L. Beril Toktay

Market Segmentation and Product Technology Selection for Remanufacturable Products

Remanufacturing is a production strategy whose goal is to recover the residual value of used products. Used products can be remanufactured at a lower cost than the initial production cost, but consumers value remanufactured products less than new products. The choice of production technology influences the value that can be recovered from a used product. In this paper, we solve the joint pricing and production technology selection problem faced by a manufacturer that considers introducing a remanufacturable product in a market that consists of heterogeneous consumers. Our analysis discusses the market and technology drivers of product remanufacturability and identifies some phenomena of managerial importance that are typical of a remanufacturing environment.

Analysis of a Forecasting-Production-Inventory System with Stationary Demand

We consider a production stage that produces a single item in a make-to-stock manner. Demand for finished goods is stationary. In each time period, an updated vector of demand forecasts over the forecast horizon becomes available for use in production decisions. We model the sequence of forecast update vectors using the Martingale model of forecast evolution developed by Graves et al. (1986, 1998) and Heath and Jackson (1994). The production stage is modeled as a single-server, discrete-time, continuous-state queue. We focus on a modified base-stock policy incorporating forecast information and use an approximate analysis rooted in heavy traffic theory and random walk theory to obtain a closed-form expression for the (forecast-corrected) base-stock level that minimizes the expected steady-state inventory holding and backorder costs. This expression, which is shown to be accurate under certain conditions in a simulation study, sheds some light on the interrelationships among safety stock, stochastic correlated demand, inaccurate forecasts, and random and capacitated production in forecasting-production-inventory systems.

Managing Product Returns: The Role of Forecasting

In this article, we discuss ways of actively influencing product returns and we review data-driven methods for forecasting return flows that exploit the fact that future returns are a function of past sales. In particular we assess the value of return forecasting at an operational level, specifically inventory control. We conclude with implications for supply chain management.

A capacity allocation problem with integer side constraints

We address a capacity allocation problem arising as a subproblem of an artificial intelligence-based scheduling system for a semiconductor wafer fabrication facility. Tooling constraints, setup considerations and differences in machine capabilities are taken into account. Focusing on the objectives of maximizing throughput and minimizing deviation from predetermined production goals, we formulate the problem as a maximum flow problem on a bipartite network with integer side constraints and develop efficient heuristics which obtain near-optimal solutions in negligible computation time. The type of network flow problem we study has not been addressed in the literature to date and is of considerable theoretical interest. From a practical point of view, the network flow model of the problem and the algorithms developed for its solution are applicable to a wide range of production settings.

Queuing for Expert Services

We consider a monopolist expert offering a service with a “credence” characteristic. A credence service is one in which the customer cannot verify, even after a purchase, whether or not the amount of prescribed service was appropriate; examples include legal, medical, or consultancy services, and car repair. This creates an incentive for the expert to “induce service,” that is, to provide unnecessary services that add no value to the customer, but that allow the expert to increase his revenues. We focus on the impact of an operations phenomenon on service inducement---workload dynamics due to the stochasticity of interarrival and service times. To this end, we model the experts service operation as a single-server queue. The expert determines the service price within a fixed and variable fee structure and determines the service inducement strategy. We characterize the experts combined optimal price structure and service inducement strategy as a function of service capacity, market potential, inducement opportunity, value of service and waiting cost. We find that service inducement is a means to dynamically skim customer surplus with state-independent prices and provision of slower service to customers that arrive when the expert is idle. We conclude with design implications of our results in limiting service inducement.

Risk Ownership in Contract Manufacturing

We consider a supply chain where a contract manufacturer (CM) serves a number of original equipment manufacturers (OEMs). Investment into productive resources is made before demand realization, hence the supply chain faces the risk of under-or overinvestment. The CM and OEMs differ in their forecast accuracy and in their resource pooling capabilities, leading to a disparity in their ability to minimize costs due to demand uncertainty. We consider two scenarios in which this risk is borne by the OEM and CM, respectively. We determine which party should bear the risk so that maximum supply chain profits are achieved. We investigate the effectiveness of premium-based schemes in inducing the best party to bear the risk, and conclude that they function well despite information asymmetry when double marginalization is not very high.

Implementing Extended Producer Responsibility Legislation

The goal of this article is to contribute to the understanding of how the multiple, and sometimes conflicting, stakeholder perspectives and prevailing conditions (economic, geographic, etc.) in the implementation locality shape extended producer responsibility (EPR) “on the ground.” We provide an in‐depth examination of the implementation dimension of EPR in a specific case study by examining concrete activities at the operational front of the collection and recycling system, and probing the varying stakeholder preferences that have driven a specific system to its status quo. To this end, we conduct a detailed case study of the Washington State EPR implementation for electronic waste. We provide an overview of various stakeholder perspectives and their implications for the attainment of EPR policy objectives in practice. These findings shed light on the intrinsic complexity of EPR implementation. We conclude with recommendations on how to achieve effective and efficient EPR implementation, including improving design incentives, incorporating reuse and refurbishing, expanding product scope, managing downstream material flows, and promoting operational efficiency via fair cost allocation design.

All-or-Nothing Ordering Under a Capacity Constraint

We consider a special case of the single-item, periodic-review inventory control problem with fixed plus linear ordering costs and an upper bound on production capacity. We assume that the fixed cost is large relative to the variable cost and restrict our analysis to full-capacity orders. We show that the optimal policy is a threshold policy, with respect to the inventory position, for a class of cost-to-go functions that include the class of convex functions.

Efficient Implementation of Collective Extended Producer Responsibility Legislation

Extended producer responsibility (EPR) is a policy tool that holds producers financially responsible for the post-use collection, recycling, and disposal of their products. Many EPR implementations are collective—a large collection and recycling network (CRN) handles multiple producers’ products in order to benefit from scale and scope economies. The total cost is then allocated to producers based on metrics such as their return shares by weight. Such weight-based proportional allocation mechanisms are criticized in practice for not taking into account the heterogeneity in the costs imposed by different producers’ products. The consequence is cost allocations that impose higher costs on certain producer groups than they can achieve independently. This may lead some producers to break away from collective systems, resulting in fragmented systems with higher total cost. Yet cost efficiency is a key legislative and producer concern. To address this concern, this paper develops cost allocation mechanisms that induce participation in collective systems and maximize cost efficiency. The cost allocation mechanisms we propose consist of adjustments to the widely used return share method and include the weighing of return shares based on processing costs and the rewarding of capacity contributions to collective systems. We validate our theoretical results using Washington state EPR implementation data and provide insights into how these mechanisms can be implemented in practice. This paper was accepted by Serguei Netessine, operations management.

The Value of Collaborative Forecasting in Supply Chains

Motivated by the mixed evidence concerning the adoption level and value of collaborative forecasting (CF) implementations in retail supply chains, in this paper, we explore the conditions under which CF offers the highest potential. We consider a two-stage supply chain with a single supplier selling its product to consumers through a single retailer. We assume that both the supplier and the retailer can improve the quality of their demand forecasts by making costly forecasting investments to gather and analyze information. First, we consider a noncollaborative model where the supplier and the retailer can invest in forecasting but do not share forecast information. Next, we examine a collaborative forecasting model where the supplier and the retailer combine their information to form a single shared demand forecast. We investigate the value of CF by comparing each partys profits in these scenarios under three contractual forms that are widely used in practice (two variations of the simple wholesale price contract as well as the buyback contract). We show that for a given set of parameters, CF may be Pareto improving for none to all three of the contractual structures, and that the Pareto regions under all three contractual structures can be expressed with a unifying expression that admits an intuitive interpretation. We observe that these regions are limited and explain how they are shaped by the contractual structure, power balance, and relative forecasting capability of the parties. To determine the specific value of collaborative forecasting as a function of different factors, we carry out a numerical analysis and observe the following. First, under noncoordinating contracts, improved information as a result of CF has the added benefit of countering the adverse effects of double marginalization in addition to reducing the cost of supply--demand mismatch. Second, one may expect the value of CF to increase with bargaining power, however this does not hold in general: The value of CF for the newsvendor first increases and then decreases in his bargaining power. Finally, whereas one may expect CF to be more valuable under coordinating contracts, rather than a simple wholesale price contract that is prone to double marginalization, the magnitude of the gain from CF is in many cases higher in the absence of quantity coordination.