Whether a retailer should enter an e‐commerce platform taking into account consumer returns

Considering consumers are increasingly shopping online nowadays and the online sales market is dominated by e‐commerce giants, traditional retailers need to choose whether to enter e‐commerce platforms. Moreover, traditional retailers need to determine whether to offer offline return services considering online return services are very popular. To address these challenges, we explore a retailer's optimal offline return strategy and channel choice of whether or not to enter a platform in the contexts of symmetric information and asymmetric information, respectively. We present conditions for the retailer to share information. Interestingly, we find that the retailer in some conditions has no motivation to improve customer satisfaction rate of offline store. Most important, we find that the retailer's channel choice depends on the magnitude of the annual service fee that is affected by offline return strategy and asymmetric information, and the offline return strategy depends on the magnitude of the average residual value of returned products.     

考虑线上线下零售商竞争的制造商双渠道定价策略

根据现有的不同电商销售平台模式,将制造商双渠道销售模式划分为自营模式、平台模式和混合模式.分析在制造商主导的双渠道供应链中,3种不同渠道模式下价格竞争系数和佣金比例系数对其他经济变量的影响,以及制造商的最优定价决策与渠道选择问题.通过模型和数值分析发现,在平台模式和混合模式中,当制造商保证佣金比例系数在一定范围内,制造商利润随佣金比例系数的增加而减少,传统零售商、网络零售商和供应链总利润随佣金比例系数的增加而增加.在3种渠道模式中,混合模式对传统零售商和制造商利润增加更有利,平台模式对网络零售商和供应链总利润增加更有利.     

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.     

市场价格信息不对称的银企融资决策

研究市场价格信息不对称下, 受资金约束的零售商为获取最优订货量从资本市场借贷的供应链运营和融资决策问题. 采用Stackelberg 博弈, 构建并分析混同契约和甄别契约模型. 研究表明: 甄别契约能更好地激励零售商透露真实信息, 使供应链整体利润增加; 银行更偏好高价格零售商以降低借贷风险, 银行在甄别契约下的期望利润总是大于混同契约下的期望利润; 高价格零售商的期望利润受到价格波动和其类型比例的双重影响, 在一定条件下选择甄别契约会得到额外的信息租金.

     

A differential game of retailer promotions

The paper identifies equilibrium marketing strategies over time in a marketing channel of distribution. A number of retailers promote locally a manufacturer's brand in order to increase their sales. However, sustained and substantial retailer promotions will damage the image of the manufacturer's brand. The manufacturer advertises nationally to improve the brand image. Demand at the retail outlets increase both by the local promotions and by the strength of brand image. First, we identify a noncooperative equilibrium of a differential game played with Markovian strategies. Next, we study a cooperative game where the players make coordinated marketing decisions and address the question whether the manufacturer can design an incentive strategy such that the retailers will stick to their parts of the agreed solution.     

Exploring Multiple‐discreteness in Freight Transport. A Multiple Discrete Extreme Value Model Application for Grain Consolidators in Argentina

There are some examples where freight choices may be of a multiple discrete nature, especially the ones at more tactical levels of planning. Nevertheless, this has not been investigated in the literature, although several discrete-continuous models for mode/vehicle type and shipment size choice have been developed in freight transport. In this work, we propose that the decision of port and mode of the grain consolidators in Argentina is of a discrete-continuous nature, where they can choose more than one alternative and how much of their production to send by each mode. The Multiple Discrete Extreme Value Model (MDCEV) framework was applied to a stated preference data set with a response variable that allowed this multiple-discreteness. To our knowledge, this is the only application of the MDCEV in regional freight context. Free alongside ship price, freight transport cost, lead-time and travel time were included in the utility function and observed and random heterogeneity was captured by the interaction with the consolidator’s characteristics and random coefficients. In addition, different discrete choice models were used to compare the forecasting performance, willingness to pay measures and structure of the utility function against.

     

The effect of backlog queue and load-building processing in a multi-echelon inventory network

A continuous-review, two-echelon inventory system with one central warehouse and an arbitrary number of non-identical retailers is considered in this study. Retailers face independent Poisson demands and apply standard (r, Nq) policies. Filled orders at the central warehouse must be consolidated into loads before shipping to the retailer level. New modeling options for the backorder processing and load-building processes are considered. Employing simulation, a set of experiments is performed to illustrate how different processing rules for the backlogging and load-building queues affect the lead-time experienced at the retailer level. Simulation results indicate that there are cases where considerable improvements can be gained from using different processing rules in backlogging and load-building queues.     

Supply chain structure and demand risk

V. Agrawal and S. Seshadri (2000) [Risk intermediation in supply chains. IIE Transactions, 32, 819-831] considered a problem in which a single risk neutral distributor supplies a short-lifecycle, long-leadtime product to several retailers that are identical except in their attitudes towards risk. They proved that the distributor should not offer the same terms to every retailer but instead offer less risky (from the demand risk perspective) contracts to more risk averse retailers. They did not prove the optimality of their menu.In this paper we reconstruct their results when the number of retailers is infinite and their coefficient of risk aversion is drawn from a continuous distribution. We use optimal control theory to solve this problem. We show that this distribution uniquely determines the channel structure. Moreover, the optimal contract menu not only has the same structure as in Agrawal and Seshadri but is also optimal among nearly all contracts. The implications of these findings for channel design are discussed.     

Analysis of the maximum level policy in a production-distribution system

We consider a production-distribution system, where a facility produces one commodity which is distributed to a set of retailers by a fleet of vehicles. Each retailer defines a maximum level of the inventory. The production policy, the retailers replenishment policies and the transportation policy have to be determined so as to minimize the total system cost. The overall cost is composed by fixed and variable production costs at the facility, inventory costs at both facility and retailers and routing costs. We study two different types of replenishment policies. The well-known order-up to level (OU) policy, where the quantity shipped to each retailer is such that the level of its inventory reaches the maximum level, and the maximum level (ML) policy, where the quantity shipped to each retailer is such that the inventory is not greater than the maximum level. We first show that when the transportation is outsourced, the problem with OU policy is NP-hard, whereas there exists a class of instances where the problem with ML policy can be solved in polynomial time. We also show the worst-case performance of the OU policy with respect to the more flexible ML policy. Then, we focus on the ML policy and the design of a hybrid heuristic. We also present an exact algorithm for the solution of the problem with one vehicle. Results of computational experiments carried out on small size instances show that the heuristic can produce high quality solutions in a very short amount of time. Results obtained on a large set of randomly generated problem instances are also shown, aimed at comparing the two policies.     

Design of Stochastic Distribution Networks Using Lagrangian Relaxation

This paper addresses the design of single commodity stochastic distribution networks. The distribution network under consideration consists of a single supplier serving a set of retailers through a set of distribution centers (DCs). The number and location of DCs are decision variables and they are chosen from the set of retailer locations. To manage inventory at DCs, the economic order quantity (EOQ) policy is used by each DC, and a safety stock level is kept to ensure a given retailer service level. Each retailer faces a random demand of a single commodity and the supply lead time from the supplier to each DC is random. The goal is to minimize the total location, shipment, and inventory costs, while ensuring a given retailer service level. The introduction of inventory costs and safety stock costs leads to a nonlinear NP-hard optimization problem. A Lagrangian relaxation approach is proposed. Computational results are presented and analyzed showing the effectiveness of the proposed approach.     

A genetic algorithm approach for location-inventory-routing problem with perishable products

In this paper, we address a location-inventory-routing model for perishable products. The model determines the number and location of required warehouses, the inventory level at each retailer, and the routes traveled by each vehicle. The proposed model adds location decisions to a recently published inventory routing problem in order to make it more practical, thus supporting the prevalent claim that integration of strategic, tactical and operational level decisions produces better results for supply chains. Given that the model developed here is NP-hard, with no algorithm capable of finding its solution in polynomial time, we develop a Genetic Algorithm approach to solve the problem efficiently. This approach achieves high quality near-optimal solutions in reasonable time. Furthermore, the unique structure of the problem requires developing a new chromosome representation, as well as local search heuristics. Finally, an analysis is carried out to verify the effectiveness of the algorithm.     

Coordination of split deliveries in one-warehouse multi-retailer distribution systems

In this paper, we consider a distribution system where a warehouse is responsible for replenishing the inventories at multiple retailers by a fleet of vehicles of limited capacity. If a distribution policy of the system involves split deliveries, that is, the inventory of at least one retailer is replenished by using multiple vehicle routes, the coordination of the deliveries can further reduce the inventory cost of the retailer. We consider the coordination where two split deliveries are realized by direct shipping and multiple-stop shipping, respectively. To the best of our knowledge, this kind of coordination was never studied in the literature but can find its application in inventory routing problems. This paper proposes and analyses a class of coordination policies for the split deliveries which can reduce the inventory costs of the retailers without increasing transportation costs. A non-linear programming model is established for formulating the class of polices. Because the optimal coordination policy corresponding to an optimal solution of the model may be hard to find and/or implement, two simple but effective coordination policies are proposed. The inventory cost savings realized by the two policies are evaluated analytically and algorithmically. Our theoretical analysis and computational experiments show that both policies are effective. Under certain conditions, they can save 50% of the inventory costs at the retailers without increasing transportation costs.     

Analysis on the effects of stock sharing on supply chain

Considering a two-period, two-part supply chain consisting of one supplier and multi-retailers, this paper studies the effects of stock sharing among retailers on the supply chain. By establishing analysis model and using simulation method, the effects of stock sharing mode and also the traditional mode without stock sharing on retailer, supplier and the whole supply chain’s performance are analyzed. The research results show that when supplier is in a dominant position, although only under certain conditions the stock sharing can stimulate retailer to increase order volume and make supplier to gain a better profit, it always make the profitability of retailer and supplier better than in the traditional mode. When supplier is in a dominant position, stock sharing can always stimulate retailer to increase their order volume, and the profitability of the retailer, supplier and the whole supply chain is always better than the traditional mode situation without stock sharing.     

Financing decisions in supply chains with a capital‐constrained manufacturer: competition and risk

Supply chain finance is a crucial topic. In this paper, we consider that a capital‐constrained manufacturer can borrow money from either a bank (bank credit financing) or a retailer (trade credit financing). Our analysis compares supply chain performance under these two financing schemes. Furthermore, we extend our model to evaluate the impacts of retail competition and supply chain member's risk aversion on supply chains, which consist of one capital‐constrained manufacturer and two competing retailers. We consider three financing schemes: only bank credit financing, dual trade credit financing, and bank and trade credit mix financing. We find that without retail competition, the retailer is always willing to use the trade credit financing; whereas with retail competition, if one retailer provides the trade credit but the other does not, the credit provider could receive the superior profit. Thus, providing an appropriate trade credit financing scheme is critically important for retailers. Moreover, we find that without retail competition, when a trade interest rate is relatively low, both the retailer and manufacturer could reach a win‐win situation in the trade credit financing. However, with retail competition, supply chain members (i.e., two retailers and one manufacturer) will not have an all‐win situation no matter which specific financing scheme is adopted and only a win‐win‐lose situation exists when using the credit mix financing scheme or the dual trade credit financing in supply chains. Last but not least, regardless of risk neutrality or aversion of supply chain members, their pricing decisions among three financing schemes are similar. This implies that the impacts of supply chain members’ risk aversion are limited in supply chain financing scheme selection. More managerial insights are discussed.     

A reinforcement learning approach to competitive ordering and pricing problem

This study analyses simultaneous ordering and pricing decisions for retailers working in a multi‐retailer competitive environment for an infinite horizon. Retailers compete for the same market where the market demand is uncertain. The customer selects the winning agent (retailer) in each term on the basis of random utility maximization, which depends primarily on retailer price and random error. The complexity of the problem is increased by competitiveness, necessity for simultaneous decisions and uncertainty in the nature of increases, and is not conducive to examination using standard analytical methods. Therefore, we model the problem using reinforcement learning (RL), which is founded on stochastic dynamic programming and agent‐based simulations. We analyse the effects of competitiveness and performance of RL on three different scenarios: a monopolistic case where one retailer employing a RL agent maximizes its profit, a duopolistic case where one retailer employs RL and another utilizes adaptive pricing and ordering policies, and a duopolistic case where both retailers employ RL.     

A closed-loop location-inventory problem with spare parts consideration

A closed-loop location-inventory problem is considered. Forward supply chain consists of a single echelon where the distribution centers (DCs) have to distribute a single product to different retailers with random demands. Reverse supply chain also contains only one echelon where the remanufacturing centers (RCs) collect the returns from the retailers, remanufacture them as spare parts and then push them back to the retailers assigned to the DCs through the forward supply chain. The problem is to choose which DCs and RCs are to be opened and to associate the retailers with them. The problem is formulated using a mixed integer nonlinear location allocation model. An exact two-phase Lagrangian relaxation algorithm is developed to solve it. The computational results and sensitivity analysis are presented.     

The profit-maximizing weber problem

We consider the location of a profit-maximizing, single-plant firm in a continuous space with a finite number of local markets. Five different spatial price policies are considered: (i) spatial discriminatory pricing, (ii) uniform delivered pricing, (iii) uniform mill pricing, (iv) zone pricing, and (v) mixed pricing where clients have the choice between pick-up or delivery. Global optimization methods are proposed for solving the corresponding problems and numerical experiments are reported on.     

Nested simulated annealing approach to periodic routing problem of a retail distribution system

This paper introduces a real-life variant of a periodic traveling salesman problem (V_PTSP) for a retail distribution system. A warehouse is allocated to serve specified retail districts where a large number of retailers are located and the deliveries are planned according to the visiting frequency of each retailer. The objective is to determine daily vehicle routes over a given planning period adhering to the visiting frequency restrictions while minimizing the total distance traveled. Initially, the V_PTSP is formulated as an integer programming model. Since this model can only solve small size problems, a nested simulated annealing procedure is developed for solving large size V_PTSPs. A series of computational tests are performed for evaluating the performance of the heuristic procedure. The results show that the proposed approach gives good solutions in reasonably short computation times.     

考虑产品替代的低碳供应链定价和政府财税

基于低碳经济背景,考虑由政府、制造商和零售商三方博弈构成的供应链,使用Stackelberg博弈和纳什均衡的分析方法求出模型的均衡解,并对比了零售商是否联合两种模式下的社会福利、制造商和零售商利润。结果表明：零售商联合情况下的零售商和制造商利润大于零售商非联合情形,同时产品需求量和社会福利与零售商非联合情形相同。高碳产品市场规模越大,社会福利、零售商和制造商利润会先下降后上升,低碳产品的生产成本增加,社会福利、制造商利润会下降,若零售商不联合,销售高碳产品零售商利润上升,销售低碳产品零售商利润下降,若零售商联合则零售商利润下降。     

An integrated approach for logistic and vendor managed inventory in supply chain

In this paper, a quaternary policy system towards integrated logistics and inventory aspect of the supply chain has been proposed. A system of multi retailers and distributors, with each distributor following a unique policy, will be analysed. The first policy is continuous time replenishment policy where the retailers’ inventory is replenished in every time interval. In the next three policies, inventory of the retailers will be replenished by some definite policy factors. The vendor managed inventory (VMI) system is used for updating the inventory of the retailers. An order-up-to policy (q, Q) is used for updating the inventory of distributors. Total erstwhile demands to the retailer will be used to determine the amount of inventory acclivity. Furthermore, the distributors will be sending the delivery vehicles to few fellow retailers who are shortlisted according to the policy, followed by the retailers and associated distributors. On the basis of random demand that the retailers are facing from end customers and the total demand that has incurred in the supply chain, products are unloaded to the selected retailers from the delivery vehicle. The path of the delivery vehicle is retrieved by dynamic ant colony optimization. In addition, a framework has been developed to measure the end-customer satisfaction level and total supply chain cost incorporating the inventory holding cost, ordering cost and the transportation cost. The framework has been numerically moulded with different settings to compare the performance of the quadruplet policies.