The discrete forward–reserve problem – Allocating space,selecting products,and area sizing in forward order picking

To reduce labor-intensive and costly order picking activities, many distribution centers are subdivided into a forward area and a reserve (or bulk) area. The former is a small area where most popular stock keeping units (SKUs) can conveniently be picked, and the latter is applied for replenishing the forward area and storing SKUs that are not assigned to the forward area at all. Clearly, reducing SKUs stored in forward area enables a more compact forward area (with reduced picking effort) but requires a more frequent replenishment. To tackle this basic trade-off, different versions of forward–reserve problems determine the SKUs to be stored in forward area, the space allocated to each SKU, and the overall size of the forward area. As previous research mainly focuses on simplified problem versions (denoted as fluid models), where the forward area can continuously be subdivided, we investigate discrete forward–reserve problems. Important subproblems are defined and computation complexity is investigated. Furthermore, we experimentally analyze the model gaps between the different fluid models and their discrete counterparts.     

Breadth of range and depth of stock: forecasting and inventory management at Euro Car Parts Ltd.

This paper investigates inventory management issues in a distribution network. The study is motivated by examining the operation of a wholesaling car parts company. Customer service requirements are of paramount importance in this market sector. The nature of the demand facing the company is characterised. The breadth of range of stock keeping units (SKUs) held at a stocking location and the quantity of each SKU held are normally treated in isolation but in this case, the rule developed to select the range of SKU was extended to determine the level of stock to hold. It is intuitively obvious that these two factors should be linked, yet the authors have not found any other literature developing the connection in a practical context. Forecasting issues are explored as the rule on stock range depends on a forecast of the number of orders received for each SKU at each stocking unit. Some implementation issues and extensions are indicated.     

Consumer returns reduction and information revelation mechanism for a supply chain

In this paper, we develop two revelation mechanism models of a supply chain consisting of one manufacturer and one retailer under asymmetric information, where the retailer provides store assistance (SA) to reduce consumer returns rate and increase demand. Under full information, we find that a higher returns rate or returns handling cost increases the SA level if the market scale is sufficiently high. In the demand information asymmetry model, we find that: (i) the low-type retailer (facing a low demand) has no incentive to distort demand information while the high-type retailer may report wrong information; (ii) the manufacturer would like to design a menu of wholesale price-order quantity contract to induce truthful demand information and the manufacturer pays an information rent to the high-type retailer if the returns rate or returns handling cost for the retailer is sufficiently low; (iii) asymmetry of information does not change the monotonicity of the unit wholesale price in the retailer’s type, and information asymmetry decreases the retail price but increases the SA level. Unlike the demand information asymmetry model, a higher retailer’s returns handling cost expands the effects of information asymmetry on the retail price and the SA level, and using revelation mechanism decreases the channel profit if the retailer’s returns handling cost is sufficiently high under the returns rate information asymmetry model.     

Inventory sharing via circular unidirectional chaining

This research studies the performance of circular unidirectional chaining – a “lean” configuration of lateral inventory sharing among retailers or warehouses – and compares its performance to that of no pooling and complete pooling in terms of expected costs and optimal order quantities. Each retailer faces uncertain demand, and we wish to minimize procurement, shortage and transshipment costs. In a circular unidirectional chain all retailers are connected in a closed loop, so that each retailer can cooperate with exactly two others as follows: receive units (if needed?available) from the left “neighbor” and send units (if needed?available) to the right, and a retailer who receives units from one neighbor is not allowed to send any units to its other neighbor. If the chain consists of at least three nodes and demands across nodes are i.i.d., its performance turns out to be independent of the number of nodes. The optimal stocking is therefore solved analytically. Analytical comparative statics with respect to cost parameters and demand distributions are provided. We also examine thoroughly the cases of uniform demand distribution (analytically) and normal demand distribution (numerically). In the uniform case with free transshipment, a unidirectional chain can save up to 1/3 of the expected cost of separate newsvendors caused by uncertainty. For three nodes, the advantage of complete pooling over unidirectional chaining does not exceed 19%.     

Inventory control in multi-channel retail

In recent years multi-channel retail systems have received increasing interest. Partly due to growing online business that serves as a second sales channel for many firms, offering channel specific prices has become a common form of revenue management. We analyze conditions for known inventory control policies to be optimal in presence of two different sales channels. We propose a single item lost sales model with a lead time of zero, periodic review and nonlinear non-stationary cost components without rationing to realistically represent a typical web-based retail scenario. We analyze three variants of the model with different arrival processes: demand not following any particular distribution, Poisson distributed demand and a batch arrival process where demand follows a Pòlya frequency type distribution. We show that without further assumptions on the arrival process, relatively strict conditions must be imposed on the penalty cost in order to achieve optimality of the base stock policy. We also show that for a Poisson arrival process with fixed ordering costs the model with two sales channels can be transformed into the well known model with a single channel where mild conditions yield optimality of an (s, S) policy. Conditions for optimality of the base stock and (s, S) policy for the batch arrival process with and without fixed ordering costs, respectively, are presented together with a proof that the batch arrival process provides valid upper and lower bounds for the optimal value function.     

Product allocation to different types of distribution center in retail logistics networks

We consider the problem of assigning stockkeeping units to distribution centers (DCs) belonging to different DC types of a retail network, e.g., central, regional, and local DCs. The problem is motivated by the real situation of a retail company and solved by an MIP solution approach. The MIP model reflects the interdependencies between inbound transportation, outbound transportation and instore logistics as well as capital tied up in inventories and differences in picking costs between the warehouses. A novel solution approach is developed and applied to a real-life case of a leading European grocery retail chain. The application of the new approach results in cost savings of 6% of total operational costs compared to the present assignment. These savings amount to several million euros per year. In-depth analyses of the results and sensitivity analyses provide insights into the solution structure and the major related issues.     

Multi-period price promotions in a single-supplier, multi-retailer supply chain under asymmetric demand information

This paper considers a two-stage supply chain in which a supplier serves a set of stores in a retail chain. We consider a two-stage Stackelberg game in which the supplier must set price discounts for each period of a finite planning horizon under uncertainty in retail-store demand. As a mechanism to stimulate sales, the supplier offers periodic off-invoice price discounts to the retail chain. Based on the price discounts offered by the supplier, and after store demand uncertainty is resolved, the retail chain determines individual store order quantities in each period. Because the supplier offers store-specific prices, the retailer may ship inventory between stores, a practice known as diverting. We demonstrate that, despite the resulting bullwhip effect and associated costs, a carefully designed price promotion scheme can improve the supplier’s profit when compared to the case of everyday low pricing (EDLP). We model this problem as a stochastic bilevel optimization problem with a bilinear objective at each level and with linear constraints. We provide an exact solution method based on a Reformulation-Linearization Technique (RLT). In addition, we compare our solution approach with a widely used heuristic and another exact solution method developed by Al-Khayyal (Eur. J. Oper. Res. 60(3):306–314, 1992) in order to benchmark its quality.     

A real-time decision rule for an inventory system with committed service time and emergency orders

In this paper, we study the inventory system of an online retailer with compound Poisson demand. The retailer normally replenishes its inventory according to a continuous review (nQ, R) policy with a constant lead time. Usually demands that cannot be satisfied immediately are backordered. We also assume that the customers will accept a reasonable waiting time after they have placed their orders because of the purchasing convenience of the online system. This means that a sufficiently short waiting time incurs no shortage costs. We call this allowed waiting time “committed service time”. After this committed service time, if the retailer is still in shortage, the customer demand must either be satisfied with an emergency supply that takes no time (which is financially equivalent to a lost sale) or continue to be backordered with a time-dependent backorder cost. The committed service time gives an online retailer a buffer period to handle excess demands. Based on real-time information concerning the outstanding orders of an online retailer and the waiting times of its customers, we provide a decision rule for emergency orders that minimizes the expected costs under the assumption that no further emergency orders will occur. This decision rule is then used repeatedly as a heuristic. Numerical examples are presented to illustrate the model, together with a discussion of the conditions under which the real-time decision rule provides considerable cost savings compared to traditional systems.     

An examination of the size of orders from customers,their characterisation and the implications for inventory control of slow moving items

This paper examines half a million observations of the size of orders from customers at an electrical wholesaler. It notes: the distribution of the size of customer orders for a single item (stock keeping unit or SKU) is very skewed and resembles a geometric distribution; while the average size of an order is different for different items, for one SKU the mean order size is effectively the same at different branches even when the branches have very different demand rates; across a range of SKUs there is a strong relationship linking the mean and the variance of order size. The general results above are shown to apply to even the slowest movers. This extension is important because for items with intermittent demand the size of customer orders is required to produce an unbiased estimate of demand. Also a knowledge of the distribution of demand is important for setting maximum and minimum stock levels and the scheme employed is described.     

Factory gate pricing: An analysis of the Dutch retail distribution

Factory gate pricing (FGP) is a relatively new phenomenon in retail distribution. Under FGP, products are no longer delivered at the retailer distribution center, but collected by the retailer at the factory gates of the suppliers. Owing to both the asymmetry in the distribution networks (the supplier sites greatly outnumber the retailer distribution centers) and the better inventory and transport coordination mechanisms, this is likely to result in high cost savings. A mathematical model was used to analyze the benefits of FGP. The main contribution of this paper is its practical approach to transport consolidation in this recently emerging supply chain concept in retail distribution. Extensive numerical results for a large real-life case study of the Dutch retail distribution are presented to show the effect of the orchestration shift from supplier to retailer, the improved coordination mechanisms, and sector-wide cooperation.     

Demand seasonality in retail inventory management

We investigate the value of accounting for demand seasonality in inventory control. Our problem is motivated by discussions with retailers who admitted to not taking perceived seasonality patterns into account in their replenishment systems. We consider a single-location, single-item periodic review lost sales inventory problem with seasonal demand in a retail environment. Customer demand has seasonality with a known season length, the lead time is shorter than the review period and orders are placed as multiples of a fixed batch size. The cost structure comprises of a fixed cost per order, a cost per batch, and a unit variable cost to model retail handling costs. We consider four different settings which differ in the degree of demand seasonality that is incorporated in the model: with or without within-review period variations and with or without across-review periods variations. In each case, we calculate the policy which minimizes the long-run average cost and compute the optimality gaps of the policies which ignore part or all demand seasonality. We find that not accounting for demand seasonality can lead to substantial optimality gaps, yet incorporating only some form of demand seasonality does not always lead to cost savings. We apply the problem to a real life setting, using Point-of-Sales data from a European retailer. We show that a simple distinction between weekday and weekend sales can lead to major cost reductions without greatly increasing the complexity of the retailer’s automatic store ordering system. Our analysis provides valuable insights on the tradeoff between the complexity of the automatic store ordering system and the benefits of incorporating demand seasonality.     

Optimal selection of retailers for a manufacturing vendor in a vendor managed inventory system

A Vendor Managed Inventory (VMI) system consists of a manufacturing vendor and a number of retailers. In such a system, it is essential for the vendor to optimally determine retailer selection and other related decisions, such as the product’s replenishment cycle time and the wholesale price, in order to maximize his profit. Meanwhile, each retailer’s decisions on her willingness to enter the system and retail price are simultaneously considered in the retailer selection process. However, the above interactive decision making is complex and the available studies on interactive retailer selection are scarce. In this study, we formulate the retailer selection problem as a Stackelberg game model to help the manufacturer, as a vendor, optimally select his retailers to form a VMI system. This model is non-linear, mixed-integer, game-theoretic, and analytically intractable. Therefore, we further develop a hybrid algorithm for effectively and efficiently solving the developed model. The hybrid algorithm combines dynamic programming (DP), genetic algorithm (GA) and analytical methods. As demonstrated by our numerical studies, the optimal retailer selection can increase the manufacturer’s profit by up to 90% and the selected retailers’ profits significantly compared to non-selection strategy. The proposed hybrid algorithm can solve the model within a minute for a problem with 100 candidate retailers, whereas a pure GA has to take more than 1 h to solve a small sized problem of 20 candidate retailers achieving an objective value no worse than that obtained by the hybrid algorithm.     

A continuous approximation approach for the integrated facility-inventory allocation problem

In today’s retail business many companies have a complex distribution network with several national and regional distribution centers. This article studies an integrated facility location and inventory allocation problem for designing a distribution network with multiple distribution centers and retailers. The key decisions are where to locate the regional distribution centers (RDCs), how to assign retail stores to RDCs and what should be the inventory policy at the different locations such that the total network cost is minimized. Due to the complexity of the problem, a continuous approximation (CA) model is used to represent the network. Nonlinear programming techniques are developed to solve the optimization problems. The main contribution of this work lies in developing a new CA modeling technique when the discrete data cannot be modeled by a continuous function and applying this technique to solve an integrated facility location-allocation and inventory-management problem. Our methodology is illustrated with the network from a leading US retailer. Numerical analysis suggests that the total cost is significantly lower in the case of the integrated model as compared with the non-integrated model, where the location-allocation and inventory-management problems are considered separately. This paper also studies the effects of changing parameter values on the optimal solutions and to point out some management implications.     

Estimating the market shares of stores based on the shopper's search and purchase behavior

Suppose that a consumer has decided to shop around several retail stores in an attempt to find a desired product or service. From his or her past shopping experience, the shopper may know: (1) the assortment size of each store, (2) the search cost per visit, and (3) the price variation among the stores. For such a situation, we first consider the optimal sequence of stores and the optimal search strategy from the shopper's search-theoretic point of view. Based on the assumption that shoppers follow the optimal sequencing and search strategy that maximizes his or her expected net gain, we then estimate the market share of each store in the market area. With a numerical example, we finally analyze the effects of the assortment size, the search cost, and the price variation on the market shares of existing retail stores. Based on the shopper's search and purchase model, we attempt to explain: (1) why shoppers visit bigger stores first, (2) why they visit fewer stores if the search cost is relatively higher than the product price, and (3) why they shop around more stores if the price variation among the stores is large.     

The impact of consumer returns policies on consignment contracts with inventory control

We consider a consignment contract with consumer non-defective returns behavior. In our model, an upstream vendor contracts with a downstream retailer. The vendor decides his consignment price charged to the retailer for each unit sold and his refund price for each returned item, and then the retailer sets her retail price for selling the product. The vendor gets paid based on net sold units and salvages unsold units as well as returned items in a secondary market. Under the framework, we study and compare two different consignment arrangements: the retailer/vendor manages consignment inventory (RMCI/VMCI) programs. To study the impact of return policy, we discuss a consignment contract without return policy as a benchmark. We show that whether or not the vendor offers a return policy, it is always beneficial for the channel to delegate the inventory decision to the vendor. We find that the vendor’s return policy depends crucially on the salvage value of returns. If the product has no salvage value, the vendor’s optimal decision is not to offer a return policy; otherwise, the vendor can gain more profit by offering a return policy when the salvage value turns out to be positive.     

Pooling of spare parts between multiple users: How to share the benefits?

Companies that maintain capital goods (e.g., airplanes or power plants) often face high costs, both for holding spare parts and due to downtime of their technical systems. These costs can be reduced by pooling common spare parts between multiple companies in the same region, but managers may be unsure about how to share the resulting costs or benefits in a fair way that avoids free riders. To tackle this problem, we study several players, each facing a Poisson demand process for an expensive, low-usage item. They share a stock point that is controlled by a continuous-review base stock policy with full backordering under an optimal base stock level. Costs consist of penalty costs for backorders and holding costs for on-hand stock. We propose to allocate the total costs proportional to players’ demand rates. Our key result is that this cost allocation rule satisfies many appealing properties: it makes all separate participants and subgroups of participants better off, it stimulates growth of the pool, it can be easily implemented in practice, and it induces players to reveal their private information truthfully. To obtain these game theoretical results, we exploit novel structural properties of the cost function in our (S − 1, S) inventory model.     

Predicting retailer orders with POS and order data: The inventory balance effect

Despite advances in retail point-of-sale (POS) data sharing, retailers’ suppliers struggle to effectively use POS data to improve their fulfillment planning processes. The challenge lies in predicting retailer orders. We present evidence that retail echelon inventory processes translate into a long-run balance or equilibrium between orders and POS, which we refer to as the inventory balance effect, allowing for more accurate order forecasting. Based on the inventory balance effect, this research prescribes a forecasting approach which simultaneously uses both sources of information (retailer order history and POS data) to predict retailer orders to suppliers. Using data from a consumable product category, this approach is shown to outperform approaches based singularly on order or POS data, by up to 125%. The strength of this novel approach – significantly improved forecast accuracy with minimal additional analysis – make it a candidate for widespread adoption in retail supply chain collaborative planning and forecasting initiatives with corresponding impact on fulfillment performance and related operating costs.     

突发事件下不对称信息供应链协调机制研究

针对一个由供应商和一个零售商构成的供应链,在零售商成本为私有信息条件下,假设产品的市场需求为零售价格的指数函数,研究如何协调供应链应对突发事件。首先,给出了对称信息下供应链协调模型;然后,研究了不对称信息下集权式与分权式供应链的协调机制;再次,在突发事件引起零售商成本分布函数扰动情况下,通过引入供应商由于可能需要调整生产计划而产生的偏差成本,研究了供应链的最优应对策略。 研究表明,供应链的最优生产计划、最优批发价格和最优零售价格均具有一定的鲁棒性,当突发事件造成零售商期望成本在一定范围内发生扰动时,三者可以保持不变,当零售商期望成本扰动超过一定范围内时,则需要对之加以调整,才能有效应对突发事件。最后,通过数值仿真验证了相关结论。     

Consignment contracting: Who should control inventory in the supply chain?

Consignment is a popular form of business arrangement where supplier retains ownership of the inventory and gets paid from the retailer based on actual units sold. The popularity of such an arrangement has come with some continued debates on who should control the supply chain inventory, the supplier or retailer. This paper aims at shedding light on these debated issues. We consider a single period supply chain model where a supplier contracts with a retailer. Market demand for the product is price-sensitive and uncertain. The supplier decides his consignment price charged to the retailer for each unit sold, and the retailer then chooses her retail price for selling the product. We study and compare two different consignment arrangements: The first allows the retailer to choose the supply chain inventory, together with her retail price, and is labeled as a Retailer Managed Consignment Inventory (RMCI) program; and the second calls for the supplier to decide the inventory, together with his consignment price, and is labeled as a Vendor Managed Consignment Inventory (VMCI) program. We show that with an RMCI program, the supply chain loses at least 26.4% of its first-best (expected) profit, while with VMCI, it loses just or no more than 26.4% of the first-best profit. Second, we demonstrate that both programs lead to an equal split of the corresponding channel profit between the supplier and the retailer. These results indicate that it is beneficial both to the supplier and to the retailer when delegating the inventory decision to the supplier rather than to the retailer in the channel.