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The Pennsylvania State University
The Graduate School
Department of Industrial Engineering
DEVELOPMENT AND EVALUATION OF SOFTWARE FOR SYSTEMATIC
BENCHMARKING OF PRODUCT FAMILIES
A Thesis in
Industrial Engineering
by
Rahul Sarnobat
© 2010 Rahul Sarnobat
Submitted in Partial Fulfillment
of the Requirements
for the Degree of
Master of Science
December 2010
The thesis of Rahul Sarnobat was reviewed and approved* by the following:
Timothy W. Simpson
Professor
Department of Industrial & Manufacturing Engineering
Thesis Advisor
Gul E. Kremer
Associate Professor
Department of Industrial & Manufacturing Engineering
Thesis Advisor
M. Jeya Chandra
Professor in Charge of Academic Programs & Graduate Program Coordinator
Department of Industrial & Manufacturing Engineering
*Signatures are on file in the Graduate School.
ABSTRACT
Benchmarking is no longer synonymous with imitation but considered an excellent means of
innovation. Benchmarking individual products with competitors or an industry’s best standards
has been gaining momentum since its first inception in the manufacturing industry. Furthermore,
many manufacturers are designing product families to make use of the strategic advantages and
valuable insights it has to offer over competitors including better part reuse, lower redesign
efforts, and rapid response to changing market requirements. However, the advantages of
benchmarking of product families are often undermined by limited capability of current
benchmarking tools. Additionally, the process is typically done manually without the aid of
software making it not only time-consuming but also subject to human variability. To address
these problems, the Product Family Analysis Toolkit (PFAToolkit) is introduced, which
combines several popular benchmarking tools to streamline and standardize the process of
product family benchmarking. The features and capabilities offered by the PFAToolkit are
discussed, and then its functionality is demonstrated using several examples. The benefits and
usability of PFAToolkit are discussed through a research study involving families of power tools.
The study aimed at assessing the efficiency, effectiveness, and satisfaction of the PFAToolkit
over manual benchmarking of product families. The results of the study show that the
PFAToolkit significantly improves the efficiency of the product family benchmarking process
and results of the satisfaction survey substantiate the above claim by indicating that majority of
the users were satisfied with the software’s capability, features and ease of use.
iii
TABLE OF CONTENT
LIST OF TABLES......................................................................................................................... vi
LIST OF FIGURES ...................................................................................................................... vii
ACKNOWLEDGEMENTS........................................................................................................... ix
CHAPTER 1 INTRODUCTION .................................................................................................... 1
CHAPTER 2 LITERATURE REVIEW AND BACKGROUND .................................................. 5
CHAPTER 3 PRODUCT FAMILY ANALYSIS TOOLKIT: SYSTEM ARCHITECTURE..... 10
3.1 Bill of Materials ............................................................................................................ 11
3.2 Design Structure Matrices............................................................................................. 12
3.3 Clustering...................................................................................................................... 13
3.4 Product Line Commonality Index................................................................................. 14
3.5 Generational Variety Index........................................................................................... 16
CHAPTER 4 PRODUCT FAMILY ANALYSIS TOOLKIT ...................................................... 18
4.1 Bill of Materials ............................................................................................................ 19
4.2 Design Structure Matrix................................................................................................ 23
4.2.1 Part Connections ....................................................................................................... 23
4.2.2 Generate Complete Design Structure Matrix............................................................ 25
4.2.3 Cluster Design Structure Matrix ............................................................................... 26
4.3 Product Line Commonality Index................................................................................. 28
4.3.1 Select Product Files................................................................................................... 29
4.3.2 PCI Analysis ............................................................................................................. 31
4.4 Generational Variety Index........................................................................................... 33
CHAPTER 5 RESEARCH STUDY RESULTS AND OBSERVATIONS.................................. 39
5.1 Summary of Experiment Protocol ................................................................................ 40
5.2 Impact of Software on Benchmarking Efficiency ........................................................ 41
5.3 Impact of Software Effectiveness on Benchmarking.................................................... 45
5.3.1 Impact of BOM Tool on Effectiveness on Benchmarking ....................................... 45
5.3.2 Impact of DSM Tool on Effectiveness on Benchmarking........................................ 51
5.3.3 Impact of PCI Tool on Effectiveness on Benchmarking .......................................... 51
iv
5.4 Satisfaction of the Software Based on Survey Results ................................................. 54
5.4.1 Evaluation of Ease of Use and Capability of the Software....................................... 54
5.4.2 Evaluation of Graphical User Interface and Usability of the Software .................... 55
5.4.3 Evaluation of BOM, DSM, and PCI Tools of the Software ..................................... 57
5.5 Summary of Overall Observations of the Software...................................................... 59
CHAPTER 6 CLOSING REMARKS AND FUTURE WORK ................................................... 61
Appendix A: Installation of PFAToolkit Add-in .......................................................................... 64
Appendix B: List of Part Names for Drill, Jig Saw, and Sander, List of Requirements for BOM,
List of Part Connections for DSM, and Formula to Calculate PCI .............................................. 67
Appendix C: Sample Assessment Table of Correct Details Comparison for Manual
Benchmarking ............................................................................................................................... 87
Appendix D: PFAToolkit Satisfaction Survey Form.................................................................... 88
REFERENCES ............................................................................................................................. 95
v
LIST OF TABLES
Table 1. GVI Rating System [37] ................................................................................................. 17
Table 2. Details of Time Taken By Participants to Perform Benchmarking ................................ 42
Table 3. Results of Mann-Whitney U Test at 95% Confidence Level for Median Times Taken for
BOM, DSMs and PCI ........................................................................................................... 44
Table 4. Details of Percentage of Correct Material, Manufacturing, and Assembly Details for
Manual and Software Benchmarking.................................................................................... 46
Table 5. Results of Mann-Whitney U tests at 95% Confidence Level for Median Time Taken for
Materials, Manufacturing Process, Assembly ...................................................................... 49
Table 6. Comparison of Parts Added or Deleted for Manual and Software Benchmarking ........ 50
Table 7. Results of Mann-Whitney U Test for Median Number of Parts Added or Deleted for
Manual and Software Benchmarking.................................................................................... 50
Table 8. Comparison of PCI Values Calculated by All Participants ............................................ 52
Table 9. Results of Mann-Whitney U tests at 95% Confidence Level for Difference of PCI value
from "Expert" Value ............................................................................................................. 52
Table 10. Summary of Adjustments Made to PCI Tables of Each Participant............................. 53
vi
LIST OF FIGURES
Figure 1. PFAToolkit Architecture ............................................................................................... 11
Figure 2. Illustration of PFAToolkit Menu................................................................................... 18
Figure 3. Illustration of the Form to Record Details of Parts for Bill of Materials ...................... 19
Figure 4. Illustration of Use of Auto Suggest Feature .................................................................. 21
Figure 5. Illustration of BOM Output in Spreadsheet................................................................... 22
Figure 6. Illustration of List of Part Connections Form................................................................ 24
Figure 7. Illustration of DSM Output ........................................................................................... 25
Figure 8. Illustration of DSM Clustering Input Parameters Form................................................ 27
Figure 9. Illustration of a Sample Clustered DSM Output ........................................................... 28
Figure 10. Illustration of Selecting Product Files for PCI Analysis ............................................. 30
Figure 11. Illustration of Form to Separate Unique and Shared Parts .......................................... 31
Figure 12. Illustration of Form for Entering PCI Factors ............................................................. 32
Figure 13. Illustration of Sample PCI Output Spreadsheet........................................................... 33
Figure 14. Illustration of GVI Phase I Form for Customer Requirements ................................... 33
Figure 15. Illustration of GVI Phase I Form for Assigning Priorities to Customer Requirements
............................................................................................................................................... 34
Figure 16. Illustration of GVI Phase I Form for Engineering Metrics ......................................... 35
Figure 17. Illustration of GVI Phase I Form for Target Values.................................................... 35
Figure 18. Illustration of GVI Phase II Form for Selecting Parts................................................. 36
Figure 19. Illustration of GVI Phase II Form for Rating Parts for Redesign Effort ..................... 36
Figure 20. Illustration of GVI Phase I Output Sheet .................................................................... 37
Figure 21. Illustration of GVI Phase II Output ............................................................................. 38
Figure 22. Comparison Between Manual and Software Benchmarking of Average Times Taken
For Benchmarking Analysis ................................................................................................. 43
Figure 23. Percentage Comparison of Identical Details for Materials between Manual and
Software Benchmarking........................................................................................................ 46
Figure 24. Percentage Comparison of Identical Details for Manufacturing Processes Between
Manual and Software Benchmarking.................................................................................... 47
vii
Figure 25. Percentage Comparison of Identical Details for Assembly between Manual and
Software Benchmarking........................................................................................................ 47
Figure 26. Overall Satisfaction Levels for "Ease of Use" of Software......................................... 55
Figure 27. Overall Satisfaction Level for "Graphical User Interface" of Software...................... 56
Figure 28. Overall Satisfaction Level for "BOM" Feature of Software ....................................... 57
Figure 29. Overall Satisfaction Level for "DSM" Feature of Software........................................ 58
Figure 30. Overall Satisfaction Level for "PCI" Feature of Software .......................................... 58
viii
ix
ACKNOWLEDGEMENTS
I would like to thank Dr. Timothy W. Simpson, Professor of Industrial and Manufacturing
Engineering at Penn State University for his constant support, guidance, and motivation
throughout the research. I would especially like to thank him for his belief and faith in my
capabilities which helped me not only to explore new and innovative topics to increase my vision
on the topic of my research but also perform to the best of my abilities. A special thanks to
Avanti Jain, who was a collaborator in this research.
I thank my family for giving me encouragement and support throughout my time in graduate
school. I also thank my lab members for all the help and support they provided. Their comfort
and company was one of the prime drivers in my completion of my masters. I would also like to
thank my friends Avinash, Dhananjay and Rajesh who made my time at Penn State truly
enjoyable.
This work is being supported by NSF Grant No. OCI-0636273 and DUE-0919724. Any
opinions, findings, and conclusions or recommendations in this paper are those of the authors
and do not necessarily reflect the views of the National Science Foundation.
CHAPTER 1
INTRODUCTION
Benchmarking techniques are now widely being used in many industries to compare product
designs, costs, manufacturing processes, packaging, and so on to competitor’s products, industry
standards or perceived best practices. Initially popularized by the Japanese industry members as
part of Total Quality Management (TQM), benefits of benchmarking have been valuable to many
US corporations [1]. Benchmarking has been defined in many ways [2], each definition trying to
capture the broad meaning of the term. Merriam-Webster’s dictionary defines benchmarking as
“something that serves as a standard by which others may be measured or judged”. More
formally, benchmarking is defined as a “systematic way to identify, understand, and creatively
evolve superior product, services, designs, equipment, processes, and practices to improve an
organization’s real performance” [3]. Thus, depending on the domain where maximum benefits
are realized, companies benchmark products, processes, internal operations, or other aspects that
have room for improvement.
The significant benefits gained from competitive benchmarking can best be explained by tracing
the history of Xerox Inc. from the late sixties to today [4]. Xerox in the sixties and seventies
experienced loss of market share primarily due to Japanese competitors. Xerox, the first
innovators of photocopiers and a company synonymous with photocopying, were getting beaten
at their own game. The Japanese were able to market higher quality products at a lower price
forcing Xerox to reorganize their product development strategy. They regained their lost
competitive advantage through extensive “teardown” of their competitors’ products and
benchmarking all aspects of their business as well against Xerox products and business
1
operations. Other instances include AT&T, which found out that their cycle time of their new
product development takes somewhere in a range of about 133 and 250 per cent of the time taken
by their Japanese competitor [5]. Realizing the vast benefits benchmarking has to offer, more
companies are setting considerable time and effort to benchmark their products, e.g., General
Motors' Vehicle Assessment and Benchmarking Activity Center performs a “teardown” analysis
of approximately 40 of its competitors’ vehicles that require almost six weeks for completion [6].
Companies design product families and platforms not only to reduce product development time
and production costs but also to diversify their product offerings [7]. With this said,
benchmarking is a time-intensive activity that is susceptible to human errors, and to achieve
effective benchmarking appropriate tools, metrics and methodologies are needed. Moreover,
benchmarking families of products greatly multiplies the complexity and variability in the
process as compared to that of an individual product [8]. Product family design involves the
design and development of multiple products that share common parts and modules, or use
similar manufacturing/assembly processes, yet target different market segments through careful
differentiation and delineation of product features [9].
Benchmarking product families and applying the right tools can provide valuable insights into
the platform architecture. For individual products, there exist various benchmarking methods
[10,11,12]; however, for families of products, there are very few approaches that address the
benchmarking needs [8,13], and the scope of existing processes is typically limited only to a few
aspects of product family design, e.g., cost, commonality and variety. Thevenot and Simpson [8]
recently developed a product family benchmarking method (PFbenchmark) based on dissection,
extending their previous work. Their process benchmarks a set of product family design
2
alternatives with respect to commonality/variety and cost estimates by means of product
dissection. A graphical evaluation method called Product Family Evaluation Graphs (PFEG) has
also been developed to compare product family design alternatives [13]. The PFEG is a two-
dimensional graph of the Variety Index (VI) versus Commonality Index (CI) that helps correlate
the marketing and engineering domains. These existing processes for benchmarking product
families are limited in number and are not very comprehensive for gathering the data needed in
today’s competitive markets. Jain [14] addresses this need by developing a systematic process to
benchmark product families. The benchmarking methodology developed integrates various tools
which provide manufacturing companies’ insights about their competitors’ products as well as
identifying opportunities to improve their own product architectures. The first step focuses on
capturing details of the products systematically and consistently which are required for further
analysis of the product family. Second step includes a metric to measure the amount of
commonality within the product family, which impacts both the economic viability of the family
and its ability to compete in the marketplace – commonality reduces design and manufacturing
costs but limits the ability to differentiate products in the family. Therefore, being able to assess
the amount of commonality in a family is an important metric for product family design, and it
provides a useful tool for benchmarking competitors’ products; however, it is not the only tool
used for product family benchmarking. The third step includes a tool to qualitatively assess
modularity and identify modules and interfaces for future product architectures which is one
concern that many companies face [15] . Lastly, the fourth step focuses on capturing changing
customer needs over a period of time and mapping them back to the product architectures to
quantify redesign efforts and flexibility of the components.
3
This thesis focuses on the development of a Product Family Analysis Toolkit (PFAToolkit) that
comprises a suite of automated and semi-automated analysis tools to aid product family
benchmarking. The analysis tools integrated into the PFAToolkit include:
(1) Bill of Materials (BOM) to record components’ details
(2) Design Structure Matrix (DSM) to display different types of connections between
components in a matrix form
(3) Clustering algorithm to cluster DSMs to assess modularity and identify modules and
interfaces
(4) Product Line Commonality Index (PCI) to analyze cost effectiveness and commonality
within a product family
(5) Generational Variety Index (GVI) to measure redesign effort for future product family
architecture requirements.
Based on experience with several industry partners, these five tools enable a comprehensive
benchmarking analysis of the products in a systematic and time-efficient manner.
The remainder of this thesis is organized as follows. Chapter 2 provides additional background
on these benchmarking tools and other tools in the same space. In Chapter 3, the proposed
software is introduced along with details of the various tools included in it. In Chapter 4 the
software is implemented, and its features and capabilities are illustrated through examples.
Chapter 5 discusses a research study involving a family of power tools. Results of the survey
conducted in the study are also presented. The thesis concludes a summary of the software’s
limitations and future work in Chapter 6.
4
CHAPTER 2
LITERATURE REVIEW AND BACKGROUND
Industry or competitive benchmarking process not only includes benchmarking of products or
services against companies in the same industry but also strategic benchmarking [9]. Pozos [16]
defines strategic benchmarking as “Proactive analysis of emerging trends, options in markets,
processes, technology and distribution that could affect strategic direction and deployment”. So,
competitive benchmarking ideally should target short-term improvements as well as long-term
improvements. Extending this to benchmarking product families, short term improvements
would include benchmarking specific product families (e.g., commonality assessment) whereas
long-term improvements would include assessing platforms designs based on predicting market
trends (e.g., product redesign effort and flexibility). The proposed PFAToolkit developed
consists of tools that would address these needs and ensure consistency in data, analysis, and
results. A review of existing tools commonly used in benchmarking product families is discussed
below.
Data collection is an integral part of any benchmarking process. In manufacturing industries, one
of the basic tools to capture components’ details in a standard format is Bill of Materials (BOM)
[17]. The first vital step when a product family is analyzed is to prepare a BOM for each
product, dissecting the products to the component level and labeling [18], and listing it in the
BOM [19,20]. Typical details included in the BOM are component’s material, mass, quantity,
classification, and function [19]. As the complexity and volume of products increases, managing
this data becomes increasingly cumbersome. Based on the size of the company, data
requirements and capital available to invest in the infrastructure, companies manage data
5
collection and manipulation through various Enterprise Resource Planning (ERP) software or
Product Data Management (PDM) software. Korpela and Tuominen [21] review these external
design repository schemas, PDM systems, other commercially available software and provide
insights including discussions on data consistency analysis and representation conventions.
These front-end and back-end databases not only provide a standardized way of information
representation but also provide design details that support designer activities. Although these
databases manage much more than simple data collection, the use and design of these databases
are still in the development phase. If we limit the scope of data requirement just to product
dissection and benchmarking product families, a simple Microsoft Excel spreadsheet data
collection with checks for data consistency and redundancy would provide a reasonable
foundation for benchmarking product families.
Qualitatively assessing modularity is one aspect of product family analysis that helps analyzing
the complexity of the product design. A modular product or sub-assembly has minimum number
of interactions between physical components (or modules) and further each physical component
performs exactly one function [20]. A Design Structure Matrix (DSM) provides a simple,
compact, and visual representation of a complex system that helps assess modularity and
supports innovative solutions to decomposition and integration problems [22]. DSMs are
excellent tools for products with many components for systematic representation. DSMs can
provide a higher level of abstraction of complex products in an easily interpretable visual format.
These DSMs can be clustered to identify modules and interfaces. The basic principle is to cluster
the DSM to minimize intra-modular connections and maximize inter-modular connections.
Pimmler and Eppinger [23] use a heuristic specialized macro based swapping algorithm. The
6
objective of their clustering algorithm is to minimize the distance of each interaction from the
diagonal. Huang and Kusiak [24] use decomposition approach and triangulation algorithm to
identify and analyze different modules. Kusiak et al. [25] and David et al. [26] identify different
types of modules based on a genetic algorithm and minimum data description. A tool specific for
clustering DSMs using MatLab macros has been developed at MIT [27]. Partitioning of DSMs
is another way of identifying modules where elimination of feedback from a DSM is the primary
objective. In other words, the DSM is reordered to obtain a lower triangulation matrix. Steven
Eppinger and his collaborators1 have developed Excel macros for partitioning and simulation of
DSMs. Each of these clustering algorithms discussed above have their own pros and cons but the
primary limitation in selecting an algorithm is capabilities of hardware and software to manage
the size of calculations along with being publicly available. Considering these limitations, the
genetic algorithm developed by Rogers et al. [28] performs satisfactorily in clustering a DSM in
Microsoft Excel.
Another aspect of assessing product families is the amount of sharing between the products
included. Numerous indices are available to measure the degree of commonality in a product
family which are useful surrogate measures to help reduce costs [29]. Commonality indices
measure the degree of sharing in a product family with respect to number of components,
manufacturing processes, assembly process, costs of components and other areas. As many as
six commonality indices have been compared and analyzed in [29]. All commonality indices
focus on providing a measure for the trade-off between product commonality and distinctiveness.
Contrary to the indices that only measure the percentage of components that are common across
7
1 http://dsmweb.org/
a product family, the Product Line Commonality Index (PCI) measures and penalizes the non-
unique components in the family given the product mix [30]. It helps capture the fine balance
between design, manufacturing, and marketing at the product family level.
To assess how well product families evolve over a period of time as customer requirements
change, the Generational Variety Index (GVI) provides an excellent indicator of the amount of
redesign required for a component to meet future market requirements [31]. GVI uses tools from
Quality Function Deployment (QFD), which is best summarized as “A set of planning and
communication routines, quality function deployment focuses and coordinates skills within an
organization, first to design, then to manufacture and market goods that customers want to
purchase and will continue to purchase. The foundation of the house of quality is the belief that
products should be designed to reflect customer’s desires and tastes - so marketing people,
design engineers, and manufacturing staff must work closely together from the time a product is
first conceived” [32]. GVI uses properties intrinsic to QFD and leverages them to predict
product evolution based on expected future customer needs.
Each of these tools addresses specific aspects of product family analysis and benchmarking;
however, none of them provide a comprehensive assessment of product families and explicitly
address product family benchmarking. Additionally, software to facilitate time efficient and
consistent benchmarking has also eluded the community thus far. Commercial tools like
ACLARO DFSS, ADePT Design, Complex Problem Solver, Lattix, and Loomeo2 are software
packages focusing on project management and product development related activities in
8
2 http://dsmweb.org/
conjunction with DSMs; however none of them addresses product family benchmarking, and
they are limited to one or two specific tools. Additionally, each of these software packages is an
individual package, and they do not utilize existing interfaces like Microsoft Excel.
Microsoft Excel gives its users enhanced flexibility in terms of data storage, data manipulation,
and data reporting in a familiar interface. This makes Microsoft Excel a Rapid Application
Development (RAD) tool. In addition, the built-in programming environment requires no
additional cost, where as using individual platforms like Visual Studio would. Appreciating the
broad range of technical backgrounds and software resources of prospective practitioners of
these analyses, a common software platform is desirable to make product family benchmarking
process cost- and time-effective with fewer errors. The PFAToolkit has the capability to
accomplish all these needs by offering a broad and integrated toolset for product family
benchmarking as discussed in the next chapter.
9
CHAPTER 3
PRODUCT FAMILY ANALYSIS TOOLKIT: SYSTEM ARCHITECTURE
The PFAToolkit is a comprehensive software package that offers multiple tools for product
family benchmarking using Microsoft Excel. The advantages of Microsoft Excel include the
capability of customization through macros, and compatibility with other data manipulation
software, extensive resources for help available online through forums and help libraries,
compatibility with other Microsoft Excel users. Its users also seldom need to worry about
converting a spreadsheet to a different format due to the wide-spread use of Microsoft Excel. It
can be easily upgraded to high-end databases like Access, SQL Server, Oracle, or MySQL if the
size of records grows too large. Microsoft SQL server, Oracle, Microsoft Access, or IBM DB2
databases are used as back-ends for their applications; Microsoft Excel is able to perform many
of the services these back-end applications provide.
This chapter reviews the benchmarking method and the tools integrated in the PFAToolkit
software. This method is adopted from the research work of Jain [14]. Figure 1 shows the
architecture of the PFAToolkit depicting the benchmarking tools integrated into the software.
The PFAToolkit includes: BOM, DSM, a clustering algorithm, PCI and GVI. First, users input
the necessary data into the BOM and then that information is extracted and applied to the other
tools with some additional inputs wherever necessary as shown in Figure 1. Integration of these
product family benchmarking tools into PFAToolkit is described in detail in the remainder of this
section.
10
Figure 1. PFAToolkit Architecture
3.1 Bill of Materials
A comprehensive list of information is recorded through the bill of materials (BOM), which
includes part name, material, manufacturing process, assembly/fastening scheme, quantity, cost,
weight, picture, manufacturing part number, vendor, and other parameters (like length for a
screw). The proposed software provides a standard user input form that prompts users to enter
all the necessary details of one component at a time to ensure that no information is left out.
Some of this information is used later to calculate PCI of the product family and hence
correctness of this information is necessary to maintain consistency and repeatability of results.
11
3.2 Design Structure Matrices
The component-based DSM [33] is a prominently used tool for analyzing product architecture
and modeling four different types of connections between components, namely spatial/physical,
energy, information, and material/mass [23]. Physical connections include the connections
between two components by means of fasteners, welded joints, bolted joints, etc. Energy flow
connections include the connections that relate to electrical energy transfer like electric cables,
wirings, etc. Information flow connections include the connections that indicate any information
exchange, for instance, sensors, display, etc. which get input from one component and produce
output accordingly conveying some kind of information. Mass flow connections include the
connections that relate to mass flow like flow of air, water, lubricant, etc.
A component-based DSM is always a square matrix. It is symmetric for physical connections, as
all physical connections are bi-directional. Practically, mass flow, energy flow and information
flow can be either uni-directional or bi-directional but from a structural standpoint they are
assumed to be bi-directional. The software is built with an input connection form that allows
users to mark all four different types of connections between components. Once all the
connections for all the components are entered, a DSM is generated. The DSM tool in the
software has been programmed to identify the symmetry of connections between the
components, and hence users only need to enter half the connections as compared to completing
the entire DSM manually.
12
3.3 Clustering
The process of clustering a DSM involves the reordering of rows and columns to group closely
related elements into modules. While it is allowed to have overlapping clusters, “the foremost
objective is to maximize interactions between elements within clusters while minimizing
interactions between clusters” [33]. One of the most widely employed clustering techniques
involves the use of genetic algorithms (GA) [34]. An Excel-based GA macro already exists for
clustering component-based DSMs [28], but its scope is limited to clustering only DSMs with
just one type of connection. This macro is used as a starting point for the development of a more
advanced macro in the software that incorporates physical, electrical, mass-flow, and
information-flow connections in the DSM. This tool is semi-automated since exact definition of
the cluster boundaries remains a matter of user judgment.
From a benchmarking standpoint, common modules within an existing product family can be
readily identified and module interface improvements can be planned based on module structure
boundaries. The proposed software provides users with an input form for GA parameters. For
users’ convenience, default values are displayed in the form, but users can change the maximum
number of generations and population size as desired (e.g., to save time based on available
computational resources).
13
3.4 Product Line Commonality Index
PCI, as given by Eq. (1), is programmed into the software as it penalizes only variants and
provides a relative measure of commonality for comparing different product families.
PCI = 100 ×
P
i i
P
i
P
i iii
nNP
nfffni
12
123121
1
1
1 (1)
where:
P = total number of non-differentiating components that can potentially be standardized
across models;
N = number of products in the product family;
ni = number of products in the product family that have component i;
f1i = Size and shape factor for component i, indicates the ratio of the greatest number of
models that share component i with identical size and shape to the greatest possible
number of models that could have shared component i with identical size and shape (ni);
f2i = Materials and manufacturing processes factor for component i, indicates the ratio of
the greatest number of models that share component i with identical materials and
manufacturing processes to the greatest possible number of models that could have
shared component i with identical materials and manufacturing processes (ni); and
f3i = Assembly and fastening schemes factor for component i, indicates the ratio of the
greatest number of models that share component i with identical assembly and fastening
schemes to the greatest possible number of models that could have shared component i
with identical assembly and fastening schemes (ni).
14
PCI varies continuously between 0 and 100. When PCI = 0, either none of the components are
shared across models, or if they are shared, then their size/shapes, materials/manufacturing
processes, and assembly schemes are all different. When PCI = 100, it indicates that all of the
components are shared across models and that they are of identical size and shape, made using
the same material and manufacturing process, and assembled in the same manner. PCI focuses
on commonality that should exist between products that share common or variant components
rather than on the unique components. It provides a single measure for the entire product family,
but it does not offer insight into the commonality of the individual products within the family.
PCI requires subjective information that leads to different results as a result of analysis by
different users. Various measures are incorporated in the proposed software to reduce variations
in PCI computation. For instance, material, manufacturing process, and assembly classification
schemes are used to design the drop-down menus in the BOM input form to provide the user
with ease to use standard options [35]. Thevenot and Simpson [36] provide a guideline to
understand the subjectivity that users might introduce into PCI calculations. As users are queried
to classify each component in previously prepared BOM as differentiating or not, there is a very
low probability that a desired component would be missing in the analysis. Also, users have the
flexibility to edit the values of f1, f2,, and f3 at any given time, which adds convenience in case of
any corrections.
15
3.5 Generational Variety Index
GVI is calculated by a seven-step process as defined in [37]. Once the market and desired life of
a product platform is defined, a customer survey is done in order to anticipate their needs and
wants. After capturing all the customers’ requirements, they are prioritized and compiled in a
QFD matrix to map them to the engineering metrics. Engineering metrics contain the parameters
that need to be changed to achieve customer’s requirements. Once this matrix is ready, then the
expected change in the customer requirements over the platform life is analyzed and categorized
as low, medium, and high. This decision depends on the existing state of the requirement. For
instance, if customers want low cost but the product is actually offered at a low price as
compared to its competitors, then it would be either given a preference of low or medium but not
high. Prioritizing customer requirements helps to define the area of focus for improvement
and/or redesign. The next step is to prepare a second QFD matrix where the engineering metrics
are mapped to the related components so as to identify which components would need to be
redesigned. For example, if a customer requirement is a quiet power tool when operating, then it
would be mapped to noise level (in decibels) in the engineering metrics, which is the first QFD
matrix, and the noise level is then mapped to the components motor, casing, etc., which is the
part of the second QFD matrix. Once the second QFD matrix is complete, GVI ratings are
assigned to each component for corresponding connections with the engineering metrics. The
ratings are based on a scale of 0-9 as described in Table 1.
16
Table 1. GVI Rating System [37]
Rating Value Description 9 Requires major redesign of the component (>50% of initial redesign costs)6 Requires partial redesign of the component (<50%) 3 Requires numerous simple changes (<30%) 1 Requires few minor changes (<15%) 0 No changes required
After assigning GVI ratings, the summation of GVI values is done for every column that
corresponds to a component. Components with high GVI values imply relatively more redesign
whereas components with low GVI values indicate very low redesign, and hence there is an
opportunity for standardizing these components for future requirements. The GVI tool is
programmed in the software in a way that it guides users through all the seven steps thereby
streamlining the process. Implementation of GVI and other tools is described in the next chapter.
17
CHAPTER 4
PRODUCT FAMILY ANALYSIS TOOLKIT
To demonstrate integration of the proposed tools, we implement the Product Family Analysis
Toolkit (PFAToolkit) as shown in Figure 2. The figure shows the menu bar of the PFAToolkit,
which appears once the Excel PFAToolkit Add-In is installed (see Appendix A). PFAToolkit is
a Microsoft Excel 2007 Add-In like other commercially available Add-Ins, e.g., SOLVER,
DATA ANALYSIS.
Figure 2. Illustration of PFAToolkit Menu
Microsoft Excel is prominently used in industry as well as universities, and therefore large
numbers of users are familiar and comfortable with using it. All tools are programmed as
Microsoft Excel macros using Microsoft Visual Basic. Each of the four features is discussed
in detail in the following sections.
18
4.1 Bill of Materials
In the PFAToolkit, component details for a BOM are recorded through a user form as shown in
Figure 3. This form ensures consistency of recorded data by minimizing errors that occur
through repetitive work. To reduce efforts of the user, pre-populated drop-down menus are
provided for each of the attributes that are being recorded through the user form. Each of the
details that are recorded has their own significance and is used in later tools for analysis.
Figure 3. Illustration of the Form to Record Details of Parts for Bill of Materials
19
The different attributes recorded in the form are explained as follows:
Part Number: This records the part number starting at 1 and increasing sequentially to help
keep track of number of parts. An error check is provided to ensure only integer numbers are
sequentially entered.
Part Name: This records the part name either by entering it manually or selecting from a
pre-populated drop-down menu of standard part names. This reduces effort and improves
naming consistency across the product family. An error check exists for repeated part names.
Upload Picture: This is used to upload a picture of the part. This helps to aid in quick
visualization of the part. An error check exists to ensure that a part number is entered before
uploading the picture.
Auto Suggest: This allows utilization of existing nomenclatures used for part names,
material, manufacturing processes, and assembly schemes. The list of names that will be
used are stored in notepad files and then extracted into the drop-down menu for future use.
This feature helps maintain consistency in nomenclature used across the product family and
customized drop-down menus according to individual user needs. Figure 4 shows a snapshot
of the Auto Suggest feature.
20
Figure 4. Illustration of Use of Auto Suggest Feature
Primary Function: This field records the primary function that a part performs. For
example, “power source” would be the primary function for a “battery”. This is very
specific, and hence each user can define customized convention to follow.
Material: This records the material type of the part. This may be selected from a pre-
populated comprehensive list [30] or entered manually.
Manufacturing Process: This records the manufacturing process used to fabricate the part.
This may be selected from a pre-populated comprehensive list [30] or entered manually.
Assembly: This records the fastening scheme used to assemble the part. This may be
selected from a pre-populated comprehensive list [30] or entered manually.
21
Other attributes can be recorded such as subassembly, quantity, weight, cost, and vendor/supplier
to provide additional details for complete information of the product; however, they are not
mandatory to be entered. They are not used for analysis in later tools. Sample BOM is shown in
Figure 5 after data for each part is entered.
The Excel worksheets are protected, which means that they cannot be edited without using the
user form used for recording part details for BOM shown in Figure 3. Here, once the item
number of the part to be edited is entered, the form automatically gets pre-populated with the
existing part information which the user can modify accordingly.
Figure 5. Illustration of BOM Output in Spreadsheet
22
4.2 Design Structure Matrix
The Design Structure Matrix (DSM) provides a matrix-based representation of the connections
within a product. This visualization of the assembly and hierarchy of a product is an important
tool and visual aid used in benchmarking.
4.2.1 Part Connections
To create a DSM based on the BOM, the connections between parts are required. This process is
performed by using “Enter Part Connections” tab under “Part Connections”, which opens the
“List of Connections Form” as shown in Figure 6. “List of Connections Form” allows the user to
enter the different connections corresponding to each part. The manner in which one part is
connected to another part is classified in four different flows, namely, physical flow, mass flow,
energy flow, and information flow as discussed in Section 3.2.
23
Figure 6. Illustration of List of Part Connections Form
The DSM for any product is symmetric, e.g., if a battery is connected to the battery charger, then
it is implied that the battery charger is also connected to the battery. Thus, a DSM for a product
is a mirror image about the diagonal. The software ensures that the DSM is symmetric and
thereby reduces the effort of the user, e.g., if the user specifies that a physical connection exists
between the battery and the battery charger, then the software replicates the inverse connection
by connecting the battery charger to the battery. Thus, repetitive work is reduced and
correctness of the information is maintained.
24
4.2.2 Generate Complete Design Structure Matrix
Once all of the connections have been defined, then the next step is to generate the DSM using
the ‘Generate Complete Design Structure Matrix’ tab which generates an unclustered DSM as
shown in Figure 7. This includes all four types of connections: physical (black filled cells), mass
(red filled cells), energy (green filled cells) and information (blue filled cells). The color-coding
helps users visualize different connections easily. Other options allow users to view the DSM by
isolating each connection by selecting the desired connection under the DSM tab.
Figure 7. Illustration of DSM Output
25
4.2.3 Cluster Design Structure Matrix
In the PFAToolkit, the Genetic Algorithm (GA) proposed in [28] is applied to cluster the DSM,
which groups components into modules based on connections, i.e., maximize connections within
a module and minimize connections between modules. The “Cluster DSM” tab accesses the
“DSM Clustering Input Parameter Form” as shown in Figure 8. Two of the six parameters
governing the clustering algorithm displayed in the form can be changed by the user. The
default values displayed are recommended for time-efficient results of clustering the DSM. For
maximum processes [28], the number of parts information is extracted from BOM. The
algorithm considers only physical connections as a focus because they mainly govern the
packaging and placement of parts in an assembly of a physical system. Yu et al. [38] have
introduced a clustering algorithm based on the Minimum Length Description (MDL) principle
[39] and a simple GA to visualize product architecture; it recognizes bus modules, allows tuning
of GA parameters depending on the type of products, and is capable of producing results that
match human experts’ clustering. GA used in the PFAToolkit can be enhanced for future
versions to incorporate these features.
26
Figure 8. Illustration of DSM Clustering Input Parameters Form
Once initiated, the clustering algorithm runs for a time that is proportional to the total number of
parts and generates a clustered DSM as shown in Figure 9. Defining modules and their
boundaries is made easier once the DSM is clustered. The exact definition of each module and
cluster boundaries is left for users to determine since their definition is specific and subjective in
nature.
27
Figure 9. Illustration of a Sample Clustered DSM Output
4.3 Product Line Commonality Index
The next tool enables commonality analysis of the product family. Currently, the software uses
the Product Line Commonality Index (PCI) [30] given its flexibility and focus on non-
differentiating components; however, other commonality metrics such as Percent Commonality
Index (%C), Degree of Commonality Index (DCI), Commonality Index (CI), and so on [29]
could easily be integrated into the software if desired.
Within PCI, commonality is divided into three factors: (1) size and shape (f1), (2) materials and
manufacturing (f2), and (3) assembly/fastening scheme (f3). The values for f1, f2, and f3 are
28
calculated using the details of each part recorded in the BOM. The user has an option to make
changes manually. The fi values vary between [1/n, 1] where n is the number of products that
have the component that is being analyzed. Once this information is entered for every part, then
PCI is computed, and an output summary is generated. The PFAToolkit guides the user through
each step for calculating PCI in an accurate and systematic manner as follows.
4.3.1 Select Product Files
The first step for calculating PCI is to select the data files that correspond to each product of the
family to be analyzed. This is done by selecting “Select Product files and start NEW analysis”
under “Product Line Commonality Analysis” tab. This opens the Windows explorer window as
shown in Figure 10.
29
Figure 10. Illustration of Selecting Product Files for PCI Analysis
The product files should contain a complete ‘Bill of Materials’ worksheet in a standard format as
generated by the PFAToolkit. Once all the product files are selected, then the next form opens as
shown in Figure 11. This allows users to separate parts that are unique (perform unique
functions) from those parts that are shared within the product family. This is repeated for all of
the products that are selected for analysis. This step is not automated since definition of unique
parts is user-dependent. Parts can be unique to provide different degrees of functionality, to
provide brand differentiation, accommodate supply chain limitations, and so on. Thus, the
definition of unique parts is left up to the users’ interpretation and judgment.
30
Figure 11. Illustration of Form to Separate Unique and Shared Parts
4.3.2 PCI Analysis
The next step is to calculate PCI for the product family by selecting “PCI Analysis” tab under the
“Product Line Commonality Index” tab, which opens the “PCI Factors form” as shown in Figure
12. The form displays the values for f1, f2, and f3 calculated based on the information recorded in
BOM. The user has an option to edit values of any of the factors for each part. The values for
the f1, f2, and f3 factors must be between 0 and 1. A check for that exists in the software. The
user goes through each part to complete the PCI analysis. Calculation of commonality using PCI
includes only those parts that are shared between products. If the user has selected parts that
have a unique occurrence in the calculation, then the user is prompted for all such parts. The
choice to include such parts is left to the user since a unique occurrence does not necessarily
imply that it is performing a unique function.
31
Figure 12. Illustration of Form for Entering PCI Factors
Once the information is entered, then the software calculates the PCI value and generates the
output shown in Figure 13. This spreadsheet summarizes the data that has been entered as well
as the values that are used to compute PCI.
32
Figure 13. Illustration of Sample PCI Output Spreadsheet
4.4 Generational Variety Index
The last step of product family analysis includes using Generational Variety Index (GVI) to
measure the extent to which a component (or module) may be redesigned to meet the
requirements for different market segments. The calculation of GVI is divided into two phases.
The first phase is initiated by using the “GVI Analysis” tab under “Generational Variety Index”
tab, which opens the “GVI Phase I” form as shown in Figure 14.
.
Figure 14. Illustration of GVI Phase I Form for Customer Requirements
33
This form allows users to enter all the customer requirements one at a time. Once all the
customer requirements are entered, then users are prompted to divide the customer requirements
into low, medium, and high priority. The form that lets users assign priorities through the second
“GVI Phase I” form as shown in Figure 15.
Figure 15. Illustration of GVI Phase I Form for Assigning Priorities to Customer Requirements
Next users are prompted to enter the engineering metric(s) that impact each of the customer
requirements. This is done by using the third “GVI Phase I” form as shown in Figure 16.
34
Figure 16. Illustration of GVI Phase I Form for Engineering Metrics
Next users are prompted to enter target values for each of the customer requirements. The values
for current and future markets are also required to be entered to complete Phase I of GVI. The
form to enter the target values is shown in Figure 17.
Figure 17. Illustration of GVI Phase I Form for Target Values
35
Phase II is initiated once the last step of entering target values in Phase I is completed. Users are
first prompted to select all of the parts that are affected by changes in the listed customer
requirements. This is done using the form shown in Figure 18.
Figure 18. Illustration of GVI Phase II Form for Selecting Parts
Next users are prompted to enter their redesign effort ratings for each part for the corresponding
future change in engineering metric (see Figure 19). Users can choose to perform this task by
entering desired values in the “GVI Phase II” worksheet.
Figure 19. Illustration of GVI Phase II Form for Rating Parts for Redesign Effort
36
Lastly, GVI is computed by summing the user-specified weights in each component column.
Note that GVI can be calculated for each individual part in the product family once all of the
parts are placed in the BOM. GVI Phase I and Phase II spreadsheet outputs are shown in Figure
20 and Figure 21, respectively. In GVI Phase II, a pie-chart displays the relative distribution of
GVI values of the components to be redesigned where higher the value of GVI greater is the area
under the pie chart.
Figure 20. Illustration of GVI Phase I Output Sheet
37
Figure 21. Illustration of GVI Phase II Output
38
CHAPTER 5
RESEARCH STUDY RESULTS AND OBSERVATIONS
To evaluate the benefits of the software implementation, a product family dissection and
benchmarking study was conducted. The study involved eight participants benchmarking a
product family of three power tools including a drill, a jigsaw, and a sander. The participant
population consisted of eight graduate and undergraduate engineering students enrolled at Penn
State University. Four volunteers were asked to perform the benchmarking of the product family
using the software while the other four did manual benchmarking.
The objectives of the case study are to assess the impact of the software on:
1. Efficiency – measured in terms of the time to complete the three tasks:
a. Recording BOM
b. Developing the DSM
c. Calculating the PCI
2. Effectiveness – measured in terms of accuracy in the three tasks:
a. BOM – number of parts catalogued and labeled correctly (manufacturing process,
materials, assembly)
b. DSM – number of correct connections noted
c. PCI – difference of calculated PCI from the “expert” value
3. Satisfaction – measured by analyzing survey responses of participants who performed the
benchmarking analysis using the software
39
Other features of the software like clustering the DSM and GVI were not tested in the study.
Clustering the DSMs manually is not only a tedious task requiring long periods of time but also
difficult to compare the two DSMs quantitatively. Additionally, the techniques to cluster DSMs
require deeper understanding of the mechanical design of products, and the results obtained
would have been biased towards the skill level and expertise of the participants. Similarly for
GVI, the understanding required is not only limited to the products but also the market
requirements and product evolution. Therefore, these tools were not included in the experimental
study.
5.1 Summary of Experiment Protocol
As stated earlier the study involved eight participants benchmarking a family of three power
tools: a drill, a jigsaw, and a sander. The participant population comprised of seven graduate
industrial engineering students and one undergraduate engineering design student enrolled at
Penn State University. Each of them had a basic understanding of manufacturing processes,
materials, and principles of mechanical devices.
All participants were first introduced to the concepts of product families, benchmarking analysis,
and the benefits of commonality while participants using the software were also given a demo of
the software including its features and capabilities. Four participants performed the
benchmarking analysis manually using either pencil/pen and paper or a simple word/spreadsheet
to do the calculations and record data. The remaining four participants performed the same
benchmarking analysis using the software. The three products, namely, drill, jigsaw, and sander,
were disassembled prior to the commencement of the activity. The parts were labeled and laid
40
out in an organized manner for the participants. Any questions or concerns of the participants
were addressed after which the participants were asked to sign the informed consent form to
enable use of their results in this study.
The steps performed by each participant (irrespective of the category assigned) individually are:
Record the BOM including part details such as part name, manufacturing process, materials,
and assembly.
Develop the DSM based on connections between parts in their list.
Calculate PCI for the product family.
Record the time taken for each step in their benchmarking process
Participants who performed the study using the software were asked to fill out a survey that
evaluated the satisfaction levels for the software used after the conclusion of benchmarking
analysis. Instruction sheets including the list of part names, list of requirements for BOM, and
formula to calculate PCI (see Appendix B for sample instructions) for each product with
individual part images for ease of identification were provided. The results of the activity and the
survey are discussed in the following sections.
5.2 Impact of Software on Benchmarking Efficiency
This section assesses the time required for manual benchmarking and benchmarking with the
software. Details of time taken by individual participants performing benchmarking analysis
manually and using the software for recording data for BOM and PCI calculation are listed in
Table 2.
41
Table 2. Details of Time Taken By Participants to Perform Benchmarking
BOM DSM PCI Participant
No. Manual (min)
Software (min)
Manual (min)
Software (min)
Manual (min)
Software (min)
1 65 65 22 7 31 4
2 80 58 28 7 22 4
3 70 60 30 4 25 5
4 65 57 40 5 25 8
It can be seen from the table that times the taken using the software for benchmarking are
consistently lower than manual benchmarking. This might be because participants using the
software used the “Save As” feature of Microsoft Excel, which allowed them to save on time for
parts that were common between the products. To give a clear comparison between the two
modes of analysis, average times taken are compared as shown in Figure 22. It can be seen that
average times to record DSM and PCI calculations using the software are five times less than
those of manual benchmarking. However, the time taken to record BOMs do not differ as
dramatically for the two modes of analysis, but the data is captured in a more systematic and
consistent format as discussed in Section 5.3. This allows users to analyze the data in multiple
ways using its other analytical capabilities.
42
30
60
6 5
26
72
0
10
20
30
40
50
60
70
80
BOM DSM PCI
Average Tim
e (m
in)
Manul Software
Figure 22. Comparison Between Manual and Software Benchmarking of Average Times
Taken For Benchmarking Analysis
43
The numbers of observations for each mode on analysis are four. For a 95% confidence level, the
sample size required for t-test is only two [40] while for Mann-Whitney U test is four, but there
are subtle issues on using t-tests for such small sample sizes. For example, a t-test with only
three patients per group (A and B) could be highly statistically significant, but the three patients
in group A might have been male and the three patients in group B female so that gender may
have explained the observed difference. The levels of understanding or knowledge of the
participants for manufacturing processes, materials, and principles of mechanical devices can be
assumed to be similar, since as discussed earlier, participants were students from mechanical or
industrial engineering; however, their knowledge levels were not measured. So, either of the tests
for central tendencies can be considered for statistical conclusions. Generally, in cases where
sample size is less than 18, Mann-Whitney U test is preferred [40]. Hence, considering all the
limitations mentioned above, Mann-Whitney U test is considered for making statistical
conclusions.
A Mann-Whitney U test at 95% confidence level was performed to compare median times taken
for BOM, DSM, and PCI between the two modes of analysis. The results of the tests are
summarized in Table 3
Table 3. Results of Mann-Whitney U Test at 95% Confidence Level for Median Times
Taken for BOM, DSMs and PCI
Parameter BOM DSM PCI
Ho Timemanual = Timesoftware
Timemanual = Timesoftware
Timemanual = Timesoftware
Ha Timemanual ≠ Timesoftware
Timemanual ≠ Timesoftware
Timemanual ≠ Timesoftware
P-value (adjusted for ties)
0.055 0.029 0.028
Remarks Statistically Not
Significant at 95% Confidence Level
Statistically Significant at 95% Confidence Level
Statistically Significant at 95% Confidence Level
Conclusion Accept Ho Accept Ha Accept Ha
It can be concluded from Table 3 that the difference between the population median times taken
for software and manual benchmarking of DSM and PCI at 95% confidence level is statistically
significant. Hence, there is sufficient evidence to conclude that the DSM and PCI tools
substantially improve the efficiency of product family benchmarking analysis. While for BOM,
the difference between the population median times taken for software and manual
44
benchmarking of DSM and PCI at 95% confidence level is statistically not significant. Thus,
there is insufficient evidence to conclude that the BOM tool significantly improves efficiency of
product family benchmarking analysis.
5.3 Impact of Software Effectiveness on Benchmarking
5.3.1 Impact of BOM Tool on Effectiveness on Benchmarking
Out of the four participants performing the benchmarking analysis manually, two of them opted
to record the BOM using pencil and paper while the remaining participants used their own
Microsoft Excel spreadsheets. A comparison study (see Appendix C for sample calculations) was
completed to evaluate consistency of part details recorded including manufacturing process,
materials, and assembly of each product across all participants. This comparison provides an
assessment of the software’s effectiveness in terms of improving data accuracy and consistency
across users. The four BOM spreadsheets of participants performing benchmarking using the
software were compared for materials, manufacturing process, and assembly. Similar assessment
was completed for the four BOMs from manual benchmarking. The percentage of correct
materials, manufacturing process, and assembly information across users are shown in Table 4.
To give a clear comparison between the two modes of analysis, the results are shown in Figure
23, Figure 24, and Figure 25, respectively.
45
Table 4. Details of Percentage of Correct Material, Manufacturing, and Assembly Details
for Manual and Software Benchmarking
Manual Software Particpant Number
% of Correct Materials
% of Correct Manufacturing Process
% of Correct Assembly
% of Correct Materials
% of Correct Manufacturing Process
% of Correct Assembly
1 75% 42% 44% 83% 55% 92%
2 62% 14% 11% 73% 38% 80%
3 75% 26% 36% 88% 43% 79%
4 89% 36% 30% 65% 31% 88%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 2 3 4
Particpant Number
Per
cen
tag
e o
f C
orr
ect
Mat
eria
ls
Manual Software
Figure 23. Percentage Comparison of Identical Details for Materials between Manual and
Software Benchmarking
46
0%
10%
20%
30%
40%
50%
60%
1 2 3 4Particpant Number
Per
cen
tag
e o
f C
orr
ect
Man
ufa
ctu
rin
g
Pro
cess
es
Manual Software
Figure 24. Percentage Comparison of Identical Details for Manufacturing Processes
Between Manual and Software Benchmarking
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 2 3 4Participant Number
Per
cen
tag
e o
f C
orr
ect
Ass
emb
ly
Manual Software
Figure 25. Percentage Comparison of Identical Details for Assembly between Manual and
Software Benchmarking
47
It can be seen from Figure 23, Figure 24, and Figure 25 that the consistency achieved for
recording data using the software is slightly higher overall than those of manual benchmarking.
As the users performing benchmarking using the software had the option of choosing materials
and manufacturing processes from a list, there was significantly higher consistency across them.
This might seem an obvious conclusion since the manual users did not have the benefit of
choosing from a list of options. However, it was observed in class activities that students are
rarely provided with a list of options from which to choose from. Thus, in case of manual
benchmarking, the type of materials or manufacturing processes considered are restricted to the
users’ knowledge and expertise. The software curbs this disparity between users’ knowledge
levels and minimizes the discrepancies of data recorded across them.
A Mann-Whitney U test at 95% confidence level was performed to compare percentage of
correct details recorded for materials, manufacturing process, and assembly between the modes
of analysis. Results of the Mann-Whitney U tests are summarized in Table 5.
48
Table 5. Results of Mann-Whitney U tests at 95% Confidence Level for Median Time
Taken for Materials, Manufacturing Process, Assembly
Parameter Material Manufacturing Process Assembly
Ho Correctmanual = Correctsoftware
Correctmanual = Correctsoftware
Correctmanual = Correctsoftware
Ha Correctmanual ≠ Correctsoftware
Correctmanual ≠ Correctsoftware
Correctmanual ≠ Correctsoftware
P-value 0.66 0.19 0.03
Remarks Statistically Not
Significant at 95% Confidence Level
Statistically Not Significant at 95% Confidence Level
Statistically Significant at 95% Confidence Level
Conclusion Accept Ha Accept Ha Accept Ha
It can be seen from Table 5 that the difference between the population median percentage of
correct assembly details for software and manual benchmarking at 95% confidence level is
statistically significant. While for correct manufacturing process and material details, the
difference between software and manual benchmarking at 95% confidence level is not
statistically significant. Thus, it can be seen that the BOM tool overall does not dramatically
impact the effectiveness of the software on product family benchmarking analysis in this study.
The number of parts added and deleted for the two modes of analysis is shown in Table 6. It can
be seen that the total number of parts added or deleted for software benchmarking were less than
half of those for manual benchmarking. Parts were added if the participant did not list it in the
BOM or deleted if they were incorrectly listed in the BOM.
49
Table 6. Comparison of Parts Added or Deleted for Manual and Software Benchmarking
Participant No.
Manual Benchmarking Software
Benchmarking
1 1 1
2 2 0
3 2 0
4 4 2
A Mann-Whitney U test was performed to evaluate statistically the difference of population
median number of parts added or deleted for the two modes of analysis. The results are show in
Table 7.
Table 7. Results of Mann-Whitney U Test for Median Number of Parts Added or Deleted
for Manual and Software Benchmarking
Parameter Description
Ho Parts added/deletedmanual = Parts added/deletedsoftware
Ha Parts added/deletedmanual ≠ Parts added/deletedsoftware
(adjusted for ties)
0.13
Remarks Statistically Not Significant at 95% Confidence Level
Conclusion Accept Ho
It can be concluded from the table that that the difference between the population median
numbers of parts added or deleted for software and manual benchmarking at 95% confidence
level there is not statistically significant. Thus, there is insufficient evidence to conclude that the
BOM tool dramatically impacts the effectiveness of the software in this study.
50
5.3.2 Impact of DSM Tool on Effectiveness on Benchmarking
To evaluate the effectiveness of the DSM tool, the DSM developed by all of the participants
were assessed for the number of correct connections listed. All participants performing the
benchmarking manually opted to use their own Microsoft Excel spreadsheet while the
benchmarking software was used by the others. All of the participants recorded the part
connections correctly; thus, the DSMs were correctly developed. The only difference between
the participants who developed DSMs manually was that each of them used different methods to
represent the DSMs. One participant chose to develop two DSMs separately, one for physical
connections and the other for energy connections, while other participants used numbers (1-
physical, 2-energy) and color coding (blue-physical, green-energy). The software overcomes
these inconsistencies by creating identically formatted DSMs. Thus, the DSM tool in a way
impacts the effectiveness of the software by acquiring data in a consistent format making it
easier for comparison.
5.3.3 Impact of PCI Tool on Effectiveness on Benchmarking
To evaluate the effectiveness of the PCI calculation tool, the PCI values calculated by all of the
participants were compared to the “expert” PCI value for the family of products. All participants
performing the benchmarking manually opted to use their own Microsoft Excel spreadsheet
while the benchmarking software was used by the others. In Table 8, PCI values for all
participants performing benchmarking manually and using the software are compared against the
calculated “expert” value of 62.3% respectively.
51
Table 8. Comparison of PCI Values Calculated by All Participants
Manual Software Participant
No. PCI
value
Difference from
"expert" value
PCI value
Difference from
"expert" value
1 64.70% 2.40% 64.80% 2.50%
2 74.00% 11.70% 59.80% -2.50%
3 61.50% -0.80% 60.80% -1.50%
4 68.80% 6.50% 66.10% 3.80%
A Mann-Whitney U test at 95% confidence level was performed to compare population
difference of PCI values from the expert value between the modes of analysis. The result of the
test is summarized in Table 9.
Table 9. Results of Mann-Whitney U tests at 95% Confidence Level for Difference of PCI
value from "Expert" Value
Parameter Description
Ho PCI Difference from Expert Valuemanual = PCI Difference
from Expert Valuesoftware
Ha PCI Difference from Expert Valuemanual ≠ PCI Difference
from Expert Valuesoftware
P(T<=t) two-tail 0.22
Remarks Statistically Not Significant at 95% Confidence Level
Conclusion Accept Ho
52
It can be seen from Table 9 that the difference between the difference of PCI values from
“expert” value for software and manual benchmarking at 95% confidence level there is not
statistically significant. Thus, there is insufficient evidence to conclude that the PCI calculation
tool dramatically impacts the effectiveness of the software in this study. However, the results
might be a little misleading since the PCI values might be similar even if the values for the f
factors considered in the analysis are different. For example, correct values for f1, f2, f3 were 0.33,
1, and 0.66, respectively. Consider that one participant incorrectly enters the f values as 1, 0.66,
and 0.33. The product of the f values in both cases would be the same even though the f values
were wrongly assigned in the later case. To evaluate the PCI values in more detail, the PCI
calculation spreadsheets were assessed and compared to the expert value. The total numbers of
parts included in PCI analysis were 14. Adjustments, including number of parts considered in the
analysis and adjustments to f values, were made to the each of the PCI spreadsheets to attain
values equal to the expert value. The results are shown in Table 10. It can be observed that in
case of manual benchmarking, the numbers of adjustments are higher than those in software
benchmarking.
Table 10. Summary of Adjustments Made to PCI Tables of Each Participant
Manual Benchmarking Software Benchmarking
Participant No.
Total number of f values adjusted
Total number of parts added or
deleted from the analysis
Total number of f values adjusted
Total number of parts added or
deleted from the analysis
1 2 - 1 -
2 4 2 1 -
3 2 1 2 -
4 5 2 3 2
53
5.4 Satisfaction of the Software Based on Survey Results
A survey (see Appendix D for Survey form) was administered to assess the overall satisfaction
with the software, the level of satisfaction for individual features of the software, suggestions to
improve it, and so on. The survey was completed by participants performing the analysis using
the software only.
The survey has three sections to comprehensively evaluate each aspect of the software:
1. Evaluation of Ease of Use and Capability of the software
2. Evaluation of GUI and Usability of the software
3. Evaluation of BOM, DSM and PCI tools
Each of the three sections had approximately ten questions each. In total, each of the four
participants had to provide responses for 29 questions. The percentages of the type of responses
for each section are discussed in detail.
5.4.1 Evaluation of Ease of Use and Capability of the Software
This section of the survey assesses the ease with which the user learns to use the software and its
capabilities. Results are shown in Figure 26. It can be observed that 83% of the responses
indicated that users were satisfied with the clarity of language, ability to navigate the features,
and ability to find required features. Majority users also were satisfied with its overall
capabilities like operating speed, recording data, saving, and editing information, but 13% of the
total responses indicated that users were undecided about “ease of use and capability” of the
software. Out of these 13% undecided responses, 80% of users were not sure if external
54
applications like Media Player or Internet Explorer ran smoothly when they were using the
software. This question was included in the survey as it is common for users to have multiple
applications open while performing the analysis. So, assessing the impact of these applications
on the performance of software is critical. The primary reason for undecided responses might be
because users did not have any other applications running while they were working on the
software.
Disagree,
5%
Can't Say,
13%
Agree, 58%
Strongly
Agree, 25%
Figure 26. Overall Satisfaction Levels for "Ease of Use" of Software
5.4.2 Evaluation of Graphical User Interface and Usability of the Software
55
This section of the survey assesses the Graphical User Interface of the software and also helps to
support the hypothesis that using Microsoft Excel reduced the learning curve for the software. As
discussed in chapter 2, Microsoft Excel provides a familiar environment to users and sufficient
capabilities at no extra cost. Figure 27 reveals that 72% of the responses positively indicate that
the software has a satisfactory GUI. All of the users “Strongly Agreed” that using Microsoft
Excel made it easier for them to understand and learn the software as compared to a new
independent platform. Furthermore, all of the users indicated that the layout of the software
features were clear and intuitive allowing the users to find what they needed. The majority of the
users also indicated that the software could be easily integrated into classroom product dissection
activities; however, 75% of the users were unsure about the commercial viability of the software.
Strongly
Agree, 36%
Agree, 36%
Can't Say,
21%
Disagree,
7%
Figure 27. Overall Satisfaction Level for "Graphical User Interface" of Software
The existing version of the software has a relatively “plain and simple” design. Creative use of
fonts and colors was not the primary focus. By integrating visually appealing backgrounds and
colors along with enhanced interaction with the user (e.g., flexibility to decide font formats,
colors, and so on), the software can be made into a more attractive package. Again, adding these
features will not cost extra in terms of software resources since the platform used to develop the
software (VBA) is available at no extra cost.
56
5.4.3 Evaluation of BOM, DSM, and PCI Tools of the Software
This section of the survey assesses the BOM, DSM, and PCI features of the software. Figure 28
reveals that 75% of responses are positive indicating that the software has a satisfactory BOM
feature; however, 3 out of 4 users disagreed or were not sure that the BOM tool prevented
redundancy of work. This feature overall was rated as the weakest feature and needs to be looked
into to reduce human effort and streamline the process of recording data.
Strongly
Agree, 25%
Agree, 50%
Can't Say,
19%
Disagree,
6%
Figure 28. Overall Satisfaction Level for "BOM" Feature of Software
The DSM tool was rated one of the best features of the software along with PCI tool. Results
shown in Figure 29 reveal that 87% responded favorably to the DSM tool. While for PCI, results
shown in Figure 30 reveal that 100% of the users agreed that the tools significantly reduced time
and avoided redundancy of work.
57
Can't
Say, 13%
Agree,
56%
Strongly
Agree,
31%
Figure 29. Overall Satisfaction Level for "DSM" Feature of Software
Agree,
62%
Strongly
Agree,
38%
Figure 30. Overall Satisfaction Level for "PCI" Feature of Software
58
5.5 Summary of Overall Observations of the Software
The DSM and PCI features provided in the software provide distinct advantages over manual
benchmarking since the automation offered in the software significantly reduces human effort
and, as a result, reduces human error. These tools also provide a consistent methodology that
guides users through the various benchmarking steps. For example, consider the DSM tool. The
user is prompted to record the connections for each part. Since, a component DSM is symmetric,
the tool automates the symmetrical connections thereby not only reducing human effort but also
avoiding human errors. Further it presents the DSM in a visually stimulating way by color
coding the different connections, sizing the DSM according to the space constraints on the
spreadsheet, formatting the texts, and so on. This helps users focus their time and effort on
analyzing the DSM for product insights (which is its core purpose) rather than developing the
DSM and making it look appealing.
Meanwhile, for PCI, the tool extracts relevant data available from the BOM for calculations
(e.g., materials, manufacturing, and assembly processes to calculate f2 and f3) and prompts the
user for any inputs required to make judgment calls (e.g., f1 or size and shape factor). This not
only helps to reduce the time for PCI calculations significantly (as seen in Figure 22) but also
gives users the ability to test the product family with different combinations of products and
analyze each scenario easily. These tools aid in reducing the resources and logistics involved in
product family benchmarking by reducing time and effort for the users. As a result, it ensures
consistency of results over time.
59
In this study, the DSM was developed for a product with only 19 parts. As the number of parts
increases, the complexity increases, and the task gets repetitive thereby not only increasing the
time required but also the possibility of errors. Furthermore, the benchmarking analysis for the
product family was performed by each individual. However, in classroom activities or especially
in industries, due to the tedious nature of benchmarking analysis, it is performed in teams rather
than individuals. The software aids in overcoming the communication gap between team
members by streamlining the benchmarking analysis process (e.g., the same input form is used
by all users resulting in same level of abstraction of detail recorded and with increase in
familiarity with the software, time required reduces), standardizing the nomenclature used (e.g.,
drop-down lists give users with list of materials, part names, and so on), and guiding the users
through the process (same steps are followed each time the analysis is performed). The impact of
these tools on data collection and benchmarking analysis is discussed in the next chapter.
60
CHAPTER 6
CLOSING REMARKS AND FUTURE WORK
In this thesis, a new Microsoft Excel-based software is presented to support product family
analysis and benchmarking. The software provides an integrated package of various
benchmarking tools currently used in industry and by researchers and faculty (for educational
purposes). It can be used by companies for benchmarking their competitors’ product families
and/or for analyzing their own products. The product family benchmarking capabilities of the
software streamlines and standardizes the workflow involved in utilizing several common
product family tools. Apart from the case study discussed in the thesis, the software has been
used as part of product dissection activities in undergraduate and graduate courses as well as
extensively in the testing activities during development. Advantages of the PFAToolkit observed
during these activities are multifold: saves considerable time and reduces human effort by
automating generation of DSM, clustering of DMS, and calculations of PCI. The software
guides the user in a methodical and organized way. This not only helped achieve more accurate
and consistent results as compared to manual benchmarking analysis but also reduced variability
in recording required data. This can be due to the numerous error checks and standard list
options including part nomenclature, materials, and manufacturing processes. Users exploited the
“Save As” option of Excel to save time on recording data for parts common between products. It
was also observed that as the number of parts increased, the disparity in data consistencies,
results and time taken for analysis increased between the modes of benchmarking analysis
(manual and software).
61
Making decisions on product family designs involves assessing various trade-offs. As the
software evolves into a more comprehensive toolkit, it will empower designers to make better
decisions by providing a systematic assessment from diverse perspectives. This can be achieved
by extending the research and improving the depth of assessment in each of the toolkits features.
For example, tools like interface matrices [41] and change propagation analysis [42] can provide
more systematic assessment of modularity of the product families. The Coupling Index [31] is
another tool that can address redesign efforts of products along with GVI. Integrating other
commonality metrics such as Percent Commonality Index (%C), Degree of Commonality Index
(DCI), Commonality Index (CI), and so on [29] would provide useful means to redesign product
families using commonality indices [43]. By introducing these features in the toolkit, the user
will have various options to explore based on individual needs. Development of these
capabilities also opens new doors for future work on benchmarking tools, as well as
opportunities for leveraging shared data in other applications. A growing area of research
involves the development of design repositories that catalog component geometry, interface, and
performance characteristics. Data entry standardization is a driving requirement in these
benchmarking capabilities and leveraging the data archived in design repositories, if combined
with standardization of metrics, offers potential for increased design automation power. In
addition, the combination with Computer Aided Design (CAD) data offers a more complete
component geometric characterization as well as potential for graphical design representations.
The usability of the tool can be improved by designing more efficient solutions for extracting
data from a company’s MRP (Material Resource Planning) systems or linking Microsoft Excel
with a database to gather data necessary for BOM and PCI analysis. Another limitation is
clustering the DSMs using GAs that only take physical connections into consideration. Future
62
work would involve considering other connections in the clustering algorithm to provide more
complete solutions. Also, scaling the clustering approach to larger DSMs is important – the
current GA implementation can only handle 200 components at a time. Other future work
includes comprehensive testing and evaluation of the proposed software including GVI tool in an
industry to determine its impact on competitive commercial benchmarking.
63
Appendix A
Installation of PFAToolkit Add-in
Following are the steps to install the PFAToolkit Microsoft Excel Add-in:
Step 1: Save ‘PFAToolkitv1.0.xla’ file in a convenient location.
Figure A. PFAToolkitv1.0.xla file icon
Step 2: Open Microsoft Excel 2007 workbook. Go to ‘Office button’ (left top corner) and select
‘Excel options’
Figure B. Excel Options in Microsoft Excel 2007
64 Step 3: In Excel Options window click on ‘Add-Ins’ (left panel). At the bottom a
tab called ‘Manage’ appears, press Go.
Figure C. Excel Options window
Step 4: Browse the xla file from the location where it was saved and the software will be added
under Add-Ins tab in top main menu once OK is pressed.
65
Figure A-1. Add-In Installation
66
Appendix B
List of Part Names for Drill, Jig Saw, and Sander, List of Requirements for BOM, List of
Part Connections for DSM, and Formula to Calculate PCI
List of Part Names and Pictures for Drill
Item No. Part Name Picture
1 Battery Charger
2 Battery
3 Left Clamshell
4 Right Clamshell
67
5 Transmission Housing
6 Contacts
7 Black Wire ack Wire
8 Red Wire
9 Switch
10 LED
68
11 Switch Cover Switch Cover
12 Direction Switch
13 Motor
14 Transmission Cap
15 Thin Planet Gear
16 Ridged UFO Gear
69
17 Thin Ring Gear
18 Speed Adjustor Housing
19 Small Planet Gear
20 Smooth UFO Gear
21 Washer
22 Thick Planet Gear
70
23 Thick Ring Gear Thick Ring Gear
24 Ball Bearing
25 Chuck
26 Horizontal Balance
27 Vertical Balance
28 Speed Switch
71
29 Direction Switch Extender
30 LED Cover
31 Long Nut
32 Screw 1
33 Bolt 1
34 Screw 5
72
35 Screw 6 Screw 6
73
List of Part Names and Pictures for Jig Saw
Item No. Part Name
Picture
1 Battery Charger
2 Battery
3 Left Clamshell
4 Right
Clamshell
5 Contacts
6 Red Wire
74
7 Black Wire
8 Switch
9 PCB
10 LED
11 Motor
12 Connector
75
13 Gear
76
14 Arm
15 Bearing
16 Blade Holder
17 Positioning
Switch
18 Blade Positioner
19 Pin
20 Safety
21 Switch Cover
22 Shield
23 Sled
24 Bracket
77
25 Square Nut
78
26 Screw 3
27 Screw 4
28 Screw 5
29 Screw 1
List of Part Names and Pictures for Sander
Item No. Part Name
Picture
1 Battery Charger
2 Battery
3 Left
Clamshell
4 Right
Clamshell
5 Switch Cover
6 Gear
79
7 Circular Bearing
8 Weight
80
9 Shaft
10 Support
11 Sander
12 Motor Gear
13 Motor
14 Switch
15 Contacts
16 Red Wire
17 Black Wire
18 Screw 1
81
19 Screw 2 Screw 2
82
Part Connections for Sander
Item No.
Part Name Physical Connections Energy Connections
1 Battery Charger
Battery Battery
2 Battery
3 Left Clamshell
Right Clamshell, Switch Cover, Circular Bearing, Support, Motor, Contacts, Screw
4 Right Clamshell
Switch Cover, Circular Bearing, Support, Motor, Contacts, Screw
5 Switch Cover Switch
6 Gear Shaft, Motor Gear
7 Circular Bearing
Shaft
8 Weight Shaft
9 Shaft Support
10 Support Sander, Screw 2
11 Sander Screw 2
12 Motor Gear Motor
13 Motor Red Wire, Black Wire Red Wire, Black Wire
14 Switch Red Wire Red Wire
15 Contacts Red Wire, Black Wire
16 Red Wire
17 Black Wire
18 Screw 1
19 Screw 2
*NOTE: ALL CONNECTIONS LISTED ARE SYMMETRICAL IN NATTURE, e.g. for Part
No. 12 “Motor Gear” has “Motor” listed connection. This also implies “Motor” is connected
to “Motor Gear”.
83
List of Requirements
There are 3 requirements of the case study.
1. Prepare Bill of Material for 3 products. The products are disassembled and laid out in an
organized manner. Drill, Sander and Jigsaw are the three products from the Durabilt product
family. You will also find list of part names along with pictures listed for each of the
products. Please use the same part names. All you need to record in addition to part name is
part number, manufacturing process, materials and assembly type. Example is shown below:
Part No. Part Name Manuf. Proc Material Assembly
1 Screw Mild Steel Machining Fastening
2 Battery Various Various Snap
After you complete recording BOM for all the 3 products, record the time taken to
complete it.
2. Next would be preparing the DSM. Prepare the DSM only for the Sander. The connections
between various parts are given in a sheet. The connections include only the lower triangular
matrix. So, e.g., if battery is connected to battery contacts then this implies that battery
contacts are connected to battery. But in the connections sheet, battery will be listed as
connected to battery contact. You have to make sure it is connected the other way round too
in the DSM. Only energy and physical connections are included.
After you complete developing the DSM, record the time taken for it to complete.
84
3. Lastly, you will need to calculate PCI for the product family of the 3 products. You will find
the formula for PCI along with other documents.
Once you have completed calculating DSM, record the time for it.
The mode of recording details is up to you. So, you can use pen/pencil or you can use MS Excel
or MS Word. You can also use a combination of it!
85
PCI Formula – Instructions
PCI formula is given by Eq. (1). It provides a relative measure of commonality for comparing
different product families.
PCI = 100 ×
P
i i
P
i
P
i iii
nNP
nfffni
12
123121
1
1
1 (1)
where:
P = total number of non differentiating components that can potentially be standardized
across models;
N = number of products in the product family;
ni = number of products in the product family that have component i;
f1i = Size and shape factor for component i, indicates the ratio of the greatest number of
models that share component i with identical size and shape to the greatest possible
number of models that could have shared component i with identical size and shape (ni);
f2i = Materials and manufacturing processes factor for component i, indicates the ratio of
the greatest number of models that share component i with identical materials and
manufacturing processes to the greatest possible number of models that could have
shared component i with identical materials and manufacturing processes (ni); and
f3i = Assembly and fastening schemes factor for component i, indicates the ratio of the
greatest number of models that share component i with identical assembly
86
Appendix C
Sample Assessment Table of Correct Details Comparison for Manual Benchmarking
1 – Indicates identical data records across users
e.g., In above figure, part 1, same material was recorded by Participant 1,2, and 4. Participant 3
recorded a different material, so Participant 3 was designated as 0.
0.5 – Indicates identical data records between 2 users
e.g., In above figure, part 12, same material was recorded by Participant 1 & 2 while same
material was recorded by Participant 3 & 4.
0 – Indicates non-identical data records across users
87
Appendix D
PFAToolkit Satisfaction Survey Form
I. YOUR VIEWS ON “EASE OF USE” OF THE SOFTWARE.
PARTICIPANT NO.: ____
1. Language is easy to understand
o o o o o Strongly
Agree Agree Can’t
Say Disagree Strongly
Disagree
2. External applications (e.g. Media Player, Internet Explorer, MS Word and so on) run
smoothly while using the software
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
3. Data recording or input method is simple
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
4. Easy to correct/edit mistakes
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
88
5. Easy to navigate to find necessary features of the software
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
6. Switching between worksheets when working on an application (e.g. Bill of Material Input
Form, Product Line Commonality Index Form is easy
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
7. Easy to exit any application you are working on
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
8. Easy to resume any application after exiting it
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
9. Software operates at an acceptable speed
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
10. Users can save at regular intervals and re‐enter the information at any point
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
89
II. YOUR VIEWS ON “GRAPHICAL USER INTERFACE and USABILITY” OF THE
SOFTWARE.
1. Background and text (fonts) are pleasing and easy to read
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
2. Colors used in the software applications are used in an effective way (e.g. DSM)
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
3. Layout is clear and intuitive; learners can always find what they need
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
4. Layout is logical & consistent on all pages
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
5. All aspects of the software can be easily integrated into classroom product dissection
activities
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
6. The software is commercially viable
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
90
7. Using Microsoft Excel as the background platform made it easier to understand and learn
the software as compared to a new independent platform
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
III. WRITE DOWN THE APPROXIMATE TIME TAKEN FOR EACH TASK OR ANALYSIS
1. Time taken to record necessary part details through BILL OF MATERIALS DATA INPUT
FORM
________
2. Time taken to generate DSM
________
3. Time taken perform PCI ANALYSIS
________
91
IV. PLEASE RATE THE FOLLOWING FOR EACH INDIVIDUAL FEATURE OF THE
SOFTWARE.
a. Bill of Materials Input Form
1. Easy to enter data
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
2. Avoids redundancy of work
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
3. Error checks integrated in the form improves data consistency (accuracy of data)
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
4. Saves time (as compared to manually recording it using an electronic spreadsheet)
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
Suggestions/Remarks:
92
b. Connections Input Form and Generating DSM tool
1. Easy to enter data
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
2. Avoids redundancy of work
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
3. Error checks provided improve data consistency (accuracy of data)
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
4. Saves time (as compared to manually recording it using an electronic spreadsheet)
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
Suggestions/Remarks:
93
c. Product Line Commonality Index (PCI) Input Form(s)
1. Easy to enter data
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
2. Avoids redundancy of work
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
3. Error checks provided improve data consistency (accuracy of data)
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
4. Saves time (as compared to manually recording it using an electronic spreadsheet)
o o o o o Strongly Agree
Agree Can’t Say
Disagree Strongly Disagree
Suggestions/Remarks:
94
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