Dynamic Inventory Control with Satisfaction-Dependent Demand

In this paper, we consider the discrete multiperiod newsvendor dynamic inventory control problem where customers follow a simple satisfaction-based demand process, where their probability of demand depends on whether their demand was satised the last time they demanded a product, and observe the differences between optimal policies and myopic policies which do not directly consider how inventory policies can affect future demand. We conrm the intuitive result that inventory managers should tend to order more than the myopic policy when satised customers are more likely to demand product, and less than the myopic policy when satised customers are less likely to demand. Moreover, we and that, when choosing a fixed order policy, even an empirically myopic solution with perfect demand distribution information will move away from the optimum towards a suboptimal solution.

How can we improve the performance of supply chain contracts? An experimental Study

Although optimal forms of supply chain contracts have been widely studied in the literature, it has also been observed that decision makers fail to make optimal decisions in these contract setups. In this research, we propose different approaches to improve the performance of supply chain contracts in practice. We consider revenue sharing and buyback contracts between a rational supplier and a retailer who, unlike the supplier, is susceptible to decision errors. We propose five approaches to improve the retailer’s decisions which are in response to contract terms offered by the supplier. Through laboratory experiments, we examine the effectiveness of each approach. Among the proposed approaches, we observe that offering free items can bring the retailer’s effective order quantity close to the optimal level. We also observe that the retailer’s learning trend can be improved by providing him with collective feedbacks on the profits associated with his decisions

An Advanced Heuristic for Multiple-Option Spare Parts Procurement after End-of-Production

After-sales service is a major profit generator for more and more OEMs in industries with durable products. Successful engagement in after-sales service improves customer loyalty and allows for competitive differentiation through superior service like an extended service period after end of production during which customers are guaranteed to be provided with service parts. In order to fulfill the service guarantee in these cases, an effective and efficient spare parts management has to be implemented, which is challenging due to the high uncertainty concerning spare parts demand over such a long time horizon. The traditional way of spare parts acquisition for the service phase is to set up a huge final lot at the end of regular production of the parent product which is sufficient to fulfill demand up to the end of the service time. This strategy results in extremely high inventory levels over a long period and generates major holding costs and a high level of obsolescence risk. With increasing service time more flexible options for spare parts procurement after end of production gain more and more importance. In our paper we focus on the two most relevant ones, namely extra production and remanufacturing. Managing all three options leads to a complicated stochastic dynamic decision problem. For that problem type, however, a quite simple combined decision rule with order-up-to levels for extra production and remanufacturing turns out to be very effective. We propose a heuristic procedure for parameter determination which accounts for the main stochastic and dynamic interactions between the different order-up-to levels, but still consists of quite simple calculations so that it can be applied to problem instances of arbitrary size. In a numerical study we show that this heuristic performs extremely well under a wide range of conditions so that it can be strongly recommended as a decision support tool for the multi-option spare parts procurement problem.Spare Parts, Inventory Management, Reverse Logistics, Final Order

Decision behavior in supply chains with random production yields

Dealing with supply risks is one of the challenges of decision makers in supply chains as producing and sourcing become more and more complex. Theoretical research on different types of supply uncertainty as well as their management is well covered. Behavioral aspects in this context, however, have not received much attention so far. In this paper, we present an experimental study which aims at investigating how subjects make decisions of ordering and producing in the presence of random production yields at a supplier, i.e. production output is a random fraction of production input. Subjects were confronted with the situation of either the buyer or the supplier in a simple two-tier supply chain with deterministic demand and had to make the respective quantity decisions. Results show that buyers have a good understanding of the situation and are likely to follow a probabilistic choice rule. In addition to that, hedging against supply risks drives their behavior of over-ordering. Suppliers on the other hand start off with moderate production decisions but improve over time which indicates learning effects. Furthermore, the study shows that additional sharing of information on yield rates is no cure for inefficient behavior of the buyer

Risk pooling via unidirectional inventory transshipments in a decentralized supply chain

We study risk pooling via unidirectional lateral transshipments between two locations under local decision-making. Unidirectional transshipments can be applicable when cost structures and/or capabilities differ between locations, and it is also a common practice in dual channel supply chains with online and offline sales channels. We show that such a system cannot be coordinated only with varying transshipment prices. The transshipment receiver orders more and the transshipment giver orders less than the respective optimal centralised order quantities. In order to remove this discrepancy, we suggest horizontal coordinationmechanisms by introducing a leftover subsidy for the location providing the transshipments or a shortage subsidy for the location receiving transshipments as well as a combination of shortage and leftover subsidy. Further, we evaluate the impact of network structure by comparing the equilibrium order quantities and profits under the uni- and bidirectional systems as well as a system without transshipments. Since demand correlation is a critical aspect in risk pooling we provide a detailed numerical study to discuss its impact on our findings