Coordinated capacity migration and expansion planning for semiconductor manufacturing under demand uncertainties

Semiconductor industry is very capital intensive in which capacity utilization significantly affect the capital effectiveness and profitability of semiconductor manufacturing companies. Due to constant technology advance driven by Moore's Law in semiconductor industry, multiple production technologies generally co-exist in a wafer fabrication facility with utilization of a pool of common tools for multiple technologies and critical tools dedicated for a specific technology. Because part of the equipment is common for products of different technologies, production managers have limited flexibility to dynamically allocate the capacity among the technologies via capacity migration. The possibility of capacity migration and interrelationship among different technologies make capacity planning difficult under demand and product-mix uncertainties.This paper aims to develop a dynamic optimization method that captures the unique characteristics of rolling demand forecast mechanism to solve capacity expansion and migration planning problems in semiconductor industry. In semiconductor industry, demand forecasts are rolling and updated when the latest market and demand information is available. This demand forecast mechanism makes forecast errors in different time periods correlated. We estimate the validity and robustness of the proposed dynamic optimization method in an empirical study in a semiconductor manufacturing company in Taiwan. The results showed practical viability of this approach and the findings can provide useful guidelines for capacity planning process under rolling forecast mechanism.     

Strategic safety stock placement in supply chain design with due-date based demand

We present a strategic safety stock placement model in supply chain design for assembly-type product with due-date based demand, where demand data are based on dates when company has to ship to customers rather than order receiving dates. We formulate multi-echelon stock placement by guaranteed-service model with demand propagation equations through backward explosion, where demand can be either stationary or nonstationary. The stock placement model is incorporated into network design problem and its optimization procedure is provided. We show effectiveness of the optimization procedure and other significant features of the model through numerical examples of a machinery product supply chain.     

Managing Capacity in the High-Tech Industry: A Review of Literature

This article surveys a new generation of analytical tools for capacity planning and management, especially in high-tech industries such as semiconductors, electronics and bio-techs. The objectives of the article are to (1) identify fundamental theory driving current research in capacity management, (2) review emerging models in operations research, game theory, and economics that address strategic, tactical and operational decision models for high-tech capacity management, and (3) take an in-depth look at capacity-optimization models developed in the specific context of semiconductor manufacturing. The goal of this survey is to go beyond typical production-planning and capacity-management literature and to examine research that can potentially broaden capacity-planning research. For instance, we explore the role of option theory and real options in modeling capacity decisions. We not only examine capacity-planning problems from the perspective of a particular firm, but also the interaction of capacity investment among supply chain partners. Not only are these issues increasingly important in the fast-changing high-tech environment, they draw on new tools from different disciplines and pose significant intellectual challenges. We also examine papers that represent the multifaceted nature of high-tech capacity planning, integrating capacity decisions with issues related to contracting, coordination, sourcing, and capacity configurations.     

Reliability evaluation and adjustment of supply chain network design with demand fluctuations

This study focuses on supply chain network design problems by considering economies of scale and demand fluctuations. A reliability evaluation method is developed to evaluate the performance of plants under demand fluctuations. In addition, two mathematical programming models are developed to determine the optimal adjustment decisions regarding production reallocation among plants under different fluctuating demands. The judgments to adjust or to do-nothing are investigated by comparing the results if the adjustment is made or not made. Results show that making adjustments benefits the manufacturers by reducing total production cost and avoiding revenue loss, which outweighs the extra costs, especially for high value-added products. Results also suggest that the manufacturer should ignore a short period abnormal state, since the benefits to respond to it might not compensate the high allocation costs. The results of this study provide a reference for the manufacturer in their decision making process of network planning with demand fluctuations, when they have to cope with benefits and costs during abnormal states.     

Integration of financial statement analysis in the optimal design of supply chain networks under demand uncertainty

Models that aim to optimize the design of supply chain networks have become a mainstream in the supply chain literature. This paper aims to fill a gap in the literature by introducing a mathematical model that integrates financial considerations with supply chain design decisions under demand uncertainty. The proposed Mixed-Integer Linear Programming (MILP) problem enchases financial statement analysis through financial ratios and demand uncertainty through scenario analysis. The applicability of the model is illustrated by using a case study along with a sensitivity analysis on financial parameters expressing the business environment. The model could be used as an effective and convenient strategic decision tool by supply chain managers.     

Demand chain management in the container shipping service industry

The growing service sector in the global economy signifies the need for applying service science to study the interdisciplinary nature of services. In particular, container shipping service is considered a key enabler of international trade and global economic development. To strengthen the role of shipping in supporting global seaborne trade, it is highly desirable to identify the determinants that influence the total capacity of the shipping industry, explain how the related business activities (e.g., demand for shipping service, vessel price, fleet size, etc.) are linked to the demand for container shipping service, and empirically verify the findings. This study builds on the demand chain management paradigm to analyze the service capacity of the container shipping industry. We establish a path-analytic model to explain how shipping demand affects such shipping-related variables as vessel price and to evaluate their effects on the service capacity of the industry. The empirically-tested model provides managers and researchers with insights on how to enhance the coordination and integration of a series of shipping-related variables from shipping demand to capacity management in the container shipping service industry.     

The bullwhip effect in capacitated supply chains with consideration for product life-cycle aspects

This paper presents an analysis of the bullwhip effect and net-stock amplification in a three-echelon supply chain considering step-changes in the production rates during a product's life-cycle demand. The analysis is focused around highly complex and engineered products (e.g., automobiles), that have relatively long production life-cycles and require significant capital investment in manufacturing. Using a simulation approach, we analyze three stages of the product life-cycle including low volumes during product introduction, peak demand, and eventual decline toward the end of the life-cycle. Parts of the simulation model have been adopted by a major North-American automotive OEM as part of a scenario analysis tool for strategic supply network design and analysis. The simulation results show that performance of a system as a whole deteriorates when there is a step-change in the life-cycle demand. While restriction in production capacity does not significantly impact the bullwhip effect, it increases the net stock amplification significantly for the supply chain setting under consideration. Furthermore, a number of important managerial insights are presented based on sensitivity analysis of interaction effect of capacity constraints with other supply chain parameters.     

Competition under capacitated dynamic lot-sizing with capacity acquisition

Lot-sizing and capacity planning are important supply chain decisions, and competition and cooperation affect the performance of these decisions. In this paper, we look into the dynamic lot-sizing and resource competition problem of an industry consisting of multiple firms. A capacity competition model combining the complexity of time-varying demand with cost functions and economies of scale arising from dynamic lot-sizing costs is developed. Each firm can replenish inventory at the beginning of each period in a finite planning horizon. Fixed as well as variable production costs incur for each production setup, along with inventory carrying costs. The individual production lots of each firm are limited by a constant capacity restriction, which is purchased up front for the planning horizon. The capacity can be purchased from a spot market, and the capacity acquisition cost fluctuates with the total capacity demand of all the competing firms. We solve the competition model and establish the existence of a capacity equilibrium over the firms and the associated optimal dynamic lot-sizing plan for each firm under mild conditions.     

Optimal Capacity,Product Substitution,Linear Demand Models,and Uncertainty

We study the capacity, pricing, and production decisions of a monopolist producing two substitutable products with flexible capacity. Although the capacity decision needs to be made ex ante, under demand uncertainty, pricing and production decisions can be postponed until after uncertainty is resolved. We show how key demand parameters (the nature of uncertainty, market size, and market risk) impact the optimal capacity decision under the linear demand function. In particular, we show that if the demand shock is multiplicative, then in terms of the “invest or not” decision, the firm will be immune to forecast errors in parameters of the underlying demand distribution. Furthermore, incorrectly modeling the demand shock as additive, when, in fact, it is multiplicative, may lead to overinvestment. On the other hand, although the concept of a growth in market size leads to similar conclusions under both additive and multiplicative demand shocks, how market risk affects the optimal capacity decision depends critically on the form of the demand shock as well as its correlation structure. Our analysis provides insights and principles on the optimal capacity investment decision under various demand settings.     

Tactical product decisions

The focus of this article is a discussion of both how and when to make tactical product decisions for existing products. Although the literature refers to these decisions in the context of strategic planning, the author considers that it is inadequate in explaining how and when these decisions should be arrived at relative to tactical planning. A range of criteria for tactical decisions is proposed.     

A hierarchical process industry production–distribution planning model

Global and multinational companies are subject to government regulations in addition to other international uncertainties due to operation in diverse geographic locations. Such government regulations often affect the cost of raw materials adversely which in turn creates adverse impact on product cost and forces the decision makers to re-evaluate current production–distribution plan. This paper presents an integrated supply chain model for simultaneous strategic and operational planning of a strategic business unit (SBU) in a global supply chain affected by government regulations. The model incorporates impact of changes in cost of inputs on expected product cost and solves for an optimal strategic and operational plan for the entire supply chain. In addition, the model includes exchange rates, border crossing costs and solves a multi-period model with due consideration of uncertainties in demand and transportation time.     

Ordering and pricing policies in a manufacturing and distribution supply chain for fashion products

In the paper, we develop a model of manufacturing and distribution supply chains that are operating to meet price-sensitive random demand for products with short life cycles such as fashion products. Two specific scenarios are considered. The manufacturer-controlled scenario is one where the distributor shares price-sensitive random demand with the manufacturer, and the manufacturer controls the supply chain stocking decisions and bears the risk of overstocking costs. The distributor-controlled scenario works in the opposite direction. Prevailing wisdom suggests that the manufacturer should control supply chain decisions (e.g., via vendor-managed inventory). Our results indicate that such an arrangement is against the interest of a distributor selling short life-cycle products. Furthermore, we find that the total supply chain profit is generally higher when the distributor controls the supply chain stocking decisions and bears the risk of overstocking costs.     

Coordination of planning and scheduling decisions in global supply chains with dual supply modes

We address the problem of coordinating aggregate planning decisions and short-term scheduling decisions in supply chains with dual supply modes. We consider long lead time, less expensive sea shipments that are based on demand forecast, and responsive but costly air shipments that are based on revised forecast closer to the demand period. The planning problem determines the sea shipment order quantity and inventory level, while the scheduling model determines the schedule and quantity of air shipments. Results from our numerical experiments suggest that our model leads to consistent cost improvements over a wide range of operating scenarios.     

Inventory decisions for substitutable products with stock-dependent demand

We study a retailer’s inventory policy for two products. The products are substitutable and have inventory dependent demand, so a higher inventory level of a product increases its sales. We model the joint effect of demand stimulation and product substitution on inventory decisions by considering a single-period, stochastic demand setting. We provide the first order optimality conditions for the profit maximizing order quantities and interpret them using marginal analysis. We also consider two heuristic solutions that separately account for either demand stimulation or product substitution. Our numerical analysis reveals that the optimal policy by appropriately using sales information that quantifies substitution and demand stimulation can produce significantly higher profits. The profit benefits are lessened under certain circumstances, such as when the two products have similar critical fractile values, suggesting that in such instances the heuristics may be used effectively.     

Sequential price and quantity decisions under supply and demand risks

Firms need to deal with not only risks from stochastic demand but also risks from supply side. The supply side risk may be due to parts/service outsourcing, third party logistics, or random yield in production processes. In this paper, we study how firms sequentially make price and quantity decisions under these two risks. The first question we try to answer is how these two risks affect the decisions and profits of the firm. We find that increased supply risk usually causes increased quantity/stocking decision, however, there exists a threshold level of supply risk above which the firm reduces quantity/stocking amount as supply risk increases. This observation may be used in a supply chain setting, where reduction of the supply risk can cause higher delivered quantity and improve supply chain performance. This observation also provides support and insights on prioritizing the risk reduction efforts from marketing and operations to achieve better coordination. At the same time, reduction of the risks help not only firms but also consumers as the optimal price decreases. To further improve decision making process under both uncertainties, we study the impact from information revelation and postponement of decisions. We compare results from different sequential decision making cases. As illustrated in the paper, firms gain competing advantage when decision postponement is available and this advantage becomes further significant as the risks increase. Our numerical examples also indicate that price postponement strategy is usually preferred but the relative profit difference between price postponement and quantity postponement become smaller as consumers become more sensitive to the price.     

Lean Launch: Managing Product Introduction Risk Through Response-Based Logistics

New product launch research has identified four strategic issues that involve activities essential to introduce a new product to its target market. The sum of these decisions is critical to new product success. Substantial research has focused on decisions guiding the proper product, price, and promotion mix to favorably impact market goals. Considerably less research has centered on determining how place capabilities such as logistics and supply chain relationships impact launch performance. Logistics and supply chain collaboration—the processes involved in planning, implementing, and controlling the efficient, effective flow and storage of goods, services, and related information from the point of origin to point of consumption for the purpose of conforming to customer requirements—can greatly reduce risk associated with new product launch. They combine to provide a structure to facilitate rapid response to developing demand by location and intensity. In this article, an effort is made to fill the gap in extant knowledge regarding new product launch strategies by reviewing relevant literature and comparing traditional launch strategies based on anticipatory demand forecasts with alternative lean launch strategies based on the principles of response-based logistics. The result is a lean launch model for continued empirical testing and managerial review. The article contrasts traditional logistics support of new product launch with an emerging logic called lean launch strategy. The traditional launch strategy is forecast driven and is based on anticipatory logistics (push). The lean launch strategy is formulated on principles of postponement and is based on response-based logistics (pull) and supply chain management. Response-based logistics systems provide flexibility that enables better management of inventory levels. Improved replenishment times and in-stock availability of products from a centralized inventory allows managers to rapidly react to actual demand. Lean launch enhances successful introduction by allowing greater flexibility in product variant selection while minimizing out-of-stock potential. Lean launch also can cut losses in product launch failures by reducing launch inventory exposure. Finally, lean launch can improve chances of new product success by helping limited volume technical successes achieve profitability.     

Efficient location of industrial activity cells in a global supply chain

Inefficient locations for production, distribution and reverse logistics plants will result in excess costs no matter how well material requirements planning (MRP), inventory control, distribution and information sharing decisions are optimized. In this paper we study ways in which aspect of activity cell location decisions can be analyzed within an extended MRP model. This model has previously been extended by including distribution and reverse logistics components in a compact form, presented in Grubbström et al. (2007). Our aim is to demonstrate the basic differences between an approach to location problems with MRP “under the same roof” as the global supply chain, in which transportation time delays and direct transportation costs have substantial influence. We discuss possibilities of how to present location aspects in the supply chain model obtained from combining input–output analysis and Laplace transforms in four sub-systems, namely manufacturing, distribution, consumption and reverse logistics, and show how the transportation costs and lead time influenced by the location of all these activities affect the resulting net present value (NPV). Our aim is to build a model supporting decisions concerning the structure of a supply chain as an alternative to a mixed integer programming formulation. The model developed is based on the use of continuous functions describing spatial distributions of cost and customer demand. Continuous functions are embedded in the MRP extension previously introduced in Grubbström et al. (2007).Location decisions influence (i) production costs, because timing influences the cost of activities involved in creating a product, cf. (Grubbström and Bogataj, submitted for publication), and (ii) logistics costs, which refer to the procurement and physical transmission of materials through the supply chain. In this current paper we wish to combine both of these aspects into a comprehensive model, where we show the interaction between the “space of flows” and the “space of places” as Giovanni Arrighi distinguishes one from the other in his book The Long Twentieth Century.     

Development of a Network-location-model for the Economic Optimization of Local Heating Systems in Urban Chile

Cogeneration of heat and electricity is an important pillar of energy and climate policy. To plan the production and distribution system of combined heat and power (CHP) systems for residential heating, suitable methods for decision support are needed. For a comprehensive feasibility analysis, the integration of the location and capacity planning of the power plants, the choice of customers, and the network planning of the heating network into one optimization model are necessary. Thus, we develop an optimization model for electricity generation and heat supply. This mixed integer linear program (MILP) is based on graph theory for network flow problems. We apply the network location model for the optimization of district heating systems in the City of Osorno in Chile, which exhibits the “checkerboard layout” typically found in many South American cities. The network location model can support the strategic planning of investments in renewable energy projects because it permits the analysis of changing energy prices, calculation of break-even prices for heat and electricity, and estimation of greenhouse gas emission savings.     

Endogenous Network Formation in Patent Contests And Its Role as A Barrier to Entry

In a setting of R&D competition, we study how collaboration affects strategic decisions during a patent contest, and how the latter influences the collaboration network structures the firms can form. We use an all pay auction approach to endogenize both network formation and R&D intensities, and to take heterogeneous and private valuations for patents into account. We find that the complete network is not always the only pairwise stable one. The other stable networks have the realistic property that some firms drop out of the contest. Thus, ‘weak’ cooperation can serve as a barrier to entry on the market for innovation.     

The Effects of Environmental Turbulence on New Product Development Strategy Planning

Managers need guidance on how to cope with turbulent environments in order to improve corporate performance. Research on environmental turbulence has suggested that firms adopt a less centralized, more organic structure in dynamic, uncertain environments. Little work has been done specifically, however, on how environmental turbulence affects strategy planning for new product development (NPD). In this article, we specify a baseline model with firm innovativeness, market orientation and top management risk taking as antecedents to NPD speed and corporate strategic planning; these in turn are modeled as antecedents to NPD program (not project) performance. Two conceptualizations of the role of environmental turbulence are examined: (1) that market turbulence and technological turbulence are additional direct antecedents to NPD program performance; and (2) that the baseline model is moderated by turbulence (that is, that the strengths of the paths differ depending on levels of turbulence). A cross-sectional survey methodology including four diverse industries [automotive, electronics, publishing, and manufacturing/research and development (R&D) laboratories] was used to test the hypotheses. The latter conceptualization is supported. In particular, the paths from innovativeness to strategic planning and from risk taking to NPD speed are significantly greater in highly turbulent environments. A set of managerial recommendations and implications are provided. First, managers must recognize the possible improvements in new product performance by actively including NPD personnel in corporate strategic planning and also by involving corporate planners in NPD activities. Second, managers also should recognize that turbulent environments heighten the need to make risky investments, and sometimes, risky decisions; risk-taking decisions ought to be encouraged in such environments.