Modeling and Managing Engineering Changes in a Complex Product Development Process

Today\u27s hyper-competitive worldwide market, turbulent environment, demanding customers, and diverse technological advancements force any corporations who develop new products to look into all the possible areas of improvement in the entire product lifecycle management process. One of the areas that both scholars and practitioners have overlooked in the past is Engineering Change Management (ECM). The vision behind this dissertation is to ultimately bridge this gap by identifying main characteristics of a New Product Development (NPD) process that are potentially associated with the occurrence and magnitude of iterations and Engineering Changes (ECs), developing means to quantify these characteristics as well as the interrelationships between them in a computer simulation model, testing the effects of different parameter settings and various coordination policies on project performance, and finally gaining operational insights considering all relevant EC impacts. The causes for four major ECM problems (occurrence of ECs, long EC lead time, high EC cost, and occurrence frequency of iterations and ECs), are first discussed diagrammatically and qualitatively. Factors that contribute to particular system behavior patterns and the causal links between them are identified through the exploratory construction of causal/causal-loop diagrams. To further understand the nature of NPD/ECM problems and verify the key assumptions made in the conceptual causal framework, three field survey studies were conducted in the summer of 2010 and 2011. Information and data were collected to assess the current practice in automobile and information technology industries where EC problems are commonly encountered. ased upon the intuitive understanding gained from these two preparation work, a Discrete Event Simulation (DES) model is proposed. In addition to combining essential project features, such as concurrent engineering, cross functional integration, resource constraints, etc., it is distinct from existing research by introducing the capability of differentiating and characterizing various levels of uncertainties (activity uncertainty, solution uncertainty, and environmental uncertainty) that are dynamically associated with an NPD project and consequently result in stochastic occurrence of NPD iterations and ECs of two different types (emergent ECs and initiated ECs) as the project unfolds. Moreover, feedback-loop relationships among model variables are included in the DES model to enable more accurate prediction of dynamic work flow. Using a numerical example, different project-related model features (e.g., learning curve effects, rework likelihood, and level of dependency of product configuration) and coordination policies (e.g., overlapping strategy, rework review strategy, IEC batching policy, and resource allocation policy) are tested and analyzed in detail concerning three major performance indicators: lead time, cost, and quality, based on which decision-making suggestions regarding EC impacts are drawn from a systems perspective. Simulation results confirm that the nonlinear dynamics of interactions between NPD and ECM plays a vital role in determining the final performance of development efforts

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Product Development Process Modeling Using Advanced Simulation

This paper presents a product development process modeling and analysis technique using advanced simulation.The model computes the probability distribution of lead time in a resource-constrained project network where iterations take place among sequential, parallel and overlapped tasks. The model uses the design structure matrix representation to capture the information flows between tasks. In each simulation run, the expected durations of tasks are initially sampled using the Latin Hypercube Sampling method and decrease over time as the model simulates the progress of dynamic stochastic processes. It is assumed that the rework of a task occurs for the following reasons: (1) new information is obtained from overlapped tasks after starting to work with preliminary inputs, (2) inputs change when other tasks are reworked, and (3) outputs fail to meet established criteria. The model can be used for better project planning and control by identifying leverage points for process improvements and evaluating alternative planning and execution strategies. An industrial example is used to illustrate the utility of the model.Center for Innovation in Product Developmen

Modeling the impact of process architecture on cost and schedule risk in product development

Title from cover. "Revised April 2000."Includes bibliographical references (leaves 30-34).Tyson R. Browning, Steven D. Eppinger

Studying the Physics of Design Flow Incorporating Early Information Using a Simulation Model

Ph.DDOCTOR OF PHILOSOPH

Structuring NPD processes: advancements in test scheduling and activity sequencing

Ph.DDOCTOR OF PHILOSOPH

COMPUTER SIMULATION OF A HOLLOW-FIBER BIOREACTOR: HEPARAN REGULATED GROWTH FACTORS-RECEPTORS BINDING AND DISSOCIATION ANALYSIS

This thesis demonstrates the use of numerical simulation in predicting the behavior of proteins in a flow environment. A novel convection-diffusion-reaction computational model is first introduced to simulate fibroblast growth factor (FGF-2) binding to its receptor (FGFR) on cell surfaces and regulated by heparan sulfate proteoglycan (HSPG) under flow in a bioreactor. The model includes three parts: (1) the flow of medium using incompressible Navier-Stokes equations; (2) the mass transport of FGF-2 using convection-diffusion equations; and (3) the cell surface binding using chemical kinetics. The model consists of a set of coupled nonlinear partial differential equations (PDEs) for flow and mass transport, and a set of coupled nonlinear ordinary differential equations (ODEs) for binding kinetics. To handle pulsatile flow, several assumptions are made including neglecting the entrance effects and an approximate analytical solution for axial velocity within the fibers is obtained. To solve the time-dependent mass transport PDEs, the second order implicit Euler method by finite volume discretization is used. The binding kinetics ODEs are stiff and solved by an ODE solver (CVODE) using Newton’s backward differencing formula. To obtain a reasonable accuracy of the biochemical reactions on cell surfaces, a uniform mesh is used. This basic model can be used to simulate any growth factor-receptor binding on cell surfaces on the wall of fibers in a bioreactor, simply by replacing binding kinetics ODEs. Circulation is an important delivery method for natural and synthetic molecules, but microenvironment interactions, regulated by endothelial cells and critical to the molecule’s fate, are difficult to interpret using traditional approaches. Growth factor capture under flow is analyzed and predicted using computer modeling mentioned above and a three-dimensional experimental approach that includes pertinent circulation characteristics such as pulsatile flow, competing binding interactions, and limited bioavailability. An understanding of the controlling features of this process is desired. The experimental module consists of a bioreactor with synthetic endotheliallined hollow fibers under flow. The physical design of the system is incorporated into the model parameters. FGF-2 is used for both the experiments and simulations. The computational model is based on the flow and reactions within a single hollow fiber and is scaled linearly by the total number of fibers for comparison with experimental results. The model predicts, and experiments confirm, that removal of heparan sulfate (HS) from the system will result in a dramatic loss of binding by heparin-binding proteins, but not by proteins that do not bind heparin. The model further predicts a significant loss of bound protein at flow rates only slightly higher than average capillary flow rates, corroborated experimentally, suggesting that the probability of capture in a single pass at high flow rates is extremely low. Several other key parameters are investigated with the coupling between receptors and proteoglycans shown to have a critical impact on successful capture. The combined system offers opportunities to examine circulation capture in a straightforward quantitative manner that should prove advantageous for biological or drug delivery investigations. For some complicated binding systems, where there are more growth factors or proteins with competing binding among them moving through hollow fibers of a bioreactor coupled with biochemical reactions on cell surfaces on the wall of fibers, a complex model is deduced from the basic model mentioned above. The fluid flow is also modeled by incompressible Navier-Stokes equations as mentioned in the basic model, the biochemical reactions in the fluid and on the cell surfaces are modeled by two distinctive sets of coupled nonlinear ordinary differential equations, and the mass transports of different growth factors or complexes are modeled separately by different sets of coupled nonlinear partial differential equations. To solve this computationally intensive system, parallel algorithms are devised, in which all the numerical computations are solved in parallel, including the discretization of mass transport equations and the linear system solver Stone’s Implicit Procedure (SIP). A parallel SIP solver is designed, in which pipeline technique is used for LU factorization and an overlapped Jacobi iteration technique is chosen for forward and backward substitutions. For solving binding equations ODEs in the fluid and on cell surfaces, a parallel scheme combined with a sequential CVODE solver is used. The simulation results are obtained to demonstrate the computational efficiency of the algorithms and further experiments need to be conducted to verify the predictions

Performance of coupled product development activities with a deadline

Title from cover. "May 31, 2000."Includes bibliographical references (leaves 24-26).Nitindra R. Joglekar ... [et al.