pull-driven scheduling for pipe-spool installation: simulation of a

ASCE Journal of Construction Engineering and Management, July/August 1998, 124 (4) 279-288

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PULL-DRIVEN SCHEDULING FOR PIPE-SPOOL INSTALLATION:SIMULATION OF A LEAN CONSTRUCTION TECHNIQUE

By Iris D. Tommelein1, Associate Member

ABSTRACT: Many construction processes include installation of unique materials in specific locations in thefacility being built: materials and locations must match before installation can take place. Mismatches due todelay and uncertainty in supplying materials or completing prerequisite work at those locations hamper fieldproductivity. This is illustrated here using a model of a materials-management process with a matching problemthat typifies fast-track process-plant projects. The uniqueness of materials and locations combined with theunpredictability in duration and variation in execution quality of various steps in the supply chain allow fordifferent ways to sequence material delivery and work area completion. Several alternatives are described. Theirimpact on process execution is illustrated by means of probabilistic process models. One model reflects totallack of coordination between delivery and work area completion prior to the start of construction; a second onedescribes perfect coordination. The corresponding materials staging buffers and construction progress areplotted based on output from discrete-event simulation models. A third probabilistic model then illustrates theuse of the lean construction technique called pull-driven scheduling. Real-time feedback regarding the status ofprogress on site is provided to the fabricator off site so process steps can be re-sequenced opportunistically. Thisyields smaller buffers and earlier project completion and, when properly accounted for, increased productivity.

1 Associate Professor, Civil and Environmental Engineering Department, 215-A McLaughlin Hall, University of California,Berkeley, CA 94720-1712, 510/643-8678, [email protected]

INTRODUCTIONConstruction involves installing materials according to projectspecifications in the facility being built. By tracking the flow ofmaterials through their supply chain (i.e., describing when andwhere materials are being engineered, fabricated, transported,staged, etc.) installation work can be most effectively plannedand executed. Flow data must be more or less detaileddepending on whether the material of concern will be availablein large quantities of identical, interchangeable units (e.g.,concrete blocks, electrical conduit, nuts and bolts); in modestquantities, possibly with some degree of interchangeability (e.g.,windows, structural steel, timber in precut lengths), or in smallquantities of units with unique properties (e.g., engineeredmaterials such as pipe spools or a custom-designed mainentrance door).

Field installation crews, responsible for the final step in thematerials flow process, must find resources that match amongthose available to them; they must ensure that the right materialgets put in the right place. For instance, they must identify thelocation where installation is to take place (e.g., area AR-123),then find the matching material (e.g., pipe spool SP-123) andretrieve the correct installation accessories (e.g., attachmentsand supports). An integral part of their work, time and again, isto solve the so-called "matching problem." In facilities thatcomprise thousands of materials of which many are unique,tackling the matching problem is an enormous task.Nevertheless, those performing installation have no way aroundit.

In contrast, those responsible for engineering and design,fabrication, delivery, and site storage of materials, as well asconstruction managers overseeing the project often overlook thematching problem that installation crews face. Dealing with

materials on an item-by-item basis means paying attention tominute details. It is a tedious task, largely irrelevant to theirown. Accordingly, matching-problem details are selectivelyabstracted away by each party so that they can focus onproblems of more direct, contractual concern to them. Forexample, structural designers do not worry about vendors'ability to deliver specialty valves or nuts-and-bolts because it isoutside of their scope of work. Pipe-spool fabricators optimizeproduction schedules to suit their plant's fabrication constraintsand other projects' needs. Shipping agents optimize travel bychoosing vehicles to meet delivery schedules; they packagematerials to ensure that loads are stable and meet weight anddimensional constraints during transportation. Laydown yardpersonnel group materials by shipment, type, or final-installation destination to ease tracking. Project managerscontrol progress based on percentages-of-total of materialsengineered, delivered to the site, or installed. The correspondingplanning systems must therefore allow for abstraction or detailas needed.

Because of this abstraction, installation crews rarely havethe data they need to optimally schedule and thus execute theirwork. They must rely on the numerous assumptions that areembedded in pre-construction schedules. How much of aproblem this creates depends on the extent to whichuncertainties in their supply manifest themselves during projectexecution. If pre-construction schedules were well thought-outand steps preceding installation had no uncertainty in durationor execution quality associated with them, then matching wouldbe easy. In practice, unfortunately, this is not the case. Manyprojects are executed on a fast track, so construction startsbefore design has been completed or materials deliveries havebeen properly sequenced. Installation crews and equipment areoften kept waiting because delays in materials supply and delays


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in completing prerequisite site work lead to mis-matches thatfoul up scheduled work sequences. This lowers the installationcrew's productivity and extends the project duration.

In order to increase understanding of these issues, a modelwas created of a process that is characteristic of the process-plant sector of the construction industry. Alternative strategiesfor sequencing materials deliveries are presented in this paperand their execution was simulated so computer data supports thecomparison between them.

RELATED WORK IN LEAN CONSTRUCTIONMatching problems and process uncertainties pose uniquerequirements on construction planning systems. An analogywith manufacturing production systems is appropriate to explainwhat these are. Specifically, the lean production philosophy isrelevant (Ohno 1988). Lean production focuses on adding valueto a raw material as it proceeds through various processing stepsto end up as a finished product. It advocates the avoidance,elimination, or at least reduction of waste from this so-calledvalue stream. By considering waste not only in or produced byindividual operations but in the value stream at large, leanproduction adopts a systems view.

The late Taiichi Ohno first articulated this philosophy andimplemented it in Toyota's production system. He classifiedsources of waste as follows (8 added by Womack and Jones1996): (1) Defects in products; (2) Overproduction of goods notneeded; (3) Inventories of goods awaiting further processing orconsumption; (4) Unnecessary processing; (5) Unnecessarymovement of people; (6) Unnecessary transport of goods; (7)Waiting by employees for process equipment to finish its workor for an upstream activity to complete; and (8) Design of goodsand services that fail to meet user's needs.

The lean production philosophy, since it emerged in the1950s, has provided major competitive advantage to Japanesemanufacturing companies. Its benefits gradually became knownoutside of Japan. In the 1980s, US manufacturing companiesbegan to convert their operations to implement lean productiontechniques and, consequently, also improved their operationsdramatically (Womack and Jones 1996). Some lean productiontechniques are: (1) Stopping the assembly line to immediatelyrepair quality defects; (2) Pulling materials through theproduction system to meet specific customer demands; (3)Reducing overall process cycle time by minimizing eachmachine's change-over time; (4) Synchronizing and physicallyaligning all steps in the production process; (5) Clearlydocumenting, updating, and constantly reporting the status of allprocess flows to all involved.

Though no one will doubt that there is much waste inconstruction, lean production has only recently become a subjectof interest in our industry. Since the publication of Koskela's(1992) seminal report, researchers around the world have beenstudying its applicability to construction (e.g., Alarcon 1997).Unfortunately, translating lean concepts from manufacturing toconstruction is not automatic because of the uniquecharacteristics of the architecture/engineering/construction(AEC) industry in addition to the geographic diversity amongprojects.

Researchers in construction have begun to realize thatconstruction management must include production controlsystems (e.g., Bernold and Salim 1993, Melles and Wamelink1993) to complement the project management systems currentlyin use. Control systems must include not only activities beingperformed at the project site but also those that make up theentire supply chain (O'Brien 1995). The work described herebelongs to this school of thought.

Some lean concepts have already been translated toconstruction. Howell et al. (1993) discussed how buffers ofmaterials can alleviate the dependencies and worker idle timeotherwise incurred when process sub-cycles interact with oneanother. Ballard formalized the Last Planner to shieldinstallation crews from uncertainties in work flow anddemonstrated its successful implementation on actual projects(Howell and Ballard 1996, Ballard and Howell 1997). Phair etal. (1997) reported how equipment manufacturers are reducingset-up time by changing product designs (e.g., buckets and otherattachments). In the same vein, this paper describes how the pulltechnique with feedback regarding progress on site tofabricators off site can improve construction processperformance (Tommelein 1997a, 1997b).

PUSH-DRIVEN VS. PULL-DRIVEN PROCESSMANAGEMENT

Push-Driven Process Management

Construction work traditionally is planned by articulatingactivities and dependencies between them, then assigningdurations and resources to each activity. A schedule isdeveloped by calculating early and late activity starts andfinishes using the Critical Path Method (CPM). Resourceleveling or allocation algorithms may yield some adjustments tothe early-start schedule, but upon project execution, activitiesare expected to start at their earliest possible date in order not todelay succeeding activities or the project as a whole.

Project control aims at adhering to the resulting schedule. Itis assumed that all resources required to perform an activity thatis about to start will indeed be available at that activity's early-start time. In this so-called "push-driven" approach, eachactivity passively waits for its ingredients (instructions, labor,materials, equipment, and space) to become available, e.g., bybeing released upon completion of predecessor activities. Whensome have become available but others needed at the same timehave not, those available will wait in a queue or buffer for thecombination of resources— the set of "matching parts"— in itsentirety to be ready. While it may be possible to start work withan incomplete set of resources, chances are this will negativelyaffect productivity (e.g., Thomas et al. 1989, Howell et al.1993).

Because of uncertainty in duration as well as variation inexecution quality and dependency logic of activities, schedulesare bound to change as construction progresses. It may bepossible to model this uncertainty during the planning stage, asis done by using probabilistic distributions to characterizeactivity durations in the Program Evaluation and ReviewTechnique (PERT). However, the actual manifestation ofuncertainty is known only upon plan execution and must thus be


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dealt with in real time. At that point, rigorously adhering to theinitial schedule may not be the best approach for successfulproject completion as network characteristics and resourceavailability will deviate from those assumed when that schedulewas generated.

Moreover, traditional CPM schedules do not necessarilyshow individual resources and their allocation to activities.Certainly, procurement schedules highlight milestone deliverydates of major items, but most materials will arrive in multi-unitshipments. If a schedule reflects only groupings, then it is toocoarse to guide work that involves unique parts. When missingparts are identified during the on-site allocation process, it ismuch too late to prevent delays.

In addition, current expediting practice is to regularly touchbase, e.g., with the engineering design firm or fabricator ofwhom goods or services are expected. Contact is made prior tothe deadline of completion of their work, in order to make surethe target delivery date, e.g., of key materials or pieces ofequipment, will be met. Yet, most expediters fail to (e.g., are notauthorized to) reschedule activities when it can be anticipatedthat deadlines will not be met. Accordingly, the traditional,push-driven approach to scheduling prior to the start ofconstruction with no corrective re-scheduling as workprogresses leads to process inefficiencies and less-than-optimalproject performance.

Pull-Driven Process Management

The main objective of a "pull-driven" approach is to producefinished products as optimally as possible in terms of quality,time, and cost, so as to satisfy customer demand. Achievinghigh process throughput while minimizing operating expensesincluding in-process inventories is key. Keeping busy byprocessing just any one of the resources in the input queue of anactivity requiring a combination of resources is insufficient. Topull means that resources must be selectively drawn fromqueues— so the activity that processes them will be busy just thesame— but chosen so that the activity's output is a productneeded further downstream in the process, and needed more sothan its output using other resources in the queue would havebeen. Resources' wait time in queues should be minimized.

To implement a pull-driven approach, selective control isneeded over which resources to draw for any given activity.This selection is driven by information not solely aboutresources in the queues immediately preceding the activityunder consideration, but also about work-in-progress andresources downstream (successor queues and activities) in theprocess. Resources will get priority over others in the samequeue if they are known to match up with resources forecast tobe or already available in queues further downstream in theprocess. This way, those downstream resources will not undulyawait their match and be in process for any time longer thanneeded, though their planned processing sequence may beviolated.

EXAMPLE PROCESS SCENARIO: PIPE-SPOOLINSTALLATIONConstructing an industrial process facility, such as an oilrefinery, involves installing many hundreds or thousands of

unique pipe spools. This process is simplified here ascomprising two chains of activities: pipe spools are designedand fabricated off site while work areas are prepared on site.After spools have been shipped to the site, the chains mergewith the installation of spools in their designated areas.

Pipe spools are fabricated off site according to theavailability of engineering design information, the fabricator'splant production capacity, etc. Individual tags denote that eachspool has unique properties and each has a designateddestination in the facility under construction as shown in theproject specifications. Spools are subject to inspection beforeleaving the fabricator's plant. The outcome of the inspectionactivity is that a spool will be found fit-for-installation with anx% likelihood, and, thus, that there will be a problem with (100- x)% of them. In the latter case, the fabricator must rework thespool to rectify the problem, prior to shipping.

Concurrently with this off-site process, construction isunder way on site. Roads are built, temporary facilities arebrought in, foundation systems are put in place, structural steelis being erected, etc. Crews of various trades must completetheir work in each area where spools are to be hung, prior tospool installation. When a specific set of ready-for-installationspools is available on site, and all prerequisite work in thematching area has been completed, spools can be installed.Completion of an area's installation work then signals to othertrades that subsequent work can start.

INDUSTRY PRACTICEThe Business Roundtable (BRT 1982) identified the pipingprocess as being critical to the success of numerous industrialprojects. However, research into improving practice has beenlagging until only a few years ago CII conducted a detailedinvestigation (CII 1996, Howell and Ballard 1996, O'Connorand Liao 1996, O'Connor and Goucha 1996). Major causes forproblems were found in the engineering development process,specifically in three areas: (1) piping and instrumentationdiagram (P&ID) problems are caused by inefficient sequencingof priotization, inefficient procedures for P&ID developmentand review, and inefficient communication of P&IDuncertainty; (2) problems in the supplier data process pertain tocommunication, coordination, and selection duration; and (3)problems in the packaged units process pertain to supplierquality and design. Whereas O'Connor et al. developed policies,procedures, and checklists to enhance the overall efficiency ofthese processes, Howell and Ballard studied the impact ofuncertainty on downstream performance.

Industry practitioners know that construction is plagued byuncertainties. In their most interesting study, Howell andBallard (1996, p. 6) describe prevailing methods for managingthem in the piping function: "Piping success requiresminimizing the extent and effects of uncertainty duringfabrication and installation. At present, uncertainty in the timingof deliveries of intermediate products from one continuingactivity to another defines the production planning andmanagement problem. Lacking tools to minimize theuncertainty in these flows, managers strive for flexibility so thatthe project can proceed in the face of erratic deliveries andunexpected problems. On piping extensive projects, they rely on


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buffers to assure progress despite variations in the timing,sequence, and quality of resources from upstream suppliers.Buffers dampen the effects of variations in the flow of resourcesand allow flexibility in the choice of work."

Howell and Ballard characterize common practice formoving pipe spools from engineering through off-sitefabrication to erection. "When engineering falls behindschedule, fabrication will be delayed, thereby also delayinginstallation work." "The order in which drawings are providedto the fabricator and the sequencing in which spools are outputby the fabrication process may bear little relationship to siteneeds, therefore requiring re-sequencing for site deliveryprovided that priority information be available." "Time delaysand out-of-sequence work make the supply of materials to thejob site unpredictable. This leads to inefficiencies because workcannot be adequately planned and executed, and thus results inlow productivity."

Figure 1 charts commodity curves from 1 of 24 projects onwhich Howell and Ballard collected data. This specific project(Project B) required installation of 2,080 pipe spools in a 57-week duration, measured from start of engineering to end ofinstallation. It was characterized as "Design well established.Rash of client changes late in project." As can be seen, scheduleslippages (deviation of actual from planned) occurred incompleting isometric drawings (ISOs) and in fabricating spools.Nonetheless, installation work progressed nearly as planned.

% PlannedISOs

% ActualISOs

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Figure 1: % Planned vs. Actual in Percent of Total of IsometricDrawings (ISOs), Fabricated Spools Delivered to Site, and

Spools in Place.[Data courtesy of Greg Howell and Glenn Ballard]

Commodity curves (which plot time vs. percent complete, alsocalled line-of-balance or velocity charts) do not reveal, however,what resources were needed to adhere to the planned installationschedule. Their usefulness is limited when managing uniquematerials because they do not help recognize matchingproblems. This is the case here as pipe spools really are notcommodities. "They [spools] differ in specificationrequirements, physical configuration, and adjacent plantfeatures. These important differences make both establishingperformance standards and comparing results very difficult"(Tatum 1985).

Howell and Ballard make no detail available to describethis project's performance, so one cannot be certain of problem

causes and possible solutions. They point out "Fast trackproject. Schedule main concern. Manpower levels aboveprojected." (p. 73). One can only speculate that uncertaintycontributed to occurrence of matching problems, hamperinginstallation work, but apparently successfully overcome. Inconclusion, Howell and Ballard recommend that pipingbacklogs be used to buffer on-site from off-site activity("successful projects have at least 60% of all pipe on hand when20% has been installed"), and that the principles of the LastPlanner be applied to shield installation from remaininguncertainties.

The present paper builds on this work by focusing on twouncertainties in the pipe-spool process: (1) uncertainty induration of fabrication and transportation and (2) quality failurein fabrication resulting in delay of shipment due to rework. Itshows how matching affects the productivity of installationcrews and the overall project duration.

PROCESS MODELING

Process-Model Representation

In order to describe and then experiment with alternativeplanning sequences, the pipe-spool installation process has beenmodeled using the STROBOSCOPE computer system fordiscrete-event simulation (Martinez 1996). Table 1 summarizesthe functionality of the STROBOSCOPE symbols that are usedhere, but note that their simplicity belies the expressiveness ofthe associated programming language.

One major feature of STROBOSCOPE is that resources canbe characterized and individually tracked as they reside invarious network nodes during a simulation run. When a queue'sresources are indistinguishable, there is only one way in whichto draw them from that queue; only 1 draw sequence exists.However, when a queue has n distinguishable resources, n! drawsequences are possible. In general n will change in the course ofa simulation run as resources join the queue (unless the queue isa source) and leave it (sink). Being able to distinguish resourcesand to draw those needed for processing when needed isnecessary when one sets out to model matching problems andpull techniques.

The sequence in which characterized resources will bedrawn from a queue during simulation depends on (1) theordering of incoming resources relative to those already in thequeue, and (2) the criteria applied in selecting resources forwithdrawal from the queue. To achieve the desired systembehavior, a STROBOSCOPE programmer can define drawsequences by specifying respectively [items in CAPS denoteSTROBOSCOPE programming statements]: (1) a queue's so-called DISCIPLINE and (2) conditions on the link emanatingfrom the queue (e.g., using RELEASEORDER andDRAWWHERE with FILTER-expressions). Example drawsequences (implemented by ordering, selection criteria, or acombination thereof) are:1. First-In First-Out or Last-In First-Out:

The ordering criterion is resource time of arrival in thequeue. First-in-first-out (FIFO) places resourcesarriving earlier at the front of the queue, so by default


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they will be drawn first (they have been waiting for thelongest time). In contrast, last-in-first-out (LIFO)places those arriving later at the front.

2. First-in-Order Based on a Property of Resources inSingle Queue:When resources can be ordered based on a property oron some externally-defined numbering system, thatorder can define draw priority. For example, trucks ofvarying size can be sorted by their loading capacity,where it may have been decided that larger ones will beloaded first; an engineer may have numbered footingsto specify the order in which concrete is to be placed,where those with lower numbers will be placed first.Selection is thus based on comparing an individualresource's "capacity" or "placement number" propertywith that of others in the same queue. Other examplesare "easiest to install first" and "highest ratio of earned-to-expended effort first" (Howell and Ballard 1996),

"materials covered by or buried in others first," and"those that can easily be damaged last."

3. Best Match Based on Properties of Resources inMultiple Queues, all Preceding a Single Activity:Resources may be drawn from one or several queues sothat the properties of those drawn from one queuematch those drawn from the other queue(s). In theworst case, matching fails. This situation typifiesmatching problems. For example, a water boiler has adesignated location in a house under construction;when the boiler is ready to be installed, workers musthave access to that designated location to work; noother location will do. Another example is to installonly complete pipe runs (Howell and Ballard 1996) orto load various structural steel shapes onto flatbedtrucks not to exceed the truck's dimensions or loadcapacity (Martinez 1996).

Table 1: Selected STROBOSCOPE Symbols

SYMBOL NAME EXPLANATION

CutSheetQueue Is a holding place (buffer) for 0, 1, or several resources waiting to become involved in the

succeeding combination activity. Queues may contain generic or characterized resources.The latter are distinct from one another and they can be traced as individuals throughvarious network nodes during simulation. The logic describing the ordering of resourcesupon entry into a queue of characterized resources is termed a DISCIPLINE.

TransportNormal

(activity)Describes a certain type of work to be done, or a delay, of a known (probabilistic) durationfrom start to finish. May require a single resource or no resource at all.

FabricateCombi

(-nationactivity)

Like a normal, describes a certain type of work to be done, or a delay, of a known(probabilistic) duration from start to finish. Unlike a normal, requires several resources incombination for its performance and draws what is needed from the queue(s) that precedeit.

AwaitTransportConsolidator Acts as a counter up to n (n is an integer value specified with the node): after n resources

have been released into the consolidator, the consolidated set will be released from it.

WA1 Link Shows flow logic. Should be labeled to meaningfully describe the resources that flowthrough it. If the link emanates from a queue, a DRAWORDER may be specified tosequence resources being drawn from the queue.

GoodBad Fork Describes a split in a resource’s flow path. Incoming resources are routed along one path oranother in a probabilistic or deterministic fashion, so the node is called a probabilistic forkor a decision node respectively. Each link emanating from it carries a likelihood or astatement evaluating to true/false for being followed by any specific resource arriving at thefork during simulation. The resource’s actual path is determined at run time.

SpoolInArea

AAssembler Shows that 2 or more resources are being assembled into a single unit resource which is of

the compound (a special kind of characterized) resource type.


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Figure 2: Process Model of Pipe-Spool Installation

4. Random:Resources are picked at random from those in thequeue. This will be appropriate when they areinterchangeable. However, when resources are notinterchangeable, random drawing (e.g., due to mis-identification of a material) results in erroneoussubstitution, thus causing problems.

Project managers and field personnel use such draw sequenceswhen planning and executing work. As is to be expected, some

sequences will be more advantageous than others. This isillustrated next by simulating alternatives.

Pipe-Spool Process Model

Figure 2 depicts a model of the example pipe-spool installationprocess. The rationale for selecting modeling elements and theirparameters is detailed by Tommelein (1997a). Admittedly,selections were somewhat arbitrary. The writer modeled salientfeatures yet represented only a few system characteristics so that


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the model's behavior and output would remain tractable.Simplifications are consistent with the aim of this paper, whichis to illustrate (1) the impact coordination planning has on theexecution of resource-matching processes and (2) the benefits ofpull over push when uncertainty is high. Using the samemethodology and tools, more complete, industrial-strengthmodels can easily be developed.

IMPLEMENTATION AND SIMULATION OFALTERNATIVESOne deterministic and three probabilistic models wereimplemented. All models describe that 600 spools are to beinstalled in 15 areas, at 40 spools per area. It has been assumedthat spools are delivered to site in loads of 10 and installation inan area will not start until all 40 matching spools are available.

Deterministic Model

Model Construction and ParametersThe deterministic model is based on the process chart with allcharacteristics shown in Figure 2 except for those listed in Table2. All durations take on their most likely value and there are noquality failures (no rework). This model illustrates how theproject progresses when everything in the system is certain,perfectly coordinated, and synchronized.

Perfect coordination means that cutsheets 1 through 600 areused for fabrication in numerical order, resulting in spools 1through 600 arriving at the site in numerical order. Similarly,work areas 1 through 15 are prepared in numerical order, so thatinstallation of spools 1 through 40 starts without delay as soonas area 1 is ready, installation of spools 41 through 80 starts assoon as area 2 is ready, and so on.

By construction, all CutSheets are available on day 0, at thestart of simulation, and WorkAreas on day 85. The durations ofactivities Design, Fabricate, PrereqWork, and Install, combinedwith their production resources DesignTeam, FabCrew,PrepCrew, and InstallCrew respectively, were chosen so thattheir throughput is equivalent to 4 spools/day. Consequently,synchronization means that all commodity curves for CutSheet,StagedSpool, WorkAreaReady, and AreaDone are parallel(Figure 3, Left, Top).

Pipe-spool Buffer SizeFigure 3 (Right, Top) illustrates that pipe spools accumulate onsite at a steady pace until day 95, when the first area opens upfor installation. The StagedSpool buffer peaks at 340. Itgradually gets depleted over time as subsequent areas open up,yet continues to get replenished until all spools have beendelivered to the site.

Note that at 20% of work done (AreaDone), more than 70%of all spools have been delivered to the site. According toHowell and Ballard's rule of thumb, this is a favorable indicatorfor project success. As will be shown later, it is no guarantee forproject success, however, particularly when out-of-sequencework leads to mismatches.

Productivity of Installation CrewSince all activities in this model are synchronized and there isno uncertainty, the installation crew's productivity is optimal.Each area gets completed in 10 days and work progressesuninterruptedly.

Project DurationThe project duration is 245 days (95 days until work in the firstarea can start plus 15 times the installation of 40 spools in eacharea where work progresses at 10 days/area: 95 + 15*10 = 245).

Alternative Probabilistic Models

Model ConstructionThe three probabilistic models also are based on the processchart as depicted, but they include all its uncertainties indurations and likelihood of rework. The variability in fabricationduration was taken from Howell and Ballard (1996). A pipingindustry rework rate ranging from 1 to 10% was quoted byBallard and the worst value is used here. Lacking other data,rework is assumed to take the same amount of time asfabrication. The three models differ from one another (1) in thedraw order of cutsheets for fabrication and (2) only the lastmodel includes the Feedback queue, the Update activity, andlinks FB1, FB2, DW3, and DW4 (shown in dotted lines inFigure 2).

Table 2: Deterministic vs. Probabilistic Models

MODELING SYMBOL DETERMINISTIC MODEL(Model D)

PROBABILISTIC MODEL(Models A, B, and C)

STRENGTH PS2 100% 90%

STRENGTH PS3 0% (no rework) 10%

DURATION Fabricate [days] 5 Pertpg [3, 5, 14]

DURATION Rework [days] N/A Pertpg [3, 5, 14]

DURATION Transport [days] 3 Normal [3, 1]


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Specs

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AreaDone

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Figure 3: Commodity Curves (Left) and Buffer Sizes (Right) for Simulation of Single Iteration


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Different draw orders for each model can be discerned in theSTROBOSCOPE source code (Tommelein 1997a) but not inFigure 2 as the graphical representation reflects only a limitednumber of model parameters. Draw sequencing does not affectthroughput of the fabrication process (by construction of themodel, all spools are characterized by the same durationdistribution for fabrication), so in terms of percent of pipespools having been fabricated, transported, and delivered to thesite, all three probabilistic models will exhibit the samebehavior: their StagedSpool commodity curves look the same.

The duration variables were chosen so that the most likelyor mean value of the off-site sequence is synchronized with theon-site sequence. Thus, the most likely values of Design,Fabricate (ignoring Rework), and Transport add up to have theright number of pipe spools— though not necessarily the rightspools— being staged on site for a desired number of days priorto the most likely completion of FieldWork and PrereqWork. Inaddition, the throughput (average number of resources outputper time unit) off site matches the throughput on site (40 spoolsget produced on average in the same amount of time needed tocomplete prerequisite work in an area). That way, presumably(if all instances of each activity had a duration close to the meanvalue of that activity and no quality failure such as reworkmanifested itself), field production should not be delayed by ashortage of materials. Nevertheless, it could be delayed due tomismatches. Table 3 lists the alternative draw sequences used ineach probabilistic model (A, B, and C). For the sake ofcompleteness, it also includes those of the deterministic modeldescribed previously (model D).

The first two probabilistic models illustrate the traditional,push-driven process-management approach, but they presentextremes in effort put into pre-construction planning. The thirdmodel exemplifies a pull-driven approach.

Output RepresentationSelected simulation outputs were charted for a single iteration toshow what a specific instance of each model might look like(Figure 3). Simulation of each probabilistic model was thenrepeated for 1,000 iterations to illustrate the extent to whichbuffer sizes and progress of activities vary. Figure 4 (Top Left)shows the superimposed commodity curves. Value ranges on

percent complete of AreaDone at any time are denoted by asolid line for the mean value, and long and short dashesrespectively for the mean plus-or-minus 1-or-2 standarddeviations. Figure 4 (Top Right, Bottom Left, and BottomRight) shows the mean plus-or-minus 1-or-2 standard deviationsfor the number of StagedSpool, and, as labeled, the standarddeviation (SD). Values were collected every 25 days.

Model A - Random SequencingModel A's Parameters

The worst-case pre-construction plan is to have no coordinationat all. The order in which pipe spools are fabricated bears norelationship to the order in which areas are being prepared onsite. With no communication between fabrication andinstallation to coordinate their respective work with one another,each crew will draw resources from the queue available to themin the order that suits them best. The fabrication crew may selectspools based, perhaps, on the order in which cutsheets areprovided to the fabrication shop. This order may have nothing todo with installation sequencing. The preparation crew mayselect one area after the other based, perhaps, on which one isnearest to their present location. This situation has beenimplemented by defining the DRAWORDER for choosingresources in the CutSheet queue through link DW2 to berandom, and leaving the discipline in the WorkArea queue asthe default FIFO (anything else would appear equally randomrelative to the randomness in drawing cutsheets).

Model A's Pipe-spool Buffer SizeBecause of lack of coordination, the likelihood for mismatchesto occur is high. When spools in StagedSpool and work areas inWorkAreaReady cannot be matched for installation, stagingareas will fill up with spools of no immediate use, and workareas remain unfinished. The peak at 570 for StagedSpool inFigure 3 (Right, Upper-middle) confirms this to be the case (590spools on site is the maximum, as there are 600 spools in total,15 areas requiring 40 spools each, and the next-to-last shipmentof 10 should at the very latest complete five required sets of 40spools).

Table 3: Alternative Sequencing Strategies

CASE DESCRIPTION CutSheet DRAWSEQUENCE

WorkArea DRAWSEQUENCE

A Probabilistic ModelRandom Sequencing

Random FIFO

B Probabilistic ModelCoordinated Sequencing

FIFO FIFO

C Probabilistic ModelPull-driven Sequencing

Priority to spools thatmatch area(s) ready

FIFO

D Deterministic ModelCoordinated Sequencing

FIFO FIFO


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Specs

CutSheet

WorkArea

StagedSpool

Work AreaReady

AreaDoneRandom

Area DoneCoordinated

AreaDonePull-driven

0 50 100 150 200 250 300 350 400 450

100

90

80

70

60

50

40

30

20

10

0

Time[days]

%Complete

Commodity Curves for Three Probabilistic Models Combined

0 50 100 150 200 250 300 350 400 450

600

500

400

300

200

100

0

Time[days]

Number

StagedSpool Buffer Size vs. Time for Random Sequencing

0

100

200

300

400

500

600

0 50 100 150 200 250 300 350 400 450 0 50 100 150 200 250 300 350 400 450

600

500

400

300

200

100

0

Time[days]

Number

StagedSpool Buffer Size vs. Time for Coordinated Sequencing

0 50 100 150 200 250 300 350 400 450

600

500

400

300

200

100

0

Time[days]

Number

StagedSpool Buffer Size vs. Time for Pull-Driven Sequencing

Figure 4: Simulation Output of 1,000 Iterations

In reality (not in the model), spools on site for a long time (atworst, a spool could be the first one delivered and last oneinstalled) are more likely to sustain damage. Additionalpreparation work (e.g., erecting scaffolds or, at least, checking ifprevious preparation work still meets specifications and if thereare no new obstructions) may be required when installationcrews finally move into an area where work is to be completed.Real project costs are incurred for keeping track of andrehandling materials on site, for working in obstructed areas,and for performing out-of-sequence work.

Model A's Productivity of Installation CrewAs mentioned previously, it has been assumed that installationin an area does not start until all 40 spools for that area areavailable. This is not necessarily industry practice (managementpressure to start work and produce "show pipe" even thoughwork cannot be executed optimally, usually is very high) but theline of balance labeled AreaDone in Figure 3 (Left, Upper-middle) confirms that accumulating a huge buffer makes itpossible for the installation crew to achieve its highest possibleproduction rate (also see Howell et al. 1993). At 20% of spoolsinstalled, nearly all spools have been delivered to site; viceversa, at 60% of spools delivered, barely any installation workhas started (also see Figure 4 Top Left). For the crew, this willbe an effective way of getting work done, provided that theyneed not be idle prior to starting to install.

In this single iteration, the crew's start time is 247 days.However, during pre-construction planning, one can onlyestimate this start date. Given the uncertainty in gettingmatching spools to site, it may make sense to build in a timebuffer or lag preceding the Install activity to enable the crew tobe optimally productive once they mobilize (e.g., start Install noearlier than day 250). Buffering protects the installation crewfrom upstream process uncertainties and buys time for them toplan and get ready, or do other work in the mean time.

Model A's Project DurationThe delay in starting installation, forced here by a shortage ofmatching materials, leads to a project duration of 397 days. Thisis by far the longest one of all scenarios shown in Figure 3.

Model B - Coordinated SequencingModel B's Parameters

Model B describes perfect coordination. The fabrication crewand the installation crew plan before starting their work anddecide on the sequence in which to draw resources. Cutsheetsand areas are assigned sequence numbers so they can be drawnin FIFO order (STROBOSCOPE's default discipline). CutSheets1 through 40 will go to fabrication before 41 through 80, and soon. Similarly, Area 1's prerequisite work will be performed priorto Area 2's, and so on.

While perfect coordination reflects an idealized situation,for many reasons it will never materialize. It seldom is acontractual requirement and it also is too restrictive to thevarious parties involved in the process (e.g., fabrication shopsare not set up to tolerate one-piece flows, that is, to changemachine setups in order to meet each spool's unique fabricationrequirements).


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Model B's Pipe-spool Buffer SizeModel B results in minimal space needed to stage spools on site:StagedSpool peaks at 200 in Figure 3 (Right, Lower-middle).Nonetheless, some spools will accumulate on site becausedeliveries get out of sequence when uncertainty manifests itselfduring fabrication and shipping, and the need for rework arisesoccasionally.

Model B's Productivity of Installation CrewDespite expedient project completion, the installation crew(which starts to work as soon as work is available and stays idlein-between activities, when materials are in short supply) wasnot able to work as productively as before (the AreaDone line ofbalance is not straight but bends to the right). This is nocoincidence! The writer crafted the model's basic template toshow how materials shortages might arise so that their impacton production could be shown. While the activities Design,Fabricate, PrereqWork, and Install can process resources at thesame average rate of 1 area/10 days or 4 spools/day, uncertaintyin the Fabricate, Rework, and Transport activities results in aStagedSpool slope much smaller than the CutSheet orWorkAreaReady slope. Consequently, the AreaDone slope issmaller as well (note that in Model A the AreaDone slope wasnot really affected by the slow delivery rate because of the largebuild-up of spools prior to its start). Because FieldWork starts85 days after OffSiteWork, the StagedSpool andWorkAreaReady lines of balance cross.

Model B's Project DurationPerfect coordination leads to project completion in the shortestduration of 275 days (Figure 3, Left, Lower-middle).

Model C - Pull-driven SequencingModel C's Parameters

Model C augments model A's random sequencing with a pullmechanism, which includes the Feedback queue, the Updatecombination activity, and four links to tie them into the existingnetwork. CutSheets initially are processed in random orderrelative to work areas, but as soon as an area is ready for spoolinstallation, area-availability feedback is transmitted and used toupdate their status. Cutsheets that match this feedback arechecked accordingly so that they will get priority over others tobe fabricated, that is, they are "pulled" to the site. In the singleiteration that is depicted, a total of 291 updates were performed.

Model C's Pipe-spool Buffer SizeRelatively few spools accumulate on site (250 maximum, Figure3, Right, Bottom). The buffer is not as small as it was withperfect coordination, but it certainly does not get as much out ofhand as it did with random sequencing either!

Model C's Productivity of Installation CrewStarting off with random sequencing and then improving thesequencing based on feedback penalizes the crew in terms offield productivity relative to the perfect-coordination case. Theslope of AreaDone has decreased further than it already had inmodel B. Luckily, this performance can be anticipated andimproved. The crew can be ordered to start later (e.g., start at

time 150 or 175, see Figure 4, Top Left), when more spools areon site so workers will be able to progress at their fastestpossible rate, or it can be scaled down in size.

Model C's Project DurationThe project duration remains fairly short, at 304 days (Figure 3,Left, Bottom).

IMPLEMENTATION HARDWARE AND SOFTWAREAll models were run in STROBOSCOPE (version 1, 2, 2, 0) ona Pentium 200-MHz computer running Windows® 95. A singleiteration takes on the order of 1 minute. Source code is available(Tommelein 1997a) so readers can reproduce and furtherexperiment with alternative inputs to this model.

Other draw sequences and feedback mechanisms couldhave been implemented and their impact studied on, forinstance, crew productivity and project completion. Thefeedback mechanism as shown does not lead to optimal systemperformance. Readers may accept this observation as achallenge.

DISCUSSION AND CONCLUSIONSThe lean-production "pull" technique has been shown toimprove performance of a construction process. It is particularlywell-suited for fast-track projects that require assembly ofunique parts and that are plagued by uncertainties. Such projectsare difficult to schedule accurately and in detail in advance. Thenature of the anticipated matching problems must determine thecomplexity and detail required of the planning system. Asuncertainties manifest themselves during project execution, thepre-construction schedule will have to be adjusted in a flexiblemanner for field work to progress efficiently and for work-in-progress inventories to remain small.

The pull technique suggests that real-time feedback fromconstruction be used to drive the sequencing of off-site work,and vice versa. By choosing upstream to process "matchingparts" first, the downstream process will proceed in a moreexpedient fashion, and completed units will be available soonerthan would be the case otherwise. Wireless communicationtechnologies, appropriate to implement this technique, arereadily available today.

The pull technique assumes that all participants in theproject supply chain are willing and able to respond to eachother's needs in order to optimize overall project performance,not just their own. This requires rethinking of contractualrelations and providing appropriate incentives. Processes alsomust become more transparent. Participants who can 'see' theother's needs, can better plan to accommodate them. Asomewhat paradoxical situation exists today, with theproliferation of specialist firms believing that they haveoptimized their own operations. Local optima may have beenreached, but at best, those are based on numerous assumptionsabout other project participants' performance. Many processuncertainties and resulting waste stems from ignorance.Increased process transparency among participants may aid notjust the project's but also the individual firm's performance.

Only one pull link was shown in the model discussed here.Obviously, choosing where, when, and how to pull is an


12

important issue. Many pull links could be created, but each costsmoney to implement and the effects of one may offset those ofanother. Investigation of this issue must be supported bycollection of process data that describes activities and durations,resources, and path-flow uncertainties of the system that is to beimproved. Discrete-event simulation can help the decisionmaker understand the system's behavior and gauge the impactpull links may have. Using the simulated data, a cost-benefitanalysis can then be performed prior to physically establishingthose links.

The collection of process data in and by itself is aworthwhile endeavor. Knowing where uncertainties exist andhow large they are will help focus on reducing thoseuncertainties. It should be obvious from the limited work thathas been conducted to date on implementing production controlin construction as is advocated by the lean productionphilosophy, that process-level analysis of construction ispromising area of research, development, and application.

ACKNOWLEDGMENTSI am most indebted to James C. Goodwin, Manager of MaterialsManagement at H.B. Zachry, for letting me study industrypractices at the Lyondell-Citgo Refinery Expansion Project sitein Houston, Texas. This study increased my awareness andunderstanding of the complexity of construction materialsmanagement.

Thanks are due to Professor Julio C. Martinez, at theVirginia Polytechnic and State University, for makingSTROBOSCOPE readily available. Thanks to him as well as hisdissertation advisor, Professor Photios G. Ioannou at TheUniversity of Michigan, this expressive and especially fastdiscrete-event simulation system now lets us model constructionprocesses effectively and efficiently.

Last, but not least, I owe thanks to my Berkeley colleague,Glenn Ballard, for introducing me to the "Last Planner" anddiscussing field implementations of lean construction, which hassharpened my own thinking about this subject. Glenn alsoprovided valuable feedback on drafts of this paper.

This research was funded by grant CMS-9622308 from theNational Science Foundation, whose support is gratefullyacknowledged. Any opinions, findings, conclusions, orrecommendations expressed in this report are those of the authorand do not necessarily reflect the views of the National ScienceFoundation.

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Howell, G.A. and Ballard, H.G. (1996). Managing Uncertaintyin the Piping Process. RR 47-13, Constr. Industry Institute,Univ. of Texas, Austin, TX, September, 103 pp.

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Phair, M., Tulacz, G.L., and Angelo, W.J. (1997). "CushierControls, Cabs, Make Cash: Ergonomics and Comfort GoHand-in-Hand with Productivity." ENR, Feb. 10, 34-39.

Tatum, C.B. (1985). "Evaluating Construction Progress."Project Mgmt. Journal, Spec. Summer Issue, August, 52-57.

Thomas, H.R., Sanvido, V.E., and Sanders, S.R. (1989). "Impactof Material Management on Productivity— a Case Study." J.Constr. Engrg. and Mgmt., ASCE, 115(3) 370-384.

Tommelein, Iris D. (1997a). Discrete-Event Simulation of aPull-Driven Materials-Handling Process that RequiresResource Matching: Example of Pipe-Spool Installation.Tech. Report 97-2, Constr. Engrg. & Mgmt. Program, Civil &Envir. Engrg. Dept., Univ. of California, Berkeley, CA.

Tommelein, Iris D. (1997b). "Models of Lean ConstructionProcesses: Example of Pipe-Spool Materials Management."In Anderson, S. (Editor). "Managing Engineered Constructionin Expanding Global Markets." Proc. Construction CongressV, Oct. 5-7 in Minneapolis, MN, ASCE, 156-164.

Womack, J.P. and Jones, D.T. (1996). Lean Thinking: BanishWaste and Create Wealth in Your Corporation. Simon &Schuster, New York, NY, 350 pp.

pull-driven scheduling for pipe-spool installation: simulation of a

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