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.     

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.     

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.     

基于提前支付的非瞬时变质产品批量订货定价策略

当供应商处于供应链节点的买方市场时,通常要求零售商提前支付一定比例购置成本作为订金.针对该问题研究零售商促销努力下存在随机需求的非瞬时变质产品批量订货定价策略,考虑零售商提前支付策略,允许部分缺货.在一定条件下可得零售商最优补货周期和局部最优定价策略,随之提出相应的求解算法.数值计算结果表明:若提前支付购置成本占比或利率增大,则零售商利润将显著减少;提前支付期限和期数均对最优利润产生消极影响;零售商采取积极促销策略可有效提升自身利润.     

A heuristic method for the inventory routing and pricing problem in a supply chain

The inventory routing problem (IRP) in a supply chain (SC) is to determine delivery routes from suppliers to some geographically dispersed retailers and inventory policy for retailers. In the past, the pricing and demand decisions seem ignored and assumed known in most IRP researches. Since the pricing decision affects the demand decision and then both inventory and routing decisions, it should be considered in the IRP simultaneously to achieve the objective of maximal profit in the supply chain. In this paper, a mathematical model for the inventory routing and pricing problem (IRPP) is proposed. Since the solution for this model is an NP (non-polynomial) problem, a heuristic method, tabu search adopting different neighborhood search approaches, is used to obtain the optimal solution. The proposed heuristic method was compared with two other methods considering the IRPP separately. The experimental results indicate that the proposed method is better than the two other methods in terms of average profit.     

The governing dynamics of supply chains: The impact of altruistic behaviour

This paper analyses an infinite horizon two-echelon supply chain inventory problem and shows that a sequence of the optimum ordering policies does not yield globally optimal solutions for the overall supply chain. First-order autoregressive demand pattern is assumed and each participant adopts the order-up-to (OUT) policy with a minimum mean square error forecasting scheme to generate replenishment orders. To control the dynamics of the supply chain, a proportional controller is incorporated into the OUT policy, which we call a generalised OUT policy. A two-echelon supply chain with this generalised OUT policy achieves over 10% inventory related cost reduction. To enjoy this cost saving, the attitude of first echelon player to cost increases is an essential factor. This attitude also reduces the bullwhip effect. An important insight revealed herein is that a significant amount of benefit comes from the player doing what is the best for the overall supply chain, rather than what is the best for local cost minimisation.     

Rationing mechanisms and inventory control-policy parameters for a divergent supply chain operating with lost sales and costs of review

We consider a static divergent two-stage supply chain with one distributor and many retailers. The unsatisfied demands at the retailers’ end are treated as lost sales, whereas the unsatisfied demand is assumed to be backlogged at the distributor. The distributor uses an inventory rationing mechanism to distribute the available on-hand inventory among the retailers, when the sum of demands from the retailers is greater than the on-hand inventory at the distributor. The present study aims at determining the best installation inventory control-policy or order-policy parameters such as the base-stock levels and review periods, and inventory rationing quantities, with the objective of minimizing the total supply chain costs (TSCC) consisting of holding costs, shortage costs and review costs in the supply chain over a finite planning horizon. An exact solution procedure involving a mathematical programming model is developed to determine the optimum TSCC, base-stock levels, review periods and inventory rationing quantities (in the class of periodic review, order-up-to S policy) for the supply chain model under study. On account of the computational complexity involved in optimally solving problems over a large finite time horizon, a genetic algorithm (GA) based heuristic methodology is presented.     

Replenishment policies for sustainable business development in a continuous credit based vendor managed inventory distribution system

In a credit supply environment in the fast moving consumer goods distribution in the developing countries, retailers are making the payment to the distributors in number of unequal installments. The distributors working with such retailers having different demand and payment policy have to accept delay of payment as a competitive strategy and achieve their own business objectives. Some distributors have the objective of minimizing the balance payment considering the financial safety. Some other distributors have the objective of maximizing the sales with an aim to go beyond business survival. In this paper two models are developed for the above two objectives and optimal replenishment policies are suggested. Genetic Algorithm and the linear programming are used together to solve the models. Two cases of biscuits and confectionery products distribution are taken for study and the results are compared with the existing system.     

Newsvendor problems with demand forecast updating and supply constraints

This study investigates an extension of the newsvendor model with demand forecast updating under supply constraints. A retailer can postpone order placement to obtain a better demand forecast with a shorter supply lead time. However, the manufacturer would charge the retailer a higher cost for a shorter lead time and set restrictions on the ordering times and quantities. This prevents retailers from taking full advantage of demand forecast updating to improve profits. In studying the manufacturer-related effects, two supply modes are investigated: supply mode A, which has a limited ordering time scale, and supply mode B, which has a decreasing maximum ordering quantity. For supply mode A, it is proven under justifiable assumptions that a retailer should order either as early or as late as possible. For supply mode B, an algorithm is proposed to simplify the ordering policy by appropriately relaxing the ordering quantity restrictions. Numerical analysis is conducted to investigate the influence of product and demand parameters on the value of demand forecast updating in the two supply modes. A comparison of the different supply scenarios demonstrates the negative effects of increased purchasing cost and ordering time and quantity restrictions when demand forecast updating is implemented.     

The benefit of VMI strategies in a stochastic multi-product serial two echelon system

We consider a multi-product serial two echelon inventory system with stochastic demand. Inventories at the downstream location are replenished periodically using an automatic ordering system. Under vendor managed inventory strategies the upstream stage is allowed to adapt these orders in order to benefit from economies of scale. We propose three different VMI strategies, aiming to reduce the order picking cost at the upstream location and the transportation costs resulting in reduced total supply chain costs. In a detailed numerical study the VMI strategies are compared with a retailer managed inventory strategy for two different demand models suitable for slow moving products. It is shown that if inventory holding costs are low, compared to handling and transportation costs, efficiencies at the warehouse are improved and total supply chain costs are reduced.     

供应链中控制多级存储的射频辨识Push/Pull混合策略设计与仿真

由于射频辨识(radio frequency identification,RFID)激励的电子看板系统能够从远端看见供应链节点企业库存的状况,使得广域分布的供应链多级存储能够实现RFID激励的Pull控制.本文根据供应链分销网络多级存储的结构特点,以及系统运作期间各阶段节点企业的功能,在不同阶段采用不同的控制策略.因此,设计了多种不同的RFID激励的Push/Pull混合控制策略.为了比较和验证各混合策略对多级存储的控制性能,建立了以总库存成本、总缺货损失、总运行成本和库存周转率作为评价策略性能的指标体系.由于供应链系统的动态性与随机性,难以进行数学建模和精确求解,因此基于离散事件系统仿真原理,设计并实现了仿真模型.通过对各策略下多种结构的供应链分销网络多级存储的仿真,验证并分析了制造商阶段采用Push控制,分销商和零售商阶段采用Pull控制的策略的有效性和最优性.     

Analysis of B2B e-marketplaces: an operations perspective

The phenomenon of business-to-business (B2B) e-marketplaces has triggered a lot of interest among researchers in recent years. Most of these studies focus on price competition or cost reduction. Few studies, however, have examined the impact of e-marketplaces on the participants’ supply chain operations, including inventory management and order fulfillment. This study aims to fill such a gap. Employing agent-based simulations, we find that supply network agents tend to keep more inventories and backlog/lose fewer orders in the e-marketplace than in traditional supply chains. And this effect is profounder for the upstream agents, i.e., distributors and manufacturers than for downstream agents, i.e., retailers. Managerial implications of these findings 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.     

资金约束下的非对称信息供应链协调研究

供应链金融能够有效解决无核心资产的中小企业资金约束的问题。针对制造商、分销商和零售商组成的三级供应链,考虑了零售商自有资金的不同对融资利率变化的影响,研究了在完全信息下采用融资前后联合契约供应链的收益变化,实现供应链协调的参数和各成员收益的变化,将零售商销售成本信息不对称下的情况作为对比,分析零售商私有信息对供应链的影响,通过算例进行了数值分析。结果表明,在完全信息下零售商存在资金约束时,融资服务能够增加供应链各成员的期望收益,回购契约与收益共享契约组成的联合契约能够协调采用融资后的供应链,在非对称信息下,供应链的效率有所减少,而资金充足的零售商会因私有成本信息获得更多的收益,但是在资金约束时,私有信息却失去了价值。     

Single period stochastic inventory problems with ordering or returns policies

In recent years, there has been an increasing adoption of returns policies in the coordination of the supply chain, where market demand is always assumed to be satisfied by manufacturing or by ordering from suppliers. However, many industries face the important decision of how to balance their inventory level. This problem has long been studied in financial institutions such as banks. This study presents an optimal inventory policy under a given stochastic demand such as a uniformly distributed demand, single-item, and single period review inventory system. The optimal inventory control policy obtained in this study is called a four-point policy: that is, when the entity’s inventory level is below a reorder point, the entity must increase his stock level by ordering and order up-to a fixed level (second point); when the entity’s inventory level is over a return point (third point); the stock level must be decreased by returns and decreased to a fixed level (fourth point); otherwise, nothing should be done. We also analyze the (K, S)-convex properties of the inventory cost function.     

Coordinated ordering decisions for products with short lifecycle and variable selling price

The supplier–buyer coordination is an important policy in the supply chain management. The buyer in the two-echelon inventory system with regular selling season has to face the uncertainty of customer demand, supplier’s delivery time and variable price change. At the same time, the supplier has to consider the inventory holding and delay cost. The objective of this study is to develop an integrated supply chain strategy for products with short lifecycle and variable selling price to entice cooperation. The strategy must provide a win–win situation for both the supplier and the buyer. A numerical case example, sensitivity analysis and compensation mechanism are given to illustrate the model.     

A vendor managed inventory model under contractual storage agreement

Vendor managed inventory (VMI) is a supply chain partnership strategy that allows a supplier to place orders on behalf of its customers. This paper considers a supply chain composed of a single vendor and multiple retailers operating under a VMI contract that specifies limits on retailers' stock levels. We address the problem of synchronizing the vendor's cycle time with the buyers' unequal ordering cycles by developing a mixed integer non-linear program that minimizes the joint relevant inventory costs under storage restrictions. We also propose a cost efficient heuristic to solve the developed optimization problem. We conducted computational experiments to assess the reduction in the total supply chain costs resulting from relaxing the restriction of equal ordering cycles. It is found that the heuristic generates greater cost savings in cases of increased variability in retailers' demand and cost parameters.     

Improving inventory effectiveness in RFID-enabled global supply chain with Grey forecasting model

A RFID-enabled global TFT–LCD supply chain associated with Grey forecasting model (GM) of Company A has been simulated and analyzed in this research. Three key performance indicates (KPI) including total inventory cost, inventory turnover and bullwhip effect are analyzed in the simulation experiments in order to compare the effectiveness of five different supply chain inventory models. The effectiveness of integrated system which is composed of supply chain operation, Grey short-term forecasting model and RFID system has been examined by aforementioned three KPIs. According to the result of Taguchi experiments, RFID-enabled R-SCI_GM supply chain model which integrates the GM(1,1) forecasting model based on (s, Q) pull-based replenishment policy reduces 43.36% of the total inventory cost compared with that of the non-RFID SCI_GM model. It apparently shows that a great improving effectiveness of supply chain inventory cost can be conducted while RFID system is incorporated with the GM(1,1) forecasting model.     

A study on inventory replenishment policies in a two-echelon supply chain system

Inventory control plays an important role in supply chain management. Properly controlled inventory can satisfy customers’ demands, smooth the production plans, and reduce the operation costs; yet failing to budget the inventory expenses may lead to serious consequences. The bullwhip effect, observed in many supply chain management cases, causes excessive inventory due to information distortion, i.e. the order amount is exaggerated while a minor demand variation occurs, and the information amplified dramatically as the supply chain moves to the upstream. In this paper, one of the main causes of bullwhip effect, order batching, is considered. A simplified two-echelon supply chain system, with one supplier and one retailer that can choose different replenishment policies, is used as a demonstration. Two types of inventory replenishment methods are considered: the traditional methods (the event-triggered and the time-triggered ordering policies), and the statistical process control (SPC) based replenishment method. The results show that the latter outperforms the traditional method in the categories of inventory variation, and in the number of backlog when the fill-rate of the prior model is set to be 99%. This research provides a different approach to inventory cost-down other than the common methods like: information sharing, order batch cutting, and lead time reduction. By choosing a suitable replenishment policy, the number of backorder and the cost of inventory can be reduced.     

Dynamic reorder point replenishment strategies for a capacitated supply chain with seasonal demand

Managing inventory and service levels in a capacitated supply chain environment with seasonal demand requires appropriate selection and readjustment of replenishment decision variables. This study focuses on the dynamic adjustment of decision variables within supply chains using continuous-review reorder point (ROP) replenishment. A framework is proposed to adjust reorder points and lot sizes based on optimal settings within different regions of a seasonal demand cycle. This framework also includes the optimal timing of adjustments defining these regions. A discrete-event simulation model of a simple, capacity-constrained supply chain is developed and simulation–optimization experiments are performed, the objective being to minimize the total supply chain inventory subject to a target delivery service level. The performance of ROP systems with optimal static and optimal dynamic decision variable settings are compared using two different seasonal demand patterns. The results confirm that performance with dynamic decision variable adjustment is better. For a given delivery service level, average work-in-process inventory levels are almost the same for both systems. However average finished goods inventory levels decrease significantly and are more stable under dynamic adjustment. The practical implication is that both finished goods holding costs and maximum storage capacity requirements are reduced.