Jan A. Van Mieghem

Bio: Jan A. Van Mieghem is an academic researcher from Northwestern University. The author has contributed to research in topics: Flexibility (engineering) & Newsvendor model. The author has an hindex of 34, co-authored 123 publications receiving 5258 citations.

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

Abstract: 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.

Price Versus Production Postponement: Capacity and Competition

Abstract: This article presents a comparative analysis of possible postponement strategies in a two-stage decision model where firms make three decisions: capacity investment, production (inventory) quantity, and price. Typically, investments are made while the demand curve is uncertain. The strategies differ in the timing of the operational decisions relative to the realization of uncertainty. We show how competition, uncertainty, and the timing of operational decisions influence the strategic investment decision of the firm and its value. In contrast to production postponement, price postponement makes the investment and production (inventory) decisions relatively insensitive to uncertainty. This suggests that managers can make optimal capacity decisions by deterministic reasoning if they have some price flexibility. Under price postponement, additional postponement of production has relatively small incremental value. Therefore, it may be worthwhile to consider flexible ex-post pricing before production postponement reengineering. While more postponement increases firm value, it is counterintuitive that this also makes the optimal capacity decision more sensitive to uncertainty. We highlight the different impact of more timely information, which leads to higher investment and inventories, and of reduced demand uncertainty, which decreases investment and inventories. Our analysis suggests appropriateness conditions for simple make-to-stock and make-to-order strategies. We also present technical sufficiency and uniqueness conditions. Under price postponement, these results extend to oligopolistic and perfect competition for which pure equilibria are derived. Interestingly, the relative value of operational postponement techniques seems to increase as the industry becomes more competitive.

Coordinating Investment, Production, and Subcontracting

Abstract: We value the option of subcontracting to improve financial performance and system coordination by analyzing a competitive stochastic investment game with recourse. The manufacturer and subcontractor decide separately on their capacity investment levels. Then demand uncertainty is resolved and both parties have the option to subcontract when deciding on their production and sales. We analyze and present outsourcing conditions for three contract types: (1) price-only contracts where an ex-ante transfer price is set for each unit supplied by the subcontractor; (2) incomplete contracts, where both parties negotiate over the subcontracting transfer; and (3) state-dependent price-only and incomplete contracts for which we show an equivalence result. While subcontracting with these three contract types can coordinate production decisions in the supply system, only state-dependent contracts can eliminate all decentralization costs and coordinate capacity investment decisions. The minimally sufficient price-only contract that coordinates our supply chain specifies transfer prices for a small number (6 in our model) of contingent scenarios. Our game-theoretic model allows the analysis of the role of transfer prices and of the bargaining power of buyer and supplier. We find that sometimes firms may be better off leaving some contract parameters unspecified ex-ante and agreeing to negotiate ex-post. Also, a price-focused strategy for managing subcontractors can backfire because a lower transfer price may decrease the manufacturer's profit. Finally, as with financial options, the option value of subcontracting increases as markets are more volatile or more negatively correlated.

Investment Strategies for Flexible Resources

Abstract: This article studies optimal investment in flexible manufacturing capacity as a function of product prices (margins), investment costs and multivariate demand uncertainty. We consider a two-product firm that has the option to invest in product-dedicated resources and/or in a flexible resource that can produce either product, but has to make its investment decision before demands are observed. The flexible resource provides the firm with a hedge against demand uncertainty, but at a higher investment cost than the dedicated resources. Our analysis highlights the important role of price (margin) and cost mix differentials, which, in addition to the correlation between product demands, significantly affect the investment decision and the value of flexibility. Contrary to the intuition also prevalent in the academic literature, we show that it can be advantageous to invest in flexible resources even with perfectly positively correlated product demands.

Dynamic Scheduling with Convex Delay Costs: The Generalized $c|mu$ Rule

Abstract: We consider a general single-server multiclass queueing system that incurs a delay cost $C_k(\tau_k)$ for each class $k$ job that resides $\tau_k$ units of time in the system. This paper derives a scheduling policy that minimizes the total cumulative delay cost when the system operates during a finite time horizon. Denote the marginal delay cost function and the (possibly nonstationary) average processing time of class $k$ by $c_k = C'_k$ and $1/\mu_k$, respectively, and let $a_k(t)$ be the "age" or time that the oldest class $k$ job has been waiting at time $t$. We call the scheduling policy that at time $t$ serves the oldest waiting job of that class $k$ with the highest index $\mu_k(t)c_k(a_k(t))$, the generalized $c\mu$ rule. As a dynamic priority rule that depends on very little data, the generalized $c\mu$ rule is attractive to implement. We show that, with nondecreasing convex delay costs, the generalized $c\mu$ rule is asymptotically optimal if the system operates in heavy traffic and give explicit expressions for the associated performance characteristics: the delay (throughput time) process and the minimum cumulative delay cost. The optimality result is robust in that it holds for a countable number of classes and several homogeneous servers in a nonstationary, deterministic or stochastic environment where arrival and service processes can be general and interdependent.

Evolution and Rationality Some Recent Game-Theoretic Results. Identification and Estimation of Local Average Treatment Effects

Abstract: We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument and a condition on the relation between the instrument and the participation status is sufficient for identification of a local average treatment effect for those who can be induced to change their participation status by changing the value of the instrument. Finally we derive the probability limit of the standard IV estimator under these conditions. It is seen to be a weighted average of local average treatment effects.

Supply Chain Coordination with Contracts

Abstract: Publisher Summary This chapter reviews the supply chain coordination with contracts. Numerous supply chain models are discussed. In each model, the supply chain optimal actions are identified. The chapter extends the newsvendor model by allowing the retailer to choose the retail price in addition to the stocking quantity. Coordination is more complex in this setting because the incentives provided to align one action might cause distortions with the other action. The newsvendor model is also extended by allowing the retailer to exert costly effort to increase demand. Coordination is challenging because the retailer's effort is noncontractible—that is, the firms cannot write contracts based on the effort chosen. The chapter also discusses an infinite horizon stochastic demand model in which the retailer receives replenishments from a supplier after a constant lead time. Coordination requires that the retailer chooses a large basestock level.