The impact of information sharing, random yield, correlation, and lead times in closed loop supply chains

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordWe investigate the impact of advance notice of product returns on the performance of a decentralised closed loop supply chain. The market demands and the product returns are stochastic and are correlated with each other. The returned products are converted into "as-good-as-new" products and used, together with new products, to satisfy the market demand. The remanufacturing process takes time and is subject to a random yield. We investigate the benefit of the manufacturer obtaining advance notice of product returns from the remanufacturer. We demonstrate that lead times, random yields and the parameters describing the returns play a significant role in the benefit of the advance notice scheme. Our mathematical results offer insights into the benefits of lead time reduction and the adoption of information sharing schemes.Japan Society for the Promotion of Scienc

Self-Selecting Priority Queues with Burr Distributed Waiting Costs

Service providers, in the presence of congestion and heterogeneity of customer waiting costs, often introduce a fee-based premier option using which the customers self-segment themselves. Examples of this practice are found in health care, amusement parks, government (consular services), and transportation. Using a single-server queuing system with customer waiting costs modeled as a Burr Distribution, we perform a detailed analysis to (i) determine the conditions (fees, cost structure, etc.) under which this strategy is profitable for the service provider, (ii) quantify the benefits accrued by the premier customers; and (iii) evaluate the resulting impact on the other customers. We show that such self-selecting priority systems can be pareto-improving in the sense that they are beneficial to everyone. These benefits are larger when the variance in the customer waiting costs is high and the system utilization is high. We use income data from the poorest and richest areas (identified by zipcode) in the United States along with the countrywide income distribution to illustrate our results. Numerical results indicate that planning for a 20–40% enrollment in the high-priority option is robust in ensuring that all the stakeholders benefit from the proposed strategy

A Newsvendor Approach to Compliance and Production under Cap and Trade Emissions Regulation

Since the 1990s, governmental agencies have increasingly turned to market based cap and trade programs to control the emission of pollutants. Firms subject to cap and trade regulation are typically required to acquire emissions allowances via open auction markets. The cost to acquire allowances may impose a substantial financial burden on a firm. While emissions reduction efforts may eliminate some firm\u27s need to acquire additional allowances, there are still numerous firms that need to purchase additional allowances on the open market. This study presents a new forward buying heuristic, designed for those firms that need to purchase emissions allowances via auctions, which reduces the impact of emissions allowance acquisitions on the firms\u27 financial performance. The heuristic, designated as the Newsvendor Production Planning with Emissions Allowance Forward Buying (NPPAFB) method, applies a forward buying algorithm to determine the number of periods for which to forward buy allowances, the current production order up to level, and the current and future emissions allowance requirements (which serves as the order up to level for allowance purchases). Additionally, NPPAFB also authorizes unused emissions allowances to be sold when market conditions are favorable. Compared against three existing production planning and allowance procurement strategies, a simulation exercise finds that the NPPAFB method significantly reduces a firm\u27s emissions allowance expenditures. These results indicate that heuristic can be readily adopted by any firm that is required to procure emissions allowances via open markets in an effort to improve the firm\u27s profitability

The impact of information sharing, random yield, correlation, and lead times in closed loop supply chains

We investigate the impact of advance notice of product returns on the performance of a decentralised closed loop supply chain. The market demands and the product returns are stochastic and are correlated with each other. The returned products are converted into “as-good-as-new” products and used, together with new products, to satisfy the market demand. The remanufacturing process takes time and is subject to a random yield. We investigate the benefit of the manufacturer obtaining advance notice of product returns from the remanufacturer. We demonstrate that lead times, random yields and the parameters describing the returns play a significant role in the benefit of the advance notice scheme. Our mathematical results offer insights into the benefits of lead time reduction and the adoption of information sharing schemes

Approximation algorithms for capacitated stochastic inventory systems with setup costs

We develop the first approximation algorithm with worst-case performance guarantee for capacitated stochastic periodic-review inventory systems with setup costs. The structure of the optimal control policy for such systems is extremely complicated, and indeed, only some partial characterization is available. Thus, finding provably near-optimal control policies has been an open challenge. In this article, we construct computationally efficient approximate optimal policies for these systems whose demands can be nonstationary and/or correlated over time, and show that these policies have a worst-case performance guarantee of 4. We demonstrate through extensive numerical studies that the policies empirically perform well, and they are significantly better than the theoretical worst-case guarantees. We also extend the analyses and results to the case with batch ordering constraints, where the order size has to be an integer multiple of a base load.National Science Foundation (U.S.) (CMMI-1362619)National Science Foundation (U.S.) (CMMI-1131249)National Science Foundation (U.S.) (DMS-0732175)National Science Foundation (U.S.) (CMMI-0846554)National Science Foundation (U.S.) (FA9550-08-1–0369

Performance of supply chain collaboration – A simulation study

In the past few decades several supply chain management initiatives such as Vendor Managed Inventory, Continuous Replenishment and Collaborative Planning Forecasting and Replenishment (CPFR) have been proposed in literature to improve the performance of supply chains. But, identifying the benefits of collaboration is still a big challenge for many supply chains. Confusion around the optimum number of partners, investment in collaboration and duration of partnership are some of the barriers of healthy collaborative arrangements. To evolve competitive supply chain collaboration (SCC), all SC processes need to be assessed from time to time for evaluating the performance. In a growing field, performance measurement is highly indispensable in order to make continuous improvement; in a new field, it is equally important to check the performance to test conduciveness of SCC. In this research, collaborative performance measurement will act as a testing tool to identify conducive environment to collaborate, by the way of pinpointing areas requiring improvements before initializing collaboration. We use actual industrial data and simulation to help managerial decision-making on the number of collaborating partners, the level of investments and the involvement in supply chain processes. This approach will help the supply chains to obtain maximum benefit of collaborative relationships. The use of simulation for understanding the performance of SCC is relatively a new approach and this can be used by companies that are interested in collaboration without having to invest a huge sum of money in establishing the actual collaboration

Behavioral Implications of Demand Perception in Inventory Management

The newsvendor problem is one of the rudimentary problems of inventory management with significant practical consequences, thus receiving considerable attention in the behavioral operational research literature. In this chapter, we focus on how decision makers perceive demand uncertainty in the newsvendor setting and discuss how such perception patterns influence commonly observed phenomena in order decisions, such as the pull-to-center effect. Drawing from behavioral biases such as over precision, we propose that decision makers tend to perceive demand to be smaller than it actually is in high margin contexts, and this effect becomes more pronounced with increases in demand size. The opposite pattern is observed in low margin settings; decision makers perceive demand to be larger than the true demand, and this tendency is stronger at lower mean demand levels. Concurrently, decision makers tend to perceive demand to be less variable than it actually is, and this tendency propagates as the variability of demand increases in low margin contexts and decreases in high margin contexts. These perceptions, in turn, lead to more skewed decisions at both ends of the demand spectrum. We discuss how decision makers can be made aware of these biases and how decision processes can be re-designed to convert these unconscious competencies into capabilities to improve decision making