Sources of iteration and a risk mitigation methodology in semiconductor new product development projects

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(1)Sources of iteration and a risk mitigation methodology in semiconductor new product development projects. Evan Murphy.

(2) Abstract Iteration is the major source of risk in semiconductor New Product Development (NPD) projects, occurring in approximately 90% of projects and causing on average approximately 30% schedule slippage. As such, iteration risk in semiconductor NPD projects is a very important issue that needs to be addressed. NPD projects bring specific challenges for a company due to the innate uncertainty involved in producing a new product. One major consequence of the inherent uncertainty involved in NPD projects is the resultant uncertainty and risk of iteration in project schedules. A challenge when trying to generate a product development schedule is to accurately account for the uncertainty caused by iteration and potential schedule slippage. The occurrence of iteration in NPD projects can directly lead to budget over-runs, schedule delays and missed product release. With time to market often being an important determinant of NPD success any factor that can potentially cause project delay must be fully understood. This research aims to further examine iteration risk in a semiconductor NPD project context, and explore how a more accurate iteration risk mitigation methodology can be established. Providing management within a firm with a process specific risk analysis regarding iteration can enable a proactive approach to NPD scheduling and contingency planning. The goal of the research is to provide an iteration risk analysis methodology where the risks of delay due to iteration in NPD projects can be identified, quantified and subsequently mitigated. A semiconductor case study company is used for the development of the risk analysis framework that aims to provide a new approach for analysing iteration risk in NPD projects. Iteration was found to be a dominant source of risk in this semiconductor NPD project environment. Investigation into the company’s NPD process, and the specific problem of iteration, identified a number of sources that contribute to iteration risk in their projects. Through the analysis of iteration in NPD in the case study firm, five general areas were identified as being contributory factors to the occurrence of iteration in semiconductor NPD projects: [1] simulation coverage, [2] requirements definition, [3] new process/technology risk, [4] human resources and [5] project planning. Follow up interviews and analysis of the five general areas resulted in the identification of 49 specific project characteristics that are used to fully define the initial conditions of an NPD project in the firm. The developed risk mitigation methodology uses the inputs for these characteristics in conjunction with historical project performance data to produce a schedule risk analysis for a current NPD project. The methodology employed uses a novel approach to defining iteration risk that uses the root causes in a process to analyse the potential schedule risk of an NPD project. To gain a deeper understanding of the methodology, data was collected for the characteristics for a sample of 30 past NPD projects. This data was used to further investigate the risk due to iteration and subsequent schedule slippage in these projects. This investigation demonstrates how the developed methodology can be deployed in a practical setting to enable the company to proactively minimise iteration risk in their NPD projects. Using the developed methodology provides a company with a sustainable approach for analysing iteration risk and facilitates ongoing organisational learning in relation to the management of product development schedules. The methodology provides a structured approach for better understanding and mitigation of iteration risk in semiconductor NPD projects..

(3) Acknowledgements I would first like to thank my supervisor Dr. Ann Ledwith for her continuous guidance, expert advice and support throughout the duration of my PhD. I would also like to thank both examiners, Dr. Con Sheahan and Professor Mike Danilovic, whose guidance and advice ensured that the final thesis was improved upon and made as strong as possible. A special acknowledgment must also go to the case study company used in this research for the level of access and cooperation provided. I would also like to acknowledge the Irish Research Council for Science Engineering and Technology (IRCSET) for the scholarship which enabled me to undertake this study. From a personal perspective I would like to thank my family for their support throughout my studies. In particular I would like to acknowledge my parents for providing the intellectually stimulating and relaxed environment growing up that I feel gave me the skills required in my academic studies. Finally I would like to offer a special thank you to my partner Allida for her intellectual and emotional support throughout the duration of my PhD. The process can be quite a solitary and consuming experience at times and I will be forever grateful to her for helping me to always maintain perspective..

(4) TABLE OF CONTENTS LIST OF FIGURES ............................................................................................................................... V LIST OF TABLES ............................................................................................................................ VIII 1. INTRODUCTION .............................................................................................................................. 1 1.1 OVERVIEW ...................................................................................................................................... 1 1.2 NPD PROJECT SCHEDULING AND ITERATION ................................................................................... 2 1.3 MOTIVATION FOR RESEARCH .......................................................................................................... 4 1.4 RESEARCH QUESTIONS AND OBJECTIVES......................................................................................... 6 1.5 THESIS STRUCTURE ......................................................................................................................... 6 2. LITERATURE REVIEW .................................................................................................................. 8 2.1 INTRODUCTION ............................................................................................................................... 8 2.2 NEW PRODUCT DEVELOPMENT AND THE IMPORTANCE OF TIME TO MARKET ................................... 8 2.3 UNCERTAINTY AND RISK IN NPD PROJECTS.................................................................................... 9 2.4 SCHEDULE DELAY IN NPD PROJECTS ............................................................................................ 13 2.5 ITERATION IN NEW PRODUCT DEVELOPMENT PROJECTS ................................................................ 15 2.5.1 Causes and types of iteration ............................................................................................... 15 2.5.2 Task overlapping .................................................................................................................. 16 2.5.3 Dependencies between project tasks .................................................................................... 18 2.5.4 Project portfolio management and resources ...................................................................... 21 2.5.5 Macro level analysis of iteration and project duration ........................................................ 23 2.5.6 Graphical Evaluation and Review Technique (GERT) ........................................................ 25 2.5.7 Minimising iteration time ..................................................................................................... 27 2.6 THE DESIGN STRUCTURE MATRIX (DSM) METHOD ..................................................................... 28 2.6.1 Defining dependencies and identifying iteration blocks ...................................................... 28 2.6.2 Adding rework probability and impact to the DSM model ................................................... 32 2.6.3 Constructing a DSM............................................................................................................. 36 2.7 MATHEMATICAL AND OPTIMISATION METHODS ............................................................................ 36 2.8 COMPUTER SIMULATION ............................................................................................................... 41 2.8.1 Simulation overview ............................................................................................................. 41 2.8.2 The use of computer simulation in project management ...................................................... 42 2.8.3 Modelling iteration using simulation ................................................................................... 44 2.8.4 System dynamics models ...................................................................................................... 47 2.9 ANALYSIS AND DISCUSSION OF ITERATION MODELLING APPROACHES .......................................... 49 2.9.1 Design structure matrix (DSM) ............................................................................................ 49 2.9.2 Mathematical and optimisation techniques.......................................................................... 50 2.9.3 Computer Simulation ........................................................................................................... 52 2.10 RELIABILITY OF MODEL INPUTS .................................................................................................. 55. i.

(5) 2.11 OPPORTUNITY FOR A DIFFERENT MODELLING APPROACH ........................................................... 56 2.12 SUMMARY .................................................................................................................................. 57 3. RESEARCH METHODOLOGY .................................................................................................... 61 3.1 METHODOLOGY SELECTION .......................................................................................................... 61 3.2 COMPANY SELECTION ................................................................................................................... 63 3.3 RESEARCH PROCESS...................................................................................................................... 64 3.3.1 Phase 1: Research question and rationale ........................................................................... 67 3.3.2 Phase 2: Initial investigation and full problem definition.................................................... 70 3.3.3 Phase 3: Root cause identification ....................................................................................... 72 3.3.4 Phase 4: NPD project characteristics definition ................................................................. 74 3.3.5 Phase 5: Risk mitigation methodology development ............................................................ 75 3.3.6 Phase 6: Analysis and interpretation ................................................................................... 77 3.4 SUMMARY .................................................................................................................................... 78 4. CASE STUDY – OVERVIEW ........................................................................................................ 79 4.1 INTRODUCTION ............................................................................................................................. 79 4.2 CASE STUDY COMPANY (SEMICO) - OVERVIEW ........................................................................... 80 4.3 SUMMARY .................................................................................................................................... 86 5. CASE STUDY – FINDINGS AND ANALYSIS ............................................................................. 87 5.1 INTRODUCTION ............................................................................................................................. 87 5.2 ITERATION AND SCHEDULING IN THE NPD PROCESS ..................................................................... 88 5.3 EFFECTIVENESS OF THE COMPANY’S CURRENT NPD PROJECT SCHEDULING PRACTICES ............... 93 5.3.1 Strengths of the current project planning practices ............................................................. 95 5.3.2 Limitations of the current project planning practices .......................................................... 95 5.4 THE IMPACT OF ITERATION ON PAST NPD PROJECTS..................................................................... 97 5.5 STATISTICAL ANALYSIS OF HISTORICAL SLIPPAGE DATA............................................................. 100 5.5.1 Descriptive statistics .......................................................................................................... 100 5.5.2 Analysis of variance ........................................................................................................... 104 5.6 FOCUS GROUPS ........................................................................................................................... 108 5.6.1 Rationale ............................................................................................................................ 108 5.6.2 Questionnaire 1: Pre-focus group questionnaire ............................................................... 109 5.6.3 Focus Group Structure....................................................................................................... 113 5.6.4 Focus Group Outcomes ..................................................................................................... 115 5.6.5 Focus Group Conclusions .................................................................................................. 128 5.7 DETERMINING SCHEDULING TOOL REQUIREMENTS ..................................................................... 129 5.8 IDENTIFIED SOURCES OF ITERATION RISK IN THE COMPANY’S NPD PROJECTS ............................ 130 5.9 DETERMINING MEASURES TO MODEL THE ROOT CAUSE VARIABLES ............................................ 135 5.9.1 Semi-structured follow up interviews ................................................................................. 135 5.9.2 Level of simulation coverage ............................................................................................. 136. ii.

(6) 5.9.3 Level of requirements definition......................................................................................... 137 5.9.4 New process / technology risk ............................................................................................ 140 5.9.5 Resources ........................................................................................................................... 143 5.9.6 Project Planning ................................................................................................................ 143 5.10 RELEVANCE OF IDENTIFIED ITERATION RISK SOURCES .............................................................. 145 5.11 SUMMARY ................................................................................................................................ 146 6. RISK ANALYSIS METHODOLOGY DEVELOPMENT ......................................................... 148 6.1 INTRODUCTION ........................................................................................................................... 148 6.2 DEVELOPMENT OF FIRST PHASE METHODOLOGY ......................................................................... 148 6.2.1 User Interface and information input ................................................................................ 150 6.2.2 Risk Analysis Calculations ................................................................................................. 153 6.2.3 Output of risk analysis tool ................................................................................................ 167 6.2.4 General NPD Project Performance and Analysis .............................................................. 174 6.2.5 Deepening the understanding of project characteristics and schedule slippage ............... 177 6.3 LONG TERM USE OF DEVELOPED METHODOLOGY ........................................................................ 193 6.5 SUMMARY .................................................................................................................................. 197 7. DISCUSSION.................................................................................................................................. 199 7.1 RESEARCH QUESTIONS DISCUSSION ............................................................................................ 200 7.2 THE RELATIONSHIP BETWEEN PRODUCT COMPLEXITY, PROCESS MATURITY AND ITERATION RISK ANALYSIS ......................................................................................................................................... 202. 7.3 USING ROOT CAUSE FACTORS FOR ANALYSING ITERATION RISK ................................................. 205 7.3.1 Simulation coverage characteristics .................................................................................. 206 7.3.2 Requirements definition characteristics ............................................................................. 207 7.3.3 New process / technology risk characteristics ................................................................... 208 7.3.4 Human resource characteristics ........................................................................................ 210 7.3.5 Project planning characteristics ........................................................................................ 211 7.3.6 Relevance of identified factors to company strategy .......................................................... 212 7.4 CURRENT LIMITATIONS IN THE FIRM AND THE BENEFIT OF THE PROPOSED METHODOLOGY ........ 213 7.5 MANAGING NPD COMPLEXITY IN THE COMPANY ....................................................................... 216 7.5.1 Planning approach ............................................................................................................. 216 7.5.2 Concurrent engineering approach ..................................................................................... 218 7.5.3 Self-organising approach ................................................................................................... 219 7.5.4 Implications of the developed methodology for the company’s NPD management approach .................................................................................................................................................... 221 7.6 DEPLOYMENT OF ITERATION RISK MITIGATION METHODOLOGY IN CASE STUDY FIRM ................ 223 7.7 GENERAL INITIAL IMPLEMENTATION OF ITERATION RISK MITIGATION METHODOLOGY .............. 228 7.8 RELATIONSHIP TO PREVIOUS RESEARCH ..................................................................................... 230 7.9 SUMMARY .................................................................................................................................. 231 8. CONCLUSIONS ............................................................................................................................. 232. iii.

(7) 8.1 INTRODUCTION ........................................................................................................................... 232 8.2 CONTRIBUTION OF THE RESEARCH .............................................................................................. 234 8.2.1 Sources of iteration in semiconductor NPD projects ......................................................... 234 8.2.2 A risk mitigation methodology for iteration in NPD projects ............................................ 234 8.2.3 Analysing iteration risk and in mature NPD environments ............................................... 234 8.2.4 Minimising the uncertainty caused by subjective estimates ............................................... 235 8.2.5 Providing a methodology for continuous improvement in NPD project planning ............. 235 8.3 BENEFITS OF ITERATION RISK MITIGATION METHODOLOGY DEPLOYMENT .................................. 236 8.4 LINKING THE THEORETICAL AND PRACTICAL ASPECTS OF THE RESEARCH .................................. 237 8.5 LIMITATIONS OF THE RESEARCH ................................................................................................. 238 8.6 FUTURE RESEARCH ..................................................................................................................... 239 PUBLICATIONS ................................................................................................................................ 240 REFERENCES ................................................................................................................................... 241 APPENDIX A: QUESTIONNAIRE 1 - SEMICO FOCUS GROUP PRE QUESTIONNAIRE .. 257 APPENDIX B: EXISTING PROCESS SEQUENCING – PARTITIONED DSM ....................... 260 APPENDIX. C:. QUESTIONNAIRE. 2. -. SEMICO. FOLLOW. UP. INTERVIEWS. QUESTIONNAIRE ............................................................................................................................ 261 APPENDIX D: MODEL - USER INTERFACE SCREENSHOTS................................................ 267 APPENDIX E: QUESTIONNAIRE 3: SEMICO – PAST PROJECT DATA COLLECTION .. 273 APPENDIX F: METHODOLOGY EXPLORATION – DETAILED RESULTS ........................ 279. iv.

(8) LIST OF FIGURES Figure 2.1: Sequential approach versus overlapped approach ..................................... 17 Figure 2.2: Different types of task interactions ........................................................... 18 Figure 2.3 Overlapped inter-dependent tasks .............................................................. 19 Figure 2.4: Relationships between task parameters (adapted from Zhang et al. (2006)) ...................................................................................................................................... 20 Figure 2.5: Collaboration between three or more tasks. (a) Cycle. (b) Communication ...................................................................................................................................... 21 Figure 2.6: Network diagram for example project (micro-representation).................. 24 Figure 2.7: Macro-level representation of different project scenarios ......................... 25 Figure 2.8: Example Activity on Arrow GERT Network ............................................ 26 Figure 2.9: Example DSM ........................................................................................... 29 Figure 2.10: (a) Original DSM & (b) Partitioned DSM............................................... 30 Figure 2.11: Example DSM showing communication times ....................................... 31 Figure 2.12: Example DSM showing rework probabilities ......................................... 32 Figure 2.13: Example DSM showing rework impacts ................................................. 33 Figure 2.14: Example DSM showing rework impacts with (a) original sequence and (b) modified sequence (adapted from Browning and Eppinger, 2002) ............................. 35 Figure 2.15: Network diagram representation of transition matrix ............................. 37 Figure 2.16: Example DSM with task durations and rework probabilities.................. 39 Figure 2.17: Reward Markov chain for example DSM (from Smith and Eppinger, 1997) ...................................................................................................................................... 40 Figure 2.18: Abstraction, Simulation & Interpretation (from Johnson, 2001) ............ 42 Figure 2.19: Comparing probability density functions of different simulation runs ... 43 (from Flanagan et al., 2005a) ....................................................................................... 43 Figure 2.20: Example causal loop diagram.................................................................. 48 Figure 2.21: System Dynamics Stock and Flow Diagram ........................................... 48 Figure 3.1: Research process steps .............................................................................. 66 Figure 4.1: NPD Process Phases .................................................................................. 82 Figure 5.1: Closed loop iteration process .................................................................... 91 Figure 5.2: Addition of rework tasks to solve iteration issues ..................................... 91 Figure 5.4: Project slippage reasons over four year period (in months) ...................... 99. v.

(9) Figure 5.6: Plot of mean slippage time defined by product line and innovation level .................................................................................................................................... 103 Figure 5.7: Slippage Time by Product Line Box-Plot ............................................... 103 Figure 5.8: Slippage Time by Innovation Level Box-Plot......................................... 104 Figure 5.9: Question 1 responses for group 1 ............................................................ 117 Figure 5.10: Question 1 responses for group 2 .......................................................... 118 Figure 5.11: Question 1 responses for group 3 .......................................................... 119 Figure 5.12: Question 2 responses for group 1 .......................................................... 121 Figure 5.13: Question 2 responses for group 2 .......................................................... 122 Figure 5.14: Question 2 responses for group 3 .......................................................... 123 Figure 5.15: Question 3 responses for group 1 .......................................................... 125 Figure 5.16: Question 3 responses for group 2 .......................................................... 126 Figure 5.17: Question 3 responses for group ............................................................. 127 Figure 5.18: Overview of proposed approach for determining risk analysis inputs for NPD project scheduling ............................................................................................. 133 Figure 6.1: User interface – welcome screen ............................................................. 151 Figure 6.2: User interface – general project information input ................................. 152 Figure 6.3: User interface – Simulation coverage inputs ........................................... 153 Figure 6.4: Simulation coverage characteristic – linear relationship example .......... 156 Figure 6.5: Multiple tools risk characteristic – piecewise function relationship ....... 159 Figure 6.6: User interface – Main output screen........................................................ 168 Figure 6.7: Model output – slippage time breakdown ............................................... 170 Figure 6.8: Model output – top 10 contributory factors............................................. 171 Figure 6.9: Model output – slippage probabilities ..................................................... 171 Figure 6.10: Model output – cumulative probabilities............................................... 172 Figure 6.11: Model output – modifying inputs .......................................................... 173 Figure 6.12: Model output – recalculations ............................................................... 174 Figure 6.13: Home page screen (1) ............................................................................ 175 Figure 6.14: Home page screen (2) ............................................................................ 175 Figure 6.15: Home page screen (3) ............................................................................ 176 Figure 6.16: Model Examination – Calculated Slippage vs. Actual Slippage ........... 179 Figure 6.17: Model Examination – Standard Deviation Analysis ............................. 185 Figure 6.18: Model Exploration – Model vs. Actual Slippage (F = 29.64; p = 0.000) .................................................................................................................................... 186 vi.

(10) Figure 6.19: Model Exploration – Comparison of TTM predictability ..................... 187 Figure 6.20: Model Exploration – Increased level of predictability .......................... 188 Figure 6.21: Breakdown of Risk Contribution – Individual Project Example .......... 190 Figure 6.22: Breakdown of Risk Contribution – All Projects ................................... 191 Figure 6.23: Advanced Modelling Framework - Flow Chart .................................... 194 Figure 7.1: Company pillars of predictability – internal strategy .............................. 213 Figure 7.2: Operational deployment of proposed iteration risk mitigation methodology .................................................................................................................................... 224 Figure 7.3: Periodic review of iteration risk mitigation methodology....................... 225 Figure 7.4: General implementation methodology .................................................... 229 Figure 7.4: Previous approach vs. proposed approach .............................................. 231 Figure 5.3: NPD Process - Partitioned DSM ............................................................. 260 Figure C1: Model - User Interface 1 .......................................................................... 267 Figure C2: Model - User Interface 2 .......................................................................... 267 Figure C3: Model - User Interface 3 .......................................................................... 268 Figure C4: Model - User Interface 4 .......................................................................... 268 Figure C5: Model - User Interface 5 .......................................................................... 269 Figure C6: Model - User Interface 6 .......................................................................... 269 Figure C7: Model - User Interface 7 .......................................................................... 270 Figure C8: Model - User Interface 8 .......................................................................... 270 Figure C9: Model - User Interface 9 .......................................................................... 271 Figure C10: Model - User Interface 10 ...................................................................... 271 Figure C11: Model - User Interface 11 ...................................................................... 272 Figure C12: Model - User Interface 12 ...................................................................... 272. vii.

(11) LIST OF TABLES Table 2.1: Dyer et al. (1999) reasons for delay in NPD projects ................................. 14 Table 2.2: Example Project Tasks ............................................................................... 24 Table 2.3: GERT nodes entrances and exits ................................................................ 25 Table 2.4: Task durations for example project ............................................................ 35 Table 2.5: 4-stage transition matrix (at end of time period 1) ..................................... 37 Table 2.6: Transition matrix at end of time period 4 ................................................... 38 Table 2.7: Transition matrix at end of time period 6 ................................................... 38 Table 2.8: Comparison of iteration modelling techniques ........................................... 54 Table 4.1: New product development assessment factors ........................................... 83 Table 5.1: Summary of project planning strengths and limitations ............................. 94 Table 5.2: Sample set cross tabulation (Product Line x Innovation Level) ............... 101 Table 5.3: Chi-square test .......................................................................................... 102 Table 5.4: Average Slippage Time for each Category (in months) ........................... 102 Table 5.5: Summary of ANOVA test results (p = 0.05, n =112, dependent variable = observed project slippage time) ................................................................................. 105 Table 5.6: Question 1 Responses ............................................................................... 116 Table 5.7: Question 2 Responses ............................................................................... 120 Table 5.8: Question 3 Responses ............................................................................... 124 Table 6.1: Risk analysis calculation example - simulation coverage characteristics 165 Table 6.2: Risk analysis calculation example – requirements definition characteristics .................................................................................................................................... 165 Table 6.3: Risk analysis calculation example – new technology/process risk characteristics ............................................................................................................. 166 Table 6.4: Risk analysis calculation example – resources characteristics ................. 166 Table 6.5: Risk analysis calculation example – project planning characteristics ...... 167 Table 6.6: Model output – Summary of project inputs .............................................. 169 Table 6.7: Model examination – Overview (all values in months)............................ 178 Table 6.8: Project comparison – Simulation coverage characteristics ...................... 182 Table 6.9: Project comparison – Requirements definition characteristics ................. 183 Table 6.10: Project comparison – New process / technology risk characteristics ..... 183 Table 6.11: Project comparison – Resources characteristics ..................................... 184 Table 6.12: Project comparison –Project planning characteristics ............................ 184 viii.

(12) Table 7.1: Simulation coverage slippage characteristics ........................................... 206 Table 7.2: Requirements definition project characteristics........................................ 208 Table 7.3: New process / technology risk project characteristics .............................. 209 Table 7.4: Human resource project characteristics .................................................... 211 Table 7.5: Project planning slippage characteristics .................................................. 212. ix.

(13) 1. Introduction 1.1 Overview In today’s highly competitive and global corporate environment, for many companies, engaging in continuous innovation is essential for survival. New Product Development (NPD) is an important process that can give an organisation a distinct competitive edge if carried out effectively. For innovative organisations, NPD projects are constantly being undertaken, often with multiple projects running concurrently. This volume of NPD projects in a company requires large amounts of time, effort and resources, at significant cost to the firm. Therefore, effective management of NPD projects is of great importance to both overall NPD performance and to achieving the wider strategic objectives of an organisation. While there are numerous components that contribute to successful management of an NPD project, this thesis is concerned with management of the project schedule. Specifically, the focus of this research study is on the risk present in NPD project schedules due to the potential occurrence of iteration in a development project. Browning (1998), provides a good definition of iteration in the context of NPD projects stating it: “…implies rework or refinement, returning to previously worked activities to account for changes. This rework can stem from new information and/or failure to meet design objectives.” Iteration can occur in NPD projects for two main reasons: [1] new information comes to light during the execution of a project that requires tasks to be reworked; [2] errors are discovered downstream in the process that requires related upstream tasks to be reworked (Browning, 1999; Browning and Eppinger, 2002; Chen et al., 2003; Cho and Eppinger, 2005; Danilovic and Browning, 2007; Maheswari and Varghese, 2005; Smith and Eppinger, 1997). The occurrence of iteration in NPD projects can directly lead to budget over-runs, schedule delays and missed product release dates (Browning and Eppinger, 2002; Kusar et al., 2004; Terwiesch et al., 2002). With time to market often being an important determinant of NPD success (Chen et al., 2005), any factor that can potentially cause project delay must be fully understood. This research aims to further examine iteration modelling in NPD and explore how more accurate and detailed predictions of iteration. 1.

(14) can be made. Providing management within a firm with a process specific risk analysis regarding iteration can enable a proactive approach to NPD scheduling and contingency planning. The goal is to provide a scheduling framework where the risks of delay can be identified and subsequently mitigated.. 1.2 NPD project scheduling and iteration Producing reliable schedules for NPD projects, and thus avoiding project delay, can be a difficult process because of the inherent uncertainty and continuous evolution involved in most development projects. Schedule uncertainty can arise from several sources, the most notable of which is task iteration occurring in a project (Browning, 1999; Browning and Eppinger, 2002; Chen et al., 2003). Unplanned iteration contributes significantly to schedule uncertainty and occurs when new information and/or the discovery of errors result in the need for project activities to be reworked (Browning, 1999). The occurrence of iteration is an essential feature of complex product development processes (Browning, 1999; Danilovic and Browning, 2007; Chen et al., 2003). Ignoring or even underestimating potential iteration in a project can have a serious negative effect with unrealistic due dates being set and costly project over-runs occurring (Browning and Eppinger, 2002). Correcting the problems that occur in NPD projects due to unplanned iteration is time consuming, costly and can directly lead to project delay (Terwiesch et al., 2002; Kusar et al., 2004). Ford and Sterman (2003) describe the critical role of iteration in explaining the ‘90% syndrome’, where a project reaches about 90% completion and then slows down significantly, almost to a halt. While the problem of iteration in NPD is widely acknowledged in the literature, there have been no broad studies that quantify the general impact of iteration in product development projects. In the study of iteration in projects at Intel, Osborne (1993) found that on average iteration accounted for one third of total project effort spent. Similarly in the semiconductor company used in this research study, on average iteration was found to account for approximately 20% of all NPD project slippage. Iteration occurs in NPD projects due to the high levels of uncertainty and the complex interdependency that can exist between project tasks. Traditional project management 2.

(15) scheduling tools such as Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT) do not account for task iteration as they allow only linear one-way progression along paths and ignore feedback loops. The uncertainty surrounding the development of new products highlights limitations of scheduling techniques such as PERT. The likelihood of encountering problems in NPD projects can result in various possible scenarios that may be executed in order to complete a project. It is this potential variability in project/ process structure that make traditional project scheduling techniques insufficient on their own. As a result, researchers have looked for alternative approaches for tackling the problem of project scheduling under uncertainty in NPD. One popular technique that has been used to incorporate iteration into project schedules is the design structure matrix (DSM) method. The DSM can represent inter-dependencies between project tasks, which are the root cause of iteration in a project. The main function of the DSM is to find the optimal sequencing of project tasks, with the goal of minimising potential iteration in a project. Mathematical and optimisation methods have also been used to model the dynamic nature of complex product development projects. Techniques such as Markov chains and specifically developed scheduling algorithms can be used to incorporate task iteration into a project schedule. Computer simulation also offers a flexible and effective method for scheduling projects with potential iteration. Using simulation, a model can be constructed that sufficiently replicates actual project conditions. Iteration can be modelled using probabilistic branching along with randomly generated numbers. A benefit of simulation is that different project conditions and structures can be evaluated with relative ease. While many effective solutions to the problem of iteration have been developed, one limitation of many of the proposals is one of practicality. The majority of the previous research that has been done in the area of modelling iteration in NPD projects has been quite theoretical with little focus on implementation in a practical industrial environment. One main motivation behind this research study is to investigate how iteration can affect NPD schedules in a practical setting and how a modelling methodology can be developed that is tailored to the unique and specific requirements of a company.. 3.

(16) 1.3 Motivation for research Regardless of the specific technique used, the vast majority of previous research in the area of iteration modelling adopts the same approach. When modelling the potential iteration in a project, two main parameters are used: [1] the probability that iteration will occur and [2] the impact that iteration will have on a schedule if it does occur (Smith and Eppinger, 1997; Cho and Eppinger, 2001; Cho and Eppinger, 2005; Browning and Eppinger, 2002). The previous models that address the problem of iteration use estimates for these parameters but do not focus on how these estimates are arrived at or whether they fully reflect how and why iteration occurs during a product development process. Instead the focus is generally on the analysis of project performance after the parameter estimates are decided upon. While this type of analysis is necessary to understand the implications of iteration, the output of any model will only be as reliable as the inputs used. An approach to ensure the accuracy of the input parameters is to have the values of the iteration probabilities and impacts in a project schedule driven by a set of identified variables. These variables can be identified through thorough investigation into the root causes of iteration in a firm’s NPD process. Using this approach to determine the inputs to a risk analysis model rather than using subjective estimates can result in a more robust model being developed. This research is motivated by this need to provide a more reliable means of incorporating the underlying causes of iteration into an NPD project schedule risk analysis. An industrial case study company is used for the development of a project scheduling risk analysis framework that aims to provide a novel approach to accounting for potential project slippage caused by unplanned iteration. Exploratory investigation into the company’s NPD process, and the specific problem of iteration, identified a number of measures that were deemed contributory factors to iteration slippage occurring in projects. One main aim of this research is to utilise the information gained from the exploratory investigation and integrate it into a practical project scheduling risk analysis methodology. This approach not only gives the user a risk analysis regarding the total duration of a given project but also highlights the most problematic areas regarding contribution to slippage. The benefit to a project manager of having such a tool is the ability to make more informed project planning decisions regarding project scheduling. In order to ensure practicality, a sustainable method that is. 4.

(17) continually updated is required. The implication of having a tool which uses actual project data and tracks the ongoing performance of projects is that it ensures a project manager can have continued confidence in model accuracy. The practicality of the risk analysis approach is a major component of this research study. To date, the majority of previous work in the area of iteration analysis has been relatively academic. An important aim of this research was to provide a practical solution that can be used in an industrial environment. Another rationale for conducting the research was the opportunity to investigate the problem of iteration in highly complex and mature NPD process. The firm in question already has established processes in place for product development projects that have been refined over time. In this scenario simple changes to the process flow and structure are not feasible and have no benefit. The challenge then becomes one of finding alternative methods of improving upon NPD schedule performance. The development of complex high-tech products also results in particular difficulties when trying to model potential iteration. Previous approaches to modelling iteration assume a simplified ‘cause and effect’ scenario where a specific problem is encountered which requires a given amount of time to rectify. However, for very complex processes this dissection of iteration slippage cannot be achieved. As is the case with the case study companies in question, project slippage time can only practically be assessed on a macro project level. With this issue arises the challenge of finding a means of analysing the occurrence of iteration in a process which provides genuine insight and is beneficial to NPD project planning.. 5.

(18) 1.4 Research questions and objectives The overriding objective of the study at its beginning is encapsulated by the primary research question. This primary research question provides a description of the general problem being addressed in the study. RQ: What are the root causes of iteration risk in new product development projects in a semiconductor company setting? A number of sub-questions further define the objectives of the study: RQ1: How has the problem of iteration risk in NPD projects been previously addressed? RQ2: How does iteration risk impact the NPD project performance of a semiconductor company? RQ3: How can the identification of iteration causes in a process be incorporated into an iteration risk mitigation methodology in semiconductor NPD projects? RQ4: How can an iteration risk mitigation methodology be utilised to facilitate continuous learning and development in NPD projects?. 1.5 Thesis structure The remainder of this thesis is structured as follows. Chapter 2 provides a review of the literature in relation to: schedule risk and uncertainty in NPD, the general problem of iteration in NPD, previous approaches for modelling iteration in NPD schedules, and a critique of the different approaches for modelling iteration. Chapter 3 describes the criteria used for selecting the case study company, the research methodology employed and the specific steps involved in the research study.. 6.

(19) Chapter 4 provides an overview of the case study company used in the investigation, including background information on the firm and a detailed description of their formal NPD process. Chapter 5 presents the findings from the investigation of the case study firm. This includes analysis of the company’s methods and procedures for handling iteration, and quantification of both total slippage and slippage due to iteration in NPD projects. Also presented in chapter 5 are the findings from all management interviews, and focus group sessions which were used to fully define the problem of iteration and its’ root causes in the NPD process. Chapter 6 describes the development of the risk mitigation methodology that uses the findings from chapter 5 to provide a process specific risk analysis tool for analysing iteration risk in NPD projects. Also detailed in chapter 6 is a theoretical framework that provides a generic and sustainable methodology for analysing iteration risk in NPD projects. Chapter 7 provides a discussion of the research and addresses the problem of modelling iteration for complex NPD and mature processes. Also discussed in chapter 7 is the concept of using the root causes of iteration in a process to develop a risk analysis methodology, as well as the issue of practicality when implementing a new scheduling methodology in an industrial setting. Finally, chapter 8 provides a summary of the research study, the limitations of the study, the contributions of the research, and suggestions for future research directions.. 7.

(20) 2. Literature Review 2.1 Introduction The next stage of the research consisted of examining relevant literature in order to frame the problem being addressed. The first areas that are examined are the general issues that exist in relation to NPD time to market performance, uncertainty and risk in NPD and the problem of NPD scheduling and delays. The next area reviewed is the general area of iteration in NPD projects and the consequence of schedule slippage in projects. A more detailed review of the previous approaches for modelling iteration and schedule slippage is then carried out. This analysis of previous modelling approaches is broken down into three broad categories: Design Structure Matrix (DSM) models, pure mathematical models and computer simulation models. An analysis of the advantages and limitations of each of the different approaches is presented. This analysis is framed by the objectives of this research study, i.e. develop a practical method for analysing slippage in complex and mature NPD processes.. 2.2 New product development and the importance of time to market A key factor for ensuring product development projects are successful is achieving a quick time to market (Montoya-Weiss and Calantone, 1994; Hendricks and Singhal, 1997; Ford and Sterman, 2003). Delays in releasing new products can have both reputational and financial consequences for a firm. In their study, Hendricks and Singhal (1997) calculated that the delay of a new product introduction can reduce the market value of a company by about 5%. The speed in which a firm can develop and release a new product is also important for particularly competitive market environments (Kessler and Chakrabarti, 1996). Releasing a product before competitors can provide an edge and lead to an increase in market share (Clark and Fujimoto, 1989; Gomory, 1989). In a study of 692 NPD projects, Chen et al. (2005) found that in the main the speed to market of a new product is positively associated with new product success. However, both the turbulence and newness of the market being entered can be moderating factors, i.e. speed to market is less important for stable and established markets. The need to speed up the product development process has resulted in firms using various methods to manage NPD projects. A common approach for achieving this 8.

(21) reduction in development time is the use of concurrent engineering practices. Where applicable, conducting tasks in parallel rather than sequentially reduces the overall duration of a project. The level of organisation and structure required in concurrent engineering projects also leads to better communication and a higher degree of responsibility among project team members (Kusar et al., 2004). The level of advantage that a short time to market gives can be dependent on [1] the type of market being entered and [2] the technological complexity of the product itself. In their study, Meyer and Utterback (1995) suggest that solely focussing on the reduction of product development cycle time does not lead to greater performance. Allowing additional time for the integration of particularly complex technologies as well as for understanding newer markets will increase overall development time but not necessarily decrease the likelihood of product success. With regard to technological complexity, the level of innovation involved in a product development project can in fact influence the effectiveness of project acceleration techniques. Kessler and Chakrabarti (1999) found that factors that were known to reduce the time to market of breakthrough projects, increased the time to market for derivative development projects.. 2.3 Uncertainty and risk in NPD projects By definition a project is a unique endeavour undertaken by a firm to achieve a specific objective (PMBOK, 2008) and NPD projects in particular contain a large amount of uncertainty. In their discussion paper, Perminova et al. (2008) provide an interesting insight into the issue of uncertainty in projects. Building on previous work and discussion in the area they explicitly distinguish between project risk and project uncertainty. Uncertainty is defined as the situation where project risks cannot be adequately quantified and defined. The authors state that “…risk is assumed as a condition in the environment in which the decision maker presumes him- or herself able to give probabilities to outcomes of events…”. This distinction between risk and uncertainty is an important one in the context of this study for two reasons. Firstly, this research is concerned with product development projects, and the role of uncertainty (not only “known risks”) is integral to the approach taken when managing these types. 9.

(22) of projects. Secondly, the concept of assigning probability values to uncertain events and scenarios is one of the fundamental issues addressed in this research. NPD projects bring specific challenges to overcome for a company due to the innate uncertainty involved in producing a new product. The use of new technology and/or new processes during a product development project causes particular difficulty for firms. In their study of large R&D projects Song et al. (2007) list five general contributors to uncertainty in development projects: [1] organisational uncertainties, [2] technological uncertainties, [3] external environmental uncertainties, [4] scope and direction ambiguity, [5] technological and market condition ambiguity. The effect of this uncertainty was found to impact the planning and decision making processes employed during the lifecycle of NPD projects. In their cross sectional study of 120 NPD projects, Tatikonda and Rosenthal (2000) found that technology novelty is positively associated with poor time to market performance of a project. One interesting finding from the study is that process technology newness is more problematic than product technology newness. This highlights the volatile nature of NPD projects, as not only is there concern regarding the new technology incorporated in a product but the means by which a project is executed is also an integral component of project success. Means to reduce uncertainty and risk in NPD projects have also been studied by numerous researchers. Cooper (2003) provides a practitioner perspective on how knowledge management systems (KMS) can be utilised to reduce risk levels in development projects. The author highlights some of the advantages, and limitations, of using KMS in NPD projects and how their use can influence the presence and mitigation of risk. While many aspects of KMS use are discussed, in relation to this research the pertinent ones relate to how the use of systematic processes can help with risk reduction through “….gathering and processing relevant information and encapsulated knowledge from a variety of internal and external sources.” Mu et al. (2009) address the use of different risk management strategies and how they impact on NPD performance. Following preliminary qualitative interviews, the authors use a survey research approach to analyse the effectiveness of risk management through three lenses: [1] Technological risk management, [2] Organisational risk management, and [3] Market risk management. These three risk management areas cover a lot of the 10.

(23) internal and external factors that can cause uncertainty and disruption in NPD projects. Of particular relevance for this research is the finding that risk management of internal processes (i.e. organisational risk management), is positively associated with NPD performance. Wang et al. (2010) propose the use of a new risk management approach for use in innovative product development projects. The comprehensive framework developed by the authors uses concepts from the balanced scorecard (BSC) and quality function deployment (QFD) techniques. The framework enables the identification and quantification of the risks present in an NPD project from four different perspectives, [1] financial, [2] customer, [3] internal business process, and [4] learning and growth. These four elements are derived from the literature and provide the structure for the balanced scorecard element of the framework. The proposed approach aims to align the risk management of a project with company strategy and NPD project performance metrics. The perceived advantage of the proposed framework is that it enables the identification, quantification, and ultimate ranking of the risks in a project. This enables management to focus on their efforts and resources on the most problematic aspects that can affect R&D projects. One major consequence of the inherent uncertainty involved in NPD projects is the resultant uncertainty in project schedules. In particular, the estimation of durations for project activities that have not been attempted before contribute to schedule uncertainty and schedule risk (Wang, 2002). The concept of schedule risk in product development projects is one which has not been investigated to any great extent. From a project management perspective, a major challenge when undertaking NPD projects is to plan and account for the uncertainty surrounding the schedule. The majority of the literature which addresses the problem of project scheduling under uncertain conditions assumes that all of the information required to produce a project schedule is readily available to a project manager (Herroelen and Leus, 2005). Herroelen and Leus (2005), provide a comprehensive review of the various approaches that have been used for scheduling under uncertain conditions. A significant amount of the literature that has addressed the problem of project scheduling under uncertainty focuses on the uncertainty surrounding activity durations 11.

(24) and the allocation of resources, known as resource-constrained project scheduling problem (RCPSP). Different computational approaches have been used when addressing the RCPSP under uncertainty, including branch-and-bound algorithms (Stork 2000), heuristic solutions (Golenko-Ginzberg and Gonik, 1997; Tsai and Gemmil, 1998; Pet-Edwards and Mollaghesemi, 1996) and stochastic activity interruptions (Valls et al., 1999). Another approach that has been used to address the problem of project scheduling under uncertain conditions is that of fuzzy project scheduling. Due to lack of previous experience in NPD, fuzzy activity durations are used to represent uncertainty rather than stochastic probability distributions. An in-depth description of fuzzy sets and scheduling is beyond the scope of this study. Fuzzy modelling uses membership functions to describe the degree of membership to a given set. In the case of project scheduling under uncertainty, the general approach when using fuzzy sets is to introduce additional freedom and flexibility in relation to activity duration, start-time, and finish-time. Wang (2002) utilises the fuzzy set approach to develop a scheduling methodology for NPD projects which aims to minimise project lateness through resource allocation optimisation. The uncertainty incorporated in the model is related to imprecise and/or incomplete information regarding activity times. Zafra-Cabeza et al. (2008) provide an optimisation model that aims to incorporate potential risks in a project schedule and the effect they can have on schedule performance. Their prediction model provides a framework for identifying factors that contribute to project schedule risk as well as mitigation techniques to reduce the impact of the defined risks. Using the well established approach of cost/time trade-off analysis, the optimisation model first calculates the expected project duration and cost by applying risk management techniques to the baseline schedule. Monte Carlo simulation is then used to apply different risk mitigation actions and the subsequent effect on project duration and cost. An interesting element of the model, and one which has relevance to this study, is the use of risk metrics in calculating project duration. The benefit of such an approach is that by providing specific measureable metrics, the inaccuracy of using subjective estimates is somewhat minimised. Another advantage of having specific metrics is the increased opportunity for post project learning. Relating. 12.

(25) inputs for the metric variables to overall project performance data can provide valuable insight that can be used when conducting future projects with similar elements. Pajares and Lopez-Paredes (2011) propose the use of two new metrics in conjunction with traditional earned value calculations which aims to integrate an element of risk management during project monitoring and control. One of the metrics introduced is the Schedule Control Index (SCoI). This metric aims to integrate project schedule uncertainty by measuring the magnitude of schedule deviation throughout the life-cycle of a project. This risk management approach to project scheduling is another interesting attempt of incorporating uncertainty in schedules even though the specific nature of the technique (i.e. earned value management) may have limitations for widespread adoption.. 2.4 Schedule delay in NPD projects While the importance of meeting time to market targets is apparent, the uncertainty that surrounds NPD poses significant problems for any company in achieving these targets. By definition, developing a new product requires the use of new technology and/or new processes. With limited past experience, there is a high risk of problems occurring during the execution of an NPD project that cause delays and result in due-dates being missed. Problems can be encountered from external sources such as: customer issues, supply chain problems, and regulatory delays. Internal factors such as technical issues, quality problems and poor planning can also cause delays of an NPD project schedule. In their meta-analysis of NPD delays in the computer industry, Rosas-Vega and Vokurka (2000) found that 79% of the reasons for delay were caused by internal problems. In their analysis of how first to market firms manage their NPD projects, Dyer et al. (1999) look specifically at factors that contribute to project delays. Based on the results of 182 surveyed technology based companies, the main internal reasons for NPD delay for three company categories were established (Table 2.1). The results indicate that there are a wide range of issues that contribute to NPD project schedules being delayed, which can ultimately result in TTM targets being missed.. 13.

(26) Table 2.1: Dyer et al. (1999) reasons for delay in NPD projects First to market companies. Fast follower companies. Late entrant companies.  Difficulty in product/market definition.  Difficulty in product/market definition.  Lack of strategic thinking.  Poor product concept definition.  Management unwillingness to take risks.  Finalising the design.  Assessing market potential.  Bureaucracy and red tape.  Poor interdepartmental relations.  Finalising the design.  Preoccupation with other things.  Defining product performance specifications.  Difficulty in product/market definition.  Making transition from R&D to manufacturing.  Uncertain technical developments.  Defining product performance specifications.  Lack of resources.  Preoccupation with other things.  Poor interdepartmental relations.  Continually changing product specifications.  Senior management short-term focus.  Senior management short-term focus.  Uncertain technical developments.  Lack of strategic thinking.  Little incentive for innovation/change.  Lack of effective team work.  Poor interdepartmental relations.  Assessing market potential.  Preoccupation with other things.  Indecision/slow response at the top. The consequences of schedule delays and missed due-dates are obvious and can be categorised into three types: [1] financial implications, [2] operational implications, and [3] reputational implications (Hendricks and Singhal, 1997; Dyer et al., 1999; Chen et al., 2007; Hendricks and Singhal, 2008). From a financial perspective, delays in a project will have cost implications due to: extended use of resources (both human and machine), reduction in product sales, loss of market share, negative impact on company share price. From an operational point of view, NPD projects that run over schedule can impact the company business as a whole due to the knock on effect of resources that are needed for other projects and operations being tied-up with over-running projects. Being late to market with a product can also damage a firm’s reputation with prospective customers. The time to market of a product can be the difference between firms being classed as: a first to market company, a fast follower company, or a late entrant company (Dyer et al., 1999). A first to market company has distinct advantages. 14.

(27) in terms of economic profits, technology patent ownership, and product life-cycle maximisation.. 2.5 Iteration in new product development projects 2.5.1 Causes and types of iteration Unplanned iteration usually occurs in projects for two reasons [1] tasks are repeated as expected additional information is obtained from related tasks, and [2] tasks are repeated due to an unexpected error being discovered in related tasks or due to a change in project direction (Smith and Eppinger, 1993). Expected iteration can also occur when tasks are intentionally started early with incomplete information from predecessor tasks. This may be done to speed up the NPD process when it is known that the expected iteration will have a minimal effect on the overall project duration. An example of expected iteration would be the modification of CAD drawings to incorporate all assembly parts of a product as they are designed. Intentional iteration can also be incorporated into a schedule to refine interrelated design tasks (Browning, 1999). Expected iteration should not disrupt a project too much as it known beforehand and thus can be planned for. Unplanned iteration on the other hand can have severe consequences on meeting a project deadline (Ford and Sterman, 2003). This type of iteration can occur for different reasons (Smith and Eppinger, 1997): 1. An upstream task may need to be repeated if a dependant downstream task discovers some sort of error or incompatibility, e.g. redesigning of a prototype because of failure during testing. 2. When tasks are executed concurrently, a downstream task may need to be repeated when modified information is passed along from a related upstream task, e.g. modifications made to the initial design of a product. In the second case, iteration occurs because of the concurrency of tasks and would not happen in fully linear sequential project. However, concurrent engineering is often employed in NPD projects as it is seen as an effective method of reducing development time and cost (Yassine and Braha, 2003). 15.

(28) Lin et al. (2008) describe two types of rework that can occur in a product development process: 1. Rework due to development errors – where rework is performed because of errors identified through review and testing activities. 2. Rework due to corruption – where rework is performed because of a change in an upstream task causes an associated downstream task to become corrupt. Costa et al. (2003) examine the question of whether iteration is avoidable or inevitable by defining different types of iteration. Three different iteration types are described, [1] rework iteration, [2] design iteration, [3] behavioural iteration. Rework iteration is described as the repetition of a task in order to correct some sort of error. Design iteration occurs when the progression of a project requires certain design tasks to be refined in order to bring them in line with a final detailed design. Behavioural iteration is the repetition of a task triggered by a change in project scope, for example a customer changing the required specification of a product after initial design tasks had been carried out.. 2.5.2 Task overlapping The use of concurrent engineering practices to speed-up NPD projects results in the need for task overlapping. When tasks are overlapped, downstream tasks will start to execute before related upstream tasks are fully completed. The difference between a sequential approach and an overlapping approach can be seen in Figure 2.1. The use of overlapping increases the likelihood of iteration occurring as downstream tasks must begin with preliminary information that may change over time (Helms, 2004). Ideally to minimise risk a project would be executed in a completely linear fashion where a task would only commence once all predecessors have been completed. However, projects that are time critical have an increased chance of failure if they do not meet specified target dates (Luh et al., 1999). This is often the case with product development efforts, as releasing a product too late into a mature market can significantly reduce the likelihood of making profit (Helms, 2004). The reason for overlapping in NPD is therefore self-evident and is achieved through effective multi-disciplinary communication and information flow (Verganti, 1997). 16.

(29) In their analytical model for estimating the cycle time of a NPD process Jun et al. (2005) also address the overlapping of project tasks. In the model, rework occurs due to the continuous release of incomplete information from the upstream task. The downstream task continuously monitors the progression of the upstream task with rework occurring at intervals throughout the overlapping period. Rather than looping back and repeating an entire task the model will continuously rework portions of a task until a suitable output is achieved. Thus, the result of rework occurring is an increase in the overall duration of a downstream task. It is assumed with overlapped tasks that rework is more likely to occur at the start of the overlap. As such, a non-homogeneous Poisson process is used to define the varying rate at which rework occurs in the downstream task. To model any specific overlap processes, interviews were conducted with experienced engineers to determine the sensitivity of the downstream task (during overlap) as the upstream task evolves. Due the high levels of uncertainty involved in NPD, the practicality of implementing such a methodology on an ongoing basis would likely be an issue. However, if implementation is possible the benefit of such an approach is apparent. Ensuring that the outputs for tasks satisfy the required inputs for downstream tasks enables a stage gate type approach where constant reviews are performed. In theory this should eliminate the occurrence of long and time consuming iteration feedback loops.. Sequential approach. Overlapped approach. Upstream Process. Upstream Process. Downstream Process. Downstream Process. Final information exchange Preliminary information exchange. Figure 2.1: Sequential approach versus overlapped approach. 17.

(30) 2.5.3 Dependencies between project tasks Due to the high levels of uncertainty and innovation that can be involved in NPD projects, complex dependencies can exist between tasks. The different types of dependencies that can exist can be seen in Figure 2.2. Independent tasks have no relationship and can be executed in parallel while dependent tasks must be executed in a fully sequential manner. Potential iteration arises from the last two types of relationship. Inter-dependent tasks can execute concurrently but may need to be repeated multiple times before they are completed. Dependent tasks with feedback loops can cause major delays in a project. Using the example in Figure 2.2, after completion task C may result in the need for task A to be repeated creating a different output for task A. This new information output can then cause successive rework in tasks B and C.. Illustration of task relationships. Type of Dependency. A. 1. Independent. B. 2. Dependent. A. B. C. A. 3. Interdependent (Coupled) B. 4. Dependent with feedback loop. A. B. C. Figure 2.2: Different types of task interactions. 18.

(31) In relation to interdependent (coupled) tasks, Joglekar et al. (2001) present a performance generation model (PGM) that helps in determining the optimal strategy for managing coupled tasks. The model is used to examine how each individual task in an interdependent relationship contributes to overall system performance and the optimal amount of overlapping that should be employed. The interdependency between two tasks is modelled by performance decline in one task due to rework generated by the other task. For example in the shaded overlapped regions in Figure 2.3, as Task A performs work to improve its own performance, it will produce rework in Task B and thus weaken the performance of task B. The aim is to improve overall system performance by ensuring neither task produces an unacceptable level of rework for the other.. Task A. Task B. Figure 2.3 Overlapped inter-dependent tasks. Zhang et al. (2006) define a method for measuring the strength of dependency between coupled tasks. Each task is broken up into its constituent information parameters and the relationships between parameters are established (Figure 2.4). The types of relationships between the parameters can then determine the strength of the coupling between tasks. For example, in Figure 2.4 there is a dependent relationship between B2 and A4, an interdependent relationship between A1 and B4 and a complex successive relationship where B5 provides A6 with information and A6 subsequently provides information to B1 and B3. Jun et al. (2005) describe four types of collaboration between inter-dependant tasks 1. Interaction 2. Feedback 3. Cycle 4. Communication 19.

(32) Interaction collaboration is the same as the coupled relationship already described, where two tasks ‘negotiate’ to find a suitable output. Similarly feedback collaboration is the same as outlined in Figure 2.2, where a number of successive tasks are required to repeat due to the output of one task. For three or more related tasks, cycle and communication collaboration can be seen in Figure 2.5. A cyclical collaboration among tasks can occur when a certain aspect of the development process is dependant on the output of a number of tasks. In this type of collaboration information is exchanged among tasks in a sequential manner until all tasks reach a suitable output. Communication collaboration is when three or more tasks must be performed concurrently in order to produce a suitable output. The tasks must simultaneously interact with each other until a reasonable result is achieved.. A1. A2. A3. Task A A4. A5. A6. B1. B2. B3. Task B B4. B5. B6. Figure 2.4: Relationships between task parameters (adapted from Zhang et al. (2006)). 20.

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