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Building blocks for a decision‐based integrated product development and system realization process
Author(s) -
Prasad Biren Brian
Publication year - 2002
Publication title -
systems engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.474
H-Index - 50
eISSN - 1520-6858
pISSN - 1098-1241
DOI - 10.1002/sys.10008
Subject(s) - computer science , process (computing) , context (archaeology) , new product development , product (mathematics) , realization (probability) , key (lock) , systems engineering , industrial engineering , management science , process management , operations research , engineering , paleontology , statistics , geometry , mathematics , computer security , marketing , business , biology , operating system
A key requirement, in a distributed product development environment at General Motors, was to provide a series of quantitative and qualitative mechanisms for integrating competing information from distributed agents. Such mechanisms must also account for provisions for combining different opinions, for resolving conflicts, and for finding a feasible or optimal solution at the end. We, at Electronic Data Systems (EDS), General Motors Account, developed a Decision‐based Integrated Product Development (DIPD) methodology to capture a system‐level optimization formulation as part of a product design, development and delivery (PD 3 ) process. The paper describes this methodology in the context of system‐level optimization. DIPD employs the inputs, requirement, constraints, and output conventions to formulate the product realization problem in a distributed manner. The purpose of this DIPD methodology is to improve the performance characteristics of the product, process, and organization (PPO) relative to automobile consumer needs and expectations. DIPD builds the theory through a systematic revision and extension of the paradigms introduced earlier by optimization experts and practitioners including this author [Prasad, 1996]. The eight parts of this DIPD methodology, called building blocks, are discussed at length in this paper. The first four blocks, 1–4, provide a conceptual framework for understanding the challenges and opportunities in DIPD. The last four parts, 5–8, of this methodology provide the building blocks for an analytical and conceptual framework for decision‐making, PPO improvements, and a large‐scale system optimization. © 2002 Wiley Periodicals, Inc. Syst Eng 5: 123–144, 2002