Order fulfillment in online retailing : what goes where

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2005.Includes bibliographical references (p. 139-146).We present three problems motivated by order fulfillment in online retailing. First, we focus on one warehouse or fulfillment center. To optimize the storage space and labor, an e-tailer splits the warehouse into two regions with different storage densities. One is for picking customer orders and the other to hold a reserve stock that replenishes the picking area. Consequently, the warehouse is a two-stage serial system. We investigate an inventory system where demand is stochastic by minimizing the total expected inventory- related costs subject to a space constraint. We develop an approximate model for a periodic review, nested ordering policy. Furthermore, we extend the formulation to account for shipping delays and advance order information. We report on tests of the model with data from a major e-tailer. Second, we focus on the entire network of warehouses and customers. When a customer order occurs, the e-tailer assigns the order to one or more of its warehouses and/or drop- shippers, so as to minimize procurement and transportation costs, based on the available current information. However, this assignment is necessarily myopic as it cannot account for any subsequent customer orders or future inventory replenishments.(cont.) We examine the benefits from periodically re-evaluating these real-time assignments. We construct near- optimal heuristics for the re-assignment for a large set of customer orders by minimizing the total number of shipments. Finally, we present saving opportunities by testing the heuristics on order data from a major e-tailer. Third, we focus on the inventory allocation among warehouses for low-demand SKUs. A large e-tailer strategically stocks inventory for SKUs with low demand. The motivations are to provide a wide range of selections and faster customer fulfillment service. We assume the e-tailer has the technological capability to manage and control the inventory globally: all warehouses act as one to serve the global demand simultaneously. The e-tailer will utilize its entire inventory, regardless of location, to serve demand. Given we stock certain units of system inventory, we allocate inventory to warehouses by minimizing outbound transportation costs. We analyze a few simple cases and present a methodology for more general problems.by Ping Josephine Xu.Ph.D

Multi-Echelon Inventory Optimization and Demand-Side Management: Models and Algorithms

Inventory management is a fudamental problem in supply chain management. It is widely used in practice, but it is also intrinsically hard to optimize, even for relatively simple inventory system structures. This challenge has also been heightened under the threat of supply disruptions. Whenever a supply source is disrupted, the inventory system is paralyzed, and tremenduous costs can occur as a consequence. Designing a reliable and robust inventory system that can withstand supply disruptions is vital for an inventory system\u27s performance.First we consider a basic type of inventory network, an assembly system, which produces a single end product from one or several components. A property called long-run balance allows an assembly system to be reduced to a serial system when disruptions are not present. We show that a modified version is still true under disruption risk. Based on this property, we propose a method for reducing the system into a serial system with extra inventory at certain stages that face supply disruptions. We also propose a heuristic for solving the reduced system. A numerical study shows that this heuristic performs very well, yielding significant cost savings when compared with the best-known algorithm.Next we study another basic inventory network structure, a distribution system. We study continuous-review, multi-echelon distribution systems subject to supply disruptions, with Poisson customer demands under a first-come, first-served allocation policy. We develop a recursive optimization heuristic, which applies a bottom-up approach that sequentially approximates the base-stock levels of all the locations. Our numerical study shows that it performs very well.Finally we consider a problem related to smart grids, an area where supply and demand are still decisive factors. Instead of matching supply with demand, as in the first two parts of the dissertation, now we concentrate on the interaction between supply and demand. We consider an electricity service provider that wishes to set prices for a large customer (user or aggregator) with flexible loads so that the resulting load profile matches a predetermined profile as closely as possible. We model the deterministic demand case as a bilevel problem in which the service provider sets price coefficients and the customer responds by shifting loads forward in time. We derive optimality conditions for the lower-level problem to obtain a single-level problem that can be solved efficiently. For the stochastic-demand case, we approximate the consumer\u27s best response function and use this approximation to calculate the service provider\u27s optimal strategy. Our numerical study shows the tractability of the new models for both the deterministic and stochastic cases, and that our pricing scheme is very effective for the service provider to shape consumer demand

Multilocation Inventory Systems With Centralized Information.

The management of multi-echelon inventory systems has been both an important and challenging research area for many years. The rapid advance in information technology and the emphasis on integrated supply chain management have new implications for the successful operation of distribution systems. This research focuses on the study of some fundamental issues related to the operation of a multilocation inventory system with centralized information. First, we do a comparative analysis to evaluate the overall performance of individual versus centralized ordering policies for a multi-store distribution system where centralized information is available. This study integrates the existing research and clarifies one of the fundamental questions facing inventory managers today: whether or not ordering decisions should be centralized. Next, we consider a multi-store distribution system where emergency transshipments are permitted among these stores. Based on some simplifying assumptions, we develop an integrated model with a joint consideration of inventory and transshipment components. An approximately optimal (s, S) policy is obtained through a dynamic programming technique. This ordering policy is then compared with a simplified policy that assumes free and instantaneous transshipments. We also examine the relative performance of base stock policies for a centralized-ordering distribution system. Numerical studies are provided to give general guidelines for use of the policies

Integrated Lead Time and Demand Risk Pooling Strategies in Multi-Echelon Distribution Systems

In this research, we first examine the impact of including quantity discounts on purchasing price in an order splitting system. Second, we try to understand how the integration of the order splitting and consolidated distribution strategies influences the total logistic cost. The results show that the integrated strategy works well in many cases if the total annual cost includes the transportation costs as well as the quantity discount in purchasing price

Retail inventory management with lost sales

The inventory control problem of traditional store-based grocery retailers has several challenging features. Demand for products is stochastic, and is typically lost when no inventory is available on the shelves. As the consumer behavior studies reveal, only a small percentage of customers are willing to wait when confronted with an out-of-stock situation, whereas the remaining majority will either buy a different product, visit another store, or entirely drop their demand. A store orders inventory on a periodic basis, and receives replenishment according to a fixed schedule. The ordered stock is typically delivered before the next ordering moment, which results in lead times shorter than the review period length. Order sizes are often constrained to integer multiples of a fixed batch size, the case packs, generally dictated by the manufacturer. Upon order receipt at the store, the stock is manually stacked on the shelves, to serve customer demand. Shelf space allocation of many products is limited, dictated by marketing constraints. Hence, surplus stock, which does not fit on the regular shelf, is temporarily stored in the store’s backroom, often a small place, poorly organized. The focus of this dissertation is on developing quantitative models and designing solution approaches for managing the inventory of a single item, under periodic review, when some or all of the following characteristics are taken into account: ?? Lost sales. Demand that occurs when no inventory is available is lost, rather than backordered. ?? Fractional lead time. Time between order placement and order delivery is shorter than the review period length. ?? Batch ordering. Order sizes are constrained to integer multiples of a fixed batch size. ?? Limited shelf space. Shelf space allocation is predetermined. The retailer’s inventory is split between the sales floor and the backroom, which is used to temporarily store surplus inventory not accommodated by the regular shelves. We consider optimal, as well as easy-to-understand inventory replenishment policies, where the objective is to minimize the long-run average cost of the system. Two types of costs are primarily recognized in the inventory models developed in this dissertation: ?? inventory related costs: for ordering, for holding products on stock, and penalty costs for not being able to satisfy end-customer demand, and ?? handling related costs: for shelf stacking, and for handling backroom stock. Despite empirical evidence on the dominance of handling costs in the store, remarkably little is reported in the academic literature on how to manage inventory in the presence of handling costs. A reason for this is that formal models of handling operations are still scarce. In this dissertation, we first formalize a model of shelf stacking costs, using insights from an empirical study. Then, we extend the traditional single-item lost-sales periodic-review inventory control model with several realistic dimensions of the replenishment practices of grocery retailers: batch ordering, handling costs, shelf space and backroom operations. The models we consider are too complex to lend themselves to straightforward analytical tractability. As a result, numerical solution methods based on stochastic dynamic programming are proposed in this dissertation, and near-optimal alternative replenishment policies are investigated. Chapter 2 addresses operational concerns regarding the shelf stacking process in grocery retail stores, and the key factors that influence the execution time of this common store operation. Shelf stacking represents the regular store process of manually refilling the shelves with products from new deliveries, which is typically time consuming and costly. We focus on products that are replenished in pre-packed form but presented to the end-customer in individual units. A motion and time study is executed, and the complete shelf stacking process is broken down into several sub-activities. The main time drivers for each activity are identified, relationships are established, tested and validated using real-life data collected at two European grocery retailers. A simple prediction model of the total stacking time per order line is then inferred, in terms of the number of case packs and consumer units. The model can be applied to estimate the workload and potential time savings in the stacking process. Implications of our empirical findings for inventory replenishment decisions are illustrated by a lot-sizing analysis in Chapter 2, and further explored in Chapter 3. Chapter 3 defines a single item stochastic lost sales inventory control model under periodic review, which is designed to handle fractional lead times, batch ordering and handling costs. We study the settings in which replenishment costs reflect shelf stacking costs and have an additive form with fixed and linear components, depending on the number of batches and units in the replenishment order. We explore the structure of optimal policies under the long-run average cost criterion and propose a new policy, referred to as the (s;QjS; nq) policy, which partially captures the optimal policy structure and shows close-to-optimal performance in many settings. In a numerical study, we compare the performance of the policy against the best (s; Q; nq) and (s; S; nq) policies, and demonstrate the relative improvements. Sensitivity analyses illustrate the impact of the different problem parameters, in particular the batch size and the handling cost parameters, on the optimal solutions and associated average costs. Managerial insights into the effect of ignoring handling costs in the optimization of replenishment decisions are also discussed. Chapter 4 extends the retail setting from Chapter 3 to situations in which there is a limited shelf space to display goods on the sales floor, and the retailer uses the store’s backroom to temporarily store surplus stock. As a result, the back stock is regularly transferred from the backroom to the sales floor to satisfy end-customer demand, which results in additional handling costs for the retailer. We investigate the effect of using the backroom on the inventory system performance, where performance is measured with respect to the optimal ordering decisions, and the long-run average cost of ordering, holding, lost-sales and merchandise handling. Two extensions of the inventory model with ample shelf space are proposed in Chapter 4, which include a (i) linear or (ii) fixed cost structure for additional handling operations. In a numerical study, we discuss several qualitative properties of the optimal solutions, illustrate the additional complexities of the second model, and compare the findings with those of the previous chapter. Furthermore, we build several managerial insights into the effect of problem parameters, in particular the shelf space capacity, on the system’s performance. Finally, we quantify the expected cost penalty the retailer may face by ignoring the additional handling costs in the optimization of inventory decisions, and illustrate the trade-off between the different cost components. Chapter 5 studies a variant of the traditional infinite-horizon, periodic-review, singleitem inventory system with random demands and lost sales, where we assume fractional lead times and batch ordering, and allow for ??xed non-negative ordering costs. We present a comparison of four situations: zero vs. positive setup costs, and unit vs. non-unit batch sizes. For all cases, the optimal policy structure is only partially known in general. We show in a numerical study that the optimal policy structure of the most general model is usually more complex than that of the models with positive setup cost, or batch ordering only. Based on the gained insights, we further test the performance of the near-optimal (s;QjS; nq) heuristic policy in the different cases, and demonstrate its effectiveness. Also, well-known inventory control policies of base-stock, or (s; S) type are extended to the case of batch ordering and studied in comparison with the new heuristic under several conditions

Mathematics in the Supply Chain

[no abstract available

Managing Reverse Logistics or Reversing Logistics Management?

In the past, supply chains were busy fine-tuning the logistics from raw material to the end customer. Today an increasing flow of products is going back in the chain. Thus, companies have to manage reverse logistics as well.This thesis contributes to a better understanding of reverse logistics. The thesis brings insights on reverse logistics decision-making and it lays down theoretical principles for reverse logistics as a research field.In particular it puts together a framework for reverse logistics identifying the elementary dimensions, providing typologies, and structuring their interrelations.With respect to aiding decision-making, this thesis comprises return handling and inventory management. On the first, the focus is on critical factors for the combination vs. separation of reverse and forward flows during material handling. On the second, the main research issue is the value of information. One of the findings is that more informed methods do not necessarily lead to the best performance.Furthermore, this thesis proposes a reflection on the future development of the field. Through a Delphi study with an international panel of academics working on the area, recommendations are made concerning both research and pedagogy. This thesis also poses the following question: is it a matter of simply managing reverse logistics or of reversing logistics management?The message is: logistics cannot go forward without reverse thinking!Twintig jaar geleden zorgden een supply chain voor deijverige logistieke fine-tuning van de goederenstromen vangrondstof tot de uiteindelijke klant. Vandaag de dag is dat geengarantie meer voor succes, aangezien een groeiende stroomproducten terug vloeit in de keten. Terwijl retourlogistiek zichwereldwijd uitbreidt in alle lagen van de keten, worden sommigeactoren gedwongen om producten terug te nemen. Anderen doen ditmeer pro-actief, aangetrokken door de waarde die geretourneerdeproducten vertegenwoordigen. In ieder geval is het goed managenvan retourlogistiek een noodzakelijke bekwaamheid in modernesupply chains.Dit proefschrift draagt bij aan een beter begrip vanretourlogistiek. Naast het verschaffen van inzicht in retourlogistieke beslissingen, wordt ook het theoretische fundament gelegd voor de ontwikkeling van retourlogistiek als onderzoeksgebied

A Restoration-Based Model for Materials Management in a Global Manufacturing Environment.

Globalization of manufacturing along with increased competition has made effective planning and control more important than ever. At the same time, it is more difficult than ever to achieve effective planning and control due to larger leadtimes and shorter product life cycles. The objective of this research is to explore the importance of control strategy on materials management in global manufacturing networks. Control strategies in common use and others that have recently been proposed in the literature are reviewed and classified along a push/pull gradient. It is shown that one of them, the restoration control strategy, can be used to represent a wide range of pull systems as well as certain elements of push systems. Using concepts underlying the restoration strategy, two models are developed for aggregate planning in a global manufacturing network. One model requires that all demands be met whereas the other allows some sales to be lost. Application of either of the models to a specific network results in values for decision variables, including target inventories and restoration coefficients. Target inventories are aggregate values that can be disaggregated to finer levels of detail. Values for restoration coefficients help identify the best control strategy. Both models apply to multi-echelon networks of any design and under known demand. Both formulations are nonlinear, mixed-integer programming models that have proven to be difficult to solve for the general case. Relaxing the integrality constraints allows the models to be solved using commercially available software although optimality cannot be guaranteed due to nonconvexity of constraints. The models were applied to a specific network. The restoration model with no lost sales was found to have severe limitations; however, the restoration model that allows lost sales provided results that were stable. The relationships between the decision variables and holding costs, labor costs, and demand variation were explored using the simulation technique of batch means. Among other things, results indicated that a control strategy very similar to base stock was most appropriate for the specific network studied