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 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.     

Smoothing inventory decision rules in seasonal supply chains

A major cause of supply chain deficiencies is the bullwhip effect, which implies that demand variability amplifies as one moves upstream in supply chains. Smoothing inventory decision rules have been recognized as the most powerful approach to counteract the bullwhip effect. Although several studies have evaluated these smoothing rules with respect to several demand processes, focusing mainly on the smoothing order-up-to (OUT) replenishment rule, less attention has been devoted to investigate their effectiveness in seasonal supply chains. This research addresses this gap by investigating the impact of the smoothing OUT on the seasonal supply chain performances. A simulation study has been conducted to evaluate and compare the smoothing OUT with the traditional OUT (no smoothing), both integrated with the Holt-Winters (HW) forecasting method, in a four-echelon supply chain experiences seasonal demand modified by random variation. The results show that the smoothing OUT replenishment rule is superior to the traditional OUT, in terms of the bullwhip effect, inventory variance ratio and average fill rate, especially when the seasonal cycle is small. In addition, the sensitivity analysis reveals that employing the smoothing replenishment rules reduces the impact of the demand parameters and the poor selection of the forecasting parameters on the ordering and inventory stability. Therefore, seasonal supply chain managers are strongly recommended to adopt the smoothing replenishment rules. Further managerial implications have been derived from the results.     

Economical production and transshipment policy for coordinating multiple production sites

In this article, we study the coordination mechanism dealing with a production–transshipment policy across the multiple regions supplying multiple products. It is assumed that each production site has its own dedicated demand region consuming multiple products. The main concern is how to determine both the production quantity and the lot-apportioning policy while minimising the relevant supply chain cost. This decision issue is formulated as a non-linear mathematical model to determine several relevant decision variables. We propose the solution procedure for deriving the production–transshipment policy minimising the overall supply chain cost.     

Two-echelon fuzzy stochastic supply chain for the manufacturer–buyer integrated production–inventory system

This paper deals with two-echelon integrated procurement production model for the manufacturer and the buyer integrated inventory system. The manufacturer procures raw material from outside suppliers (not a part of supply chain) then proceed to convert it as finished product, and finally delivers to the buyer, who faces imprecise and uncertain, called fuzzy random demand of customers. The manufacturer and the buyer work under joint channel, in which a centralized decision maker makes all decisions to optimize the joint total relevant cost (JTRC) of entire supply chain. In this account, in one production cycle of the manufacturer we determine an optimal multi-ordering policy for the buyer. To be part of this, we first derive the JTRC in stochastic framework, and then extend it in fuzzy stochastic environment. In order to scalarize the fuzzy stochastic JTRC, we use an evaluation method wherein randomness is estimated by probabilistic expectation and fuzziness is estimated by possibilistic mean based on possibility evaluation measure. To derive the optimal policies for both parties, an algorithm is proposed. A numerical illustration addresses the situations of paddy procurement, conversion to rice and fulfillment of uncertain demand of rice. Furthermore, sensitivity of parameters is examined to illustrate the model and algorithm.     

The impact of information sharing and inventory control coordination on supply chain performances

The lack of coordination in supply chains can cause various inefficiencies like bullwhip effect and inventory instability. Extensive researches quantified the value of sharing and forecasting of customer demand, considering that all the supply chain partners can have access to the same information. However, only few studies devoted to identify the value of limited collaboration or information visibility, considering their impact on the overall supply chain performances for local and global service level. This paper attempts to fill this gap by investigating the interaction of collaboration and coordination in a four-echelon supply chain under different scenarios of information sharing level. The results of the simulation study show to what extent the bullwhip effect and the inventory variance increase and amplify when a periodic review order-up-to level policy applies, noting that more benefits generate when coordination starts at downstream echelons. A factorial design confirmed the importance of information sharing and quantified its interactions with inventory control parameters, proving that a poor forecasting and definition of safety stock levels have a significant contribution to the instability across the chain. These results provide useful implications for supply chain managers on how to control and drive supply chain performances.     

Optimal inventory control policy and supply chain coordination problem with carbon footprint constraints

Carbon footprint constraints exert pressure on supply chains to reexamine decisions. In this paper, we consider carbon transfer cost and carbon holding cost in a supply chain. A multiperiod dynamic programming model with carbon footprint constraints is presented to investigate the impact of carbon transfer cost and carbon holding cost on inventory control policy as well as the supply chain coordination problem. A two‐control limit inventory control policy is proved to be optimal and a contract with wholesale price, subsidy, and fixed setup cost is verified analytically to coordinate the supply chain. Finally, a numerical study is conducted to reveal managerial insights. We find that when the supply chain is coordinated, the chain's profit is more sensitive to carbon transfer cost while inventory level is more sensitive to carbon holding cost. Additionally, because of the complexity of the coordinated contract, when it is not easy to coordinate the supply chain, it is better to keep the values of wholesale price, subsidy, and fixed setup cost below the corresponding values for the coordinated supply chain.     

Exploring the interaction of inventory policies across the supply chain: An agent-based approach

The Bullwhip Effect, which refers to the increasing variability of orders traveling upstream the supply chain, has shown to be a severe problem for many industries. The inventory policy of the various nodes is an important contributory factor to this phenomenon, and hence it significantly impacts on their financial performance. This fact has led to a large amount of research on replenishment and forecasting methods aimed at exploring their suitability depending on a range of environmental factors, e.g. the demand pattern and the lead time. This research work approaches this issue by seeing the whole picture of the supply chain. We study the interaction between four widely used inventory models in five different contexts depending on the customer demand variability and the safety stock. We show that the concurrence of distinct inventory models in the supply chain, which is a common situation in practice, may alleviate the generation of inefficiencies derived from the Bullwhip Effect. In this sense, we demonstrate that the performance of each policy depends not only upon the external environment but also upon the position within the system and upon the decisions of the other nodes. The experiments have been carried out via an agent-based system whose agents simulate the behavior of the different supply chain actors. This technique proves to offer a powerful and risk-free approach for business exploration and transformation.     

On ordering adjustment policy under rolling forecast in supply chain planning

Rolling forecast is a useful tool for lowering total cost with regard to practical inventory management. The details regarding a rolling forecast are obtained from a customer’s projected ordering data. The customer estimation of a rolling forecast may deviate from actual orders because of unstable conditions or customer’s deliberation. This study investigates what measures a customer might apply in responding to a situation where the rolling forecast deviates from the actual order. In addition, an appropriate ordering adjustment policy is proposed for better monitoring the supply chain performance with regard to a variant level of error concerning rolling forecast data. This study also considers the influence of lead time and inventory cost structure. We adopted a simulation approach, employing a model developed and examined in several different settings. The proposed ordering adjustment policies are determined by AVG, SD, and RMSE calculated from differences existing between historical forecasts and realized data. Levels of estimate error and estimate bias in a rolling forecast are included in the experimental procedure. Results reveal that the RMSE ordering adjustment policy is the most effective in situations of normal and downside estimation bias, whereas the AVG policy is more appropriate in the case of upside estimation bias. The level of estimation error is irrelevant to the selection of ordering adjustment policies, but it is positively associated with inventory costs. Stock-out costs and lead time are positively associated with inventory costs. Accuracy of the rolling forecast is therefore deemed to be essential in a situation involving a long lead time with high stock-out costs.     

Modelling and analysis of inventory replenishment for perishable agricultural products with buyer–seller collaboration

In this article, we study the inventory replenishment model for perishable agricultural products in a simple two-level supply chain. Collaborative forecasting is introduced into the inventory replenishment decisions to avoid overstocking and understocking of agricultural products, and to maximise profits. We analyse the model with ordering cost, holding cost, shortage cost, deterioration cost and opportunity lost cost of perishable agricultural products. Extensive numerical analysis is carried out to study the performance of the inventory policy. The optimal replenishment policy that minimises the total cost can be obtained from the model. It has demonstrated that the supply chain cost decreases with supplier and retailer's collaborative forecasting.     

A production base-stock policy for recycling supply chain management in the presence of uncertainty

This study focuses on solving master planning problems for a recycling supply chain with uncertain supply and demand. A recycling supply chain network includes collectors, disassemblers, remanufacturers, and redistributors working from the collection of returned goods to the distribution of recovered products to the market. The objective of this study is to maximize the total profit of the entire recycling supply chain. Considering the stochastic property of the recycling supply chain, this study institutes stocking and processing policies for each member of the recycling supply chain to better respond to unknown future demand. We propose a heuristic algorithm called stochastic recycling process planning algorithm (SRPPA) to address master planning problems in the recycling supply chain and its supply and demand uncertainties. The main SRPPA process consists of three phases. In the leader determination phase, SRPPA identifies the most important node as the leader of the recycling supply chain. In the candidate policy set generation phase, SRPPA defines the search range for the inventory policy and forms the candidate policy sets based on the characteristics of the leader. In the best policy set selection phase, SRPPA constructs the simulation process for each inventory policy candidate to identify the best policy set. A scenario analysis is then presented to show the effectiveness and efficiency of SRPPA.     

Performance modeling of a two-echelon supply chain under different levels of upstream inventory information sharing

The advancement in information technology has facilitated the sharing of information in supply chain networks (SCNs), resulting in effective management of inventory and storage capacity. In this paper, our focus is on upstream inventory information sharing. Existing analytical performance evaluation models of SCNs are not capable of assessing the impact of inventory information sharing. To address this need, we develop performance evaluation models of SCNs that explicitly consider production capacity, inventory related decisions, variability, transit delays and inventory information sharing in a unified manner. We employ a two-echelon SCN configuration with two retail stores and two production facilities as a test bed. The retail stores have inventory information from the production facilities. We model three levels of inventory information sharing in our study; the information shared ranges from the stock-out information at the lowest level to inventory and backorder level information at the highest level. We develop analytical models first for Poisson arrivals and exponential processing times under all levels of inventory information sharing. We extend these models to general inter-arrival and processing time distributions and subsequently include transit delays between the production facilities and the retail stores. We demonstrate the performance prediction capability of the analytical models developed via extensive numerical experimentation.     

Adaptive logistic controller for integrated design of distributed supply chains

Usually each manufacturing stage in a supply chain makes its own decision regarding quantity and timing of parts it purchases from its suppliers, thereby controlling its inventory position and overall supply chain dynamics. Such decisions, although good for each individual stage, can adversely affect the overall performance of the supply chain. This can be viewed as distributed control of inventory, in which each controller is making autonomous decisions based on local objectives. Because these controllers do not have information about inventory position or order quantities at other stages, the safety stock tends to be higher, leading to higher inventories and cost. This also causes demand amplification and the bullwhip effect. This paper presents a distributed feedback control algorithm, called the Adaptive Logistics Controller (ALC), which simultaneously decides the order quantities for each stage of the supply chain to minimize the total WIP in the entire supply chain for a given demand. In this approach, simulation is used to provide the feedback to the ALC controller, leading to an iterative numerical computational approach. Computational experiments compared with the traditional centralized (Q,r)$(Q,r)$ policy model show that the order quantities calculated by the distributed ALC are much superior in terms of total overall WIP and hence result in lesser total costs for the entire supply chain.     

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.     

供应商具备多级生产率的供应链控制策略仿真

研究供应商具有多级生产率的供应链库存成本控制策略问题。在满足客户与销售商随机性订单需求的基础上,以供应链的整体运营成本最低为目标,运用Arena仿真平台对由供应商、销售商、客户构成的多级供应链库存系统进行了建模和仿真优化,制定合理的销售商的库存策略以及供应商的生产库存策略。为具备多级生产率的供应链系统控制策略提供了一种更科学合理的决策方法,对以后的类似研究具有一定的借鉴意义。     

The impact of information sharing on ordering policies to improve supply chain performances

Bullwhip effect represents the amplification and distortion of demand variability as moving upstream in a supply chain, causing excessive inventories, insufficient capacities and high operational costs. A growing body of literature recognizes ordering policies and the lack of coordination as two main causes of the bullwhip effect, suggesting different techniques of intervention. This paper investigates the impact of information sharing on ordering policies through a comparison between a traditional (R, S) policy and a coordination mechanism based on ordering policy (a combination of (R, D) and (R, S) policies). This policy relies on a slow, easy to implement, information sharing to overcome drawbacks of the effect, in which replenishment orders are divided into two parts; the first is to inform the upstream echelons about the actual customer demand and the second is to inform about the adjustment of the inventory position, smoothing at the same time the orders of the different levels of the supply chain. A simulation model for a multi-echelon supply chain quantifies the supply chain dynamics under these different policies, identifying how information sharing succeeds to achieve an acceptable performance in terms of both bullwhip effect and inventory variance.     

Two-echelon supply chain inventory model with controllable lead time and service level constraint

This paper considers a two-echelon supply chain inventory problem consisting of a single-vendor and a single-buyer. In the system under study, a vendor produces a product in a batch production environment and supplies it to a buyer facing a stochastic demand, which is assumed to be normally distributed. Also, buyer’s lead time is controllable which can be shortened at an added cost and all shortages are backordered. A model has been formulated for an integrated vendor–buyer problem to jointly determine the optimal order quantity, lead time and the number of shipments from the vendor to the buyer during a production cycle while minimizing the total expected cost of the vendor–buyer integrated system. It is often difficult to estimate the shortage cost in inventory systems. Therefore, instead of having a shortage cost term in the objective function, a service level constraint (SLC) is included in the model that requires a certain proportion of demands to be met in each cycle. An efficient procedure has been suggested to find the bounds on number of shipments and then, an algorithm is developed to obtain the optimal solution of the proposed model. A numerical example is included to illustrate the algorithmic procedure and the effects of key parameters are studied to analyze the behavior of the model. Finally, the savings of buyer and vendor are investigated from implementation of joint optimization model over the model in which they minimize their own cost independently.     

Controlling general multi-echelon distribution supply chains with improved reorder decision policy utilizing real-time shared stock information

The objective of this paper is to introduce an improved reorder decision policy for controlling general multi-echelon distribution systems utilizing shared stock information. Since traditional reorder policies sometimes show poor performances when applied to distribution systems, the order risk policy, which utilizes shared stock information more accurately, was developed for the two-echelon distribution system in the previous research. In this paper, the order risk policy is extended to general multi-echelon systems. Since the calculation of the exact order risk for general multi-echelon systems is complex, a practical approximation method is developed. Through computational experiments, the superior performance of the order risk policy, compared to the existing reorder policies, is shown and the value of shared stock information varying with the characteristics of the supply chain is analyzed.     

Supply chain modeling in uncertain environment with bi-objective approach

Supply chain is viewed as a large-scale system that consists of production and inventory units, organized in a serial structure. Uncertainty is the main attribute in managing the supply chains. Managing a supply chain (SC) is very difficult, since various sources of uncertainty and complex interrelationships among various entities exist in the SC. Uncertainty may result from customer’s demand variability or unreliability in external suppliers. In this paper we develop an inventory model for an assembly supply chain network (each unit has at most one immediate successor, but any number of immediate predecessors) which fuzzy demand for single product in one hand and fuzzy reliability of external suppliers in other hand affect on determination of inventory policy in SCM. External supplier’s reliability has determined using a fuzzy expert system. Also the performance of supply chain is assessed by two criteria including total cost and fill rate. To solve this bi-criteria model, hybridization of multi-objective particle swarm optimization and simulation optimization is considered. Results indicate the efficiency of proposed approach in performance measurement.     

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.