufacturing and overall organizational strategies. It provides many concretesexamples of JIT manufacturing, the Theory of Constraints, and Production Scheduling practices.

Mishina, Kazuhiro, Toyota Motor Manufacturing, USA, Inc., 1992. This is a case study ofapplying the Toyota Production System to a Toyota plant in the United States.

Monden, Yasuhiro, Toyota Production System: An Integrated Approach to Just-In-Time,Industrial Engineering Press, 1993. Descriptions and examples of TPS process integration.

Nahmius, S., Production and Operations Analysis, 3 edition. McGraw-Hill, 1997. This is atextbook outlining factory operations and planning. It was useful to outline the basics of kanban-controlled processes and setting kanban quantities. The book also provides sections on inventorycontrol for both known and uncertain demands, with a section on low volume demandsapplicable to Instron's market.

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Ohno, Taiichi, Toyota Production System: Beyond Large-Scale Production, Productivity Press,1988. This reference directly describes the Toyota Production System from its origins at Toyotadirectly from its founder Ohno.

Raymond, Arthur, "Applicability of Toyota Production System to Commercial AirplaneManufacturing," Masters Thesis, MIT Leaders for Manufacturing program, 1992.

Schonberger, Richard J., World Class Manufacturing - The Lessons of Simplicity Applied,Macmillan, 1986. This book outlines many simple techniques to apply lean manufacturingprinciples to actual production processes. It also provides many descriptions of successfulimplementation of such techniques in American companies.

Suri, Rajan, Quick Response Manufacturing, A Companywide Approach to Reducing LeadTimes, Productivity Press, 1998. This book compares and contrasts the Lean Manufacturing(Toyota Productin System) that is focused on reducing waste to shorten lead times with a revisedmethod called Quick Response Manufacturing that is focused on shortening lead times whichprovides reductions in waste as a result. It is truly a variant of the TPS, with many examplessimply reversed to fit the QRM model. Topics include manufacturing time response, capacity,material planning and replenishment, and supplier relations. Further, it extends the conceptsfrom manufacturing into product development and general office operations. It is a clear readingsource that can be used to complement a Lean Manufacturing initiative.

"Toyota Motor Manufacturing, USA, Inc.," Case study from the Harvard Business School,Revised 1995. Article that reviews examples of successful TPS implementation.

Vining, G., Statistical Methods for Engineers, Duxbury Press, 1998. This is an introductory texton statistics. It was used as a reference to provide information on normal distributions and z-statistical calculations.

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