Simme Douwe P. Flapper

A characterisation of logistics networks for product recovery

Recovery of used products is receiving much attention recently due to growing environmental concern. Efficient implementation requires appropriate logistics structures to be set up for the arising goods flow from users to producers. We investigate the design of such logistics networks. As a basis for our analysis we review recent case studies on logistics network design for product recovery in different industries. We identify general characteristics of product recovery networks and compare them with traditional logistics structures. Moreover, we derive a classification scheme for different types of recovery networks.

Reverse Logistics: A Review of Case Studies

Products, components, materials and other equipment stream forward and back wards and back in their supply chains. Reverse Logistics deals with the processes associated with the reverse stream from users/owners to re-users. This paper provides a review and content analysis of scientific literature on reverse logistics case studies. Over sixty case studies are included. In addition, we give an overview of particular issues, which we link with propositions, unanswered questions and thus directions for future research.

A facility location allocation model for reusing carpet materials

Re-using the huge quantities of carpet waste that are yearly generated has become a must. A facility location–allocation model for the collection, preprocessing and redistribution of carpet waste is presented. This model differs from other mathematical models for supporting the design of the logistic structure of reuse networks among others in a completely free choice of the locations for preprocessing and in explicitly taking into account depreciation costs. Two applications of the model, one in Europe and one in the United States of America, are described.

Lot-sizing for a single-stage single-product production system with rework of perishable production defectives

Abstract. We consider a single-stage single-product production system. Produced units may be non-defective, reworkable defective, or non-reworkable defective. The system switches between production and rework. After producing a fixed number (N) of units, all reworkable defective units are reworked. Reworkable defectives are perishable or can become technologically obsolete. We assume that the rework time and the rework cost increase linearly with the time that a unit is held in stock. Therefore, N should not be too large. On the other hand, N should not be too small either, since there are set-up times and costs associated with switching between production and rework. For a given N, we derive an explicit expression for the average profit (sales revenue minus costs). Using this expression, the optimal value for N can be determined numerically. Moreover, it is easy to perform a sensitivity analysis, as we illustrate.

Logistic planning of rework with deteriorating work-in-process

Abstract Rework is described as the transformation of production rejects into re-usable products of the same or lower quality. Rework can be very profitable, especially if disposal costs are high and if materials are expensive and limited in availability. Furthermore, rework can contribute to a ‘green image’. Although there is quite a lot of literature on logistic planning and control of rework, up to now no attention has been paid to rework of production rejects that deteriorate while waiting to be reworked. Such situations occur, for example, in the food industry. This paper deals with the above in a production line that is dedicated to a single product and that uses the same facilities for production and rework. Produced lots may be non-defective, reworkable defective or non-reworkable defective. Reworkable defective lots deteriorate over time, which effects the rework time and the rework cost. We consider a disposal strategy and two types of rework strategies. We derive expressions for the average profit per time unit, including costs for production, rework, disposal, procurement of input materials, and storage of reworkable defective lots. Using those expressions, the strategies can be compared numerically, as is illustrated for a few examples.

Condition-based maintenance policies for systems with multiple dependent components: A review

Condition-based maintenance (CBM) has received increasing attention in the literature over the past years. The application of CBM in practice, however, is lagging behind. This is, at least in part, explained by the complexity of real-life systems as opposed to the stylized ones studied most often. To overcome this issue, research is focusing more and more on complex systems, with multiple components subject to various dependencies. Existing classifications of these dependencies in the literature are no longer sufficient. Therefore, we provide an extended classification scheme. Besides the types of dependencies identified in the past (economic, structural, and stochastic), we add resource dependence, where multiple components are connected through, e.g., shared spares, tools, or maintenance workers. Furthermore, we extend the existing notion of structural dependence by distinguishing between structural dependence from a technical point of view and structural dependence from a performance point of view (e.g., through a series or parallel setting). We review the advances made with respect to CBM. Our main focus is on the implications of dependencies on the structure of the optimal CBM policy. We link our review to practice by providing real-life examples, thereby stressing current gaps in the literature.

Production planning for product recovery management

Production Planning and Control (PPC) in product recovery systems faces complications due to several characteristics which typically require tools different from, and in addition to, those known from traditional forward production and logistics systems (e.g., see Guide, 2000, and Inderfurth and Teunter, 2002). Many of these characteristics are due to additional specific operations necessary for disassembling, reprocessing, and rearranging recoverable products. Furthermore, in product recovery management, considerable sources of uncertainty have to be taken into consideration concerning the arrival of recoverables as well as the outcome of disassembly and reprocessing activities. The specific environment that poses challenges for PPC under product recovery will be demonstrated by two case examples, one of them concerning remanufacturing of used products in the field of discrete products’ manufacture, and the other considering rework of by-products in the field of process industries.

Control of a production-inventory system with returns under imperfect advance return information

We consider a production–inventory system with product returns that are announced in advance by the customers. Demands and announcements of returns occur according to independent Poisson processes. An announced return is either actually returned or cancelled after a random return lead time. We consider both lost sale and backorder situations. Using a Markov decision formulation, the optimal production policy, with respect to the discounted cost over an infinite horizon, is characterized for situations with and without advance return information. We give insights in the potential value of this information. Also some attention is paid to combining advance return and advance demand information. Further applications of the model as well as topics for further research are indicated.

Recovery of car engines: the Mercedes-Benz case

The following describes the situation in 1997. Although many changes occurred since then, these changes do not concern the issues that play a role, but how they are dealt with. In 1997, Mercedes-Benz (MB) belonged to the Daimler-Chrysler-Corporation with headquarters in Stuttgart, Germany. MB is, among others, active in the marketing, production, distribution, and after-sales activities for cars, vans, and trucks. MB offers the owners of an MB car, van, or truck the option of replacing their present engine with a remanufactured engine, of the same or different type, with the same quality as a new engine, but for a price 20-30% lower than the price of a similar new engine. This offer holds for at least 20 years after a new car, van, or truck has been purchased. MB offers similar options for waterpumps, crank cases, crank shafts, and other parts produced by MB itself. In this chapter, we focus on the recovery network for engines for cars and vans, further denoted by the MB-MTR network, based on the MBMTR plant in Berlin, Germany, where the actual remanufacturing takes place. In 1996 MB-MTR remanufactured about 60 of the above engines a day, selling 14,250 remanufactured engines in total, about 40% of the total world-wide market for remanufactured MB engines for cars and vans.

On the optimal control of manufacturing and remanufacturing activities with a single shared server

We consider a single-product make-to-stock manufacturing–remanufacturing system. Returned products require remanufacturing before they can be sold. The manufacturing and remanufacturing operations are executed by the same single server, where switching from one activity to another does not involve time or cost and can be done at an arbitrary moment in time. Customer demand can be fulfilled by either newly manufactured or remanufactured products. The times for manufacturing and remanufacturing a product are exponentially distributed. Demand and used products arrive via mutually independent Poisson processes. Disposal of products is not allowed and all used products that are returned have to be accepted. Using Markov decision processes, we investigate the optimal manufacture–remanufacture policy that minimizes holding, backorder, manufacturing and remanufacturing costs per unit of time over an infinite horizon. For a subset of system parameter values we are able to completely characterize the optimal continuous-review dynamic preemptive policy. We provide an efficient algorithm based on quasi-birth–death processes to compute the optimal policy parameter values. For other sets of system parameter values, we present some structural properties and insights related to the optimal policy and the performance of some simple threshold policies.