Using online pickup site inclusion policies to manage demand in retail/E-tail organizations

A major development in online retailing is the significant increase in the number of traditional “offline” retailers extending their brands online. Many of these retail/e-tail firms are attempting to leverage channel synergies by allowing customers to purchase products over the internet and then pick their orders up at one of the firm's local stores. This paper proposes that the firm presents only a subset of its stores to online customers as available pickup locations, rather than simply listing all local stores with inventory. By doing so, the firm can protect stores with critically low inventory levels and thereby reduce backorder costs. Specifically, we develop and evaluate a dynamic pickup site inclusion policy that incorporates real-time information to specify which of the firm's e-fulfillment locations should be presented at online checkout. Computational results indicate that managing in-store demand via such policies can decrease total cost (holding, backorder, and lost or redirected pickup sale costs) by as much as 18% over allowing customers to pick online orders up from any site with available inventory. The percentage of pickup sales and customers' sensitivity to travel are critical in determining the magnitude of the benefit.     

零售业卷烟库存决策模型研究

为了“按客户订单组织货源”,规范和优化企业内部流程,降低资源配置成本和经营风险,提高客户服务水平,快速响应零售市场,提出了零售业库存决策模型.该模型结合当前烟草公司的实际情况与业务需求,以烟草公司管理零售库存为切入口,引入抽样调查机制对不同零售户类别的卷烟库存及销量数据进行采集和抽样,利用因果关系预测法推算出社会库存及销量,根据其所得到的存销比来预测未来零售市场的需求,辅助决策者在后续投放工作中做出准确的决策.以此确保零售库存不囤积不脱销,最大限度地满足零售市场需求.     

Fuzzy inference systems and inventory allocation decisions: Exploring the impact of priority rules on total costs and service levels

Inventory allocation decisions in a distribution system concern issues such as how much and where stock should be assigned to orders in a supply chain. When the inventory level of an inventory point is lower than the total number of items ordered by lower echelons in the chain, the decision of how many items to allocate to each ``competing'' order must take into consideration the trade-off between cost and service level. This paper proposes a decision-support system that makes use of fuzzy logic to consider inventory carrying, shortage and ordering costs as well as transportation costs. The proposed system is compared through simulation with three other inventory allocation decision support models in terms of cost and service levels achieved. Conclusions are then drawn.     

考虑单位车辆运力的运输-库存随机需求决策模型

针对提前期和客户需求不确定的生产、运输和库存协调控制问题, 基于整车直达运输策略, 从优化系统物流成本角度, 建立了在决策中明确体现单位车辆运力影响的运输-库存系统协调模型, 设计了求解模型的优化搜索机制并从数学上证明了其有效性. 最后, 进一步对单位车辆运力进行敏感性分析并得出以下结论: 在其他条件不变时, 单位车辆的运力会影响系统决策结果, 该运力既不是越大越好, 也不是越小越好, 而是某个适中值.

     

Winner's curse and parallel sales channels—Online auctions linked within e-tail websites

A sample of 416 online auctions was examined to determine the extent of overpayment (winner's curse) where online auctions and e-tail websites were linked together to form a parallel sales channel. The results indicated that 8.7% of the highest winning online auction bidders exceeded e-tail posted reference prices of identical retail merchandise found at the same website. Significantly, such bids exceeded the reference prices by a mean percentage dollar amount of 14.1% thus suggesting the existence of a winner's curse. The results also indicated that (1) there was a significant negative association between reference price and mean percentage dollar amount overbid; (2) there was a significant negative association between auction lot size and mean percentage dollar amount overbid; and (3) there was no significant association between overtime auctions and mean percentage dollar amount overbid. While manipulation of reference price and auction lot size might minimize winner's curse, erratic or irrational behavior (by online auction and/or e-tail websites) may lead to disinformation.     

Heuristics for solving an internet retailer’s dynamic transshipment problem

Throughout the world, electronic retailing has grown exponentially. In this study, we explore the transshipment strategies of a large online retailer with multiple distribution centers in a geographically dispersed network. We investigate transshipment strategies in a continuous review system with dynamic demand. The theoretical optimal transshipment schedule by a dynamic program is developed as a benchmark solution. Then, three computationally simple heuristic transshipment rules are proposed for practitioners’ decision making. Through comprehensive numerical evaluations, we recommend an acceptable heuristic rule that could closely approximate the same performance obtained under the theoretically optimal transshipment. We further demonstrate that a retailer can significantly reduce total fulfillment costs by combining an inventory allocation policy with an appropriate heuristic transshipment rule.     

Production and allocation policies in a two-class inventory system with time and quantity dependent waiting costs

This paper presents an integrated production and inventory allocation model in a two-echelon supply chain system. The higher echelon is a manufacturer, who produces a single commodity. The lower echelon consists of two types of major commodity distributors who might face stochastic or deterministic demands from multiple retailers. Our analytical model provides optimal decision policies that minimize total production and customer waiting costs from the manufacturer's perspective when there are time and quantity dependent customer waiting costs. We identify the value of the integrated policy and compare it with typical approximations such as aggregating the multiple demands or applying the single demand multiple times.     

Continuous review inventory model with controllable lead time,lost sales rate and order processing cost when the received quantity is uncertain

In practice the quantity received may not match the quantity ordered due to worker's strike, rejection during inspection, damage during transportation, human errors in counting, etc. Accordingly, the managers often must make decisions under uncertain quantity received circumstances. In this study, we investigate the continuous review inventory model with shortages include the case where the quantity received is uncertain, in which the lead time, lost sales rate and order processing cost are decision variables. Here, we consider the lead time crashing cost is an exponential function of lead time, and the order processing cost and lost sales rate are logarithmic functions of capital investment. The objective of this study is to minimize the total relevant cost by simultaneously optimizing the order quantity, lost sales rate and order processing cost. In addition, an efficient algorithm is developed to determine the optimal policy, and our approach is illustrated through a numerical example. From the results of numerical example, it can be shown that, the significant savings can be achieved through the reductions of order processing cost and lost sales rate.     

A decision rule for coordination of inventory and transportation in a two-stage supply chain with alternative supply sources

This paper deals with a two-stage supply chain that consists of two distribution centers and two retailers. Each member of the supply chain uses a (Q,R) inventory policy, and incurs standard inventory holding and backlog costs, as well as ordering and transportation costs. The distribution centers replenish their inventory from an outside supplier, and the retailers replenish inventory from one of the two distribution centers. When a retailer is ready to replenish its inventory that retailer must decide whether it should replenish from the first or second distribution center. We develop a decision rule that minimizes the total expected cost associated with all outstanding orders at the time of order placement; the retailers then repeatedly use this decision rule as a heuristic. A simulation study which compares the proposed policy to three traditional ordering policies illustrates how the proposed policy performs under different conditions. The numerical analysis shows that, over a large set of scenarios, the proposed policy outperforms the other three policies on average.     

连锁零售供应链多级库存协同决策研究

针对连锁零售供应链多级库存资源的动态优化配置问题,提出了在上层对库存策略和下层对物流分配方案协同寻优的多级库存双层规划模型。借鉴细粒度模型遗传算法的遗传操作具有局部性的特点,模拟微观群体交互作用的局部性,基于细粒度模型遗传算法的Agent群体行为优化算法和基于复杂适应系统涌现机理的协同决策机制,进行连锁零售供应链多级库存协同决策研究。通过算例实验对模型的有效性进行了验证。仿真实验结果表明,通过连锁零售供应链微观个体Agent的群体行为优化,从系统工程的角度,实现了连锁零售供应链多级库存的动态资源优化配置和信息共享,降低了多级库存管理与运营的总成本。     

A recovery model for a two-echelon serial supply chain with consideration of transportation disruption

Supply chains are becoming increasingly competitive and complex in order to effectively meet customer demands. These characteristics make supply chains vulnerable to various risks, including disruptions. In this study, a recovery model is explored for a two-stage production and inventory system with the possibility of transportation disruption. This model is capable of determining the optimal ordering and production quantities during the recovery window, and ensuring that the total relevant costs are minimized, while seeking to recover the original schedule. An efficient heuristic was developed to solve the model. The results showed that the optimal recovery schedule is highly dependent on the relationship between the backorder cost and the lost sales cost parameters. In addition, the heuristic was able to give quality solutions for the model, with very small deviations of the heuristic solutions from the optimal value. Such tools are useful in assisting managers towards effective decision making, particularly in determining the optimal recovery strategy for the longevity and sustainability of their firms undergoing disruptions.     

Pricing and inventory management in a system with multiple competing retailers under (r, Q) policies

We consider a multi-retailer system operated on an infinite horizon, in which each retailer faces stochastic demand following a Poisson process and adopts a continuous-review (r, Q) policy for replenishing inventory to satisfy customer demand. The system involves decisions of pricing and inventory management with the goal of maximizing profit, which equals the sales revenue minus the purchase and inventory costs. Taking Cournot competition into account, models are formulated to optimize simultaneously the expected sales volumes and (r, Q) policies of all retailers. An efficient approach is proposed to calculate the approximate inventory cost. Based on this approach, solution methods for centralized and decentralized scenarios are developed. A great number of numerical computations are provided to evaluate the efficiency of the solution methods, and their performance in the two scenarios. Moreover, system performance under sequential decisions (first pricing and then inventory management) is also investigated.     

按单装配系统中组件生产和库存分配控制策略研究

针对由两种组件、三类顾客需求组成的按单装配系统, 本文研究了其中的组件生产控制与库存分配问题. 在各类顾客需 求是泊松到达过程, 各种组件加工时间服从指数分布的假设下, 我们运用马尔科夫决策理论建立了无限期折扣总成本模型, 根据Lippman转换得到了相应归一化后的离散最优方程, 在此基础之上分析了生产和库存分配联合最优控制策略的结构性质. 本文证明了最优策略是依赖于系统状态的动态策略. 组件的最优生产策略是动态基库存策略, 其中基库存水平是关于系统中其他组件库存水平的非减函数. 而最优的分配策略是动态的阈值策略, 对于只需一种组件构成的顾客需求, 组件的分配阈值是系统中另一组件库存水平的增函数; 而对于同时需要两种组件组成的顾客需求, 其各组件的分配阈值是另一组件库存水平的减函数. 最后通过数值试验给出了各个参数对联合最优控制策略的影响, 并得到了相应的管理启示.     

Competitive Delivered Pricing by Mail-Order and Internet Retailers

Mail-order and internet sellers must decide how customers pay shipping charges. Typically, these sellers choose between two pricing policies: either “uniform pricing,” where the firm delivers to any customer at a fixed delivery charge (that may be volume dependent), or “mill pricing,” where the firm bills the customer a distance-related shipping charge. This paper studies price competition between a mail-order (or internet) seller and local retailers, and the mail-order firm’s choice of pricing policy. The price policy choice is studied when retailers do not change price in reaction to the mail-order firm’s policy choice, and when they do. In the second case, a two-stage non-cooperative game is used and it is found that for low customer willingness to pay, mill pricing is favored but as willingness to pay rises, uniform pricing becomes more attractive. These results are generalized showing that larger markets, higher transportation rates, higher unit production cost, and greater competition between retailers all increase profit under mill pricing relative to uniform pricing (and vice versa). On the other hand, cost asymmetries that favor the mail-order firm will tend to induce uniform rather than mill pricing. Some empirical data on retail and mail-order sales that confirm these results are presented.     

Regression approximation for a partially centralized inventory system considering transportation costs

Inventory centralization for multiple stores with stochastic demands reduces costs by establishing and maintaining a central ordering/distribution point. However the inventory centralization may increase the transportation costs since either the customer must travel more to reach the product, or the central warehouse must ship the product over longer distance to reach the customer. In this paper, we study a partially centralized inventory system where multiple central warehouses exist and a central warehouse fulfills the aggregated demand of stores. We want to determine the number, the location of central warehouses and an assignment of central warehouses and a set of stores. The objective is the minimization of the sum of warehouse costs and transportation cost. With the help of the regression approximation of cost function, we transform the original problem to more manageable facility location problems. Regression analysis shows that the approximated cost function is close to the original one for normally distributed demands.     

A general model for EOQ inventory systems with partial backlogging and linear shortage costs

We present a mathematical model which generalises several known deterministic Economic Order Quantity (EOQ) inventory systems with partial backlogging. This inventory model considers purchasing cost, holding cost, shortage costs and replenishment cost. Shortage costs (backorder cost and lost sales cost) are both made up of a fixed cost and a variable cost which depends on the length of the waiting time for the next replenishment. The optimal policy is characterised through a sequential optimisation procedure. To illustrate the model, numerical examples and sensitivity results are given.     

Particle Swarm Optimization and Pure Adaptive Search in Finish Goods’ Inventory Management

Abstract

Inventory management deals with a tradeoff between the benefits of keeping stocks of goods that allows fulfillment of the customer’s demand, and the cost of carrying inventory. Inventory control techniques are very important components and the most organizations can substantially reduce their costs associated with the flow of materials. This paper presents new inventory management model based on particle swarm optimization and pure adaptive search global optimization algorithm in production-inventory system. The proposed model is focusing on planned level of demand for finished goods, production and raw materials cost, production capacity as the norm, change of the production cost and inventory capital cost, all of which are typical factors in automobile manufacture industry. The model determines different factors such as the minimizing inventory quantity, minimizing inventory value, and minimizing production cost based on demand for production items. The model is tested with original real-world dataset obtained from the automotive company Lear from US and its factory in Novi Sad, Serbia.     

A coordinated approach to hedge the risks in stochastic inventory-routing problem

The inventory routing problem (IRP) studied in this research involves repeated delivery of products from a depot to a set of retailers that face stochastic demands over a long period. The main objective in the IRP is to design the set of routes and delivery quantities that minimize transportation cost while controlling inventory costs. Traditional IRP focuses on risk-neutral decision makers, i.e., characterizing replenishment policies that maximize expected total net present value, or equivalently, minimize expected total cost over a planning horizon. In this research, for incorporating risk aversion, a hedge-based stochastic inventory-routing system (HSIRS) integrated with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and Forward Option Pricing (FOP)model based on Black-Scholes model, from hedge point of view, is proposed to solve the multi-product multi-period inventory routing problem with stochastic demand. Computational results demonstrate the importance of this approach not only to risk-averse decision makers, but also to maximize the net present value at an acceptable service level. As a result, an optimal portfolio (R, s, S) system of product group can be generated to maximize the net present value under an acceptable service level in a given planning horizon. Meanwhile, the target group needed to be served and the relative transportation policy also can be determined accordingly based on the time required to be served as a priori partition to minimize the average transportation costs; hence, the routing assignment problem can be successfully optimized through a Predicting Particle Swarm Optimization algorithm.     

A heuristic for rationing inventory in two demand classes with backlog costs and a service constraint

We study the rationing policy in an inventory system with two demand classes and different service criteria for backorders. Due to the difference of customer values, system performance sometimes has to be measured with a mixture of penalty cost and service level in managing inventory. With a continuous review (r,Q) system, we develop a critical level rationing policy in which a threshold mechanism is adopted to allocate backorders when multiple outstanding orders exist. Due to the complexity of the problem, a heuristic is developed based on the principle that both demand classes are served with respective target service levels. We also introduce bounds so that the search ranges of decision variables become restrictive. The numerical examples indicate an excellent performance of our heuristic. In addition, when ordering cost is medium or high, the threshold clearing mechanism has the same results as the optimal one. When ordering cost is small (set to zero), different clearing mechanisms should be used depending on the priorities of demand classes. Further analysis indicates that transforming the service constraint into a cost parameter and then applying the existing algorithm will not be a good approach for this problem with mixed performance criteria. It either increases the costs or violates the service constraint. This study also shows the importance of applying rationing policy when high priority class has a low demand volume, target service levels between two classes have a large gap, or replenishment lead time is long. The results of this study should enhance our understanding of how to implement rationing policies in practice.     

Investigating impact of the order activity costing method on product cost calculations

Inaccuracies in calculated product costs have existed since the development of costing systems. A key contributor to the issue is the use of inappropriate bases for the application of overhead costs. This research proposes and provides preliminary evaluation in a virtual environment for a new allocation base that is believed to be better matched to the consumption rate of the indirect costs being allocated. Using a generalized manufacturing operational framework incorporating multi-period simulation, this research investigates the relationship between allocated cost categories and production or sales order activity. The existing cost allocation methods of full absorption and activity based costing (ABC) are used for comparison to the proposed method. Results show that at the aggregate reporting level (that is, income statement), the use of sales order or production order activity as an allocation base tracks closely with performance levels experienced using more traditional allocation bases. However, the results indicate that the impact on calculated product costs would influence decision making within a firm in terms of sales emphasis, mix, and markets in which to expand and from which to exit. This approach toward cost allocation would be equal to other Enterprise Resource Planning system based solutions in terms of simplicity of maintenance while offering product cost accuracies relatively equal with unit-level focused ABC systems, without requiring the substantial maintenance costs.