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We study the impact of three well-known inventory allocation mechanisms, including proportional, linear, and uniform, on the ordering behavior of retailers serviced from a central distribution center. Based on the allocation mechanism, retailers may have an incentive to adjust (either inflate or deflate) their orders to gain a more favorable allocation, a behavior that may reduce allocation efficiency from a system perspective. We find that while all three mechanisms are centrally optimal under common knowledge of local demands, only the uniform allocation incentivizes retailers to set orders truthfully. Consistent with theory, our experimental results show that using proportional or linear allocation results in larger and more frequent order adjustments, with the degree of strategic ordering being largest under the linear mechanism. Across all mechanisms, order adjustments decrease both allocation efficiency and local retail profits. While uniform allocation results in smaller and less frequent adjustments overall, it may not be feasible to implement in more general settings. Hence, we propose and test a new mechanism, tailored uniform, that leverages the uniform principle while overcoming some practical limitations. It provides more flexibility by allowing for differences in the allocated quantities among retailers, while still providing no incentive for order manipulation. The tailored uniform mechanism performs similarly to uniform in terms of order adjustments, and further increases allocation efficiency when retailers have heterogeneous demands.

In this study, we consider a company that uses two channels for trading: long‐term contracts and spot markets. With a quantity flexibility long‐term contract, the buyer commits to purchasing at least as much as the minimum order quantity and is able to reserve capacity with the forward contract supplier up to the maximum order quantity in each period at a predetermined price. Through the spot market, the buyer can order or sell inventory at an uncertain future spot price without quantity limitations. A fixed setup cost is incurred in each instance of buying or selling in the spot market. This study considers very general inventory‐related (not necessarily convex) costs, and demand is random following a one‐sided Polya distribution. We introduce a concept called “non‐(K1, K2)‐decreasing with changeovers” and characterize the optimal trading policy in the spot market. We show that the optimal policy can be characterized by five critical points. We also characterize optimal procurement from the contracted supplier under certain conditions. Two simple, yet efficient, heuristics that yield near‐optimal results in many cases are presented as well in order to calculate procurement quantities. Moreover, we show that our results also apply to uniform demand, which is not Polya, and numerical tests assuming uniform demand are performed to discern which factors are most important and to gain managerial insights.

This chapter focuses on the identification of challenges that supply chains of the future will most likely face. The primary input in this process are the potential optimistic/pessimistic/intermediate future scenarios based on trends within political, economic, social, technological, legal, and environmental dimensions. Based on such input, we present a list of major challenges/opportunities in relation to the design and operations of Supply Chains (SCs) in the near future. The preliminary list is calibrated and validated based on the input from industry stakeholders (to account for the perspectives of different supply chain actors such as buyers, suppliers, policy makers, and supply chain facilitators) in order to make sure that these challenges are indeed of practical relevance and grounded in reality. The aforementioned challenges are aggregated into several clusters aiming at providing decision makers with a tool that would enable them to quickly and easily spot the relevant challenges and take proper actions to mitigate any potential risk.

We study the impact of three well-known inventory allocation mechanisms, including proportional, linear, and uniform, on the ordering behavior of retailers serviced from a central distribution center. Based on the allocation mechanism, retailers may have an incentive to adjust (either inflate or deflate) their orders to gain a more favorable allocation, a behavior that may reduce allocation efficiency from a system perspective. We find that while all three mechanisms are centrally optimal under common knowledge of local demands, only the uniform allocation incentivizes retailers to set orders truthfully. Consistent with theory, our experimental results show that using proportional or linear allocation results in larger and more frequent order adjustments, with the degree of strategic ordering being largest under the linear mechanism. Across all mechanisms, order adjustments decrease both allocation efficiency and local retail profits. While uniform allocation results in smaller and less frequent adjustments overall, it may not be feasible to implement in more general settings. Hence, we propose and test a new mechanism, tailored uniform, that leverages the uniform principle while overcoming some practical limitations. It provides more flexibility by allowing for differences in the allocated quantities among retailers, while still providing no incentive for order manipulation. The tailored uniform mechanism performs similarly to uniform in terms of order adjustments, and further increases allocation efficiency when retailers have heterogeneous demands.

Inventory decisions made at a centralized level often rely on demand forecast information passed from regional managers within a supply chain. Such managers often have unique insights into the demand patterns at their local sites that can help inform how much inventory to order for the system as a whole. Problems can arise with this setup, however, if these managers have incentives to misreport their forecasts and the central planner (CP), in turn, mistrusts this information. The goal of our research is to shed light on the existence, magnitude, and causes of such coordination problems using a combination of analytical models and behavioral experiments. The analytical analysis reveals that incentives are misaligned in this setting and no truth-telling equilibrium exists in general, unless inventory competition or demand uncertainty is removed. With this theoretical grounding, we conduct a series of controlled laboratory experiments to test the magnitude of these problems and how they are impacted by inventory competition, strategic concerns regarding the other parties, and the demand environment. We find that inventory competition and market uncertainty harm the efficacy of forecast sharing and channel efficiency, while the possibility that untrustworthy reports are detected (identical local demand information) does not deter regional managers from misreporting their forecasts. Removing strategic concerns regarding the CP (automating the central ordering decision) does not improve profit but reduces order variability. Information sharing appears to be a dominant policy as it significantly improves profit in all treatments compared with no information sharing.

Governments have put in place instruments to control the transit of goods and ensure that national regulatory frameworks are followed by transport companies, in particular to ensure revenue collection and reduce security risks, e.g., violation of import/export restrictions, money laundering, evasion of local taxes or duties, etc. Nevertheless, many of these instruments are known to bring additional burdens to companies. In this chapter, these burdens are analyzed under the form of financial and operational risks. The results, based on a case study of intermodal transport (road and sea) performed across five countries in the BSEC region (Serbia, Bulgaria, Turkey, Georgia, and Azerbaijan), show which cross-border financial instruments could be more convenient for companies. In addition, the study highlights the presence of hidden risks that should be considered when choosing among available options. QC 20190304

In this paper, we study the impact of coordinated replenishment and shipment in inventory/distribution systems. We analyze a system with multiple retailers and one outside supplier. Random demand occurs at each retailer, and the supplier replenishes all the retailers. In traditional inventory models, each retailer orders directly from the supplier whenever the need arises. We present a new, centralized ordering policy that orders for all retailers simultaneously. The new policy is equivalent to the introduction of a warehouse with no inventory that is in charge of the ordering, allocation, and distribution of inventory to the retailers. Under such a policy, orders for some retailers may be postponed or expedited so that they can be batched with other retailers’ orders, which results in savings in ordering and shipping costs. In addition to the policy we propose for supplying inventory to the retailers, we also consider three other policies that are based on these well-known policies in the literature: (a) can-order policy, (b) echelon inventory policy, and (c) fixed-replenishment interval policy. Furthermore, we create a framework for simultaneously making inventory and transportation decisions by incorporating the transportation costs (or limited truck capacities). We numerically compare the performance of our proposed policy with these policies to identify the settings in which each policy would perform well.

Cataloged from PDF version of article. We consider supplier diversification in an EOQ type inventory setting with multiple suppliers and binomial yields. We characterize the optimal policy for the model and show that, in this case, it does not pay to diversify, in contrast to previous results in the random yield literature. © 2008 Elsevier B.V. All rights reserved.