Analysis of product development decision rules and effects on product performance

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Analysis of Product Development Decision Rules and Effects on

Product Performance

By

Hans D. Schumacher

Dipl.-Ing. (M.S.) Mechanical Engineering RWTH Aachen (Germany), 1994

and

Donald J. Mecsey

B.S. Biomedical Engineering University of Iowa, 1986

Submitted to the System Design and Management Program in Partial Fulfillment of the Requirements for the Degree of Master of Science in Engineering and Management

at the

Massachusetts Institute of Technology February 2002

distribute publicly paper and electronic copies of this thesis document in whole or in part.

Signatures of Authors

Hans D. Schumacher and jonad J. Me tem Desi and Maiageent Prog 2

/ /1/ 1 O 7/~ February 2002

Certified by

O- Joel Cutchr-Gershenfeld7 Thesis Supervisor Execu e Director, Engineering Systems Learning Center, MIT and Senior Research Scientist, Sloan School of Management, MIT Accepted by

Steven D. Eppinger Co-Director, LFM/SDM K'G ML Professor of Manageient Science and Engineering Systems Accepted by

Paul A. Lagace

MASSACHUSETTS INSTITUTE Co-Director, LFM/SDM

OF TECHNOLOGY Professor of Aeronautics & Astronautics and Engineering Systems

JUL 1

8 2002

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Analysis of Product Development Decision Rules and Effects

on Product Performance

BY

Hans D. Schumacher

Dipl.-Ing. (M.S.) Mechanical Engineering RWTH Aachen (Germany), 1994

Donald J. Mecsey

B.S. Biomedical Engineering University of Iowa, 1986

Submitted to the System Design and Management Program in Partial Fulfillment of the Requirements for the Degree of

Master of Science in Engineering and Management AT THE

Massachusetts Institute of Technology FEBRUARY 2002

ABSTRACT

Key product development decisions at the onset and during the product development process shape the product's subsequent value stream and, ultimately, the firms' competitive edge. Even though decisions can be key differentiators between success and failure, literature research and interview research suggests that decision-making is a highly variable process with many unintended

consequences. Our focus here is on product development decisions about supplier sourcing and downstream execution, which are particularly important areas of a firm's decision-making. While the topic of decision-making is discussed extensively in the literature, decision process

interdependencies within organizational hierarchies in product development are largely unexplored. In particular, product success correlations between program enterprise senior management and front line staff decision rules require in depth studies, which this thesis addresses.

Ultimately, it is corporate, functional and personal decision rule drivers that impact actual decision-making. These sets of decision rules may or may not be aligned between enterprise and frontline levels - a key issue investigated here. We find that positive product performance outcomes can happen even when decision rules are not aligned. We also find that differing rule sets between both enterprise and front line rules impact product performance. While enterprise sourcing decisions that value supplier capability can have a positive effect on product performance, this product

performance improvement can be undermined by fire fighting on the front line level, which tends to quickly degrade the OEM-Supplier relationship.

Although not the initial focus of the research, the analysis also surfaced unexpected implications for career development, skill building, reward systems, and even recruitment of engineering

professionals. Many of these human resource factors ended up playing important reinforcing or undercutting roles in the product sourcing decision-making process - a finding with important organizational policy implications. In addition, it was substantiated that intangible variables such as employee motivation (i.e. emphasis on value engineering versus project management) within the OEM-Supplier product development system impact tangible product performance variables, with substantial time lags, but enable long-term competitiveness.

Thesis Supervisor: Dr. Joel Cutcher-Gershenfeld

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LIST OF FIGURES

Figure 1 - Decision-Making Tree ... 19

Figure 2 - Product Development Funnel ... 20

Figure 3 - Decisions and Organizational Hierarchy ... 21

Figure 4 - Decision Rule Drivers... 22

Figure 5 - Influence of Objectives ... 23

Figure 6 - Corporate Decision-Making Framework ... 25

Figure 7 - Component Functional Interfaces... 34

Figure 8 - Knowledge Dependency Flow ... 36

Figure 9 - Outsourcing vs. Architecture ... 37

Figure 10 - Decision Rule Categories ... 42

Figure 11 - Survey Demographics ... 46

Figure 12 - Component Outsourcing ... 47

Figure 13 - Engineering Responsibilities ... 47

Figure 14 - Decision Rule Priorities ... 48

Figure 15 - Decision Rule Drivers... 49

Figure 16 - Alignment Segmentation ... 50

Figure 17 - Shareholder Value Misalignment... 51

Figure 18 - Organization Infrastructure Alignment Gap ... 52

Figure 19 - Quality of Competitive Product Alignment Gap... 53

Figure 20 - Number and Type of Functional Interface Alignment Gap...54

Figure 21 - Number of Programs Alignment Gap ... 54

Figure 22 - OEM's Knowledge and Expertise Misalignment ... 55

Figure 23 - WCR, DVP&R, FMEA Adherence Misalignment ... 56

Figure 24 - Supplier Manufacturing Assets Alignment Gap ... 57

Figure 25 - Supplier's Component Knowledge Misalignment ... 57

Figure 26 - Number of Other OEM's Supplier Supplies Alignment Gap ... 58

Figure 27 - Level of Supplier Responsibility Alignment Gap ... 58

Figure 28 - Part Cost and Timing vs. Objective Alignment Gap... 59

Figure 29 - Number of Competitive Suppliers Misalignment ... 59

Figure 30 - Supplier Technical Assistance Involvement Alignment Gap... 60

Figure 31 - Decision Rule Driver Importance Rankings ... 60

Figure 32 - Decision Rule Importance Rankings ... 61

Figure 33 - Decision Rule Category Reliability (Initial)...62

Figure 34 - Decision Rule Driver Category Reliability (Initial) ... 62

Figure 35 - Decision Rule Category Reliability (Final) ... 63

Figure 36 - Decision Rule Drier Category Reliability (Final)...63

Figure 37 - Decision Rule Driver to Product Outcome Correlation ... 64

Figure 38 - Decision Rule to Product Outcome Correlation ... 66

Figure 39 - Product Performance Graph As Boundary Object ... 70

Figure 40 - R/1 000 Graph As Boundary Object ... 71

Figure 41 - Dynamic Causal Loop Structure ... 75

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Figure 43 Figure 44 -Figure 45 -Figure 46 -Figure 47 -Figure 48 -Figure 49 -Figure 50 -Figure 51 Figure 52 Figure 53-Figure 54-Figure 55* Figure 56 Figure 57 Figure 58 Figure 59 Figure 60 Figure 61 Figure 62 Figure 63 Figure 64 Figure 65 Figure 66 Figure 67 Figure 68 Figure 69 Figure 70 Figure 71 Figure 72 Figure 73 Figure 74 Figure 75 Figure 76 Figure 77 Figure 78 Figure 79 Figure 80 Figure 81 Figure 82 Figure 83 Figure 84 Figure 85 Figure 86 Figure 87

Inter-Com pany Relationship Loop ... 80

Inter-Functional Relationship Loop ... 81

Dynam ic Relationship Loop ... 81

Evolutionary Relationship Loop ... 82

M odel View 1 ... 85 M odel View 2... 86 M odel View 3... 87 M odel View 4... 88 M odel View 5... 89 M odel View 6... ... 91

M odel Correlation to Com ponent Level 1 ... 94

M odel Correlation to Com ponent Level 2 ... 95

M odel Correlation to Com ponent Level 3 ... 96

Base M odel O utput... 98

M isalignm ent of Supplier Capability Priority ... 102

M isalignm ent of Com petition Priority ... 102

Hypothesis 1 Test Results...103

Product O utcom e vs. M isalignment...104

Decision Rule Category to Decision Rule Driver Correlation...109

System s Dynam ics Next Steps ... 111

Decision Rule Driver Influence ... 113

Product O utcom e vs. O rganizational Level Test 1 ... 116

Product O utcom e vs. Organizational Level Test 2 ... 117

Product O utcom e vs. O rganization Level Test 3 ... 118

Product O utcom e vs. Upfront Planning ... 120

Product O utcom e vs. Knowledge of Supplier Ability ... 120

Loop Notation ... 123

M anagem ent Vision/Leadership Loop ... 123

Product Outcome vs. Management Leadership and Vision ... 125

New Hire Q uality Loop ... 126

Product O utcom e vs. Motivation to Stay ... 127

Fire Fighting Loop ... 128

Product O utcom e vs. Upfront Planning Rewards ... 129

Value Engineering Loop ... 130

Product O utcom e vs. Program s to Do ... 131

Product O utcom e vs. Program M anagem ent W ork ... 132

System Engineering Efficiency Loop ... 133

Product O utcom e vs. Cascade Efficiency ... 135

Com m odity Loop ... 136

Supplier Capability vs. Pressure to Reduce Cost ... 137

Internal Capability vs. Pressure to Reduce Cost ... 137

Product O utcom e vs. Pressure to Reduce Cost ... 138

Cost Reduction Em phasis Switch...139

Supplier Capability Loop ... 140

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1 Objectives and Discussion

1.1 Introduction

All competitive advantage is temporary. This principle has held true throughout history. The

Greeks, Romans, and the Ottomans reigned with substantial power and influence at one point in time. Today, one might consider the Western civilization as the most influential power in the world. While Ford stood out as the leader in automotive production early in the past century,

General Motors was considered the dominant automotive firm in the 1950's and 1960's. In the 1980's, Japanese firms led the industry due their progressive manufacturing initiatives.

Disruptions in competitive advantage occur in many dimensions. And the authors of this thesis would argue that those disruptions are enabled by the decisions and decision rules made along the way.

Product decisions before and during the product development process shape the product's subsequent value stream and, ultimately, the firms' competitive edge. Even though decisions can be key differentiators between success and failure, literature research and interview research suggests that decision-making is a highly variable process with many unintended consequences.

While the topic of decision-making is discussed extensively in literature, decision process interdependencies within organizational hierarchies in automotive product development efforts are largely unexplored. The specific area of research concentrating on the interrelations and product success correlations between enterprise level senior management and front line staff decisions and decision rules, will be addressed in this thesis. For purposes of this research we define enterprise as the executive management level of a product development organization within a large corporation. This thesis provides an initial test of the following five hypotheses:

HYPOTHESIS 1: Misalignment of decision rules impedes achievement of corporate goals

HYPOTHESIS 2: Decision rules for a particular PD team vary over time

HYPOTHESIS 3: Corporate, functional and personal goals directly shape the decision

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HYPOTHESIS 4: Decision rules vary by the level within the organization, with enterprise

level decision rules heavily influenced by corporate goals and front line level decisions heavily influenced by personal goals

HYPOTHESIS 5: Enterprise decision rules at pivotal PD events have a greater impact (on

product outcome) than front line engineering decisions

In order to better understand decision-making processes in the corporate environment, a practical framework on how to think about product development decision-making was developed. This framework, initially developed based on interviews within the corporation, intends to map product development decision-making in light of organizational hierarchies and corporate, functional, and personal boundaries. The theoretical decision model developed in this thesis attempts to contrast the upper level management decision-making in organizations concerned with strategic goals and the lower level engineering staff of an organization concerned with product development execution of such strategies. This generic framework is then applied to the area of automotive OEM-Supplier relationships via independent research approaches within an engineering organization. Our focus here is on early product development decisions regarding supplier sourcing and effects on downstream engineering execution, which are particularly important areas of a firm product development process.

Key variable relationships are quantified by means of an original survey of 33 executives and lead engineers, involved in sourcing decisions and product performance consequences on nine selected components, along with statistical data analysis of the results. The supplier sourcing and subsequent product development decision rule categories (product complexity, internal capability, supplier capability, supplier proximity, customer knowledge, competition, and product performance) were decomposed into sub-elements. These sub-elements were based on

technical features, product architecture, performance tuning characteristics, manufacturing assets and data exchange which are critical, but not necessarily unique, to the specific product and organization analyzed (automotive engine). This enabled the development of a survey eliciting the importance of pragmatic decision rules. Statistical survey data analysis yielded quantitative decision rule category relationships given intervening parameters, such as corporate, functional, and personal decision rule drivers.

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The qualitative portion of the research commenced with developing interview guides for both interview and focus group data gathering. P. Carlile's'2 _{work in boundary object theory and}

systems dynamics reference modes presented by Lyneis and Warmkessel3 and J. Sterman4 laid the foundations for eliciting critical information from research participants. Traditional "voice of

the customer" tools5 _{and processes were then applied to the interview and focus group}

transcriptions and evaluations. Qualitative data on variable relationships were derived from in depth interview and focus group research from the same population. Here, simple relationships, identified in interviews, between key variables were developed, ultimately producing a complex web of relationships. This web contains several surprising and unique reinforcing loops that can cause instability in the system. Thus, recommendations were developed to prevent such

instability.

Further, a system dynamics model was developed based on the aforementioned organization and supplier relationships. The systems dynamics model was developed using real world data from several engine component development case studies, providing an opportunity to

understand the interrelationships between the OEM-Supplier product development system. Ultimately, relationships between enterprise and front line decision objectives are better understood through this triangulation of research methods, which increases confidence in the validity and reliability of the findings.

After developing a decision process, the authors contend that there is misalignment between those organizationally distant groups, namely senior leaders and front line staff. Data research

informed by corporate interviews, focus groups and statistical survey analysis suggests that misalignment is largely influenced by differences in corporate, functional, and personal decision drivers across organizational hierarchies.

' Carlile, Paul. Organizational Behavior and Processes. Class Notes. Cambridge, MA: MIT, January 6, 2000

2 Carlile, P. A Pragmatic View of Knowledge and Boundaries: Boundary Objects in New Product Development.

Working Paper. Cambridge, MA: MIT, August 15, 2000

3 Lyneis, Jim and Warmkessel, Joyce. System and Project Management. Class Notes. Cambridge, MA: MIT,

September 10, 2000.

4 Sterman, John D., Business Dynamics - Systems Thinking and Modeling for a Complex World, Irwin McGraw-Hill, 2000.

5 Brodie, Christina Heperner and Burchill, Gary. Voices into Choices - Acting on the Voice of the Customer, Joiner Associates Inc., 1997.

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While researching the issue of component outsourcing in particular, this study expands on C. Fine and D. Whitney's6 _{capability dependency and outsourcing model that focuses on enterprise}

level outsourcing decision objectives. This study looks to expand the work of Fine and Whitney work by highlighting success and failure relationships between enterprise level component outsourcing and front line product execution decision objectives for an automotive product development activity.

While interrelating simple and known relations between key variables, the research attempts to honor the core principle of systems engineering in its dealing with the system as a whole. Furthermore this research finds that decision rules correlate well to product outcome metrics. Both enterprise and front line decision rules impact product performance successfully given differing rule sets between these organizational levels. While enterprise sourcing decisions value supplier capability positively, fire fighting can cause a skewed vision of the OEM-Supplier relationship within engineering. This results in lost opportunities adversely impacting OEM capability and decision-making. In this research we will provide evidence on how these and other key variables, such as cost reduction initiatives and lack of upfront planning, can initiate downward product performance spirals that may take years to overcome.

Although not the initial focus of the research, the analysis also surfaced unexpected implications for career development, skill building, reward systems, and even recruitment of engineering professionals. Many of these human resource factors ended up playing important reinforcing or undercutting roles in the product sourcing decision-making process - a finding with important organizational policy implications. In addition, it was substantiated that intangible variables such as employee motivation (i.e. emphasis on value engineering versus project management) within the OEM-Supplier product development system impact tangible outcome variables, such as product performance with substantial time lags, but enable long-term competitiveness.

1.2 Literature Search

Decision-making represents a very crucial skill set that all individuals and organizations must possess in order to achieve goals and objectives. Given the organizations' stated mission statement or corporate vision, it will ideally attempt to realize its vision by generating both a 6 _{Fine, C., Whitney, D. (1996), "Is the Make-Buy Decision Process a Core Competence", MIT Center for}

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strategy and an execution plan. Thus, decisions made in both strategy and execution will have a strong impact in realizing the vision.

Literature provides many examples where decisions in either strategy or execution led to consequences for organizations, in some cases unintended and in others intended. Specific to product development organizations, Crawley7 argues that product architecture actually

represents the product strategy. Here, product architecture can be thought of as the product's strategic design intent ranging from the organization executing the product design to the manufacturing strategy developed to produce the product. Furthermore, product architecture includes the supply chain architecture mapping the product value stream from raw materials to the product beneficiary and beyond, and the market strategy enabling product sales and revenue growth. As evidenced by R. Henderson and K. Clark8 (product architecture drives organizational architecture), C. Christensen9 (architectural innovations drive market

discontinuities), and C. Fine' (system architecture drives supply chain design), dominating firms failed and lost competitive advantage once the impact of the product architecture on

downstream efforts was no longer sufficiently understood. Thus, decisions made during the strategic design deriving the product architecture have success altering consequences. Similar impact of decisions can be identified on the product development execution side. For instance, a risky decision to disregard design information and seal failures during verification testing ultimately led to the catastrophic failure of Challenger seventy-four seconds after launch in January of 1986. Therefore, one can see how only a single product execution decision can lead to adverse outcome.

Above evidence confirms the importance of product development decision-making. We will demonstrate for an automotive organization that in fact both strategic and product execution

7 Crawley, Edward. 16.882 System Architecture. Class Notes. Cambridge, MA: MIT, September 10, 2000.

8 Henderson, R.M. and K.B. Clark (1990). 'Architectural innovation: The reconfiguration of existing product

technologies and the failure of established firms', Administrative Science Quarterly, 35, pp. 9-30

9 Christensen, C. M. (1995). Explaining the attacker's advantage: technological paradigms, organizational dynamics,

and the value network, Research Policy 24, pp. 233-257.

10 Fine, Charles H. (1998), Clock Speed: Winning Industry Control in the Age of Temporary Advantage, Perseus Books, Reading, MA.

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decision-making is crucial for product quality. However, as this paper will illustrate, this finding is dependent on which set of decision rules were applied.

Interview and literature research indicates that decision-making processes are highly variable and that corporations have difficulties arriving and standing by decisions. Quotes from interview research within several corporate activities, such as human resources, product planning, purchasing and others, at an automotive manufacturer suggest some of the difficulties real world decisions are subjected to. They are follows:

Quote 1:

"We have difficulty in representing 'intangible benefits' with long-term consequences during decision-making events. The mindset is cost driven and short-term."

This is not too dissimilar from the quote by Albert Einstein3: "Not everything that counts can be counted and not everything that can be counted, counts". The suggestion is that each decision process is dependant on its particular circumstances and that both intangible and tangibles need representation in its conclusion. Unfortunately, we must realize that benefits from some of the most important factors, such as the intangibles, often cannot be quantified. Similarly, we tend to only understand those things that we are measuring: Your organization will become what is measured" (John Hauser3); "Tell me how you will measure me and I tell you how I will

behave" (Eli Goldratt3). We will further develop the implications of tangible and intangible variables later in this paper.

Quote 2:

"Decision-making events require many meetings, because there are many stakeholders."

This reference indicates that decision rules, and ultimately the decision, change based on the perspectives represented. Interviewees indicated the power of presentation has a significant impact on the decision made. This will become specifically important for the results in our study, as well demonstrate how stakeholder decision-making is not only dependent on corporate goals, but also on functional and even personal objectives.

3 Lyneis, Jim and Warmkessel, Joyce. System and Project Management. Class Notes. Cambridge, MA: MIT,

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Quote 3:

"There are many decision tools, but we don't really use them."

The observation is consistent with the authors' product development experience. While CAE tools and testing results are used to influence product decisions, generic decision tools

addressing the trade-off of conflicting decision objectives are rarely applied. Clemen12 indicates that the value of decision analysis (DA) tools is difficult to demonstrate, as it is not easy to measure the value of the chosen course of action relative to paths not taken. However, he indicates intangible benefits to DA in general which include facilitating discussions among stakeholders with different preferences, providing a common language for discussing elements of a decision problem, focusing on specific disagreements and helping to build consensus, which in turn speeds implementation. Such contributions can improve the overall functioning of

the organization, thereby contributing to the bottom line. Interestingly, Yassine1 3 indicates that no body of literature exists that shows unequivocally whether the use of DA techniques actually helps individuals to make better decisions. As part of this research, we will evaluate both the human element (in the form of strong leadership) and the brick and mortar element (cascade efficiency throughout an organization) and how they both play a substantial role in transforming decision rules into positive product actions. Thus, we contend that decision tools alone are not sufficient for product development decision-making.

Quote 4:

"Why can't I get information that exists to make this decision?"

The answer to this age old senior management question, as described to us by many

interviewees, could be because: 1) people are invested in their knowledge and they are careful in volunteering it, or 2) systems are locally optimized and information formats useful to one part of the organization cannot easily be transferred to other parts of the system, or 3) knowledge

exists in people, it is tacit, thus not easily detectable, or simply 4) because 'George' is on

vacation. We will show that product development decision rules vary over time and with that the key information required for decision-making changes. Thus locally optimized systems, which are a function of people and knowledge transfer and databases, are not efficient.

1 Clemen, Robert T., Kwit, Robert C. (2000), The Value of Decision Analysis at Eastman Kodak Company, 1990-1999, Joint Research Paper between Duke University and Eastman Kodak Company.

" Yassine, Ali and Chelst, Kenneth. Opportunities for Decision Analysis in Engineering and Manufacturing Management: An Overview, MIT Research Paper.

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Without trying to embark on the impossible task of citing and describing the massive amount of published literature on decision-making, referring to some scholars and their work maybe helpful to understand difficulties with decision-making in general.

Scholars in the field of decision sciences suggest many reasons for why decision-making is difficult. They address some of issues described in the quotes listed above. In 1772, Benjamin Franklin in a letter titled "Morale or Prudential Algebra"14 made the following statement: "When those difficult cases occur, they are difficult, chiefly because while we have them under

consideration, all the reasons pro and con are not present to the mind at the same time; but sometimes one set present themselves, and at other times another, the first being out of sight. Hence the various purposes or inclinations alternatively prevail, and the uncertainty that perplexes us." Simon, who developed the concept of "bounded rationality", emphasizes the cognitive limitations of decision-makers, limitations of both knowledge and computational

capacity. Miller16 _{highlights limitations in the human span of attention (i.e. recognizing number of}

objects at a glance), of absolute judgment (i.e. distinguishing between categories), and of immediate memory (i.e. number of items that can be recalled). Thus Franklin, Simon, and Miller each addressed the limitations of human capacities on decision-making. Our research further verifies that for a product development organization, the relationship mapping that we

developed clearly indicated that the organizational dynamics represented relationships so complex and large in number that it would be infeasible for any one or two individuals to

understand them well enough to be able to make efficient decisions.

Another large body of decision-making research is the area of Decision Analysis (Raiffa17).

Within the context of decision analysis, Hammond, Keeney, and Raiffa18 describe what they call the "even-swaps method" - thinking about the value of one objective in terms of another. The thought here is that decision objectives have relative importance and alternatives have different

14 Franklin, Benjamin (London Sep. 19th, 1772), A Letter Titled "Morale or Prudential Algebra", published in Harvard Business Review (March-April 1998), Reprint 98206.

15 Simon, Herbert A. Models of Bounded Rationality, Volume 3, The MIT Press, Cambridge, MA. 16 _{Miller, G.A, "The magic number seven, plus or minus two: Some limits on our capacity for processing} information," An Essay in the psychology of communication, Basic Books, 1967.

17 Raiffa, H., Decision Analysis. Addison-Wesley, Reading, MA, 1968.

18 Hammond, John S., Keeney Ralph L., and Raiffa Howard, Even Swaps: A Rational Method for Making Trade-offs, Harvard Business Review (March-April 1998), Reprint 98206.

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utilities. The research contends that if alternatives are equally good in one objective, this

objective can be deleted from decision. Clemen'9 indicates that a good decision making process is a process where "if I still know what I exactly knew back then (when I made the decision), I still would make the same decision". In "The Effective Decision", Drucker20 _{suggests the}

following pre-described steps for efficient decision-making. However, given the complex cultural, political, and strategic dimensions influencing product development decisions, the methodology seems impractical to undertake in the corporate environment. A more realistic perspective presented by Hammond, Keeney, and Raiffa2 1, warns of various traps that decision-makers are exposed to. We will show that the decision maker must understand more than just the process

by which to weigh competing variables and objectives. The relationship mapping performed in

our research indicates that it is just as important to have an understanding of the balancing and reinforcing network loops that define the decision-making environment. Various loops and their associated behavior will be presented later in greater detail.

Literature on the automotive industry tends to be centered on the decision-making culture differences between Japanese and American OEM's22_'23_. _{In the Toyota model, set based}

concurrent engineering and the ability to delay decisions until later in the PD process govern. This may provide advantages as product development activities circumvent upfront ambiguities

in an environment that slowly converges to the final product. US automotive manufactures tend to converge early to an end solution only to have that solution refined as product development continues. This approach causes constant tuning of one good idea rather than the convergence of sets of ideas. The difference product development approaches at US OEM's and Toyota is significant for decision-making. Our research shows that product development decision rules vary over time and thus the Toyota approach may be a better competitive approach, because it

19 Clemen, Robert T., Making Hard decisions - An Introduction to Decision Analysis, Duxbury Press, 1996.

20 Drucker, Peter F., The Effective Decision, Harvard Business Review (January-February 1967), Reprint 67105.

21 Hammond, John S., Keeney Ralph L., and Raiffa Howard, The Hidden Traps in Decision Making, Harvard

Business Review (September-October 1998), Reprint 98505.

22 Sobek, Durward K., Ward, Allen C., Liker, Jeffrey K., Toyota's principles of set based concurrent engineering,

MIT Sloan Management Review (Winter 1999, Vol. 40, Number 2), Reprint 4025.

23 _{Ward, Allen C., Liker, Jeffrey K., Cristiano, John J., Sobek, Durward K., The second Toyota paradox delaying}

decisions can make better cars faster, MIT Sloan Management Review (Spring 1995, Vol. 36, Number 3), Reprint 3634.

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allows for decision space and reaction to changing consumer wants and economic developments late in the product development effort.

From above quotes and literature references, it becomes clear that issues surrounding uncertainty, timeliness of decisions, multiple objective trade-offs, stakeholder views, and complex system interdependencies make decision-making a highly unpredictable and variable process with unforeseen consequences. Decision-making becomes an extremely complicated task as information overload makes it difficult to decipher key information under ever-increasing budget and time constraints. As described by Apostolakis2 4, decision-making depends on variables, such as outcome probabilities, outcome consequences, and a person's utility towards risk, none of which are easily controlled or measured.

Ultimately, decisions are reached by the application of decision rules. And decision rules are informed by corporate, functional, and personal goals or objectives. Forrester25, Hines and House26 _{similarly equate decision rules in terms of policies or procedures that people actually}

use to make a series of decisions. Yet another interesting writing in the field of decision-making comes from Hines and House27 _{who equate organizational evolution to biological evolution.}

Hines and House contend that organizational policies correspond to biological genes; policy innovation corresponds to genetic mutation; and organizational learning corresponds to genetic recombination. Policies, like genes, are a fulcrum on which evolution can operate. If policies or decision rules evolve, so will the company. Thus, consistent with research from Forrester and Hines, and House, organizational evolution assumes decision rule evolution. Thus, the study of decision rules as applied to an organization's pivotal decision-making events, is key not only to understanding how the organization operates, but also to identifying rules that, when adhered to, will effect positive change. Given that decision rules are applied during strategy and product execution events, the question for product development organizations becomes whether decision rule success patterns exist and can be identified so that product performance can be enhanced.

24 Apostolakis, George. Engineering Risk-Benefit Analysis. Class Notes. Cambridge, MA: MIT, February 7, 2000.

25 Forrester, Jay W. 1961. Industrial Dynamics. Portland, OR: Productivity Press.

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1.3 Research Framework

All product development teams encounter decisions that involve everything from routine

tradeoffs to the career-defining actions. For the most part, corporate decisions are based on data that tends to be limited by the manner in which the problem was framed, the amount of time available to gather the data, the knowledge and experience of the team assigned to collect the data, and the effectiveness of the risk analysis process (see figure below). In addition, once the decision is made, success of the solution is dependant upon the changes inherent in the business climate and the ability of the team to execute the decision.

Figure 1 - Decision-Making Tree28

No

Specify Objectives

(performance, quality, profit, etc.)

Frame Decision

(team members, decision makers, stakeholders, economic state, etc.)

No An Alterna Stil Reasoi Yes Make Decision

select best alternative

Implement/Execute

Chosen Alternative

__

One might ask then "is the outcome a result of the strategy set forth in the decision making, the execution of the chosen direction, the intangible external factors, or some combination of all of these?" We can assume that the latter is the correct explanation, but how can we test for this?

28 Unless noted, all Figures are original works developed jointly by both thesis authors.

Establish Alternatives (choices, resources, timeframe. etc.) Risk Analysis (uncertainties/probabilities, costs, benefits. etc.)

Apply Decision Rules

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This research attempts to do just that by examining the decisions, decision rules engaged, and the net effect of both as encountered in an automotive product development firm.

Decision processes in automotive product development can be divided into two segments, strategy and product execution. On the same product program, these general types of decisions are separated by both time and by organizational hierarchy. While strategic decisions are made upstream in a product program by senior level management, lower level management and lead engineers make the downstream product execution verdicts. The separation of time and organizational level also leads to differences in the decision rules, those factors used to weigh various alternatives, employed. Utilizing the product development process framework as

expressed by Ulrich and Eppinger 9, enterprise pivotal decisions are predominantly made during the product planning and concept development stages of the process. Front level engineering decision-making occurs downstream during system and detail design, as well as throughout testing and refinement phases (see Figure 2).

Figure 2 - Product Development Funnel

System -Level D tail Testing and

Design Design Refinement

Enterprise Level Decisions Front Line Engineering Decisions

Enterprise decisions include all PD pivotal events that impact the product strategic design. Here, a product's strategic design is defined by parameters such as quality, cost, weight and functional objectives, product development/manufacturing/component supply chain strategy and its time to

29 Ulrich K.T. and S.D. Eppinger. Product Design and Development, McGraw-Hill, New York, 1995 (1st edition) and 2000 (2nd edition).

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market and volume requirements. Given the product strategic design, front line engineering activities make design and development decisions (see Figure 3).

Figure 3 - Decisions and Organizational Hierarchy

ecisions Decision Rules Enterprise 0 , N ---C _ _ _ecisions Decision Rules 0

Front Line Engineering

Strategy Execution

PD Process

Decision rules set the constraints and objectives for the decision to be made. At the enterprise level, decisions might include the outsourcing of a specific component or the selection of one of many available suppliers for a particular component. In this instance, enterprise level decision rules may be composed of elements weighing the corporation's internal capabilities to produce the component, the supplier's knowledge of leading edge technology, or the competitive advantage of holding manufacturing in-house vs. outside. The enterprise level team may choose to outsource a component to the most technologically advanced supplier rather than

maintaining capability in-house and risk becoming non-competitive. At the front line level, one can think of decision rules as the factors that weigh product feature and development trade-offs. For instance, a shock absorber team may need to trade-off functional attributes such as noise, vibration, dampening characteristics, friction, and weight versus variable cost and investment implications. A decision rule might be that customer satisfaction in ride and handling as a function of dampening characteristics and friction are most important. Thus, among available alternatives, the product execution team will select the design with the most optimized vehicle dynamics performance.

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As described above, decision rules are a function of time and organizational level. That is, they routinely change during the course of the product development cycle, due to evolution of the product from prototype to production ready, and based on which team, enterprise or front line, is responsible for making the decision. The authors contend that decision rules are driven by three objectives, corporate, functional and personal (see Figure 4). Answering three simple questions can identify these objectives: What's in it for the corporation? What's in it for my

department? And What's in it for me? Examples of each of these include:

Corporate Objectives:

" Corporate mission or vision * Shareholder value " Customer satisfaction " Employee satisfaction * Community obligation Functional Objectives: " Departmental goals

" Supply chain management

" Organizational structure * Local processes * Culture/tradition Personal Objectives: " Merit rewards * Experience . Work-life balance

Figure 4 - Decision Rule Drivers

...

Decision Rules

=F {t, L}

Corporate

Functional

Personal

Objectives

Rules change

over time

Weighting of influences vary per org. level

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Corporate objectives are cascaded from the highest level of the organization in the form of a corporate mission statement, directive or list of values. The authors contend that as these objectives are cascaded downward through the organization, it could be assumed that these objectives are more easily understood, and therefore adhered to, at the top of the organization, than at the bottom levels. This might explain the American slang phrase 'flavor of the month'. Although the corporate mission statement or key principles may be both admirable and

achievable, inefficiencies in cascading these goals prevents the lowest levels of the organization from truly understanding and adopting them. Functional objectives are naturally unique to the various levels of the organization because they are based on local product development deliverables and budget. These objectives tend to be well understood as they are most often tied to some year-end performance metric. Personal objectives, such as better pay or a better position within the organization, on the other hand are not unique to any one particular level within the organization. However, the authors contend that since enterprise level managers typically have their merit pay and bonuses tied to higher-level corporate objectives, there is less of a tendency for those at the enterprise level to make decisions based on personal

considerations. Likewise, it was assumed by the authors that cascade inefficiency causes dilution of corporate objectives so that by the time these objectives make there way down to the front line engineers, they are not heavy drivers in decision making processes. Instead, front line level employees are more likely to be motivated by personal rewards, opportunities for

advancement and avoidance of job induced stress (see Figure 5).

Figure 5 - Influence of Objectives

Corporate Functional Personal

Enterprise Executive

Level

wlin Org _Middle_Mgmnt

Front Line

Low Med High

00 0

0.

0 e

2.I7

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The scope of this research was narrowed to concentrate on the study of the enterprise and front line levels only. It is the intent of the authors to illustrate the observed differences in the very top and bottom levels of the organization, as it is presumed that this data would be the least

confounded and that data from the executive and middle management levels would simple indicate incremental progression from the enterprise to the front line levels (Figure 5). As a result, executive and middle management cope with all objectives at the same time. In addition, since teams make the vast majority of corporate product development decisions, this research focuses only on the decisions and decision rules of teams, rather than individuals. The scope of this research was further restricted to the study of nine separate components within the engine development organization.

1.4 Objective of the Research

This research seeks to discover the combinations of product development decisions, decision rules, and motivating factors that influence decision making behavior within the organization and their correlation to product performance (as measured by traditional automotive quality metrics).

Choosing the "right" set of decision rules is critical to a product's, and ultimately an

organization's, success. It is the author's belief that alignment of decision rules at the top and bottom levels of an organization builds upon the organization's capability to make efficient decisions, ultimately reinforcing the corporation's vision, albeit for better or worse (see figure below). This thesis will focus primarily on the technical/engineering rules used for supplier outsourcing and product development execution as well some of the 'softer' personal decision rules. In addition, this thesis will provide recommendations pertaining to improving downstream product performance based on upstream enterprise and front line level decisions.

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Figure 6 - Corporate Decision-Making Framework Reinforces Impedes i.e.: Experience Personal Incentives Functional Incentiy.qs.. (implicit/explicit) Impedes Customers Shareholders ... ,...--see

. '''Community Corp. Climatee

Segment Leader in

Product(s) Produced Disseminated

Through

... ---. - G uiding

i.e.: _Principles

Customer Satisfaction Shareholder Value

Stakeholder Satisfaction Promotes

Front Lind Decision Ru es

F'

Confu Generates sin oProduces Produces Environment i4e.: -I-Org. Structure Tradition Processes Enables c !um Reinforces

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1.4.1 Hypotheses

Hypotheses were developed around the areas of automotive product development decision rules, decision rule drivers and organizational levels. This body of research was framed around the premise that the decision rules at the top and bottom of an organization are misaligned due to factors such as the business environment (org. structure, tradition, processes), local

motivation (experience, personal incentives) and inefficient cascading of corporate objectives. In addition, it was hypothesized that alignment of decision rules (at enterprise and front line levels of org.) leads to capabilities and efficiencies that reinforce the Corporate Vision (see Figure 6 above). In general terms, the corporate vision of the firm studied was to be the

"segment leader in specific product produced".

HYPOTHESIS 1: Misalignment of decision rules impedes achievement of corporate goals Decision rules, as defined by the authors, are the criteria that product development teams use to weigh and prioritize available information during key decision making events. The authors further define decision rules as a function of time and level within the organization, driven by

corporate, functional and personal objectives (see Figures 3, 4 and 5 above).

HYPOTHESIS 2: Decision rules for a particular PD team vary over time

HYPOTHESIS 3: Corporate, functional and personal goals directly shape the decision

rules teams apply during key product development decisions

HYPOTHESIS 4: Decision rules vary by the level within the organization, with enterprise

level decision rules heavily influenced by corporate goals and front line level decisions heavily influenced by personal goals

A typical product development funnel, as illustrated in Figure 2 above, often depicts pivotal

decision points in the product development cycle pertaining to suppliers and supplier sourcing. Early on in the process, the enterprise level makes the decisions to outsource particular components and identify competitive suppliers. This portion of the funnel can be deemed 'strategy'. Further into the funnel, the front line engineers make decisions involving supplier sourcing and product design and manufacturing. This portion of the funnel can be deemed 'execution'. It is recognized that valuing customer satisfaction can improve product outcome and that valuing personal goals can degrade product outcome. If Hypothesis 4 holds true, than one could surmise that enterprise level decisions, centered on customer satisfaction and other

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on the other hand, which maybe be heavily influenced by personal objectives that would tend to negatively effect product outcome. Consequently, one could consider enterprise level decisions as reinforcing (corporate vision) while front line decision-making reinforces, degrades or has no

net effect on product outcome, given the same primary intent by both teams of achieving product success.

HYPOTHESIS 5: Enterprise decision rules at pivotal PD events have a greater impact (on

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2 Research Design and Survey Descriptive Statistics

2.1 Introduction

Today's automotive products are so complex, that no single entity can entertain all knowledge and assets required to autonomously design, develop, and manufacture products and its subcomponents. Thus, companies outsource a fair amount of components to full service suppliers. A full service supplier not only manufactures the component according to a drawing,

but more importantly takes responsibility for the component design, development, and validation as well. Given that these components are embedded in complex systems, that are difficult to integrate, it comes to no surprise that full service suppliers attempt to expand their area of expertise to complete systems. Suppliers that in the past were considered job shops are now converting to become systems providers. An example of that are fuel delivery systems. In the early nineties, suppliers just manufactured filler pipes, fuel lines and such. Now they are designing and developing aforementioned components. Furthermore, they attempt delivering the entire system from fuel tank to fuel pump driver module3 0_{. As a result of these}

developments, automotive OEM manufactures have become less vertically integrated and more dependent on outside partners. Strategic supplier sourcing and selection decision-making becomes increasingly important. Furthermore, decision-making in product development and execution with external entities as opposed to internal departments adds new challenges. Given this development in the automotive industry, we will to focus our data research on product development decision rules that are concerned with strategy and product execution decision-making in the regards to the OEM-Full Service Supplier relationship.

Research literature in the field of product development efforts between automotive

manufactures and their suppliers is mainly concerned with enterprise level outsourcing decision objectives. For instance, literature by Fine and Whitney6 _{as well as Fine}3

' has dealt in particular

with make-buy decision-making from an OEM strategic point of view. Hence, the effect of downstream engineering decisions based on upfront sourcing decisions is widely uncorrelated and the question arises, whether upfront strategic or downstream executive decision rules has more impact on the product outcome. Are there any success or failure relationships between

30 Pilot industries brochure, Steel Fuel Tank Sales, 2001.

6 Fine, C. and Whitney, D. "Is the Make-Buy Decision Process a Core Competence", MIT Center for Technology,

Policy, and Industrial Development.

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sourcing decision rules applied upfront and decision rules used during downstream product execution? For instance, given an upfront decision requiring sourcing of a less capable supplier, can the application of certain downstream product development decision rules still lead to product success? And that lead us to question how product development rules vary? Supplier sourcing and subsequent product development decisions are influenced by a number of

complex sets of decision rules. These decision rules may vary between functional organizations, such as engineering, purchasing, and product planning. As discussed in previously, in order to cover a wide array of potential parameters a mixture of informal interviews at various

organizational levels (within a leading US automotive), literature research, frequent discussions with MIT-SDM cohorts, and supplier interviewing was performed. Through literature reviews and interviews there tends to be great debate as to the segmentation and complex

interrelationships of component outsourcing decision rules. We will explore the broader areas of outsourcing decision rules in the following sections.

2.2 Data Collection

A total of nine engine sub component teams were selected as case studies for this project.

Beyond the condition that the mix of selected components to be studied varied by performance history (quality/warranty metrics over past 10 years were not identical), they were randomly selected. In addition, the level of supplier design and development responsibility and other variables for each of these components also varied over the past ten years.

Given the scope of this research, a large amount of complex data needs to be collected and analyzed in order to derive valuable conclusions. However, given the constraints associated with this project, we took the approach to collect data utilizing three research methods;

quantitative surveys, qualitative interviews and focus groups, and the development of a systems dynamics model. Ultimately, we will triangulate significant results from these three independent but limited research methods to reach scientifically significant conclusions. Key variable

relationships are quantified by means of an original survey of 33 executives and lead engineers, involved in sourcing decisions and product performance consequences on nine selected

components, along with statistical data analysis of the results. Qualitative data on variable relationships are derived from in depth interview and focus group research from the same population - all based on original protocols developed for this research. Finally, the systems dynamic model calibrated with real world data from several engine component development case studies provides an opportunity to understand the complex web of causal relationships within the OEM-Supplier product development system. Ultimately, relationships between

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enterprise and front line decision objectives are better understood through this triangulation of research methods, which increases confidence in the validity and reliability of the findings. In order to acquire useful data for each research method, we analyzed a specific engine

engineering organization within a major automotive manufacturer.

2.3 Decision Rule Identification

2.3.1 Core Criteria

Supplier sourcing and subsequent product development decisions are influenced by a complex set of decision rules. These decision rules may vary between functional organizations, such as engineering, purchasing, and product planning. Therefore, in order to cover a wide array of

potential parameters, the authors conducted informal interviews with employees of varying levels of responsibility from a number of departments within aforementioned automotive OEM. This interview information was combined with literature research6

,9,3 2

,33 _{frequent discussions with} co-workers and MIT-SDM professionals, and supplier interviews. Synthesis of supplier sourcing and product development decision variables yielded the following array of decision rules,

randomly listed below:

6 Fine, C. and Whitney, D., "Is the Make-Buy Decision Process a Core Competence", MIT Center for Technology, Policy, and Industrial Development, 1996.

9 Fine, Charles H. (1998), Clock Speed: Winning Industry Control in the Age of Temporary Advantage, Perseus Books, Reading, MA.

32 Ford Motor Company, -_{Europe, Potential High Impact Supplier Selection Rationale, Ford Motor Company}

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Quality of Vendors

Vendor Accountability (i.e. Warranty Sharing) Supplier Financial Capability

Warranty Cost

Supplier Risk-Taking Behavior Part Supply Volume

Supplier R&D Responsibility Supplier R&D Capability

Time until engineer has proficient component knowledge

Time until Component Engineer moves on Component Complexity

Technical Innovation

Internal Component Expertise / Capability Supplier Mfg. Capability

Component Functional Requirements / Attributes Component Mfg Feasibility

Internal Special Knowledge (i.e. materials etc.) Supplier Accessibility (i.e. co-location) Competitive Component Cost Pressure Supplier-OEM Partnership / Teamwork Supplier Responsiveness

Supplier Attitude

Program Staffing (early in Program) Program Staffing (throughout Program) Cost Cutting Pressure

Quality

Supplier Economies of Scale Number of engine programs Number of unique Components Engine Program Timing

Number & degree of difficulty of regulatory wants Special / Focus Customer Wants

Internal Behavior towards risk Level of component mgmt leadership Program turnover / life cycle Special warranty causes Supplier Productivity Six Sigma Black Belt Staffing Amount of Program Rework

Intensity of supplier shadow engineering Internal Program Workload

Number of short-term projects OEM Profitability

Supplier Profitability OEM internal capability Supplier Employee Turnover

Supplier Willingness to become Full Service Supplier Competitive Pressures

OEM teaching Capability

Engineering Change Validation Capability Supplier Business Objectives

OEM Business Objectives

Sharing of Drwg's, Tools among suppliers Design Change Leverage

Level of internal tool development capability

56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94. 95. 96.

Engineers learning to become proficient Average Engineer in Job time

OEM Competitor Capability Component Attribute Knowledge Management of Resources Management of Facilities Management of Documentation Systems Engineering Concentration Program Management Concentration Leadership in issue resolution Number of suppliers per component Supplier Process Capability Time to force bad supplier out Perception of analytical tools Supplier Quality

Supplier Engineering Support Supplier Product Delivery Number of Quality Vendors

Level of Internal Vision (know your goals) Level of Internal Strategy Knowledge Level of Internal Strategy Execution Time to develop FEA/CAE Tools Level of maintaining / extending in house capability

Late changes

Level of Technical Innovation Commonality

Amount of non-value added work Incorporation of lessons learned Ability to predict customer duty cycles

Level of Upfront planning Management support

Level of component documentation Amount of component testing Ability to prioritize work

Component Section Structure Time to see strategy benefits Want to be an engineer Want to be a manager

Flexibility in schedule, relationship, outside pursuits

Real Engineer - empowerment

Satisfaction in doing real engineering work 97. Section learning

98. Doing the right thing

99. Doing the politically correct thing 100. Component Performance 101. Supplier Continuity

102. Ability to see weakness and resolve

103. OEM teaching ability

104. Internal Engineer Training 105. Product complexity

106. Capacity (Resources, Mfg. Etc) 107. Supplier Location

108. Supplier Organization 109. Supplier Reputation

Analysis of product development decision rules and effects on product performance

Analysis of Product Development Decision Rules and Effects on

Product Performance

Hans D. Schumacher

Donald J. Mecsey

JUL 1

8

2002

Analysis of Product Development Decision Rules and Effects

on Product Performance

Hans D. Schumacher

Donald J. Mecsey

TABLE OF CONTENTS

LIST OF FIGURES

1

Objectives and Discussion

1.1 Introduction

1.2 Literature Search

1.3 Research Framework

__

PD Process

Decision Rules

Corporate

Functional

Personal

Objectives

Objectives

Objectives

0.

0

e

2.I7

1.4 Objective of the Research

F'

2 Research Design and Survey Descriptive Statistics

2.1 Introduction

2.2 Data Collection

2.3 Decision Rule Identification