This paper is a review of research in product development, which we define as the transformation of a market opportunity into a product available for sale. Our review is broad, encompassing work in the academic fields of marketing, operations management, and engineering design. The value of this breadth is in conveying the shape of the entire research landscape. We focus on product development projects within a single firm. We also devote our attention to the development of physical goods, although much of the work we describe applies to products of all kinds. We look inside the "black box" of product development at the fundamentaldecisions that are made by intention or default. In doing so, we adopt the perspective of product development as a deliberate business process involving hundreds of decisions, many of which can be usefully supported by knowledge and tools. We contrast this approach to prior reviews of the literature, which tend to examine the importance of environmental and contextual variables, such as market growth rate, the competitive environment, or the level of top-management support.

/pdf/product-development-decisions-a-review-of-the-literature-1jhatri7bn.pdf

Product Development Decisions: A Review of the Literature

The impact that scheduling can have on the performance of semi-conductor wafer fabrication facilities is assessed. The performance measure considered is the mean throughput time (sometimes called cycle time, turnaround time or manufacturing interval) for a lot of wafers. A variety of input control and sequencing rules are evaluated using a simulation model of a representative, but fictitious, semiconductor wafer fabrication. Certain of these scheduling rules are derived by restricting attention to the sub-set of stations that are heavily utilized, and by using a Brownian network model, which approximates a multi-class queuing network model with dynamic control capability. Three versions of the wafer fabrication model, which differ only by the number of servers present at particular stations, are studied. The three versions have one, two, and four stations, respectively, that are heavily utilized (near 90% utilization). The simulation results indicate that scheduling has a significant impact on average throughput time, with larger improvements coming from discretionary imput control than from lot sequencing. The effects that specific sequencing rules have are highly dependent on both the type of input control used and the number of bottleneck stations in the fabrication. >

Scheduling semiconductor wafer fabrication

A heuristic method for identifying modules for product architectures

This article reviews the literature on strategic capacity management concerned with determining the sizes, types, and timing of capacity investments and adjustments under uncertainty. Specific attention is given to recent developments to incorporate multiple decision makers, multiple capacity types, hedging and risk aversion. Capacity is a measure of processing abilities and limitations and is represented as a vector of stocks of various processing resources, while investment is the change of capacity and includes expansion and contraction. After discussing general issues in capacity investment problems, the article reviews models of capacity investment under uncertainty in three settings: The first reviews optimal capacity investment by single and multiple risk-neutral decision makers in a stationary environment where capacity remains constant. Allowing for multiple capacity types, the associated optimal capacity portfolio specifies the amounts and locations of safety capacity in a processing network. Its key feature is that it is unbalanced, i.e., regardless of how uncertainties are realized, one typically will never fully utilize all capacities. The second setting reviews the adjustment of capacity over time and the structure of optimal investment dynamics. The article ends by reviewing how to incorporate risk-aversion in capacity investment and contrasts hedging strategies involving financial versus operational means.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2213357_code560268.pdf?abstractid=2213357&mirid=3

Capacity Management, Investment, and Hedging: Review and Recent Developments

This paper reviews the literature on strategic capacity management concerned with determining the sizes, types, and timing of capacity investments and adjustments under uncertainty. Specific attention is given to recent developments to incorporate multiple decision makers, multiple capacity types, hedging, and risk aversion. Capacity is a measure of processing abilities and limitations and is represented as a vector of stocks of various processing resources, while investment is the change of capacity and includes expansion and contraction. After discussing general issues in capacity investment problems, the paper reviews models of capacity investment under uncertainty in three settings:The first reviews optimal capacity investment by single and multiple risk-neutral decision makers in a stationary environment where capacity remains constant. Allowing for multiple capacity types, the associated optimal capacity portfolio specifies the amounts and locations of safety capacity in a processing network. Its key feature is that it is unbalanced; i.e., regardless of how uncertainties are realized, one typically will never fully utilize all capacities. The second setting reviews the adjustment of capacity over time and the structure of optimal investment dynamics. The paper ends by reviewing how to incorporate risk aversion in capacity investment and contrasts hedging strategies involving financial versus operational means.

/pdf/commissioned-paper-capacity-management-investment-and-iq5gxpp7lx.pdf

Commissioned Paper: Capacity Management, Investment, and Hedging: Review and Recent Developments

Queueing networks have been used to model the performance of a variety of complex systems. Since exact results exist for only a limited class of networks, the decomposition methodology has been used extensively to obtain approximate results. In this paper, we consider open queueing networks with multiple product classes, deterministic routings and general arrival and service distributions. We examine the decomposition method for such systems and show that it provides estimates of key parameters with an accuracy that is not acceptable in many practical settings. Recognizing this weakness, we enrich the approach by modeling a phenomenon previously ignored. We consider interference among products and describe its effect on the variance of the departure streams. The recognition of this effect significantly improves the performance of this methodology. We provide extensive experimental results based on the data of a manufacturer of semiconductor devices.

Multiproduct queueing networks with deterministic routing: decomposition approach and the notion of interference

Hierarchical production planning

This paper examines a multiproduct dynamic investment model for making technology choices and expansion decisions over a finite planning horizon. The motivation for our problem comes from recent developments in the field of flexible technology such as CAD, CAM, and CIM that permit firms to invest in these more expensive, flexible technologies to provide a competitive edge in the form of an ability to respond rapidly to changing product mix. On the other hand, more specialized dedicated equipment may be less costly. The decisions on appropriate mixes of dedicated and flexible capacity involve many complex considerations such as economies of scale, demand patterns, and mix flexibility. We formulate the problem as a mathematical program with the objective of minimizing total investment cost. Since the problem is difficult to solve optimally, we develop a two-phased approach and present heuristics to obtain good expansion schedules. These procedures are based on an easily solvable sequence of subproblems derived from the planning problem. Our computational results suggest that these methods work well and provide acceptable solutions with reasonable effort.

Dynamic Capacity Expansion Problem with Multiple Products: Technology Selection and Timing of Capacity Additions

A scenario-based stochastic programming approach for technology and capacity planning

In this paper, we introduce the notions of tradeoff curves, targeting and balancing in manufacturing systems to describe the relationship between variables such as work-in-process, lead-time and capacity. We consider multiproduct manufacturing systems modeled by open networks of queues and formulate the targeting TP and balancing BP problems as nonlinear programs. These formulations are based primarily on parametric decomposition methods for estimating performance measures in open queueing networks. Since TP and BP typically are hard to solve, we show that under fairly realistic conditions they can be approximated by easily solvable convex programs. We present heuristics to obtain approximate solutions to these problems and to derive tradeoff curves. We also provide bounds on the performance of the heuristics, relative to the approximation problems, and show that they are asymptotically optimal under mild conditions.

Devanath Tirupati

Papers

Multiproduct queueing networks with deterministic routing: decomposition approach and the notion of interference

Hierarchical production planning

Dynamic Capacity Expansion Problem with Multiple Products: Technology Selection and Timing of Capacity Additions

A scenario-based stochastic programming approach for technology and capacity planning

Tradeoff Curves, Targeting and Balancing in Manufacturing Queueing Networks