Moritz Fleischmann

Quantitative models for reverse logistics: A review

Abstract This article surveys the recently emerged field of reverse logistics. The management of return flows induced by the various forms of reuse of products and materials in industrial production processes has received growing attention throughout this decade. Many authors have proposed quantitative models taking those changes in the logistics environment into account. However, no general framework has been suggested yet. Therefore the time seems right for a systematic overview of the issues arising in the context of reverse logistics. In this paper we subdivide the field into three main areas, namely distribution planning, inventory control, and production planning. For each of these we discuss the implications of the emerging reuse efforts, review the mathematical models proposed in the literature, and point out the areas in need of further research. Special attention is paid to differences and/or similarities with classical ‘forward’ logistics methods.

Quantitative models for reverse logistics

I. Reverse Logistics: An Introduction.- 1. Introduction.- 1.1 Scope and Definition of Reverse Logistics.- 1.2 Research Goals and Methodology.- 1.3 Outline of this Monograph.- 2. Reverse Logistics at IBM: An Illustrative Case.- 3. Structuring the Field.- 3.1 Dimensions of the Reverse Logistics Context.- 3.2 Categories of Reverse Logistics Flows.- 3.3 Literature Review.- 3.3.1 General Reverse Logistics Issues.- 3.3.2 Marketing Channels for Reverse Logistics Flows.- 3.3.3 Production and Operations Management Issues.- II. Reverse Logistics: Distribution Management Issues.- 4. Product Recovery Networks.- 4.1 Introduction to Reverse Distribution.- 4.2 Evidence from Current Practice.- 4.3 Recovery Network Characteristics.- 4.3.1 Commonalities of the Surveyed Business Cases.- 4.3.2 Comparison with Other Logistics Networks.- 4.4 Classification of Recovery Networks.- 4.4.1 Dimensions of the Network Context.- 4.4.2 Product Recovery Network Types.- 4.5 Vehicle Routing Issues.- 5. A Facility Location Model for Recovery Network Design.- 5.1 Recovery Network Design Models in Literature.- 5.2 A Generic Recovery Network Model.- 5.3 Examples.- 5.3.1 Example 5.1: Copier Remanufacturing.- 5.3.2 Example 5.2: Paper Recycling.- 5.4 Parametric Analysis and Network Robustness.- 5.5 Extensions.- Conclusions of Part II.- III. Reverse Logistics: Inventory Management Issues.- 6. Inventory Systems with Reverse Logistics.- 6.1 Exemplary Business Cases.- 6.2 Characteristics of Recoverable Inventory Management.- 6.3 A Review of Inventory Models in Reverse Logistics.- 6.3.1 Deterministic Models.- 6.3.2 Stochastic Periodic Review Models.- 6.3.3 Stochastic Continuous Review Models.- 7. Impact of Inbound Flows.- 7.1 A Basic Inventory Model with Item Returns.- 7.2 The Unit Demand Case.- 7.3 General Demand Case: Analysis of the Cost Function.- 7.4 General Demand Case: Optimal Policy Structure.- 7.5 Numerical Examples.- 7.6 Extensions.- 8. Impact of Multiple Sources.- 8.1 Tradeoffs Between Recovery and Procurement.- 8.2 The Capacity Aspect of Product Returns.- Conclusions of Part III.- IV. Reverse Logistics: Lessons Learned.- 9. Integration of Product Recovery into Spare Parts Management at IBM.- 9.1 The Current Dismantling Process.- 9.2 Logistics Alternatives for Integrating Dismantling.- 9.2.1 Design of the Dismantling Channel.- 9.2.2 Dismantling Decision Rule.- 9.2.3 Co-ordination with Other Sources.- 9.3 Performance of Alternative Planning Approaches.- 9.3.1 A Simulation Model.- 9.3.2 Numerical Results.- 9.4 Recommendations.- 10. Conclusions.- List of Figures.- List of Tables.- References.

E-fulfillment and multi-channel distribution - A review

This review addresses the specific supply chain management issues of Internet fulfillment in a multi-channel environment. It provides a systematic overview of managerial planning tasks and reviews corresponding quantitative models. In this way, we aim to enhance the understanding of multi-channel e-fulfillment and to identify gaps between relevant managerial issues and academic literature, thereby indicating directions for future research. One of the recurrent patterns in today’s e-commerce operations is the combination of ‘bricks-and-clicks’, the integration of e-fulfillment into a portfolio of multiple alternative distribution channels. From a supply chain management perspective, multi-channel distribution provides opportunities for serving different customer segments, creating synergies, and exploiting economies of scale. However, in order to successfully exploit these opportunities companies need to master novel challenges. In particular, the design of a multi-channel distribution system requires a constant trade-off between process integration and separation across multiple channels. In addition, sales and operations decisions are ever more tightly intertwined as delivery and after-sales services are becoming key components of the product offering.

Integrating Closed-Loop Supply Chains and Spare-Parts Management at IBM

IBM is among the pioneers recognizing the benefits of closed-loop supply chains that integrate product returns into business operations. We worked on a project exploiting product returns as a source of spare parts. Key decisions concern what recovery opportunities to use, the channel design, and coordinating alternative supply sources. Our analytic inventory-control model and a simulation model showed that procurement-cost savings largely outweigh reverse logistics costs and that information management is essential. These findings provide a basis for significantly expanding the usage of the novel parts supply source, thereby cutting procurement costs.

Controlling inventories with stochastic item returns: A basic model

Abstract Environmental legislation and customer expectations increasingly force manufacturers to take back their products after use. Returned products may enter the production process again as input resources. Material management has to be modified accordingly. One of the areas concerned is inventory management. The present paper provides a step towards a systematic analysis of inventory control in the context of reuse. A basic inventory model is presented comprising Poisson demand and returns. For this model, an optimal control policy is derived and optimal control parameters are computed. Moreover, a numerical analysis is provided of the impact of the return-flow on the inventory system. Comparison with traditional ( s , Q )-inventory models is central throughout the analysis.

Periodic review, push inventory policies for remanufacturing

Sustainability has become a major issue in most economies, causing many leading companies to focus on product recovery and reverse logistics. This research is focused on product recovery, and in particular on production control and inventory management in the remanufacturing context. We study a remanufacturing facility that receives a stream of returned products according to a Poisson process. Demand is uncertain and also follows a Poisson process. The decision problems for the remanufacturing facility are when to release returned products to the remanufacturing line and how many new products to manufacture. We assume that remanufactured products are as good as new. In this paper, we employ a “push” policy that combines these two decisions. It is well known that the optimal policy parameters are difficult to find analytically; therefore, we develop several heuristics based on traditional inventory models. We also investigate the performance of the system as a function of return rates, backorder costs and manufacturing and remanufacturing lead times; and we develop approximate lower and upper bounds on the optimal solution. We illustrate and explain some counter-intuitive results and we test the performance of the heuristics on a set of sample problems. We find that the average error of the heuristics is quite low.

Dynamic demand fulfillment in spare parts networks with multiple customer classes

We study real-time demand fulfillment for networks consisting of multiple local warehouses, where spare parts of expensive technical systems are kept on stock for customers with different service contracts. Each service contract specifies a maximum response time in case of a failure and hourly penalty costs for contract violations. Part requests can be fulfilled from multiple local warehouses via a regular delivery, or from an external source with ample capacity via an expensive emergency delivery. The objective is to minimize delivery cost and penalty cost by smartly allocating items from the available network stock to arriving part requests. We propose a dynamic allocation rule that belongs to the class of one-step lookahead policies. To approximate the optimal relative cost, we develop an iterative calculation scheme that estimates the expected total cost over an infinite time horizon, assuming that future demands are fulfilled according to a simple static allocation rule. In a series of numerical experiments, we compare our dynamic allocation rule with the optimal allocation rule, and a simple but widely used static allocation rule. We show that the dynamic allocation rule has a small optimality gap and that it achieves an average cost reduction of 7.9% compared to the static allocation rule on a large test bed containing problem instances of real-life size.

On optimal inventory control with independent stochastic item returns

To a growing extent companies take recovery of used products into account in their material management. One aspect distinguishing inventory control in this context from traditional settings is an exogenous inbound material flow. We analyze the impact of this inbound flow on inventory control. To this end, we consider a single inventory point facing independent stochastic demand and item returns. This comes down to a variant of a traditional stochastic single-item inventory model where demand may be both positive or negative. Using general results on Markov decision processes we show average cost optimality of an (s,S)-order policy in this model. The key result concerns a transformation of the model into an equivalent traditional (s,S)-model without return flows, using a decomposition of the inventory position. Traditional optimization algorithms can then be applied to determine control parameter values. We illustrate the impact of the return flow on system costs in a numerical example.

Reverse Logistics Network Design

The different chapters of this book highlight manifold examples of reverse logistics programs. While these cases vary substantially with respect to products, actors, and underlying motivations, as discussed in Chapter 1, they share a number of fundamental managerial issues. One of these is the need for an appropriate logistics infrastructure.

Time Slot Management in Attended Home Delivery

Many e-tailers providing attended home delivery, especially e-grocers, offer narrow delivery time slots to ensure satisfactory customer service. The choice of delivery time slots has to balance marketing and operational considerations, which results in a complex planning problem. We study the problem of selecting the set of time slots to offer in each of the zip codes in a service region. The selection needs to facilitate cost-effective delivery routes, but also needs to ensure an acceptable level of service to the customer. We present a fully automated approach that is capable of producing high-quality delivery time slot offerings in a short amount of time. Computational experiments reveal the value of this approach and the impact of the environment on the underlying trade-offs.