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Models for the Development of Precision Machine Tools
by
David J. Mintz
Submitted to the Department of Mechanical Engineering
in Partial Fulfillment of the Requirements for the Degrees of
Bachelor of Science in Mechanical Engineering
and
Master of Science
at the
Massachusetts Institute of Technology
February 1993
Massachusetts Institute of Technology, 1993All Rights Reserved
Signature fSignature RedactedAuthor
Department of Mechanical EngineeringJanuary 22, 1993
Ceried Signature Redactedby
Professor Alexander H. SlocumThesis Supervisor
Accepted Signature Redactedby
Professor Ain A. SoninChairman, Department Graduate Committee
MASSACHUSETTS INSTITUTEOF TFrNr1v IyMASACHUSETTS INSTUTE
OF TECHNOLOGY
FEBj16 2001
ARCHIVES LIBRARIES
Models for the Development of Precision Machine Toolsby
David J. Mintz
Submitted to the Department of Mechanical Engineering
on January 22, 1993, in Partial Fulfillment of the Requirements for the Degrees of
Bachelor of Science in Mechanical Engineering and Master of Science
Abstract
The purpose of this research is to develop two distinct modeling tools to facilitate the
development of precision machine tools. Both modeling tools are intended to help
companies minimize the time and cost of developing precision machine tools, while
maximizing the performance of these machines and the learning achieved by the
organization.
The first tool is a systematic, spreadsheet-based error budgeting methodology for
predicting machine tool accuracy. The error budget spreadsheet is based on rigid body
kinematics and includes error matrix representations. Using this method, a machine
concept is modeled by freely assigning up to ten coordinate systems to the machine's
structure. Each of these coordinate systems represents a rigid body within the machine.
Taking into account the geometric, kinematic, external load induced, thermal,
instrumentation, and computational errors for each coordinate system, a first-order estimate
of the machine's accuracy is determined.
The second tool is a model for optimizing ramp-up for precision machine tools. Ramp-up
is the process of taking new machine tools from initial prototype to full production. The
model identifies the factors that influence ramp-up. These factors include the criteria upon
which ramp-up is measured, as well as the drivers which shape how ramp-up is carried
out. The model then uses matrix analysis and the Pugh concept selection process to
analyze these factors as a collective whole. The matrix analysis indicates which drivers will
have the largest positive and negative impacts on ramp-up. The Pugh concept selection
process is used to determine superior approaches for conducting ramp-up.
Thesis Supervisor: Alexander H. Slocum
Title: Associate Professor of Mechanical Engineering
2
Acknowledgments
I would like to thank Alex Slocum, my mentor and friend. Professor Slocum is
one of those special people that we come across too few times in our lives. His genius, his
creativity, and his determination are a source of inspiration for me. He is also one of the
most decent people that I have ever met. He is goodhearted and kind. And, I believe that I
am a better person for having known him. I hope that we will always be friends.
I would like to thank Joe Pack. Joe is someone who I feel fortunate to call both my
manager and friend. As my manager, he has helped to make my work fulfilling,
challenging, and fun. His expertise in process development and business have proved
invaluable in my professional growth. Joe is also a goodhearted and kind person, whose
advice I respect very much.
I would like to thank Jim Bryan. Jim introduced me to Precision Engineering and
its guiding principles. He has been very supportive and patient in teaching me about this
field, he has been very gracious in introducing me to many of his colleagues, and he has
been a lot of fun to be around. I hold Jim in high regard as a person, as a teacher, and as a
friend.
I would like to thank Marcie Tyre. Professor Tyre is an expert in the areas of
implementation of new manufacturing technologies and transfer of technology from R&D
to production. Without her help and advice, Chapter 2 would not have been possible.
I would like to thank Woodie Flowers. Professor Flowers is one of the most
interesting and engaging people that I have ever met. While listening to him lecture for the
first time, I realized then that Mechanical Engineering would be my major and that design
would be my passion. Throughout my education at MIT, he has been very supportive, and
it has been enjoyable getting to know him.
I would like to thank Rosendo Fuquen for giving me the opportunity to be in the
EIP program. This program has been invaluable to my growth professionally and
personally. Rosendo is also a friend, whose advice I respect very much.
I would like to thank Tom Ballas, Jim Charmley, Dave Cogdell, Mike Dreher, Pete
Harding, and Dave Webb for all of their help and assistance in conducting this research, as
well as their friendship.
I would like to thank Heinz Gaub for all of his help in developing the error
budgeting spreadsheet and for being a good friend.
I would like to thank Steve Immerman. Steve was my undergraduate advisor and
has been a good friend throughout my years at MIT.
3
I would like to thank Mark Bower, Rob Mitra, Doug Smith, and Jim Szafranski for
their friendship and for making summers in Ohio a lot of fun.
I would like to thank Dan Stephens for a friendship that has endured since
childhood and for all of his support through the years.
I would like to thank Paul Dans, Mike and Beth Garatoni, Chris Harris, Erik Kerr,
Kathy Metz, Brian Quinn, and Bob Rizika for their friendship over the years.
I would like to thank the faculty and administration of MIT, especially the
Mechanical Engineering Department. I appreciate the education that I have received, and
hope to represent MIT well in the years to come.
Most of all, I would like to thank my family, especially my Mom and Dad, Gram-a-
gram and Papa, my sister Michele, and my Aunt Barb for their love and support over the
years. I would also like to thank them for the financial sacrifices that they made so that I
could attend MIT.
4
to my Mom and Dad
5
Table of Contents
Spreadsheet-Based Error Budgeting for Predicting Machine Tool
Accuracy
1.1 Introduction 8
1.2 Classification of Error Budgets 9
1.2.1 Correlation Models 9
1.2.2 Trigonometric Analysis 9
1.2.3 Error Matrix Representations 10
1.2.4 Rigid Body Kinematics 10
1.3 Homogeneous Transformation Matrix Representation of a
Machine Tool 11
1.4 Classification of Errors 14
1.4.1 Geometric 14
1.4.2 Kinematic 18
1.4.3 External Load Induced 20
1.4.4 Thermal 24
1.4.5 Instrumentation 26
1.4.6 Computational 28
1.4.7 Miscellaneous 28
1.5 Error Gain Matrix 29
1.6 Combinational Rules 31
1.6.1 Random, Systematic, and Hysteresis Errors 31
1.6.2 Complete Detail, Upper Bound, Lower Bound,
and Expected Value 31
1.7 Position and Orientation Errors of the Tool 32
1.8 Architecture of Error Budget Spreadsheet 35
1.9 Case Study: Precision Coordinate Measuring Machine 35
1.9.1 Introduction 35
1.9.2 Correlation Model 36
1.9.3 Trigonometric Analysis 36
1.9.4 Error Gain Matrix Representation 36
1.9.5 Rigid Body Kinematics Analysis 37
1.9.6 Discussion 37
1.10 Conclusion 37
6
2 Model for Optimizing Ramp-Up for Precision Machine Tools
2.1 Introduction 38
2.2 Ramp-Up 39
2.3 Development of Model 41
2.4 Interpretation of Model 47
2.5 Discussion 56
2.6 Case Study: Precision Coordinate Measuring Machine 58
2.6.1 Introduction 58
2.6.2 Preliminary Recommendations 60
2.7 Conclusion 64
Figures
Figure 1: Geometric Errors in a Linear Motion Carriage 15
Figure 2: Geometric Errors in a Spindle 17
Figure 3: Orthogonality and Horizontal and Vertical Parallelism Errors 19
Figure 4: Thermal Effects in Manufacturing and Metrology 27
Figure 5: Ramp-Up Criteria and Drivers 42
Figure 6: Affinity Diagram 43
Figure 7: Tree Diagram 44
Figure 8: 'House of Quality' Matrix Structure 46
Figure 9: Pugh Concept Selection Matrix 54
Figure 10: Evaluation of Ratings 55
Figure 11: Matrix Analysis of ECMM 57
Appendices
Appendix A: Ramp-Up Definitions and Metrics 67
Appendix B: Ramp-Up Interview Questions 83
7
Chapter 1
Spreadsheet-Based Error Budgeting for Predicting Machine
Tool Accuracy
1. 1 Introduction
Increasing global competition in advanced manufacturing technology requires
developing new machine tools with higher accuracy, less cost, and shorter development
lead time. Currently, most machine tools are designed by designers who rely on long-term
experience and only basic calculations to arrive at a final design. The machine's accuracy is
typically determined empirically in long test runs, often resulting in major design changes
and multiple costly and time consuming prototype iterations. Further, novice designers or
designers of new types of process technologies often do not have the time available to
develop an accurate model that will allow them to predict the accuracy of a machine before a
prototype is built. To help companies minimize the cost and development time of new
manufacturing process technologies, as well as to help machine design engineers develop
more accurate models of these process technologies, a systematic spreadsheet-based
method for error budgeting has been developed.
The error budget spreadsheet is based on rigid body kinematics and includes error
matrix representations. Using this method, a machine concept is modeled by freely
assigning up to ten coordinate systems to the machine's structure. Each of these coordinate
systems represents a rigid body within the machine. Taking into account the geometric,
kinematic, external load induced, thermal, instrumentation, and computational errors for
each coordinate system, a first-order estimate of the machine's accuracy is determined. By
using the spreadsheet as a modeling tool, the design of a machine becomes more
predictable, and numerous prototype iterations can be avoided. This allows a design
8
engineer more time to explore 'what-if scenarios before any hardware is built. As a cost
minimization tool, the spreadsheet can also be used to make trade-off alternatives to
minimize overall system cost.
The spreadsheet is written on Microsoft Excel and can be run on a personal
computer. The software is currently being developed and tested on the design and
development of a precision coordinate measuring machine. The results along with the
spreadsheet-based error budgeting methodology will be presented.
1.2 Classification of Error Budgets
1.2.1 Correlation Models
Correlation models graphically represent the sources of error for a machine tool and
show how these errors interact with each other. They also show how these errors
contribute to the total position error between the tool and workpiece. In the early stages of
design, detailed specifications of machine tool concepts rarely exist. Correlation models
are useful at this level of design in that they provide a qualitative analysis of accuracy when
actual values of error are not available. In identifying sources of error for a machine tool,
correlation models also facilitate the development of trigonometric and rigid body
kinematics analyses.
1.2.2 Trigonometric Analysis
Chronologically, trigonometric analysis is often the next type of error budgeting
performed in developing machine tools. It is usually performed concurrently with the
subsystem design phase. Trigonometric analysis is based on simple geometry and takes
into consideration geometric, kinematic, and probe errors (when applicable). Keeping in
9
mind the plane(s) of interest for the machine, contributions of each of these errors are
estimated and summed. For error estimation, product catalogs serve as accurate and easily
accessible sources of information. By providing a quantitative estimate of a machine's
accuracy, trigonometric analysis is useful in identifying components and technologies that
will be critical to the success of the machine early in the design phase.
1.2.3 Error Matrix Representations
Error matrix representations are performed from the time that a system design has
been selected till the time that firm dimensioned layout drawings are completed. They are
based solely on the geometry of the machine. Error matrix representations provide a
sensitivity analysis of the machine's errors by listing the amplification factor by which each
individual error component for each coordinate system contributes to the total position and
orientation error. By indicating this sensitivity of errors to different layouts, error matrix
representations are useful in qualifying the machine's structural loop dimensions.
1.2.4 Rigid Body Kinematics Analysis
Rigid body kinematics is the most mathematically rigorous and the most robust
error budgeting methodology. Homogeneous transformation matrices are used to three-
dimensionally model the machine tool. The analysis is capable of taking into account
geometric, kinematic, external load induced, instrumentation, and computational errors.
The analysis is robust in that it is capable of performing trigonometric analysis as well as
providing error matrix representations. The rigid body kinematics analysis can therefore be
performed from early on in the design phase till the detailed design is complete.
10
1.3 Homogeneous Transformation Matrix Representation of a Machine
Tool
Homogeneous transformation matrices (HTMs) represent the position and
orientation of a coordinate system (e.g. xi, yl, zi) with respect to a reference coordinate
system (x, y, z). In doing so, HTMs define the spatial relationships of objects in three-
dimensional space. These matrices can therefore be used to represent machine tool
structures based on a rigid body model. Because they three-dimensionally define a
machine tool, HTMs can be used to generate the error gain matrix for a machine.
Furthermore, HTMs can be used to take into account linear and angular offsets between
coordinate frames. These linear and angular offsets correspond to position and orientation
errors intrinsic to individual machine components. These errors referenced with respect to
individual machine components are then transformed so that they can be referenced with
respect to the toolpoint and the workpiece.
The HTM is a 4 x 4 matrix as shown:
Oix Oy Oiz PxRT= OjX Ojy Oz PY
Okx OGky Okz Pz0 0 0 Ps
The first three columns of the HTM are the direction cosines (unit vectors i, j, k)
representing the orientation of the rigid body's xN, yN, and zN axes with respect to an
adjacent coordinate frame. The last column represents the position of the rigid body's
coordinate system origin with respect to the reference coordinate frame. The value in the
last column and last row, Ps, represents the scale factor which is set to unity for error
budgeting. The pre-superscript, R, represents, the reference frame in which the result is to
be represented. The post-subscript, n, represents the reference frame from which one is
11
l
transferring (e.g. xYzTxiyizi represents the location of the xj, yi, zi coordinate system in
the x, y, z coordinate system.).
If a coordinate system (e.g. xi, yl, zi) is translated by an amount x along the X-
axis, an amount y along the Y-axis, and an amount z along the Z-axis with respect to the
reference coordinate system (x, y, z), then the HTM is:
XYZTxiyizi =~1 0 0 x
S 0 y0 0 1 z0 0 0 1]
If the coordinate system (xi, yl, zl) is rotated an amount Ox about the X-axis with respect
to the reference coordinate system, then the HTM is:
xYzTxiyizi =
_ ~z
1 0
0 cos0x
0 sin0x
0 0
If the coordinate system (xl, yl, zi) is rotated an amount Oy about the Y-axis with respect
to the reference coordinate system, then the HTM is:
XYZTxiyizi =-'
cos0y
0
-sin0y
0
0
1
0
0
sin0y
0
cos0y
0
0
0
0
1
(4)
If the coordinate system (xl, yi, zi) is rotated an amount 0, about the Z-axis with respect
to the reference coordinate system, then the HTM is:
12
(2)
0
-sin0x
cosOX
0
0
0
0
1 _
(3)
XIYZTXIYiZi =LcosOz -sin6z 0 0
sinOz cosOz 0 0
0 0 1 0
0 0 0 1i
Coordinate systems which involve a combination of these motions can be represented by a
single HTM. This HTM is the product of other HTMs, each representing a single
translation or rotation, multiplied in series. For instance, if the coordinate system (xi, yi,
zi) is translated by the amounts x, y, and z along the X-, Y-, and Z-axes, respectively, and
if rotated an amount 0x about the X-axis, rotated an amount Oy about the new Y'-axis, and
rotated an amount Oz about the new Z"-axis, then the HTM is:
x
sin0xsin0ycos0z
+ cos0xsin0z
-cos0xsin0ycos0z
+ sinOxsinOz
0
cos0xcos0z -sin0xsinysin0z
sin0xcosOz +cos0xsin6ysin0z
0
-sinxcos0y y
cosOXcos0y z
0 1
In modeling a machine tool, each HTM represents either the position of the
machine's axes or intermediate locations on the machine structure that facilitate modeling
(e.g. bearings, joints). HTMs in series are then used to model the rigid bodies from the
machine's tool tip to a reference location. Similarly, HTMs in series are used to model the
rigid bodies from the machine's workpiece to the same reference location. Thus, both the
tool tip and workpiece positions are known from the reference location. These positions
are found by taking the sequential products of all the HTMs from the tool tip or workpiece
to the reference coordinate system:
13
(5)
xYZTxiyizi
NRTN = 11 m-Tm = T 1 T2 2 T3 ... (6)
m=1
1.4 Classification of Errors
1.4.1 Geometric
Geometric errors are concerned with the quasi-static accuracy of surfaces which
move relative to each other, such as components of linear and rotary axes. These errors
may be systematic, exhibit hysteresis, and/or exhibit random behavior. Geometric errors
are affected by:
* surface straightness - influences deviation from straight-line motion
. surface roughness - profile of surface affects high frequency straightness
" bearing preload - susceptibility to 'rough spots' influence high frequency
straightness
" kinematic vs. elastic design principles - errors caused by breaking, repositioning,
and reestablishing mechanical contact
* structural design philosophies - affect repeatability and error-mapping techniques
A rigid body has six degrees of freedom. Therefore, a rigid body, such as a linear
or rotary axis, has six sources of (geometric) error, three translational and three rotational.
Figure 1 shows a single-axis linear motion carriage. For this linear motion carriage, the six
sources of error are the following:
* Translational Errors
. 8x: Linear displacement error
14
pp
Verticalstraightness error
XR
IZZR
Horizontalstraightness error 5.
Figure 1: Geometric Errors in a Linear Motion Carriage 1
1Figure obtained from Alexander H. Slocum, Precision Machine Design, EnglewoodCliffs, NJ: Prentice-Hall, Inc., 1992, p. 64.
15
S y
Jn
Yaw E y
Rol E X
Xn
ZnPitch e
X axis servoerror 8
z
* Sy: Straightness error in the X-Y plane
* 8z: Straightness error in the X-Z plane
Rotational Errors
ex: Angular motion of the X-axis about the X-axis: Roll
. Ey: Angular motion of the X-axis in the X-Z plane: Yaw
ez: Angular motion of the X-axis in the X-Y plane: Pitch
The HTM for an ideal (i.e. no translational or rotational errors) linear or rotary axis with x,
y, and z offsets of a, b, and c, respectively, is:
1 0 0 alRTn = 0 1 0 b (7)
0 0 1 c.0 0 0 11
The HTM that represents the errors of the linear carriage is found by multiplying the HTMs
representing the errors terms 5x, 8y, 8z, x, Ey, and ez in series. Neglecting second-order
terms and assuming x, y, and z offsets of a, b, and c, respectively, this HTM is found to
be:
1 -Ez ey a + 8x
Ez 1 -ex b + 8yRTff = -Ey Ex 1 c + z (8)
0 0 0 1
Figure 2 shows a rotary spindle. For this rotary axis, the six sources of error are
the following:
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Reference axis Z R Axis of rotation Z n
Radial displacement 0z W)measurements aremade parallel to the z Rotating bodyX axis (spindle)
X RR
X n
Figure 2: Geometric Errors in a Spindle2
2 Figure obtained from Alexander H. Slocum, Precision Machine Design, EnglewoodCliffs, NJ: Prentice-Hall, Inc., 1992, p. 66.
17
. Translational Errors
* 8,: Radial error motion of rotary axis in X-Z plane
* 8y: Radial error motion of rotary axis in Y-Z plane
* 8z: Axial motion of rotary axis
. Rotational Errors
. Ex: Tilt motion of rotary axis about the X-axis
. Ey: Tilt motion of rotary axis about the Y-axis
. Ez: Angular displacement error
As in the linear case, the HTM that represents the errors of the rotary spindle is found by
multiplying the HTMs representing the errors terms 8x, 5y, 8z, ex, Ey, and ez in series.
Neglecting second-order terms, making small angle approximations (i.e. cos E = 1 and sin
E= ), and assuming x, y, and z offsets of a, b, and c, respectively, this HTM is found to
be:
cosOz -sin0z ey a+ 8x
RTner = sin0z cosOz -ex b + 5y (9)Exsin0z - Eycos0z excos0z + Eysin0z 1 c + 8z
0 0 0 1
1.4.2 Kinematic
Kinematic errors are errors in an axis's trajectory that are caused by misaligned or
improperly sized components. Misalignments of component's axes are measured in terms
of angular deviations from orthogonality (i.e. squareness, perpendicularity) and parallelism
(horizontal and vertical). Figure 3 shows the alignment of axes as well as their
misalignment error.
18
A
y R YR900 E
ZR XR
-Zs R X
yXZR XR ..... R.
900 E Y90* E
Figure 3: Orthogonality and Horizontal and Vertical Parallelism Errors3
3 Figure obtained from Alexander H. Slocum, Precision Machine Design, EnglewoodCliffs, NJ: Prentice-Hall, Inc., 1992, p. 84.
19
HTMs for modeling the alignment of axes should be modeled as occurring at the
intersection of component axes. The HTM for ideal (i.e. no kinematic errors) alignment
between axes and x, y, and z offsets of a, b, and c, respectively, is:
1 0 0 alRTn= 0 1 0 b (10)
0 0 1 c-0 0 0 1.
The HTM that represents kinematic errors is found by multiplying the HTMs representing
the errors terms ex, Ey, and Ez in series. This is the same methodology used in constructing
the HTM for the linear carriage with geometric errors. Neglecting second-order terms and
assuming x, y, and z offsets of a, b, and c, respectively, the HTM for the orthogonality
error shown in Figure 3 is found to be:
1 0 Ey aRTneff 0 1 0 b
ner= -y 0 1 c0 0 0 1
Similarly, the HTM for the horizontal and parallelism errors shown in Figure 3 is found to
be:
1 -Ez Ey a
RTne _ z 1 0 br -E 0 1 c
0 0 0 1
1.4.3 External Load Induced
External load induced errors are caused by loads such as gravity loads, cutting
loads, and axis acceleration loads. Gravity loads are due to the weights of parts of the
20
machine structure and the workpiece. Cutting loads are applied at the tool tip and act on
every element of the machine. To satisfy productivity demands, machine tools are being
required to operate at increased speeds, which increases the forces acting on the machine
due to accelerating axes. All of these forces cause a machine tool structure to deform. The
errors caused by external loads are difficult to model in that they are often distributed
throughout the machine structure. Bearing interfaces are often the most compliant part of
the structure. Therefore, load induced errors are often modeled as being lumped at bearing
interfaces.! Additional HTMs are required in more complex structures. Nevertheless, each
HTM of a machine's structure is modeled as having loads acting on it. These loads are
either generic loads (e.g. gravity loads, cutting, loads, axis acceleration loads) originating
at the HTM or loads transferred to the HTM from another HTM in the chain, or both.
Modeled at an HTM, these loads may involve up to three forces acting along and three
moments acting about the X-, Y-, and Z-axes. In order to determine the six load induced
error components, each HTM has three linear and three angular stiffnesses assigned to it.
The six load induced error components for each coordinate system are computed by
dividing the load components by the corresponding stiffnesses.
Forces and moments are vector quantities. In order to determine the loads
transferred from one HTM to another, the forces and moments have to be transformed from
one HTM to the other. In other words, given forces and moments at one HTM, what are
the equivalent forces and moments at another HTM. Equivalent refers to forces and
moments that will have the same effect on the object. The equivalent moments are
calculated from the following relationships:
Mx = Ox. ((F x P) + M)
MY = Oy. ((F x P) + M) (11)
Mz= Oz. ((F x P) + M)
21
where F is the force vector, M is the moment vector, Ox, Oy, and Oz are the first three
columns and P is the last column of the HTM that the forces and moments are being
transformed into. The forces are calculated from the following dot products:
Fx= Ox. F
Fy = Oy- F
Fz= Oz. F
(12)
For example, given: Position of coordinate system t5 with respect to coordinate system t4:
t5-Ox =0 deg
t5-Oy = -90 deg
t5-Oz =0 deg
The HTM for t5 in t4 is therefore:
0100
-1000
-0.51-0.20.1
I .
The generic loads in t5 are:
t5Fx = 100 N
t5Fy = 50 N
t5Fz = 0 N
t5Mx = 0 Nm
tOMy =0 Nm
t5Mz = 0 Nm
Find: What are the total loads acting at t4, when the rigid body which connects t4 and t5
has a weight of 50 kg? The force vector in t5 is:
22
t5-x =
t5-y =
t5-z =
-0.5 m
-0.2 m
0.1 m
t4 Tt5 =
0010
t5 F =100500-
The moment vector in t5 is:
t5M=[0100 .
The HTM for t4 in t5 is:
00
-10
0 1 -0.111 0 0.20 0 -0.50 0 1
The first three columns denote the coordinates of the three t4 unit vectors in t5:
0 ~t50xt4 =0
--.
0 =
t50yt4 = 1. 0 .I t5 0zt4 = 0
- 0
The last column denotes the position of t4 in t5:
-0.1t5Pt4= 0.2
L-0.51
Using equations 11 and 12, the resulting forces and moments in t4 are:
t4 Fx = 0 N
t4Fy = 50 N
t4 Fz = 100 N
t4Mx = -25 Nm
t4My = 50 Nm
t4Mz = -25 Nm
23
A
I
t5Tt4 =t4Tt5-1
The generic loads in t4 are forces and moments originating in t4 from the 50 kg weight of
the rigid body. These forces and moments are:
t4 Fxgen = 0 N
t4 Fygen = -500 N
t4 Fzgen = 0 N
t4 Mxgen = 500 N x 0.1 m = 50 Nm
t4Mygen = 0 Nm
t4 Mzgen = 500 N x 0.15 m = 75 Nm
The total loads in t4 are the loads transferred from t5 plus the generic loads in t4:
t4Fxtotaj = t4Fx + t4 Fx,gen = 0 N
t4Fy,total = t4Fy + t4 Fy,gen = -450 N
t4 Fztotal = t4Fz + t4 Fz,gen = 100 N
t4Mx,total = t4 Mx + t4 Mxgen = 25 Nm
4My,total = t4 My + t4My,gen = 50 Nm
t4 Mztotal = t4 Mz + t4 Mzgen = 50 Nm
The load vectors 4F and t4 M would then be used to calculate the forces and moments in t3
being transferred from t4. The process continues until all the forces and moments at each
coordinate system are calculated.
1.4.4 Thermal
Thermal errors are often the largest and most misunderstood sources of error for
machine tools. These errors can be divided into three categories: (1) the effect of average
temperatures other than 68 *F (20 'C), (2) the effect of cyclic temperature variation, or
'drift', and (3) the effect of gradients in the environment's temperature.
A meter is the distance between two points in space. It is defined as the distance
that light travels in 1 of a second in a vacuum. A meter does not vary with299,792,458
temperature. On the other hand, the lengths of most materials that people deal with do
change with temperature. As a result, the International Committee on Weights and
24
Measures in 1931 agreed that when the length of an object is described it is at a temperature
of 68 'F (20 *C). Thus, there are errors in the workpiece being manufactured or measured
when the mean environmental temperature is other than 68 F (20 *C). These errors come
in two forms: errors in the value of the temperature recorded during manufacture or
measurement, and, errors in the values of the coefficients of thermal expansion for the
materials of the machine and workpiece. The errors in recorded temperature are due to
defects in the instruments making the measurements (e.g. inaccurately calibrated, intrinsic
source of error such as self-heating effect of resistance-bulb thermometers) and the location
of where measurements are made, which is significant if temperature gradients are present
(in workpiece or environment). The errors in the coefficients of thermal expansion are due
to differences in chemical composition, physical composition, or both. The errors in the
coefficients are also due to experimental bias, because most coefficient values are the result
of averaging data from several experiments and experimenters. The result of these errors in
temperature recorded and errors in thermal coefficient values appears in the calculations to
correct for a mean environmental temperature other than 68 *F (20 'C). These calculations
to estimate the expansion or contraction of an object are based on the time-mean
temperature and the coefficient of thermal expansion. Therefore, if there are errors in either
or both of these values, then there will be errors in determining the dimensions of the
workpiece.
Temperature variation affects the dimensions of every object in a different way.
This influence varies as a function of the frequency and magnitude of temperature
oscillation. The difference in response between any two objects is referred to as the
differential response. The differential response reaches a maximum at some particular
frequency of oscillation in a manner analogous to resonance in vibration work. This
differential response between machine and workpiece is difficult to model. However, the
response of a machine tool is often found by means of a drift test. The drift test is an
25
experiment to determine the drift inherent in a machine tool under normal operating
conditions:
Of all of the non-ideal temperature conditions to asses for error effects, gradients in
the environment temperature are the most difficult to model and calculate. Gradients refer
to portions of the machine tool environment that are not at the same temperature. They
occur because of heat sources/sinks that exist within the boundaries of the environment.
These sources/sinks are shown in Figure 4. In the figure, six sources/sinks of thermal
influences are shown:
. Heating or cooling influence provided by the room environment
" Heat added or removed by the coolant systems
. Heat added by people
. Heating or cooling created by the machine
. Heat generated by the cutting process
. Thermal memory of any previous environment
Of these influences, only room environment and the coolant systems can create uniform
temperatures. The remaining heat sources will cause either steady-state temperature
gradients, temperature variation, or both. All of these sources/sinks occur through
conduction, convection, and/or radiation.
1.4.5 Instrumentation
Instrumentation errors involve errors with sensors (e.g. position, velocity,
acceleration, temperature, humidity, etc.) associated with the control of the machine tool
and/or associated with its environmental control, and, errors associated with equipment
used for the calibration of the machine tool. Errors associated with sensors include:
26
Heat Heat added or removed by coolant systemssource/ Room Coolants Peoplesinks environment Electronic Hydraulic Frame lCutting| Lubricating
systems oil stabilizingl fluid I oil
Heat created by the machineElectrical Frame stabilization
and Motors, transducerselectronic Amplifiers, control cabnets
Friction Spindle bearingsOther
HydraulicMiscellaneous
[ Conductionj ConvectionIRadiation
Uniform temperatureother than 20 degrees C
| Part |
Conduction| Convection Radiation Conductio Convection Radiation
Temperature gradiants Temperature variationsor static effects or dynamic effects
Nonunifor temperatures |Memory of previousenvironment
. e
IqFmF-irI
Station-chaneeffect
orm error Size error
Total thermal error]
Figure 4: Thermal Effects in Manufacturing and Metrology 4
4 Figure obtained from Alexander H. Slocum, Precision Machine Dsign, EnglewoodCliffs, NJ: Prentice-Hall, Inc., 1992, p. 97.
27
Heatcreatedby thecuttingprocess~ ~ - -
Heatflowpaths
Temperaturefield
AffectedStructure
Errorcomponents
intrinsic accuracy, interpolation errors, and mounting errors (e.g. position, mounting
stress). Calibration errors stem from errors intrinsic to the calibration instrumentation and
artifacts, errors introduced into the instrumentation due to the environment, and improper
use of the equipment during the mastering process.
1.4.6 Computational
Computational errors come in the form of both software and hardware. In
dimensional measurement, coordinate data on part surfaces is often converted to substitute
geometry using various analysis (software) algorithms. The accuracy in the results that
these algorithms provide depends on several factors: sampling strategy, sampling density,
form error, errors in the analysis algorithms, etc. If any of these factors are nonoptimal,
then errors will be introduced by software. Errors caused by hardware are occurring less
often with the advances in and the availability of computer hardware. However, some
older computers are susceptible to rounding off errors that should be taken into account for
error budgeting.
1.4.7 Miscellaneous
There are additional sources of error that may impact the accuracy of a machine
tool. These errors are difficult to model, because the manner and magnitude with which
they impact each machine varies. Nevertheless, a machine tool should be analyzed with
respect to its susceptibility to these errors. If estimates of error can be made for these
additional sources, then they should be included in the error budget. These additional
sources of error include:
28
. Humidity
. Loose joints
. Vibration (e.g. external environment, cutting process, rotating masses)
. Inadequate resolution
eDirt
. Coulomb friction
. Sliding oil films
. Variations in supply of electricity
. Variations in supply of air/fluid pressure
* Random procedures of operator
. Material instability
" Control system (e.g. algorithm type, stick-slip friction, varying stiffness)
1.5 Error Gain Matrix
There are six individual error components for each coordinate system, three
position (e.g. Ax, Ay, Az) and three orientation (e.g. A0, Apy, Aoz). Each individual
error component for each coordinate system contributes to the total position and orientation
error between the tool and workpiece. The error gain matrix lists the amplification factor
by which each individual error component for each coordinate system contributes to the
total position and orientation error. For the error components of a coordinate system (e.g.
t4), the error gain matrix would resemble the following:
29
I
Ax Ay Az A4x A4y AO,
t4x -0.05 -0.23 0.97 0 0 0
t46y 0.84 -0.54 -0.09 0 0 0
t45z 0.54 0.81 0.22 0 0 0
t4Ex -0.04 0.14 0.03 2.15 -1.14 0.83
t4Ey -0.02 -0.10 0.05 1.30 -0.30 -0.54
t4Ez 0.07 0.06 -0.11 0.05 0.27 1.04
The error gain for an individual error component for a particular coordinate system is
determined numerically by:
" Set all errors in all of the HTMs equal to zero.
* Then, set the value of the individual error of interest equal to a small percentage of
the machine size (e.g. 0.1%). This small value minimizes numerical errors while
maintaining linearity.
. The resulting total position and orientation errors of the tool with respect to the
workpiece are divided by the amount of the error entered.
. The result is the gain of the particular error on the total position and orientation
errors for the tool with respect to the workpiece.
The error gains have the following bounds:
" Linear on linear are less than or equal to 1
- Linear on angular are always 0
. Angular on linear may be less than, equal to, or greater than 1
* Angular on angular are less than 1
30
1.6 Combinational Rules
1.6.1 Random, Systematic, and Hysteresis Errors
There are three types of errors: random, systematic, and hysteresis. The errors
mentioned previously (e.g. geometric, kinematic, thermal, etc.) fall into one or more of
these categories. For instance, geometric errors may be systematic, may exhibit
hysteresis, or may exhibit random behavior. Random errors, which under apparently
equal conditions at a given position, do not always have the same value and can only be
expressed statistically. Systematic errors always have the same value and sign at a given
position and under given circumstances. Systematic errors can generally be corrected for,
if the relative accompanying random error is small enough. Hysteresis is a systematic
error, which in this instance is separated out for convenience. It is usually highly
reproducible, has a sign depending on the direction of approach, and has a value partly
dependent on the travel. Hysteresis errors may be used for correcting the measured value if
the direction of approach is known and an adequate pre-travel is made. In performing an
error budget, random, systematic, and hysteresis errors should be summed independently.
The next section explains how to combine these three types of errors together to predict the
accuracy of a machine tool.
1.6.2 Complete Detail, Upper Bound, Lower Bound, and Expected Value
The are four ways in which sum the random, systematic, and hysteresis errors for a
machine tool: complete detail, upper bound, lower bound, and expected value. The upper
bound or over-conservative estimate is based on all errors occurring at a maximum
simultaneously. To perform this summation, the individual displacement and orientation
errors are added arithmetically:
31
NYEupper bound = YEsystematic + XEhysteresis + Erando, PV (13)
The lower bound or under-conservative estimate neglects possible error correlations. To
perform this summation, an RMS amplitude is performed on the random error:
- N ~1-
Elower bound = LEsystematic + YEhysteresis + 1 (Erandom, PVj)2 2 (14)
where K is a numerical factor depending on the probability distribution of the error signal
between two parallel lines containing the error signal. K is 3.46 for a uniform distribution
and 4.0 for a 4 sigma Gaussian distribution. Experimentally, it has been found that the
accuracy for most machines fall midway between the upper and lower bound values. The
expected value is an estimate of composite error found by averaging the upper and lower
bound estimates:
Eexpected = 4,[Eupper bound + Elower bound] (15)
Complete detail is different from the other summations in that it is used once a machine tool
has been developed. It is most often used for the purpose of error correcting a machine
tool. For complete detail, direct addition can be used to generate a map of the resultant
displacement and orientation errors as a function of slideway position, time, etc.
1.7 Position and Orientation Errors of the Tool
The position and orientation of the tool are determined from the ideal location of the
tool tip with respect to the ideal location of the workpiece. The actual position of the tool
32
tip and the actual position of the workpiece are determined from the position and orientation
errors occurring at each rigid body location that is modeled. The difference between the
ideal and actual locations of the tool tip given with respect to the workpiece represent the
total position and orientation errors for the machine tool. In the spreadsheet, the ideal
location of the tool with respect to the workpiece is given by the HTM without error
components:
w5 ,dealTt5ideal = refTw5,ideal-1 refTt5,ideal = w5,idealTref refTt5,ideal (16)
The actual location of the tool in the workpiece is given by the HTM including all error
components:
w5,actualTt5,actual = refTw5,actual-1 refTt5,actual = w 5 ,actualTref refTt5,actual
The HTMs w5,idealTt5,ideal and w5,actualTt5,actual are of the form:
[Qixo.JxOx
0
jyOky
0
OjzOkz0
Px
PyPzPS
(17)
ITo determine the three Euler angles for the orientation of the tool with respect to the
workpiece, a 3 x 3 submatrix of these HTMs are used. This 3x3 matrix is:
OixOjxOkx
Oiy
OjyOky
Oiz
OjzOkz.
The 3x3 submatrix represents three subsequent rotations $x, $y, and $z:
33
OixOixOkx
Oiy
OjyOky
Oiz
OjzOkz
1 0 0
0 cosox -sinox
0 sinox cosox
=cosoy 0 sinoy
0 1 0
-sinoy 0 cosOy
The solutions for $x, $y, and Oz are found by the inverse transform method of inverse
kinematics. Premultiplying with the $x-1 matrix results in:
1 0
L0
0 cosox -sinox
0 sinox cosox
cosoy 0 sinoy
0 1 0
-sinoy 0 cosOy_
-1 -O xO ix
x Ojx- Okx
x sinoz
0
Oiy
OjyOky
-s
c
Oiz
OjzOkz1=
(19)inoz 0
)Oz 0
0 1
$x, $y, and $z can be solved for by expanding both sides of the above matrix equation.
The final equations for $x, $y, and Oz are found to be:
$x = arcta niz = arctan2(Okz, -Ojz)
$y = arc Oiz = arctan2(-Ojzsinox + Okzcosox, Oiz)-Ojzsinox + Okzcos4x
$z = arc O xcos4x + Okxsin,'
\Ojycoso, + Okzsinx /
arctan2(Ojycosox + Okzsinox, Ojxcosox + Oysinox)
(20)
(21)
(22)
34
(18)-sinoz 0
cosOz 0
0 1_
cosOz
x sinoz
-0
As mentioned previously, the difference between the ideal and actual locations of
the tool given with respect to the workpiece represent the total position and orientation
errors for the machine tool. These errors can be found by multiplying the inverse HTM
representing ideal position and orientation by the HTM representing actual position and
orientation:
t5, idealTt5, actual = w5, idealTt 5, ideal-1 w5, actualTt5, actual (23)
The orientation errors are determined from the inverse transform method applied to this
resulting HTM. The position errors in the X, Y, and Z directions are equal to Px, Py, and
Pz, respectively, in the resulting HTM. For a system without errors, these components
would be zero.
1.8 Architecture of Error Budget Spreadsheet
When the spreadsheet is completed, this section will present its architecture. This
section will discuss all of the inputs, outputs, and how the information is processed.
1.9 Case Study: Precision Coordinate Measuring Machine
1.9.1 Introduction
The specification and design of a precision coordinate measuring machine began in
February 1992. Construction will be completed by March 1993. The four error budgeting
methodologies previously described will be used in modeling the accuracy of this machine.
The machine's volumetric accuracy will be measured and an error map of the machine will
35
be constructed. The results will then be used to assess the accuracy and utility of the
models.
1.9.2 Correlation Model
Research will be performed to develop a correlation model for the machine. This
model will graphically represent the sources of error and show how these errors interact
with each other. It will also show how these errors contribute to the total position error
between the probe and workpiece.
1.9.3 Trigonometric Analysis
A trigonometric analysis will be performed on the machine. This model will utilize
trigonometry to assess the impact of the geometric, kinematic, and probe errors on the total
position error between the probe and workpiece.
1.9.4 Error Gain Matrix Representation
An error gain matrix representation of the machine will be constructed. This
representation is solely based on the geometry of the machine. It will provide a sensitivity
analysis of the machine's errors by listing the amplification factor by which each individual
error component for each coordinate system contributes to the total position and orientation
error.
36
1.9.5 Rigid Body Kinematics Analysis
A rigid body kinematics analysis will be performed on the machine. This model
will take into account geometric, kinematic, external load induced, instrumentation,
computational, and a number of miscellaneous errors. The machine environment will be
controlled to 68 F 0.1 'F. Therefore, thermal errors will be considered; however, it is
expected that they will be negligible for modeling purposes.
1.9.6 Discussion
This section will discuss the results of each error budgeting methodology. This
discussion will include the amount of time and difficulty in performing each methodology,
the value of the information that each methodology provides, and a comparison between the
accuracy measured on the machine and the accuracy predicted by the trigonometric and
rigid body kinematics analysis methodologies.
1.10 Conclusion
This section will summarize the findings of the research: the accuracy of the
spreadsheet in modeling the precision coordinate measuring machine, as well as the level of
difficulty of using the spreadsheet to model the machine. This section will also make
suggestions for further research to enhance the spreadsheet as a modeling tool.
37
Chapter 2
Model for Optimizing Ramp-Up for Precision Machine Tools
2.1 Introduction
The current business environment emphasizes bringing new products and processes
to market quickly, cost efficiently, and with high performance. As a result, many
companies are looking at how to optimize ramp-up. Ramp-up is the process of taking new
products and manufacturing process technologies (e.g. machine tools, material handling
equipment, etc.) from initial prototype to full production. There now appears to be a strong
correlation between how well a company ramps-up new products and processes and the
long term health of the company. Keeping this in mind, there is a need to develop a model
or intellectual framework that can help companies determine how to optimize ramp-up for
new products and processes. The model need not be a road map that tells a company each
turn that it must take. But rather, it should be more of a compass which indicates the
general direction to be taken and allows companies to chart a course that they should follow
in order to reach their goal. The purpose of this research is to develop such a model for the
ramp-up of new manufacturing process technologies.
Existing studies on ramp-up and related development topics can broadly be
classified into one of two categories. The first is task relationships. This category focuses
on the sequence of and the technical relationships among the many development tasks to be
performed. The second category is factors that influence ramp-up. This is based upon the
criteria which ramp-up is measured, as well as the drivers which shape how ramp-up is
carried out. The model developed in this paper is based on the latter category. While
previous studies have limited themselves to establishing and discussing specific factors that
influence ramp-up, this model uses matrix analysis and the Pugh concept selection process
38
to study these factors as a collective whole. The model as it stands is a work in progress.
The objective for this paper is to begin to demonstrate the feasibility that such a model can
be constructed and can be useful.
The organization of this paper will be the following. First, ramp-up will be
reviewed. Explanations of how the model was developed and how the model is to be
interpreted are then presented. A discussion section follows. Then, a case study on a new
manufacturing process technology, a precision coordinate measuring machine, is presented
and the preliminary recommendations based on the model are discussed. Finally, the
conclusion will summarize the main findings of the research thus far and suggest additional
research to be pursued.
2.2 Ramp-Up
Ramp-up of manufacturing process technologies involves taking technologies from
initial prototype to full production. Rarely, if ever, is this process as simple as 'plugging
in' a prototype machine tool and immediately having a production-ready manufacturing
process. There are three empirically grounded reasons why this is so. First, new
technologies are almost never perfect upon initial introduction (Tyre, 1991). Very few
companies pay close attention to technology readiness. Technology readiness occurs only
when all of the basic scientific problems for a technology have been solved. As a result,
time pressures and/or miscalculations tend to push manufacturing process technologies out
of the nest before they can fly in an optimal fashion (Cohen, Keller, and Streeter, 1979).
Another reason for technologies experiencing difficulties during introduction is that many
are susceptible to scale-up problems when they go from being a prototype in the laboratory
to a production process in the plant.
Second, a new technology almost never fits perfectly into the user environment
(Leonard-Barton, 1988). Leonard-Barton (1988) stated that, 'Usually technologies are
39
introduced into processes to increase the quality of output or increase efficiency. .
.However, introducing a small island of scientific rigor into a craft-dominated production
process can appear far from beneficial to advocates of the old technology.' In reality, the
dichotomy between old and new technology is often not as large as going from 'art' to
'science'. However, there are often misfits between new process technology and the
production plant where it is to be used. These misfits come in three forms: (1)
misalignments between the technology and its original specifications or with the production
process into which it is introduced, (2) misalignments between the technology and the user
organization infrastructure (e.g. supporting hardware, software, educational programs),
and (3) misalignments between the technology and job performance criteria in the user
organization. These misfits are corrected by altering the technology, changing the user
environment, or both.
Thirdly, there are numerous opportunities for improvement that were not apparent
before introduction, but that are needed to meet users' needs, objectives, or changing
circumstances (Rosenberg, 1982; Dutton and Thomas, 1985). In short, there is a need to
correct mistakes, optimize critical functional parameters, and to make the manufacturing
process technology more robust.
Because new manufacturing process technologies need to be matured, and
mismatches between new manufacturing process technologies and their user environments
need to be corrected, and new manufacturing process technologies need to be optimized
and made more robust, ramp-up will take time, will cost money, will affect performance of
the process technology, and will affect learning achieved by the organization. How well
ramp-up is orchestrated will determine how much time, .how much money, how much
performance, and how much learning. History has shown that most American companies
are not good at orchestrating ramp-up for manufacturing process technologies. As Thurow
(1987) pointed out, 'The prime reason for America's poor productivity, quality, and trade
performance is easily isolated. . .In industry after industry if one plots the speed with
40
which new process technologies are first adopted and the speed with which they are put in
place, U.S. firms lag behind foreign firms'. Thus, the challenge is to learn how to
introduce and exploit new manufacturing process technologies and how to integrate them
fully into production. The purpose of this research is to provide a conceptual framework
for doing so.
2.3 Development of Model
A literature search was performed in the areas of ramp-up criteria and drivers,
organizing qualitative data, and matrix analysis. From the literature on ramp-up criteria and
drivers, ideas and attributes were written down in an unstructured manner. An affinity
diagram was constructed from these ideas regarding ramp-up criteria and drivers, and it is
illustrated in Figure 5. The definitions and metrics for these criteria and drivers can be
found in Appendix A. An affinity diagram is a methodology for building an overall
hierarchical structure of ideas from a set of unstructured ideas. An affinity diagram is
constructed in the following manner: Without any preconceived structure in mind, ideas
(from the literature search) are compared to one another. If the ideas seem to be related in
some intuitive way, then they are placed in the same group. Once this process is complete,
the groups then represent some theme or topic. Then just like the ideas before, the groups
are compared to one another. And intuitively, these groups, if related, are put together to
arrive at higher level themes or topics. This process continues until a hierarchy of themes
or topics has been established as shown in Figure 6. After the affinity diagram was
completed, a tree diagram was constructed. The tree diagram is an extension of the affinity
diagram as can be seen in Figure 7. It builds on the affinity diagram by filling in omissions
at every level of the hierarchy to complete the structure.
A matrix diagram is a familiar systematic structure for evaluating ideas in one
dimension against ideas of another dimension. Putting one set of ideas along a horizontal
41
Ramp-Up Criteria
(1) Time (e.g. speed)(A) Ramp-Up Prior to Final Environment(B) Ramp-Up at Final Environment
(2) Cost(A)(B)(C)(D)
DirectIndirectOpportunityStartup
(3) Performance (e.g. quality)(A) Accuracy(B) Availability(C) Cycle Time(D) Setup Time(E) Flexibility(F) Maintainability(G) Reliability
(4) Learning (e.g. understanding, knowledge)(A) Individual Technologies(B) Process Technology(C) Manufacturing Process(D) Ramp-Up
Ramp-Up Drivers
(1) Stage of Knowledge(A) External(B) Internal
(2) Preparatory Search(A) Information Acquired(B) Ambiguity Remaining
(i) Politics
(3) Purpose(A) Service Factory (or Cell)
(i) Consultant(ii) Dispatcher(iii) Laboratory(iv) Showroom
(B) Traditional
(4) Staffing(A) Roles
(i) Id(ii) P(iii) S(iv) V(v) G
(a(b
(B) Selection
(5) Structure(A)(B)
ea Generators)litician/Shield Makerturdy Pillarsisionary/Moderatorroup Mix) Functional Overlap) Joint Search
IntegrationSeparation
(6) Systematic Learning and Problem Solving Methodology(A) Experimentation
(i) Experimental Influences(a) Cost(b) Fidelity(c) Individual Habits(d) Information Turnaround Time(e) Manpower Applied(f) Signal-to-Noise Ratio(g) Manufacturing Process Resources
(ii) Experimental Types(a) Natural(b) Controlled(c) Simulation(d) Ad Hoc(e) EVOP
(B) Technological Adaptation(i) Technological Adaptation Causes
(a) Delivery System(b) Technology(c) Value
(ii) Technological Adaptation Influences(a) Conditions(b) Congealing Influences
(7) Technology(A) Systemic Shift(B) Technological Complexity
Figure 5: Ramp-Up Criteria and Drivers
42
UnstructuredIdeas
Figure 6: Affinity Diagram
43
MaintainabilityRamp-Up
Performance
Time Direct Availability
Individual Technologies
Cost FlexibilityIndirect Setup Time
Startup
Accuracy
Cycle Time LManufacturing Process
Ramp-Up Criteria
Time
Cost
Direct
Indirect
Opportunity
Sta
Performance
Accuracy
Availability
Cycle Tm~e
Setup Time
Flexibility
Maintainability I
Reliability7 1
Learning
ndividual Technologies
Manufacturing Process
Ramp-Up
Ramp-Up Criteria-
Time -
Cost -
Ramp-Up Prior to Final Environment
Ramp-Up at Final Environment
Direct
Indirect
Opportunity
Startup
Accuracy
Availability
Cycle Time
Performance - Setup Time
Flexibility
Maintainability
Reliability
Learming -
Individual Technologies
Process Technology
Manufacturing Process
Ramp-Up
Figure 7: Tree Diagram
44
axis and the other set along a vertical axis allows each idea of one dimension to be
correlated with each idea of the other dimension. Upon comparing different types of matrix
analysis, it was decided that 'The House of Quality' structure was the most appropriate
option. It was chosen because its structure was ideally suited for supporting all of the
necessary correlations for ramp-up as shown in Figure 8. These include ramp-up criteria,
ramp-up drivers, correlations between ramp-up criteria and ramp-up drivers, ramp-up
drivers to ramp-up drivers correlations, and a planning matrix. The planning matrix is used
for recording the assessment of a variety of factors that combine to rank the various ramp-
up criteria.
With the ramp-up criteria and drivers ascertained from the above methodology, the
matrix can be filled in. First the correlation matrices, drivers-to-criteria and drivers-to-
drivers, are evaluated. In the case of the drivers-to-criteria matrix, one asks the question:
To what extent will the driver at the head of the column contribute to meeting the criteria at
the left of the row? In the case of the drivers-to-drivers matrix, one asks the question: To
what extent will a change in one driver affect another driver? In both cases, the scales
range from -5 to 5, with -5 denoting 'strong negative relationship', -3 denoting 'moderate
negative relationship', -1 denoting 'weak negative relationship', 0 denoting 'no
relationship', 1 denoting 'weak positive relationship', and so on (Cohen, 1988).
Next is the planning matrix. The first column is titled Company Value. This
reflects how important each ramp-up criteria is to the company. Numbers are assigned
from 1 to 5, with 1 denoting 'unimportant' and 5 denoting 'very important'. The second
column is titled Company Performance. This reflects how well the company is doing today
in meeting its criteria. Similarly, a 1 to 5 scale is used. The third column is titled Goal. In
this column, we evaluate on a 1 to 5 scale how well does the process development team
wish to meet the company's criteria. Next, is the Improvement Ratio column. This value
is determined by dividing the Goal value by the Company Performance value. The Sales
Point column is next. It deals with the evaluation of features that would directly influence a
45
Ramp-UpDrivers-to-Ramp-Up
DriversCorrelations
Criteria andUp Drivers
Ramp-Up Drivers
Planning Matrix
117 L III~LJ111 ~L ll
II ~ ill
Figure 8: 'House of Quality' Matrix Structure
46
CorrelatiRamp-Up
Ramp-
ons Between
Ramp-UpCriteria
~IL
z
I
I
sale. There are three possible values for this column. 1.5 indicates a feature that is a
significant sales point, 1.2 indicates a moderately powerful sales point, and 1 indicates
features that have no sales point. Finally, the Raw Weight is computed. For each criteria,
it is the product of Company Value times Improvement Ratio times Sales Point.
The product of the correlation factor and the Raw Weight of the associated
Company Value is then entered into the appropriate cell. Add the contributions entered into
all cells for each column and record the sum at the bottom of the matrix. This algebraic
sum in the matrix is referred to as the 'Total Positive Impact'. These Total Positive Impact
sums at the bottoms of all the columns give the rank ordering of the drivers with respect to
the influence they have on meeting the company's needs. The other summation is the
'Total Impact of Any Sort'. For this value, the magnitude (absolute value) of all cells for
each column are added together. Also, the sign for each Total Impact of Any Sort
summation should be made to match the sign for its corresponding Total Positive Impact
summation.
2.4 Interpretation of Model
At the most basic and perhaps the most important level, the model is the
identification and hierarchical representation of the various drivers and criteria that influence
ramp-up. The reason for this importance is the following. In order to optimize ramp-up, it
is essential to identify and understand the factors and challenges that influence ramp-up.
The model is intended to be used by multidisciplinary process development teams. The
teams may use the model generated in this paper or may generate their own from the
previously described methodology. Teams using the model generated in this paper should
be aware that the secondary level criteria for Performance (e.g. Accuracy, Availability,
Cycle Time, Setup Time, Flexibility, Maintainability, Reliability) are specific to the
Precision Coordinate Measuring Machine described later in the case study. In other words,
47
teams should replace these secondary level criteria for Performance with criteria applicable
to their new process technology. All other criteria and drivers in the model are generic for
ramp-up of any new manufacturing process technology. Once a process development team
has identified these drivers and criteria, the model is analyzed in two ways.
The first way in which the model is analyzed is categorically. The process
development team interviews numerous development, implementation, and operations
personnel within the company to determine what are the company norms for the secondary
level ramp-up drivers. Numerous personnel are interviewed because very few companies
have structured methodologies for conducting ramp-up. As a result, ramp-up is shaped by
the individual habits and philosophies of the process development team members.
Therefore, in addition to determining the status quo, there is the opportunity to learn about
multiple approaches to ramp-up and to identify multiple sources of ramp-up expertise
within the company. The following is a list of the primary and secondary level ramp-up
drivers, as well as brief definitions (See Appendix A for complete definitions and metrics).
Primry Level
Secondary Level
(1) Stage of Knowledge
. Understanding of individual technologies (e.g. linear motors,
LVDTs), the process technology (e.g. CMMs, surface grinders),
the manufacturing process (e.g. gaging, grinding), and ramp-up.
(A) External
* Knowledge outside of the company (e.g. consultants).
(B) Internal
* Knowledge within the company.
(2) Preparatory Search
* Problem solving prior to ramp-up.
(A) Acquired Information
. Useful information acquired during preparatory search.
48
(B) Ambiguity Remaining
. Amount of uncertainty remaining when ramp-up begins.
(3) Purpose
* The type of facility or cell where the process will end up.
(A) Service Factory (or Cell)
* Factory or cell that bundles services with products to meet
comprehensive range of customer needs.
(B) Traditional
* Factory or cell that produces products under historical
manufacturing drivers.
(4) Staffing
. Selection or nonselection of team members, determination of roles
if any, and types (e.g. marketing, production) of people on team.
(A) Roles
* Establishing roles (e.g. idea generator, moderator) that provide
critical functions in the innovation process.
(B) Selection
" Team members 'hand picked', assigned, or somewhere in
between.
(5) Structure
" Relationship between project and corporate culture.
(A) Integration
e Project maintains corporate culture.
(B) Separation
* Project structure avoids collocation to foster creativity.
(6) Learning/Problem Solving Methodology
* Methodology implemented during ramp-up to increase
knowledge.
(A) Experimentation
* Source of learning and problem solving.
49
-4
(B) Technological Adaptation
* Nature of adjustments (e.g. hardware, specifications, end users'
job performance criteria) to manufacturing process during ramp-
up.
(7) Technology
* The technology of the process itself.
(A) Systemic Shift
. Effect of technology on established systems (e.g. movement to
FMS, JIT) and skill sets.
(B) Technological Complexity
* The evolutionary jump and/or sophistication of the technology.
In determining prior approaches to the secondary level ramp-up drivers, the process
development team examines a variety of issues (Appendix B may serve as preliminary
guide). For example, in the case of 'Experimentation', the team would examine issues
such as:
* What types of experiments (controlled, natural, simulation, ad hoc, and/or EVOP
(see Appendix A for definitions)) do the process development teams perform during
ramp-up to learn more about a manufacturing process?
* What is the approximate distribution of the experiments that are conducted?
* During experimentation, how much effort and/or what measures do the process
development team take to improve the signal-to-noise ratio?
* During experimentation, does the process development team do anything to
improve the information turnaround time between beginning an experiment and
getting results from it?
* How do process development teams deal with the issue of fidelity (i.e. the degree
of similarity between the actual environment where the process ends up and the
environment where experiments are conducted during ramp-up)?
50
. How much are direct and indirect costs weighed in terms or running experiments
during ramp-up?
. Does the process development team apply more or less resources (e.g. engineers,
operators, and problem solvers) during ramp-up to learn about a new
manufacturing process?
. How does the process development team deal with engineers' individual habits for
designing, conducting, analyzing, and especially documenting experiments during
ramp-up?
In the interest of continuous improvement, the status quo must be challenged. The team
should inquire as to why each of the responses for the above questions was chosen. The
team needs also to determine what are the benefits and drawbacks associated with each
approach in terms of time, cost, performance, and learning. After the interview process is
complete, the company norms, alternative approaches, the motivations behind each
approach, and the benefits and drawbacks associated with each approach for the secondary
level ramp-up drivers should be documented. Once documentation is completed, the
challenge to the team is to brainstorm alternative approaches for each of the secondary level
drivers that would provide improvements in terms of time, cost, performance, and
learning.
The second step is to perform a matrix analysis on the ramp-up drivers and criteria.
The matrix is filled in as prescribed in the previous Development of Model section. As
with any development project, there is always a finite amount of time, money, and
manpower. Thus, the challenge is to efficiently budget these resources so that the
maximum success can be attained despite these limitations. The matrix analysis can be
interpreted as a sort of ROI calculation or cost/benefit analysis that gives insight into how
the team's resources should be budgeted.
51
nThe sums at the bottom of the columns of the matrix (Total Positive Impact) indicate
the influence that each driver has on meeting the company's needs and goals. For the
positive sums, the larger the number is, the greater the positive influence that driver will
have in helping the company meet its goals. For the negative sums, the larger the
magnitude of the number, the greater the negative influence that driver will have in
preventing the company from reaching its goals. From these Total Positive Impact totals,
prioritization of where time, effort, and money should be spent to maximize success can be
performed. In other words, more resources should be invested in drivers where the
rewards are the largest, as well as on drivers which have large negative influences so as to
minimize their impact.
The matrix analysis also indicates 'breakthrough' opportunities. A breakthrough
opportunity implies that a single driver both hurts and helps ramp-up simultaneously.
Breakthrough opportunities are identified at the bottom of the columns of the matrix when
the Total Positive Impact for a driver does not equal the Total Impact of Any Sort for that
driver. The larger the difference is, the larger the breakthrough opportunity is. The
objective with a breakthrough opportunity is not to make compromises, but rather to
develop a better set of alternatives that maximize the positive and minimize the negative
effects of that driver. The two correlation matrices (drivers-to-criteria and drivers-to-
drivers) in the matrix analysis provide insight into the drivers that may help in developing
better alternatives. Nevertheless, by identifying breakthrough opportunities early with the
use of matrix analysis, the likelihood of developing a better set of alternatives is improved.
Having conducted the matrix analysis, it is now possible to go back to the
categorical analysis of ramp-up and determine whether the status quo approach or the
alternative approach (or perhaps even a hybrid approach) is appropriate for each driver. To
make these decisions, the Pugh concept selection process can be used. The Pugh concept
selection process (Clausing, 1992) has ten steps and is carried out by the process
development team.
52
* Normally the first step is choosing the criteria. For this application, the criteria is
already known from the model (see Figure 5).
* Next, the matrix is formed. The criteria are used as the row headings and the
concepts are used as the column headings as shown in Figure 9.
. Third, the team discusses each concept so that every team member has a high level
of understanding about each concept.
* Choosing the datum concept is the fourth step. Here, one of the concepts is chosen
as a reference concept to which all others are compared. The team should try to
guess the best concept and use it to serve as the reference concept.
* The matrix is ready to be evaluated. Each concept is compared to the datum concept
for each criterion. The first criterion is applied to each concept, then the second
criterion, and so on. A three-level rating system is used for comparing the
concept(s) to the datum concept. For each criterion, a '+' symbol is used if the
concept is better than the datum, a '-' symbol is used if the concept is worse than
the datum, and a 'S' symbol is used if the concepts are relatively equal.
* The sixth step is the evaluation of the ratings. For this step, the number of pluses
and minuses are recorded at the bottom of the column for each concept as shown in
Figure 10.
* The concept(s) should then be examined to see if they can be changed so that the
negatives can be overcome. In other words, brainstorming is performed on
concepts that have minus signs for any of the criteria. The goal is to develop a
better approach that would change the minuses to S's and (ideally) pluses.
. Aftei this step is completed, a new datum is selected and the matrix is rerun. This
is done to get additional insight and to help generate improved hybrid concepts.
* If necessary, further work is planned to gather more information, conduct more
analyses, interview more people, etc.
53
Approaches (Concepts)
Prior to FinalEnvironment
FinalEnvironment
StatusQuo'
Altern- Hybrid Hybridative I #1 I #2
Direct
IndirectU
Opportunity
Startup
Accuracy
Availability
Cycle Time
Setup Time
Flexibility
Maintainability
Reliability
IndividualTechnologies
ProcessTechnology
ManufacturingProcess
Ramp-Up
Figure 9: Pugh Concept Selection Matrix
54
CdC4
.b
- I
Approaches (Concepts)
StatusQuo
V V I
Altern- Hybrid Hybridative I #1 #2
-Y V 0 * I 4
Prior to FinalEnvironment
FinalEnvironment S
S
+
+
+
Direct - S S
Indirect - - -
Opportunity S - S
Startup S S S
Accuracy S S S
Availability S S S
Cycle Time S S S
Setup Time S S S
Flexibility - S +
Maintainability S S S
Reliability S S S
IndividualTechnologies
+ S S
ProcessTechnology -
Manufacturing 5 S S
Process
Ramp-Up
6+1-
+
2+2-
+
4+1-
Figure 10: Evaluation of Ratings
55
U
Q
&
.b
* Finally, the matrix is rerun till a dominant concept emerges by consensus. If adominant concept (approach) does not emerge, then the results of the matrix
analysis can be used. In this case, the Raw Weight column of the planning matrix,as shown in Figure 11, is used to weigh which criteria are most critical for each
driver and thus indicate which approach is superior.
2.5 Discussion
There are many benefits of this kind of model. First, it is valuable to complete the
exercise of what drivers influence ramp-up and what criteria ramp-up will be evaluated.
This is truly the first step in optimizing ramp-up. If .ne d 1o t have 0 understanding _Q:
at feeling for the factors .tha influence ramp-up, then it ji virtually impossible IQ a
to optimize it. It makes one become aware of not only the factors that influence ramp-up,
but also which factors that can be controlled (optimized). Along these lines, this exercise
helps to not only identify factors that influence ramp-up, but fill in the holes and remove the
roadblocks that are found in many companies.
Another benefit is that of the 'House of Quality' structure. The House of Quality
structure helps to impart a methodical analysis on the interrelationships of ramp-up drivers
and criteria. Furthermore, because it is a very specific structure, it helps to make process
planning a very convenient step-by-step approach. This, by its nature, is critical to not
only the development of new technologies, but to their implementation and use as well.
Generating the model, structuring the hierarchy, and analyzing the correlations
between drivers and criteria are best done with a multidisciplinary group. By doing this in
a group of people with diverse backgrounds, there is a better chance that the model
developed will be closer to the 'truth'. The reason for this is that group dynamics tend to
weed out personal biases. And, there is the likelihood that a diverse group will incorporate
key ideas into the model that otherwise may have been omitted. Another benefit is that each
56
5S5
2 5 3
3 3 4 51 5 4 5 1
0 3 4 3 1 01 2 3 3 3 0 3
1 3 2 2 4 2 4 54 3 3 -2 3 0 1 5 2
2 5 3 -1 -3 -3 4 2 4 3-1 4 0 -1 5 3 4 2 4 -
3 -3 3 -1 5 0 2 5 53 4 -5 -1 -5 0 4 -1 -3 4 4 4 2
Rmqp-l.Drivers
Knoledge- oaPrc po Stafag SuIte Technologyprnoalatg r I I I Imetbo"Olgy
I .1 9
CIpaLy Improvenment SalesI Raw(A Value PerfOImarejGON al bRatio inj Weight
TeDgie 4, 12 4,12 1,3 0,0 0,0 2,6 3 I9 1 Z6 3,9 , 15 2,6 2, 6 5, 15
1,3 3, 2,6 -1, -3 14,12 0,0 4,12 3.9 3,9 4,12 412 2,6 5,15 4,12
4
4
4
3- t-l-t-l*-t-l-l-I-I-4-~-l.-l.-l.----I-----1------I--1
244.4 4033 2533 -10.41100.6 0 157.91 281.5 169.4 233.5 434.4 -146.5 164 224.8
Total pactof Any Sort 4 10A 173. 0 157. 287.3 1 40.21236. 44 -2353 320 432.6
6 0
1.3
3'
KEY: 5 - Sumag ositive Relationship3 - Moderate Positie Relationship
- Weak ositive Relationship0. No Relationship
-1 -Weak Negativ Relationship-3 -Moderate Negative Relationship-S - Soong Negative Relationship
* -NregakthronugblOppoctuniy- Negatie nlhae
Figure 11: Matrix Analysis of ECMM
57
I-uI
FinalEaet
3,18 15,0 3,18 .3,-181-2,12 0,0 3.18 4,24 2,12 1 3,18 15,30 -5,30 -3,18 -5,-3
1,5 5,27 3,16 4,-21 ,-3,16 0,0 1,5 4.21 3,161 1.5 14,21 -5,27 -5,27 ,16f
6
53
Dkect 1.2 4.1 2 4 -2- 4 -3,-4 0,0 1,2 3,6 2,4 -2,-4 3,6 -4,- -3, -6 , -10 2 2 2 1 1 2
lndnurt 1,3 5,14 2,5 -2,-5 -1,-3 0,0 2,5 2,5 -2 -5 0,0 1, 3 -2,-3 -5,-14 -1,-3 2 3 4 1.3 1 2.7
Opprnnnity 2,3 2.3 2, 3 -3,-5 -2,-3 0,0 1,2 -2, -3 1,2 -1.-2 1,2 -2, -3 -4,4 -4,-6 3 2 1 0.5 1 1.5
StaituP 1,2 2,4 3,6 -,3-6 -1,-2 0,0 0,0 2, 4 3,6 ,-6 2,4 -1,-2 4,4 -5.-1 3 3 2 0.7 1 2
Accuracy 4,50 5,63 3,38 -1,-13 0,0 0,0 1,13 3,38 1,13 2,25 4,50 0,0 0,0 5,63 3 3 5 1.7 1.5 12.5
Availability 1,7 5.34 1,7 0,0 3,20 0,0 1,7 3,2 3,20 1,7 4,27 -3,-20 0,0 -2-13 5 3 4 1.3 1 6.7
Ccle Tie 3.13 3.15 4,20 -3.-5 1,5 0,0 1,5 2,10 1,5 3,15 4,20 -3,-5 0.0 3,15 4 4 5 1.3 1 5Setup Time 1,10 440 3,30 -3,-30 3,30 0,0 1,10 3,30 1,10 3,30 4,40 -3,-30 5,50 5.50 5 3 5 1.7 1.2 10
Fleuility 1,15 2,30 1, 13 0,0 1, 15 0,0 1,15 2,30 1, 15 3,45 2,30 -1,-3 5,75 5,75 5 2 5 2.5 1.2 15
M aki- blky 1,5 3,16 4,21 0,0 3,16 0,0 1,5 3,16 2,11 2,11 4,21 -34-16 0,0 -3,-16 4 3 4 1.3 1 5.3Reliability 5,50 5,50 4,40 -2,2- 3,30 0,0 1,10 3,30 35,50 -2,-20 5,50 5,50 5 3 5 1.7 1.2 10
ProcessTe.mcclogy 5,30 5,30 2,12 -1, -6 4,24 0,0 4,24 3,18 3.18 4,24 5.30 3, 1 3,111 5,30 4 4 5 3 1.3 2 6
Process 3,14 3,14 2, 10 -1,-S 4,19 0, 0 4,19 3,14 4,19 3,14 3,24 3,14 ,24 4,19 2 2 4 2 1.2 4.1
2,6
RImp-Up
Total a eImpact
I I
Prior to FimalEnirmet
I
3 2 I2
4 3 4 I
..5
3 I I 3
I I SI 3
issue that arises during this process will either result in group agreement or the need for
more information. Either result is beneficial to the ramp-up planning process.
The matrix and its analysis also serve as a record of the ramp-up planning process.
This is useful in creating a database for ramp-up planning of a particular process
technology or ramp-up in general. Furthermore, evaluating this record as ramp-up is going
on and/or in a post mortem fashion creates a good learning opportunity. And, having
existing records including lessons learned will make the planning process faster and more
accurate the next time around.
2.6 Case Study: Precision Coordinate Measuring Machine
2.6.1 Introduction
For those unfamiliar with the details of an error-corrected coordinate measuring
machine (ECMM) one must first understand what a coordinate measuring machine is.
Coordinate measuring machines emerged in the early 1960's, as an efficient and accurate
means to inspect manufactured parts. The way that they operate is the stylus (ball- or
cylindrically-shaped tip) on the measuring probe of the coordinate measuring machine is
used to contact the object (workpiece) under test. The spatial coordinates of the point of
contact between the stylus and the object are recorded. This process is repeated at different
predetermined locations as many times as needed as determined by the data sampling
strategy. Based on these recorded spatial coordinates, a numerical picture of the object is
built. Once built, a computer compares the features of this real object to that of what it
ideally should be. From this comparison, it is determined whether each feature of the
object is within dimensional tolerance.
Error-correction is a methodology that was developed to increase the accuracy of
machine tools, which include coordinate measuring machines, by compensating for
58
inherent systematic errors within the machine. Inherent systematic errors (translational and
rotational) of each individual element of the machine are measured as a function of position
and time, and then stored in the machine controller's memory. For instance on a linear
motion carriage, one would measure the three translational errors (two components of
straightness error, and linear displacement accuracy of the actuator) and three rotational
errors (pitch, roll, and yaw) at discrete points along the axes' travel. These inherent
systematic errors for each individual element are then put into a mathematical model that
relates the error in the position of the measuring probe (in the case of the ECMM) with
respect to the workpiece to the errors of the individual elements of the measuring machine
structure as a function of position and time. This mathematical model can then be used in
the data analysis to compensate for the inherent systematic errors.
Without this model, the computer that runs the ECMM has no knowledge that these
inherent systematic errors are present when it records the spatial coordinates of the points
of contact between the stylus of the probe and the workpiece. Thus, there are errors in the
spatial coordinates that are recorded. By using this model, the computer can correct the
spatial coordinates that were recorded to take into account the inherent systematic errors.
By doing so, the accuracy of the recorded spatial coordinates is improved, and thus the
accuracy of the ECMM is improved.
The ECMM project began in February 1991. It involves the specification, design,
construction, testing, and ramp-up of a prototype error-corrected coordinate measuring
machine. .Construction will be completed during February 1993. Ramp-up for the
prototype is expected to last a year and will take place in a pilot plant located adjacent to the
company's R & D facility. Furthermore, five production versions of the ECMM have to be
fully installed and fully functional at a designated plant by December 1994.
The project in general can be characterized as representing a relatively high amount
of technical complexity, based on interviews at the company (see Appendix B). The
project also represented a very high amount of systemic shift.
59
2.6.2 Preliminary Recommendations
The matrix analysis, as shown in Figure 11, and categorical analysis of ramp-up for
the ECMM provide several pieces of information that have implications for action. First,
the matrix analysis indicates that the Experimental Influences and Internal Stage of
Knowledge drivers will have the largest positive impacts with regard to optimizing ramp-
up. In addition, Staff Selection followed by Preparatory Search, External Stage of
Knowledge, and Separation will also have large positive impacts on ramp-up. This implies
that all of these drivers should be optimized and maximized as much as cost and other
factors allow. Based on interviews at the company followed by categorical analysis, some
preliminary observations and recommendations include:
Secondary Level Driver: Experimentation (6.A)
Status Quo:
. Most experiments run during ramp-up are either natural or ad hoc. Few process
development teams conduct design of experiments.
. Few measures are taken to improve signal-to-noise ratio and information
turnaround time.
. Although there is general knowledge about experiments that each team member
conducts, there is usually no formal structure for conducting experiments and
documenting results, as well as no centrally located documentation.
. Fidelity was often mentioned as a large problem when ramp-up moves from the
pilot plant into the manufacturing plant. To counter this problem, work has begun
on the development of microenvironments. Microenvironments are enclosures for
machine tools and coordinate measuring machines that provide temperature and
humidity control.
60
. Direct and indirect costs are monitored during ramp-up, but rarely influencedecisions to conduct experiments.
. The process development team is formed from individuals (e.g. engineers,
analysts, line managers) primarily in research and development. The team size is
generally reduced during ramp-up.
Recommendations:
* It is important to continue to run natural and ad hoc experiments, however there
should be more of an emphasis on controlled and simulation experiments. To run
controlled and simulation experiments, engineers and problem solvers have to
design experiments and determine boundary conditions. This requires gathering
more data and analyzing the process more. Also, the opportunity for incorrect
interpretation of results is reduced. In short, learning about the manufacturing
process is increased as well as about conducting experiments. Another benefit is
that information turnaround time is often shorter in controlled and simulation type
experiments.
" Continue developing a microenvironment for the ECMM. By developing this, a
laboratory environment will be simulated for the ECMM when it is in the
manufacturing plant. In addition, it is important to simulate the process control and
workpiece flow that the ECMM will experience in the plant environment, preferably
with it future end users involved. It would also be worthwhile to take the ECMM
to the intended plant for a brief time during ramp-up to see if there are any
unforeseen environmental (plant) noises (problems) that otherwise might not be
apparent at the pilot plant location. By improving fidelity and increasing simulation
of actual conditions, time required for ramp-up will be reduced. And with reduced
time, these recommendations will also reduce cost from a time-value-of-money
perspective.
. Documentation is very important, so it would be worthwhile to determine a
structure in which all information is recorded in the same manner and can easily be
found at a central location. This would reduce problems associated with many
individuals running experiments, taking data, and documenting results each in
61
his/her own way. This will improve information turnaround time and increase theopportunity for learning.
Secondary Level Driver: Integration (5.A)
Status Quo:
As noted above, the process development team is formed from individuals (e.g.
engineers, analysts, line managers) primarily in research and development.
Normally somewhere between the middle and end of ramp-up at the pilot plant,
members from technology implementation are added to the team. In the case of the
ECMM, technology implementation personnel were added during development. In
general, end users from the intended plant do not participate in ramp-up until the
manufacturing process arrives at their plant. Usually within three months after the
manufacturing process arrives at the intended plant, process development team
members from research and development get reassigned to new responsibilities.
Recommendations:
* Critically important is early involvement of the end user. The recommendation is to
bring the operators and maintenance people, who will initially operate and maintain
the ECMM, from the manufacturing plant to the pilot plant to get their input and
feedback, as well as to provide better knowledge transfer. Furthermore, the earlier
end users are brought in (ideally in the design phase), the easier plant buy-in of the
new manufacturing process technology will be. And, by bringing end users in
early, ramp-up will run in more of a parallel structure, rather than series, which will
reduce the time required for ramp-up. And as before, reduced time results in
reduced cost.
Technological Adaptation followed by Ambiguity Remaining were the two negative
influences. This implies that efforts should be maximized via Preparatory Search and other
means to reduce these drivers as much as possible. Based on interviews at the company
and categorical analysis, some preliminary recommendations include:
62
Secondary Level Drivers: Technological Adaptation (6.B)Status Quo:
" Job performance criteria is not modified for end users during ramp-up.Performance and pay are based on production.
" During ramp-up at the production plant, there is usually no cognitive and/orphysical separation from the normal production activities of the plant.
. Setting or not setting goals during ramp-up is determined by the project leader. In
general, it was found that most do not set goals.
Recommendations:
. One of the major and one of the most overlooked issues in terms of technological
adaptation is misalignment between the end users' responsibilities during ramp-up
and job performance criteria. During ramp-up, experimenting with and optimizing
the manufacturing process should precede focusing on production volume.
However, it is difficult for the end user to do this when his/her paycheck is based
on producing production quantities. The recommendation is thus to change the end
users' job performance criteria during ramp-up so that it promotes learning about
and optimizing the manufacturing process.
. To further foster an environment of experimentation and optimization for the end
users, consideration should be given to 'things' that help end users cognitively
and/or physically separate themselves from the production imperative. Cognitive
items include a designated conference room for problem solving and updates;
shirts, caps, and jackets denoting a team or project logo; etc. Physical items might
include walls or special demarcation around the manufacturing process, being
located in a more isolated area of the plant, etc. It is important to note that all of
these 'things' are for the benefit of helping end users do their job. Therefore, they
should have input into what these 'things' should be or if they want them at all.
* Another suggestion is periodic goals for the group. These goals should help keep
the group focused and should reduce the opportunity for congealing influences to
63
come into play. Congealing influences reduce and eventually eliminate continuous
improvement of manufacturing processes.
The matrix analysis also indicated that there were some 'breakthrough'
opportunities. A breakthrough opportunity is shown in the matrix when the Total Positive
Impact value does not equal the Total Impact of Any Sort value for a particular driver. The
two largest 'breakthrough' opportunities were not surprisingly Technological Complexity
and Systemic Shift. In both of these cases, they offer the opportunity for high performance
and high learning opportunities. However, a higher cost and a longer ramp-up time are
also predicted. The implication therefore is to use the two correlation matrices (drivers-to-
drivers and drivers-to-criteria) to minimize the negative impacts, while maximizing the
positive effects.
2.7 Conclusion
The feasibility of this model has been demonstrated by illustrating that ramp-up
drivers and criteria can be organized into a hierarchical structure and analyzed as a collective
whole. This is not to say that there are not still issues to be resolved and fine tuning to do,
but the proposed methodology offers a viable solution. Matrix analysis is the most widely
accepted means by which to compare ideas of different dimensions. Further, the 'House of
Quality' structure is most conducive to all the correlations and planning that are necessary
in the analysis of ramp-up. An extensive literature search, interviewing numerous people
who have ramped-up new manufacturing process technologies, and working with a
multidisciplinary team are the best ways to determine the drivers and criteria of ramp-up, as
well as their appropriate hierarchy.
As mentioned previously, the challenge for industry is to learn how to introduce
and exploit new manufacturing process technologies and how to integrate them fully into
64
-A
production. Optimizing ramp-up is necessary in meeting this challenge, but it is only part
of the solution. The other part involves optimizing the development of new manufacturing
process technologies. To accomplish this, three facets of process development must be
optimized and integrated together. These facets are (1) a set of technical tasks and activities
that if carried out properly will ensure a robust process technology, (2) an analytical and
visual tool that facilitates the planning, organizing, and monitoring of tasks in development
projects, and (3) a set of factors (criteria and drivers) which influence the development of
manufacturing process technologies. These three facets coupled with integrated business,
product, and technology strategies determine the success of a project. Clausing, Taguchi,
et al. (1991) have developed a set of technical tasks and activities that if carried out properly
will ensure a robust process technology. Through a variation of Steward's design structure
matrix (Steward, 1981), Eppinger, Whitney, Smith, and Gebala (1990) have developed an
analytical and visual tool that facilitates the planning, organizing, and monitoring of the
tasks in development projects.
This then leads to suggestions for further research. The remaining facet to be
developed and optimized is a model that represents the factors which influence the
development of new manufacturing process technologies. As can be imagined, a model
similar to the one presented in this paper could be developed. A literature search and
interviews in the areas of development criteria and drivers would have to be conducted.
After which, the model could be generated by the same procedure described in this paper.
Once completed, the three facets would be integrated together in the following manner.
The set of tasks and activities developed by Clausing, Taguchi, et al. create the technical
structure for a project. From this structure, thousands of subtasks are generated. These
subtasks can involve the work and coordination of hundreds of technical personnel (e.g.
engineers, analysts, operators, consultants) and can lead to thousands of decisions that
have to be made. As a parallel activity, there is a need to create an optimal set of factors
which influence these development activities and tasks. After the development model is
65
generated and the analysis performed, this parallel activity involves planning, organizing,
and monitoring the implementation. Finally, from a project management perspective, there
is a need to plan, organize, and monitor all of the dependent, independent, and
interdependent tasks created from both of these activities, as well as a need to facilitate the
coordination of all people involved on the project. This is achieved by using the above
mentioned variation of the design structure matrix. Integrated together, these three facets
would lead to an improved total development process for new manufacturing process
technologies.
66
4
Appendix A: Ramp-Up Definitions and Metrics
Ramp-Up Criteria
Time:
. Goal: Minimize Time.
" The amount of time that it takes to ramp-up a process technology in
either a particular environment, in a series of environments, or to
ramp-up the process technology completely.
* Measured in terms of time to ramp-up prior to final environment and
time to ramp-up at final environment.
Ramp-Up Prior to Final Environment:
" Time spent on ramp-up at location(s) (e.g. pilot plant) prior to the
final environment for the process technology. Note: If the process
technology was ramped up entirely at the final environment, then
this time would be zero.
" Measured in months.
Ramp-Up at Final Environment:
. Time spent on ramp-up
technology.
. Measured in months.
at the final environment for the process
Cost:
. Goal: Minimize Cost.
* The cost or expense associated with ramp-up of a process
technology.
. Measured in terms of direct, indirect, opportunity, and startup costs.
Direct:
" Cost of resources (e.g. materials, labor time) during ramp-up.
. Measured in dollars.
67
(1)
(L.A)
(L.B)
(2)
(2.A)
(2.B) Indirect:
" Cost of adverse impact on normal production during ramp-up (e.g.
bottleneck aggravation, setup time, increased confusion).
. Measured in dollars.
(2.C) Opportunity:
. Cost that occurs during ramp-up when problem solving competes
with production for precious resources.
. Measured in dollars.
(2.D) Startup:
. Fixed cost incurred for experimentation on process technology
during ramp-up.
. Measured in dollars.
(3) Performance:
" Goal: Maximize Performance.
. The performance or level of success of the primary operating
characteristics upon which the process technology is judged.
. Measured in terms of accuracy, availability, cycle time, setup time,
flexibility, maintainability, and reliability.
(3.A) Accuracy:
. The maximum translational or rotational error between any two
points in the process technology's work volume.
* Measured in position error (e.g. microinches or microns) and
orientation error (e.g. arcseconds or microradians).
(3.B) Availability:
* Obstacles to availability include breakdowns, downtime for
maintenance, and setup time.
. Measured as percentage of time that the process technology is fully
functional.
68
(3.C) Cycle Time:
" The amount of time it takes for a part to be loaded onto the process
technology, worked on, and then removed from the process
technology.
" Measured in time (e.g. seconds or minutes).
(3.D) Setup Time:
. Measured as the amount of time that it takes to setup (changeover)
the process technology for a different product model.
* Measured in time (e.g. seconds or minutes).
(3.E) Flexibility:
" Flexibility for the process technology involves being flexible to
accommodate variable production yields, being able to accommodate
diverse product models, and being able to be upgraded in the future
without requiring extensive modification or expansion.
. Variable production yields are measured as range (i.e. 100 to 1,000
parts) of cost efficient production yields, diverse product models are
measured as percentage of product models able to accommodate,
and flexibility to upgrade is measured as ratio of cost (dollars) of
modification including development cost if necessary to cost of
premodified process technology.
(3.F) Maintainability:
. The process technology does not break down easily or often, and, is
easy to repair when maintenance problems do occur.
. Breakdown is measured as time (days or months or years) in
between breakdowns while the process is running (i.e. If the
process does not run during the third shift, then do not count the
time during the third shift.), and, repair is measured as the amount
of time (minutes or hours) it takes to repair the process technology
so that it is fully functional.
(3.G) Reliability:
. The dependability, consistency, or repeatability of the process
technology.
69
. Measured as the error in position (microinches or microns) and
orientation (arcseconds or microradians) between a number of
successive attempts to move the object of interest (e.g. cutting tool,measuring probe) to the same position.
(4) Learning:
* Goal: Maximize Learning.
* Knowledge or understanding acquired during ramp-up of a process
technology.
* Measured in terms of individual technologies, process technology,
manufacturing process, and ramp-up.
(4.A) Individual Technologies:
. Learning about individual technologies that comprise the process
technology.
. Measured as useful information acquired.
(4.B) Process Technology:
* Learning about a type of process technology from a systems
perspective. (Example: Coordinate measuring machines (CMMs):
Column-type CMM, Gantry-type CMM, Ring bridge CMM, etc.)
. Measured as useful information acquired.
(4.C) Manufacturing Process:
* Learning about the manufacturing process (e.g. turning, grinding,
gaging, etc.) that the process technology is to be used for.
. Measured as useful information acquired.
(4.D) Ramp-Up:
" Learning about ramp-up of process technologies in general.
. Measured as useful information acquired.
70
A
Ramp-Up Drivers
(1) Stage of Knowledge:
" Current understanding of the individual technologies that comprise
the process technology, the type of process technology, the
manufacturing process that the process technology is to be used for,
and ramp-up in general.
. Measured as stage of knowledge. Example: Process technology
(Leonard-Barton):
1. Recognition of prototype (e.g. what is a good product).
2. Recognition of attributes within prototypes (i.e. ability to
define some conditions under which process gives good
output).
3. Discrimination among attributes (those that are important;
recognition of patterns. Experts may differ about relevance
of patterns; apprenticeship is common).
4. Measurement of attributes (some key attributes; measures
may be qualitative and relative).
5. Local control of attributes (repeatable performance; process
designed by expert, but technicians can perform).
6. Recognition and discrimination of contingencies; production
process can be mechanized and monitored manually.
7. Control of contingencies; process can be automated.
8. Complete procedural knowledge and control of
contingencies. (Process is completely understood.)
(L.A) External:
" Current understanding outside of the organization of the individual
technologies that comprise the process technology, the type of
process technology, the manufacturing process that the process
technology is to be used for, and ramp-up in general.
. Measured as stage of knowledge.
71
(L.B) Internal:
. Organization's current understanding of the individual technologiesthat comprise the process technology, the type of process
technology, the manufacturing process that the process technology
is to be used for, and ramp-up in general.
. Measured as stage of knowledge.
(2) Preparatory Search:
. Problem solving before new process is physically put in place.
" Measured in terms of information acquired and ambiguity
remaining.
(2.A) Information Acquired:
* Preparatory Search reduces ambiguity. Ambiguity is any
uncertainty (including technical understanding) or undefined aspect
that exists for the ramp-up of a particular process technology.
. Measured as acquired useful information content.
(2.B) Ambiguity Remaining:
. Regardless of the amount of Preparatory Search, there will always
be some ambiguity or uncertainty when ramp-up begins.
. Measured as useful information content remaining.
(2.B.i) Politics:
. There is the possibility that politics could come into play on any
issue or decision in which there are reasonable amounts of
ambiguity or uncertainty remaining.
" To measure the possibility that politics can come into play is a
function of the level of decision, history of events leading to
decision, and amount of ambiguity remaining.
(3) Purpose:
. The purpose of the facility or cell where the process will end up will
affect ramp-up. The purpose may affect staffing, the systematic
learning and problem solving methodology, structure, etc.
72
* Measured as the type of facility or cell (Service, Traditional, orhybrid) that the process will be a part of.
(3.A) Service Factory (or Cell):
. Factory or cell within a factory that bundles services with products,anticipating and responding to a truly comprehensive range of
customer needs.
. Measured as the type of facility or cell (Consultant, Dispatcher,
Laboratory, Showroom, or hybrid).
(3.A.i) Consultant:
" Type of service factory or cell. To add value to the product, factory
or cell workers' expertise is offered to customers.
" Measured as whether factory or cell meets this criteria (yes or no).
(3.A.ii) Dispatcher:
* Type of service factory or cell. The factory or cell serves as the
linchpin of aftersales support.
* Measured as whether factory or cell meets this criteria (yes or no).
(3.A.iii) Laboratory:
" Type of service factory or cell. Factory or cell provides data on
product performance to R&D, process parameters to designers,
capacity restrictions to sales and marketing, etc.
" Measured as whether factory or cell meets this criteria (yes or no).
(3.A.iv) Showroom:
* Type of service factory or cell. Factory or cell serves as a
demonstration of the systems, processes, and products it
manufactures. It can also represent a company's manufacturing
capability, quality, and reliability.
* Measured as whether factory or cell meets this criteria (yes or no).
(3.B) Traditional:
* Factory or cell that produces manufactured products under historical
manufacturing drivers.
73
. Measured as whether factory or cell meets this criteria (yes or no).
(4) Staffing:
. Staffing includes the selection (or nonselection) of team members,determination of roles (if any), and what types of people and how
many of each of those types of people are represented on the team.
" Measured in terms of Roles and Selection.
(4.A) Roles:
* In terms of staffing, establishing roles that provide for the
accomplishment of critical functions in the innovation process.
* Measured as what type of roles (Idea Generators, Politician/Shield
Maker, Sturdy Pillars, Visionary/Moderator) are established.
(4.A.i) Idea Generators:
. Type of role. Person(s) that generates ideas, solves key problems,
and/or serves as a creative component on the project team.
* Measured as whether role is established (yes or no).
(4.A.ii) Politician/Shield Maker:
. Type of role. Person(s) that provides resources (e.g. time, money,
people) and can buffer resistance from various areas of the
company.
. Measured as whether role is established (yes or no).
(4.A.iii) Sturdy Pillars:
. Type of role. Person(s) who has knowledge of company history
both in terms of culture and the manufacturing process that the
process technology is intended for. Using this knowledge, this
person(s) provides a reality check and a defense of the status quo for
the project team.
" Measured as whether role is established (yes or no).
74
(4.A.iv) Visionary/Moderator:
* Type of role. Person(s) who manages the project, provides a vision
for the team, and can strike a balance between the idea generators
and the sturdy pillars of the project team.
* Measured as whether role is established (yes or no).
(4.A.v) Group Mix:
. In terms of staffing, what types of people and how many of each of
those types of people are represented on the team. The appropriate
group mix is a function of the 'technical complexity' and 'systemic
shift' of a project. Therefore, the group mix will partially be
determined by the amount of joint search and functional overlap that
is needed during ramp-up.
. Measured as what types of people (e.g. development, operations,
field support) and how many of each are represented on the team
during ramp-up.
(4.A.v.a) Functional Overlap:
" Functional overlap is merging the roles of a plant's technical and
production personnel. In general, the greater the systemic shift, the
greater the positive impact of functional overlap on project success.
. Measured on 1-5 scale the primary mode of communication and
contribution between engineering and production personnel: 1 =
Simple handoff; 2 = Significant direct contact; 3 = Special liason
role created; 4 = Special task force includes individuals in both
separate functions; 5 = Fully integrated team made up of multi-
functional personnel.
(4.A.v.b) Joint Search:
. Joint search involves using outside technical experts to help solve
problems during the actual startup process. In general, the greater
the technical complexity, the greater the positive impact of joint
search on project success.
" Measured on 1-5 scale the role of outside experts in the startup
process: 1 = Not a partner in the problem solving process; 5 = Part
of the problem solving team.
75
(4.B) Selection:
. In terms of staffing, whether the team members are 'hand picked',
whether they are just assigned, or somewhere in between.
- Measured on 1-5 scale: 1 = Team members assigned without input;
5 = Team members hand picked.
(5) Structure:
* The cognitive and physical relationship between the project and the
company and the company's culture.
* Measured in terms of integration (or separation).
(5.A) Integration:
" Structure that increases access to existing knowledge, overcomes
resistance easier, and smoothes transitions.
. Measured on 1-5 scale: 1 = Full time, differentiated unit devoted
solely to innovation activities; 2 = Multiple full and part time
individuals working on innovation activities in a project
environment; 3 = Part time individuals working on innovation
activities in a project environment; 4 = Full time assignment of an
individual to work on innovation activities; 5 = Part time assignment
of an individual to work on innovation activities.
(5.B) Separation:
* Structure that avoids collocation and fosters creativity.
* Measured on 1-5 scale: 1 = Part time assignment of an individual to
work on innovation activities; 2 = Full time assignment of an
individual to work on innovation activities; 3 = Part time individuals
working on innovation activities in a project environment; 4 =
Multiple full and part time individuals working on innovation
activities in a project environment; 5 = Full time, differentiated unit
devoted solely to innovation activities.
(6) Systematic Learning and Problem Solving Methodology:
* Methodology implemented during ramp-up to increase stage of
knowledge.
76
. Measured in terms of Experimentation and Technological
Adaptation.
(6.A) Experimentation:
* Through experimentation, systematic learning and problem solving
are achieved.
* Measured in terms of Experimental Influences and Experimental
Types.
(6.A.i) Experimental Influences:
* Experimental influences are factors that determine the effectiveness
of learning by experimentation.
* Measured in terms of Cost, Fidelity, Individual Habits, Information
Turnaround Time, Manpower Applied, Signal-to-Noise Ratio, and
Manufacturing Process Resources.
(6.A.i.a.) Cost:
* The cost or expense associated with experimentation on a process
technology. Cost criteria includes direct, indirect, startup, and
opportunity.
Direct: Cost of resources dedicated to performing the experiment
(e.g. materials, labor time).
Indirect: Cost of adverse impact on normal production (e.g.
bottleneck aggravation, setup time, increased confusion).
Startup: Fixed cost incurred when a new method of
experimentation is first being used.
Opportunity: Cost that occur when problem solving competes with
production for precious resources during experimentation.
. Direct, indirect, startup, and opportunity costs are measured in
dollars.
(6.A.i.b) Fidelity:
. The degree of similarity between the actual environment where the
process ends up and the environment where experiments are
conducted.
77
. Measured on 1-5 scale: 1 = Low degree of similarity between actual
manufacturing plant environment where process is intended for and
environment where ramp-up experiments are being conducted; 5 =
High degree of similarity between actual manufacturing plant
environment where process is intended for and environment where
ramp-up experiments are being conducted.
(6.A.i.c) Individual Habits:
. Problem solvers' and engineers' individual habits for designing,
conducting, analyzing, and especially documenting experiments.
. Measured on 1-5 scale: 1 = Individual conducts, analyzes, and
documents experiments in his/her own way; 5 = Structured
methodology that fosters individuals to conduct, analyze, and
document experiments in an integrated and unified manner with
other team members.
(6.A.i.d) Information Turnaround Time:
" Time from beginning an experiment and getting results from it.
" Measured in time (e.g. seconds, minutes, hours, days).
(6.A.i.e) Manpower Applied:
* Applying more problem solvers and engineers during
experimentation to improve the speed of learning.
. Measured as percentage increase or decrease of manpower for ramp-
up.
(6.A.i.f) Signal-to-Noise Ratio:
* Ratio that indicates how well the environment is being controlled
during experimentation, so that variations in measurements of the
process reflect the impact of an experimental change rather than
seemingly random process variations.
" Measured as the S/N ratio of the quality characteristic (an
appropriate response variable) for the process.
78
(6.A.i.g) Manufacturing Process Resources:
. Supply and quality of equipment, materials, etc. used for
experimentation.
" Supply measured as the available quantity in terms of number and
variety of resources that would serve a given purpose for the
process technology, and, quality measured as percentage of
resources that are supplied for the process technology that do not
posses an unusable defect.
(6.A.ii) Experimental Types:
. Different types of experimentation.
* Measured in terms of types of experimentation (Natural, Controlled,
Simulation, Ad Hoc, and EVOP) conducted, as well as approximate
distribution (%) of each.
(6.A.ii.a) Natural:
. Type of experiment that involves observing the normal operation of
manufacturing process.
. Measured as whether this type of experiment is performed (yes or
no).
(6.A.ii.b) Controlled:
. Type of experiment that involves making predetermined and
deliberate changes to the manufacturing process.
. Measured as whether this type of experiment is performed (yes or
no).
(6.A.ii.c) Simulation:
. Type of experiment that involves using mathematical models of the
process.
. Measured as whether this type of experiment is performed (yes or
no).
(6.A.ii.d) Ad Hoc:
* Type of experiment that involves 'before' and 'after' comparisons.
79
. Measured as whether this type of experiment is performed (yes or
no).
(6.A.ii.e) EVOP:
* Type of experiment that involves making slight changes to the
manufacturing process at irregular intervals.
* Measured as whether this type of experiment is performed (yes or
no).
(6.B) Technological Adaptation:
* The nature (causes, environment, and causes to congeal) of
adjustments required to improve the performance of the technology
and to help it fit better into its user environment.
* Measured in terms of Technological Adaptation Causes and
Technological Adaptation Influences.
(6.B.i) Technological Adaptation Causes:
. Types of misalignments that cause a process technology not to fit
perfectly into its user environment. Since the process technology
does not fit perfectly into its user environment, there is the need for
technological adaptation.
. Measured in terms of Delivery System, Technology, and Value.
(6.B.i.a) Delivery System:
. Misalignment between the technology and the user organization
infrastructure (e.g. supporting hardware, software, educational
programs).
" Measured in terms of significance (high or low) and impact (positive
or negative).
(6.B.i.b) Technology:
. Misalignment between the technology and its original specifications
or with the production process into which it is introduced.
- Measured in terms of significance (high or low) and impact (positive
or negative).
80
(6.B.i.c) Value:
* Misalignment between the technology and job performance criteria
in the user organization.
* Measured in terms of significance (high or low) and impact (positive
or negative).
(6.B.ii) Technological Adaptation Influences:
* Technological adaptation influences are factors that determine the
effectiveness of technological adaptation.
. Measured in terms of Environmental Conditions and Congealing
Influences.
(6.B.ii.a) Environmental Conditions:
. The environmental conditions are the conditions under which users
and experts can gather data, reflect on it, formulate questions, and
develop solutions. Key conditions include 'continuity' among
people to link problem solving and production, 'new people and
new perspectives' to provoke a new way of viewing the technology
and the user environment, enough 'physical or cognitive distance' to
offer a haven from influences of production, and enough 'proximity
to production' to allow users and experts to observe and investigate
how the process technology will work in its intended user
environment.
. Measured in terms of what measures are taken to increase
'continuity', 'new people and new perspectives', and 'proximity to
production'.
(6.B.ii.b) Congealing Influences:
. Congealing influences are factors that cause process technologies to
congeal fairly rapidly after their introduction. These influences
include 'competency traps' in that users gain proficiency with a
given set of procedures, 'expectations adjust to fit reality' in that the
project team learns to live with various difficulties, 'production
imperative' in that it is difficult to try to correct problems after the
need to maintain the steady pace of production has occurred, and
81
-4
'teams break down' in that project teams tend to lose momentum and
coherence once members are pulled off and enthusiasm wanes.
. Measured in terms of what measures are taken to reduce and prevent
'competency traps', 'expectations to fit reality', 'production
imperative', and 'teams breaking down'.
(7) Technology:
" The technology of the process itself.
. Measured in terms of Systemic Shift and Technological Complexity.
(7.A) Systemic Shift:
* The effect the technology has on established systems, assumptions,
and/or skill sets.
* Measured on 1-5 scale as to how much experience does intended
manufacturing plant have with equipment such as new process
technology in terms of probing (or tooling) concepts (1-5 scale), in
terms of material handling (loading) concepts (1-5 scale), in terms of
control concepts (1-5 scale), and in terms of the flow of production
(e.g. integrated line) (1-5 scale). Note: 1 = Significant experience;
5 = No experience.
(7.B) Technological Complexity:
" The evolutionary jump, the relative novelty, and/or sophistication of
the technology.
. Measured on 1-5 scale: Is the new process technology based on
standard, well-known technology or on new technological
developments in terms of probing (or tooling) concepts (1-5 scale),
in terms of material handling (loading) concepts (1-5 scale), in terms
of control concepts (1-5 scale), and in terms of terms of all other
features (1-5 scale). Note: 1 = Well-known; 5 = New.
82
Appendix B: Ramp-Up Interview Questions
(1) How does the company deal with problems on a new manufacturing process that
occur after the need to maintain the steady pace of production?
(2) Does the company have any mechanisms to deal with users gaining proficiency
with a given set of procedures that constrain further experimentation and reduce the
possibility of further optimizing the manufacturing process?
(3) After a satisfactory (in terms of duration) ramp-up period, how does the company
view and/or approach a manufacturing process that does not measure up to original
expectations?
(4) Will project team members will be pulled off of the project during either
development or ramp-up? If yes, are there any measures that the company takes to
maintain the momentum and coherence of the team?
(5) During ramp-up, does the process development team create any discrepant events to
adapt and change the manufacturing process?
(6) During ramp-up, how does the process development team frame unexpected events
and problems?
(7) During ramp-up, is job performance criteria (especially for the users) altered? If
yes, in which ways?
(8) If a problem arises during ramp-up that could be solved by altering the technology
of the manufacturing process, the environment in which the process is located, or
both, which solution does the process development team normally choose?
(9) During experimentation in ramp-up, how much effort and/or what measures does
the process development team take to improve the signal-to-noise ratio?
(10) During experimentation in ramp-up, does the process development team do
anything to improve the information turnaround time between beginning an
experiment and getting the results from it?
83
(11) How does the process development team deal with the issue of fidelity (i.e. the
degree of similarity between the actual environment where the process ends up and
the environment where experiments are conducted during ramp-up)?
(12) How much does the company weigh direct and indirect costs in terms of running
experiments during ramp-up?
(13) Does the company apply more resources (engineers and problem solvers) during
ramp-up to improve the speed of learning about a new manufacturing process?
(14) What types of experiments (controlled, natural, simulation, ad hoc, and/or EVOP)
do the process development team perform during ramp-up to learn more about a
manufacturing process?
(15) How does the process development team deal with engineers' individual habits for
designing, conducting, analyzing, and especially documenting experiments during
ramp-up?
(16) At the company, how cross functional are the development and ramp-up teams?
(17) At the company, how are project team members selected?
(18) During ramp-up, is there a focus on tangible results?
(19) During process development and ramp-up, how much effort is oriented toward
users?
(20) Describe ramp-up at the eventual manufacturing plant in terms of physical and
cognitive distance from production operations.
(21) At the company, how much effort is made for ramp-up in terms of preparatory
search (i.e. investing in problem solving before equipment is installed)?
(22) At the company, how much effort is made for ramp-up in terms of joint search (i.e.
using outside technical experts to help solve problems during the actual ramp-up
process)?
84
A
(23) At the company, how much effort is made for ramp-up in terms of functional
overlap (i.e. merging the roles of a plant's technical and production personnel)?
(24) In terms of company culture during ramp-up, how much uncritical acceptance is
there of the existing structure?
(25) Does the company have and/or employ analytical techniques if political situations
arise?
(26) Within the company, how can employees representing desired sources of
information and expertise be found?
(27) When staffing a project team, are there particular roles (i.e. politicians/shield maker,
visionary/moderator, sturdy pillars, idea generators, etc.) that have to be filled?
(28) Is the Error-Corrected Coordinate Measuring Machine (ECMM) based on standard,
well-known technology or on new technological developments in terms of probing
and loading concepts? (1-5 scale)
(29) Is the ECMM based on standard, well-known technology or on
developments in terms of electronic controls? (1-5 scale)
(30) Is the ECMM based on standard, well-known technology or on
developments in terms of all features? (1-5 scale)
(31) Does the company expect the introduction of the ECMM to be a
units: 1 = No; 5 = Yes?
new technological
new technological
series of prototype
(32) Does the company expect the ECMM to be fully proven at the laboratory level: 1 =
Not true; 3 = True for one or two major features; 5 = True for overall technology?
(33) How much experience does the intended plant have with this kind of equipment
prior to the introduction, in terms of probing and loading concepts? (1-5 scale)
(34) How much experience does the intended plant have with this kind of equipment
prior to the introduction, in terms of controls concepts? (1-5 scale)
85
(35) How much experience does the intended plant have with this kind of process prior
to the introduction, in terms of the flow of production (e.g. integrated line)? (1-5
scale)
(36) How big a change is expected compared to existing equipment, in terms of the flow
of production (e.g. leadtime and flexibility)? (1-5 scale)
(37) The ECMM is based on: 1 = Basic technological strengths of the company; 3 =
Other technologies employed by the company; 5 = New technological approach for
the company.
(38) Describe the expected process of pretesting the equipment at the vendor prior to
delivery and startup: 1 = We will have a runoff test, but it will be an exercise; 5 =
We will have extensive runoff testing.
(39) How helpful do you expect the following groups to be during this introduction: (a)
Equipment vendor? (b) Personnel from sister plants? (c) Outside advisors? (d)
Company experts from technical center? (1-5 scale)
(40) What kind of people from technology implementation and the intended plant do you
expect to be involved during ramp-up?
(41) In terms of learning during ramp-up about the process technology and/or ramp-up
in general, does the process development team or company do anything to promote,
document, and/or diffuse this learning?
(42) With regard to ramp-up of new manufacturing processes, what are traditionally the
company's largest problems?
86
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