Valuation of inventories in systems with product recovery

Valuation of inventories has different purposes, in particularaccounting and decision making, and it is not necessary for a firmto use the same valuation method for both purposes. In fact, it isnot uncommon to use accounting books as well as management books.In this chapter, we will only consider inventory values from theperspective of decision making. More specifically, we will analyzethe effect of inventory valuation on inventory control decisions(and not the corresponding financial results) for systems withproduct recovery.

Simple heuristics for push and pull remanufacturing policies

Inventory policies for joint remanufacturing and manufacturing have recently received much attention. Most efforts, though, were related to (optimal) policy structures and numerical optimization, rather than closed form expressions for calculating near optimal policy parameters. The focus of this paper is on the latter. We analyze an inventory system with unit product returns and demands where remanufacturing is the cheaper alternative for manufacturing. Manufacturing is also needed, however, since there are less returns than demands. The cost structure consists of setup costs, holding costs, and backorder costs. Manufacturing and remanufacturing orders have non-zero lead times. To control the system we use certain extensions of the familiar (s,Q) policy, called push and pull remanufacturing policies. For all policies we present simple, closed form formulae for approximating the optimal policy parameters under a cost minimization objective. In an extensive numerical study we show that the proposed formulae lead to near-optimal policy parameters.inventory control;remanufacturing;heuristics

Average Costs versus Net Present Value

While the net present value (NPV) approach is widely accepted as the right frameworkfor studying production and inventory control systems, average cost (AC) models are morewidely used. For the well known EOQ model it can be verified that (under certain conditions)the AC approach gives near optimal results, but does this also hold for more complex systems?In this paper it is argued that for more complex systems, like multi-source systems, one hasto be extremely careful in applying the AC approach on intuition alone, even when thesesystems are deterministic. Special attention is given to a two-source inventory system withmanufacturing, remanufacturing, and disposal, and it is shown that for this type of modelsthere is a considerable gap between the AC approach and the NPV approach.inventory control;remanufacturing;average costs;holding costs;net present value

An Inventory Model with Dependent Product Demands and Returns

In this paper an inventory model for a single reusable product is investigated,in which the random returns depend explicitly on the demand stream. Further,the model distinguishes itself from most other research in this field by consideringleadtimes and a finite planning horizon. We show that neglecting the dependencybetween demands and returns of products may lead to bad performance with respectto total average relevant costs. Additionally, our results enable us to determine theminimal recovery probability or the minimal length of the planning horizon forwhich reuse is profitable.remanufacturing;Markov Chain;order-up-to policy;reusable products

Inventory control with product returns: the impact of (mis)information

Product returns are often characterized by a dual uncertainty on time and quantity. In the literature on inventory management with product returns, best forecasts have been associated with methods that use the most informationregarding product return history.In practice however, data is often scarce and unreliable. In this paper we investigate the impact of (mis)information on inventory performance. An exact analysis shows that in case ofmisestimation the most informed method does not necessarily lead to best performance. Further we provide an extensive simulation study to investigate the impact of misinformation w.r.t. inventory costs.This has relevant implications regarding the investments to make on product return information systems.Forecasting;Inventory control;Product returns;Information management

Inventory Management with product returns: the value of information

This paper evaluates the impact of misinformation for inventory systems with product returns. Ifone could exactly know how much is going to be returned and when, one would certainly benefitfrom incorporating this perfect information a priori in the management of production, inventory,and distribution. In practice, one has to attempt to forecast the timing and the amount of productreturns, by hypothesizing about the return flow properties. To do so, historic data on demandand returns can be used. The available literature on information and inventory managementwith product returns commonly 1) assumes known return probabilities; or 2) considers specificcases where the most informed method does not necessarily lead to the best performance,investigating the impact on inventory related costs.product returns;forecasting;information management;inventory management

An NPV and AC Analysis of a Stochastic Inventory System with Joint Manufacturing and Remanufacturing

While the net present value (NPV) approach is widely accepted as the right framework for studying production and inventory control systems, average cost (AC) models are more widely used. For the well known EOQ model it can be veri_ed that (under certain conditions) the AC approach gives near optimal results. This paper investigates whether the same holds for two-source systems with joint manufacturing and remanufacturing. It appears that the performance of the AC approach stands or falls with the right choice of the holding cost parameters. Through the analysis of a deterministic model a theoretical basis is provided for choosing the parameters. Then, given this set of holding cost parameters, the performance of the AC approach is tested in a stochastic model

End-of-Life Inventory Problem with Phase-out Returns

We consider the service parts end-of-life inventory problem of a capital goods manufacturer in the final phase of its life cycle. The final phase starts as soon as the production of parts terminates and continues until the last service contract expires. Final order quantities are considered a popular tactic to sustain service fulfillment obligations and to mitigate the effect of obsolescence. In addition to the final order quantity, other sources to obtain serviceable parts are repairing returned defective items and retrieving parts from phase-out returns. Phase-out returns happen when a customer replaces an old system platform with a next generation one and returns the old product to the original equipment manufacturer (OEM). These returns can well serve the demand for service parts of other customers still using the old generation of the product. In this paper, we study the decision-making complications stemming from phase-out occurrence. We use a finite horizon Markov decision process to characterize the structure of the optimal inventory control policy. We show that the optimal policy consists of a time varying threshold level for item repair. Furthermore, we study the value of phase-out information by extending the results to cases with an uncertain phase-out quantity or an uncertain schedule. Numerical analysis sheds light on the advantages of the optimal policy compared to some heuristic policies.spare parts;end-of-life inventory management;phase-out returns

Inventory strategies for systems with fast remanufacturing

We describe hybrid manufacturing/remanufacturing systems with a longlead time for manufacturing and a short lead time for remanufacturing.We review the classes of inventory strategies for hybrid systems inthe literature. These are all based on equal lead times. For systemswith slow manufacturing and fast remanufacturing, we propose a newclass. An extensive numerical experiment shows that the optimalstrategy in the new class almost always performs better and often muchbetter than the optimal strategies in all other classes.logistics;remanufacturing;stochastic inventory control

Extended Producer Responsibility in the Aviation Sector

In this paper we investigate recent initiatives with respect to extended producer responsibility in the aviation sector. We compare those with the existing practices in the automobile sector and the emerging regulations in the shipping sector. We describe the challenges and the lessons to be learned from the evolution and state of extended producer responsibility in these two industries.End-of-life aircraft;Environmental legislation;Extended producer responsibility