The value of postponing online fulfillment decisions in multi-channel retail/e-tail organizations

Many retail/e-tail organizations assign responsibilities for online sales immediately and to the closest fulfillment location that has available stock. Unfortunately there is little research on the value of using such policies in retail/e-tail companies. To fill this gap, this paper examines two aspects of the online fulfillment assignment decision that differ from current practice. We propose that online sales should be accumulated before they are assigned to a fulfillment site and that more inventory position information should be leveraged into the fulfillment decision. Specifically, we develop and evaluate a “quasi-dynamic” allocation policy that assigns accumulated online sales to fulfillment locations based on expected inventory, shipping, and customer wait costs. Computational results show that our policy can reduce costs (i.e., holding, backorder, transportation cost) at the fulfillment locations by as much as 23% on average over a commonly used transportation cost policy. In addition, postponing the allocation decision and allowing sales to accumulate can reduce inventory costs at the fulfillment sites by 14% over common practice of instantaneously assigning online sales responsibilities. The magnitude of the benefit depends critically on the number of allocations made each period and the fraction of total sales coming from the online channel. Although postponement delays receipt of online sales, our findings suggest that explicitly incorporating customer service in the allocation decision can improve product availability at little or no additional cost.     

Quick response and omnichannel retail operations with the ship‐to‐store program

To better integrate online businesses with offline stores, fast‐fashion retailers roll out the “ship‐to‐store” (STS) initiative, which is an omnichannel fulfillment method that allows consumers to order online and have their parcels delivered to selected stores for pickup. To study the impact of STS on fast‐fashion operations, we consider four scenarios: multichannel retailing, multichannel retailing with quick response (QR), omnichannel retailing with STS, and omnichannel retailing with both STS and QR. Moreover, we investigate how STS influences the value of QR. The results show that although STS benefits firms in many situations, STS under a QR strategy can reduce a retailer's profits when inventory information disclosure due to STS dampens cross‐selling opportunities. Our findings also reveal that the implementation of STS may lower the value of QR when customers who encounter stockouts offline opt to purchase online directly rather than use STS.     

An integrated vendor–buyer inventory model with backorder price discount and effective investment to reduce ordering cost

The single-vendor single-buyer integrated production inventory system has been an object of study for a long time, but little is known about the effect of investing in reducing ordering cost on the integrated inventory models with backorder price discount and variable lead time. The purpose of this article is to investigate in the continuous review model with backorder price discount and variable lead time to effectively increase investment and to reduce the joint expected annual total cost. The integrated strategy discussed here is one in which the buyer orders a quantity, then the vendor produces n times order quantity in each production cycle, in order to reduce setup cost. In addition, the buyer offers backorder price discounts to the customers that may motivate the customers’ desire for backorders, and buyer ordering cost can be reduced through effective investment. An integrated inventory model is established to find the optimal solutions of order quantity, ordering cost, backorder price discount, lead time, and the number of shipments from the vendor to the buyer in one production run, so that the joint expected annual total cost incurred has the minimum value. Furthermore, numerical examples are used to demonstrate the benefits of the model.     

季节性产品零售商的网络订单履行策略研究

以一个销售季节性产品的零售商为研究对象,该零售商拥有两个实体店,分别位于两个独立的地区,通过Drop-shipping方式接收网上的订单,以扩大销量。初始库存量是确定的,产品卖给网上顾客为零售商带来的收益要比卖给店内顾客的收益少（履行网上订单获得的收益是零售商与订单提供者共享）;因此,每当网上订单到达时,零售商需要在接收该订单、还是把产品留给能带来更高收益的未来的店内顾客间做决策;如果接收该订单,还要决策将由哪个实体店来履行订单效益更好;以最大化未来期望收益为目标,建立动态规划模型,并从实体店对单个产品期望收益的角度为这两个问题的决策提供依据;分别在两个实体店处于平等地位和其中一个实体店被确定为优先供应商两种不同的情况下,研究零售商决策结果的差异。     

A simulation-optimization approach for integrated sourcing and inventory decisions

In this paper, we study a generalized vendor selection problem that integrates vendor selection and inventory replenishment decisions of a firm. In addition to vendor-specific procurement and management costs, we consider inventory replenishment, holding, and backorder costs explicitly to meet stationary stochastic demand faced by the firm. Our goal is to select the best set of vendors along with the optimum inventory decisions at each plant of the firm so that we minimize the system-wide total costs and achieve desired service and reliability levels. Due to uncertainties inherent in the problem related to demand observed by the firm, quality provided by vendors, and disruptions observed by vendors, we utilize a simulation-optimization approach to solve the problem. More specifically, we build a discrete-event simulation model to evaluate the objective function of the problem that works in concert with a scatter search-based metaheuristic optimization approach to search the solution space. Computational results not only provide managerial insights and measure the significance of intangible factors in the vendor selection process but also highlight the importance of computational tools such as simulation-optimization for the vendor selection problem.     

Pricing and promotion strategies of an online shop based on customer segmentation and multiple objective decision making

The advent of the Internet and web technologies has enabled the prosperity of virtual stores, which greatly reduce customers’ search costs and retailers’ overhead. However, the furious competition between online shops makes it difficult for them to generate profits. This study attempts to establish pricing and promotion strategies for online shops to enhance their profitability. The pricing decision is based on the concept of customer relationship management, where a greater margin of price concession is given to customers who are more valuable to the shop. The process of our approach is: clustering customers into different classes based on their RFM data, computing and presenting the list prices of products to customers according to their classes, allowing customers to bargain over the price and offering conceded prices which are computed based on customer classes and a multi-objective decision making model, and finally providing promotion options to customers to reinforce their purchase inclination. The proposed approach is implemented at an online shop of a computer peripherals retailer. Transaction data before and after the implementation are collected and compared to assess the performance of the proposed approach.     

Impacts of supply disruptions and customer differentiation on a partial-backordering inventory system

In this paper, we study a single-product inventory system which involves a supplier, a retailer, and differentiated customers. The supplier provides products to the retailer which in turn sells products to its customers. The supplier is unreliable and is subject to random disruptions. The retailer adopts a periodic-review inventory policy, under which the retailer reviews its inventory position every a fixed period of time and decides whether a replenishment is needed or not. Partial backordering is applied when a stockout occurs. That is, under the situations of stockouts, customers can choose either to backorder products or to abandon the purchase. Customers are segmented into two classes according to their backordering probabilities. The customer class with high backordering probability is provided with high priority to receive backorders. In this paper we allow more than one outstanding orders to exist, which is not the case in the previous literature. We develop a simulation model for such an inventory system and investigate the impacts of supply disruptions and customer differentiation on this inventory system.     

The lot size-reorder level inventory system with customers impatience functions

In this paper, we study a continuous review inventory model with deterministic demand. The model allows shortages, which are partially backlogged. The backlogging is characterized using an approach in which customers are considered impatient. Total profit function is developed using three general costs: holding cost, order cost and shortage cost. Holding cost is based on average stocks and order cost is fixed per replenishment. In shortage cost, we include three significant costs: the unit backorder cost (depending on the shortage time), the goodwill cost (constant) and the opportunity cost. A general approach is presented to determine the economic lot size, the reorder level and the minimum total inventory cost. We consider two customers impatience functions to illustrate the application of the procedure. This paper extends several models studied by other authors.     

Price versus privacy: an experiment into the competitive advantage of collecting less personal information

In previous privacy studies, consumers have reported their unease with online retailers that collect a lot of personal data. Consumers claim they will switch to alternative providers or cancel transactions if data collection is deemed excessive. Therefore, privacy appears to be a competitive factor in electronic commerce. This paper describes a study which quantifies the degree to which privacy is a competitive advantage for online retailers. In an experiment, we offered 225 participants the option to purchase one DVD from one of two online stores. Throughout the study, one online shop asked for more invasive personal data—as confirmed by an exit-questionnaire. In the test treatment, the privacy-invasive store sold DVDs for one Euro less than the other, and in the control treatment, both stores sold DVDs for the same price. Across both treatments, 74 participants made a purchase and had the DVD they bought delivered. In our study we found that, when the price of DVDs was the same between both stores, the shop asking for less personal data did not amass the entire market. When consumers were offered a trade-off between price and privacy, the vast majority of customers chose to buy from the cheaper, more privacy-invasive, firm; this firm got both a larger market share and higher revenue. The cheaper shop generated strong dissatisfaction with their privacy practises; in contrast, consumers of the more expensive store displayed only weak dissatisfaction with price. We established the validity of our analysis by checking users made informed choices, and did not select one firm over the other due to hasty decision-making or ordering effects. We found no support for either a materialistic lifestyle nor the quest for immediate gratification as to why customers chose the cheaper but privacy-unfriendly store.     

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.     

A vehicle routing problem with pickup and delivery time windows,and coordination of transportable resources

This paper addresses a pickup and delivery problem with time window constraints, using two types of delivery resources, which allows for coordination. A heavy resource (in this case a van) may carry both delivery items and one or more units of the lighter resource (foot couriers) on its single- or multi-route assignment. Foot couriers can pick up and deliver items independently, or travel with a van on its outbound and/or return leg. Coordination between resources can save time and cut costs. The optimization of this type of coordinated logistics system has received scant coverage in the literature. The objective criterion is the total cost (i.e., the sum of fixed cost and travelling cost) of both heavy and light resources. The problem is first formulated as a mixed integer program, assuming a reasonable coordination strategy. A two-stage heuristic is designed by employing both exact approaches and an ejection chain based on modifying the shortest path algorithm implemented in a dynamic network. The two-stage heuristic is compared with optimization models for an independent strategy and a partial coordination strategy on both local and simulated instances of up to 200 customers. Under certain conditions, the benefit of coordination is realized over the independent strategy in terms of lower average total cost and usage of heavy resource.     

Multiproduct multiple-buyer single-vendor supply chain problem with stochastic demand, variable lead-time, and multi-chance constraint

In this paper, a multi-product multi-chance constraint joint single-vendor multi-buyers inventory problem is considered in which the demand follows a uniform distribution, the lead-time is assumed to vary linearly with respect to the lot size, and the shortage in combination of backorder and lost-sale is assumed. Furthermore, the orders are placed in multiple of packets, there is a limited space available for the vendor, there are chance constraints on the vendor service rate to supply the products, and there is a limited budget for each buyer to purchase the products. While the elements of the buyers’ cost function are holding, shortage, order and transportation costs, the set up and holding costs are assumed for the vendor. The goal is to determine the re-order point and the order quantity of each product for each buyer such that the chain total cost is minimized. We show the model of this problem to be a mixed integer nonlinear programming type and in order to solve it a particle swarm optimization (PSO) approach is used. To justify the results of the proposed PSO algorithm, a genetic algorithm (GA) is applied as well to solve the problem. Then, the quality of the results and the CPU times of reaching the solution are compared through three numerical examples that are given to demonstrate the applicability of the proposed methodology in real world inventory control problems. The comparison results show the PSO approach has better performances than the GA method.     

Multi-objective optimization of hybrid backorder inventory model

This paper addresses inventory problem for the products that are sold in monopolistic and captive markets experiencing hybrid backorder (i.e., fixed backorder and time-weighted backorder). The problem with stochastic demand is studied first by developing single objective (cost) inventory model. Computational results of a numerical problem show the effectiveness of hybrid backorder inventory model over fixed backorder inventory model. The model is later extended to multi-objective inventory model. Three objectives of multi-objective inventory model are the minimization of total cost, minimization of stockout units and minimization of the frequency of stockout. A multi-objective particle swarm optimization (MOPSO) algorithm is used to solve the inventory model and generate Pareto curves. The Pareto curves obtained for hybrid backorder inventory model are compared with the existing Pareto curves that are based on fixed backorder. The results show a substantial reduction in stockout units and frequency of stockout with a marginal rise in cost with proposed hybrid backorder inventory system in comparison to existing fixed backorder inventory system. Sensitivity analysis is done to study the robustness of total cost, order quantity, and safety stock factor with the change in holding cost. In the end, the performance of the MOPSO algorithm is compared with the multi-objective genetic algorithm (MOGA). The metrics that are used for the performance measurement of the algorithms are error ratio, spacing and maximum spread.     

Stochastic (Q, r) inventory model with customer reneging

This paper presents a stochastic inventory model for situations in which, during a stockout period, unsatisfied demands are initially backordered but may renege (leave) probabilistically at the rate ρ to be filled elsewhere. The model is suggested by the customers' different reactions to a stockout condition: some patient customers wait until their demand is satisfied, while other impatient or urgent customers cannot wait long and have to fill their demand from another source. The cost of a backorder is assumed to be proportional to the length of time for which the backorder exists, and a fixed penalty cost is incurred per unit of lost demand. Based on a heuristic treatment of a lot-size reorder-point policy, a mathematical model representing the average annual cost of operating the inventory system is developed. The optimal operating policy variables minimizing the average annual cost can be calculated iteratively. At the extremes when ρ = 0 and ρ = ξ, the model presented reduces to the usual backorders and lost sales case, respectively.     

基于购买行为的随机生命周期易逝品库存策略

研究需求和商品生命周期均为随机的零售商库存管理问题,提出按照购买行为特征对需求进行分类,在不同类型的客户之间进行库存分配并允许缺货的库存策略.通过构建动态规划模型,求解出零售商最优库存策略,包括补货策略和库存分配策略.与先到先服务策略相比,该策略能显著提升零售商的利润,减少商品损坏的损失.     

A tabu search procedure for coordinating production,inventory and distribution routing problems

This paper addresses the problem of optimally coordinating a production‐distribution system over a multi‐period finite horizon, where a facility production produces several items that are distributed to a set of customers by a fleet of homogeneous vehicles. The demand for each item at each customer is known over the horizon. The production planning determines how much to produce of each item in every period, while the distribution planning defines when customers should be visited, the amount of each item that should be delivered to customers and the vehicle routes. The objective is to minimize the sum of production and inventory costs at the facility, inventory costs at the customers and distribution costs. We also consider a related problem of inventory routing, where a supplier receives or produces known quantities of items in each period and has to solve the distribution problem. We propose a tabu search procedure for solving such problems, and this approach is compared with vendor managed policies proposed in the literature, in which the facility knows the inventory levels of the customers and determines the replenishment policies.     

Supply Chain Management by H-Infinity Control

In this paper, a H _infin control technique is proposed for the management of a supply chain (SC) model linearized about nominal operating conditions. The performance considered is a weighted H _infin norm comprising the inventory and the orders placed by each site, hence controlling the bullwhip effect together with local management costs. It is shown how the optimization of local costs at each site is related to the performance of the whole chain and a decentralized control methodology is proposed based on this relation.     

A simulation based heuristic discrete particle swarm algorithm for generating integrated production–distribution plan

Deciding the strategy for production and distribution in a stochastic demand scenario is important for the manufacturing industries. An integrated production–distribution plan considering regular, overtime and outsourced production costs along with inventory holding, backorder, hiring/laying-off and trip-wise distribution costs is developed for a renowned bearing manufacturing industry producing three types of products at three locations. Demand is assumed to vary uniformly and a novel simulation based heuristic discrete particle swarm optimization (DPSO) algorithm is used for obtaining the best production–distribution plan that serves as a trade-off between holding inventory and backordering products. The algorithm also uses an innovative regeneration type constraint handling method which does not require a penalty operator. In addition to the bearing manufacturing industry data set, two other test data sets are also solved. The simulation based optimization approach gives good approximate solutions for the stochastic demand problems.     

Integration of strategic and tactical decisions for vendor selection under capacity constraints

We examine a generalized vendor selection problem of a multi-store firm where the goal is the simultaneous determination of (i) the set of vendors the firm should work with and (ii) how much each store should order from the selected vendors. In addition to the typical costs associated with vendor selection and delivery between the vendors and their assigned stores, we explicitly consider the inventory-related costs and decisions of the stores. We emphasize the relationship between facility location applications and the problem at hand, and we propose an integrated vendor selection and inventory optimization model. Also, arguing that our model creates a venue for precise incorporation of realistic capacity constraints, we model throughput and dispatch capacities, explicitly. The model is a challenging mixed integer nonlinear program for which we propose an efficient solution approach that relies on Generalized Benders Decomposition (GBD).     

CIO roles and responsibilities: Twenty-five years of evolution and change

We investigated how the CIO's job has changed over the past years and found that the CIOs’ role has evolved to reflect both the firm's IS infrastructure and strategy. This has led to two versions of the role: an executive-level manager focused on the firm's strategy and processes, and a technical manager focused on minimizing costs by rationalizing and leveraging the existing IS infrastructure. The degree to which a firm has standardized its IS architecture infrastructure, and the degree to which IS enables core products, services, processes, or competitive advantage of the firm impact the nature of the role.