Fuzzy knowledge-based token-ordering policies for bullwhip effect management in supply chains

The “Bullwhip Effect” is a well-known example of supply chain inefficiencies and refers to demand amplification as moving up toward upstream echelons in a supply chain. This paper concentrates on representing a robust token-based ordering policy to facilitate information sharing in supply chains in order to manage the bullwhip effect. Takagi–Sugeno–Kang and hybrid multiple-input single-output fuzzy models are proposed to model the mechanism of token ordering in the token-based ordering policy. The main advantage of proposed fuzzy models is that they eliminate the exogenous and constant variables from the procedure of obtaining the optimal amount of tokens which should be ordered in every period. These fuzzy approaches model the mentioned mechanism through a push–pull policy. A four-echelon SC with fuzzy lead time and unlimited production capacity and inventory is considered to survey the outcomes. Numerical experiments confirm the effectiveness of proposed policies in alleviating BWE, inventory costs and variations.     

损失厌恶下考虑参照利润效应的供应链决策模型

针对由一个风险中性供应商和一个损失厌恶零售商构成的二级供应链,研究随机需求下考虑零售商参照利润效应的供应链决策问题.在回购政策下,建立以供应商为主方、零售商为从方的Stackelberg主从博弈模型.结合参照依赖偏好模型分别得到集中和分散供应链决策,分析供应链最优决策与损失厌恶程度、参照利润强度和零售商乐观水平之间的关系,并进一步设计能够实现供应链完美协调的回购契约机制.研究结果表明,在集中和分散供应链决策下,零售商订货量均随着损失厌恶和乐观程度的增加而减少.而当零售商损失厌恶程度较低时,订货量随参照利润强度的增加而增加;反之,亦成立.对于批发价格决策,则存在一个阈值,当高于该阈值时,批发价格随着零售商损失厌恶、乐观程度和参照利润强度的增大而增加;低于该阈值时,批发价格随着损失厌恶、乐观程度和参照利润强度的增大而降低.     

Random network models and sensitivity algorithms for the analysis of ordering time and inventory state in multi-stage supply chains

Supply chains in reality face a highly dynamic and uncertain environment, especially the uncertain end-customer demands and orders. Since the condition of product market changes frequently, the tasks of order management, product planning, and inventory management are complex and difficult. It is imperative for companies to develop new ways to manage the randomness and uncertainty in market demands. Based on the graphical evaluation and review technique, this paper provides a simple but integrated stochastic network mathematical model for supply chain ordering time distribution analysis. Then the ordering time analysis model is extended so that the analysis of inventory level distribution characteristics of supply chain members is allowed. Further, to investigate the effects of different end-customer demands on upstream orders and relative inventory levels, model-based sensitivity analysis algorithms for ordering fluctuations and inventory fluctuations are developed. A detailed numerical example is presented to illustrate the application of the proposed models to a multi-stage supply chain system, and the results of which shows the effectiveness and flexibility of the proposed stochastic network models and algorithms in order and inventory management.     

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.     

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.     

Quantifying and mitigating the bullwhip effect in a benchmark supply chain system by an extended prediction self-adaptive control ordering policy

An undesired observation known as the bullwhip effect in supply chain management leads to excessive oscillations of the inventory and order levels. This paper presents how to quantify and mitigate the bullwhip effect by introducing model predictive control (MPC) strategy into the ordering policy for a benchmark supply chain system. Instead of quantifying the bullwhip effect with commonly used statistical measure, we derive equivalently the expression of bullwhip metric via control-theoretic approach by applying discrete Fourier transform and (inverse) z-transform when the demand signal is stationary stochastic. A four-echelon supply chain is formulated and its dynamical features are analyzed to give the discrete model. An extended prediction self-adaptive control (EPSAC) approach to the multi-step predictor is implemented in the development of MPC formulation. The closed-form solution to MPC problem is derived by minimizing a specified objective function. The transfer function for MPC ordering policy is then obtained graphically from an equivalent representation of this closed-form solution. A numerical simulation shows that MPC ordering policy outperforms the traditional ordering policies on reducing bullwhip effect.     

Decentralized and centralized model predictive control to reduce the bullwhip effect in supply chain management

Mitigating the bullwhip effect is one of crucial problems in supply chain management. In this research, centralized and decentralized model predictive control strategies are applied to control inventory positions and to reduce the bullwhip effect in a benchmark four-echelon supply chain. The supply chain under consideration is described by discrete dynamic models characterized by balance equations on product and information flows with an ordering policy serving as the control schemes. In the decentralized control strategy, a MPC-EPSAC (Extended Prediction Self-Adaptive Control) approach is used to predict the changes in the inventory position levels. A closed-form solution of an optimal ordering decision for each echelon is obtained by locally minimizing a cost function, which consists of the errors between predicted inventory position levels and their setpoints, and a weighting function that penalizes orders. The single model predictive controller used in centralized control strategy optimizes globally and finds an optimal ordering policy for each echelon. The controller relies on a linear discrete-time state-space model to predict system outputs. But the predictions are approached by either of two multi-step predictors depending on whether the states of the controller model are directly observed or not. The objective function takes a quadratic form and thus the resulting optimization problem can be solved via standard quadratic programming method. The comparisons on performances of the two MPC strategies are illustrated with a numerical example.     

Coordinating a two-supplier and one-retailer supply chain with forecast updating

We consider the component-purchasing problem for a supply chain consisting of one retailer and two complementary suppliers with different lead-times. The retailer purchases a specific component from each supplier for assembling into a fashionable product. After ordering from the long-lead-time supplier (Supplier 1) and before ordering from the short-lead-time supplier (Supplier 2), the retailer can update its demand forecast for the product. The retailer can partially cancel its order from Supplier 1 after forecast updating. By formulating the problem as a dynamic optimization problem, we explore the measures that can be deployed to coordinate the retailer’s ordering decisions with forecast updating. We analytically show that the supply chain can be coordinated if both suppliers offer a returns policy and Supplier 1 charges an order-cancelation penalty to the retailer. We find that the coordination mechanism is independent of demand distribution and the forecast updating process. We further show that it is easier for the suppliers to coordinate the supply chain if market observation indicates the future market demand is sufficiently large. We also study the case where demand is price-dependent and propose a generalized revenue-sharing contract to coordinate the supply chain. We discuss the academic and managerial implications of the theoretical findings.     

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.     

供应链库存的模糊机会约束规划模型

研究了不确定环境下的供应链库存优化问题。考虑需求为模糊量,且可能在一定条件下不满足约束条件的决策前提,用三角模糊数表示需求,结合可能性理论中的可信性测度,建立了多品种联合补充的模糊机会约束规划模型,目标函数为最小化供应链订货成本和库存成本的期望值。用遗传算法对优化模型求解,以目标函数值作为染色体适应度,给出了编码方案及选择、交叉、变异算子。用数值实例进行了仿真计算,证明了模型和算法的有效性和性能,并给出了不同置信水平下的计算结果。     

An integrated supply chain model for new products with imprecise production and supply under scenario dependent fuzzy random demand

In the present day business scenario, instant changes in market demand, different source of materials and manufacturing technologies force many companies to change their supply chain planning in order to tackle the real-world uncertainty. The purpose of this paper is to develop a multi-objective two-stage stochastic programming supply chain model that incorporates imprecise production rate and supplier capacity under scenario dependent fuzzy random demand associated with new product supply chains. The objectives are to maximise the supply chain profit, achieve desired service level and minimise financial risk. The proposed model allows simultaneous determination of optimum supply chain design, procurement and production quantities across the different plants, and trade-offs between inventory and transportation modes for both inbound and outbound logistics. Analogous to chance constraints, we have used the possibility measure to quantify the demand uncertainties and the model is solved using fuzzy linear programming approach. An illustration is presented to demonstrate the effectiveness of the proposed model. Sensitivity analysis is performed for maximisation of the supply chain profit with respect to different confidence level of service, risk and possibility measure. It is found that when one considers the service level and risk as robustness measure the variability in profit reduces.     

Data envelopment analysis for a supply chain

Data envelopment analysis (DEA) is a method for evaluating the management efficiency of decision-making units (DMUs). This article proposes a DEA model for supply-chain management. Traditional studies focused on the selection of partners and the construction of the supply chain. Therefore, this study considers how to optimize the supply chain itself in order to maximize the benefit by DEA. In addition, a significant matter is that supply chains have sometimes unbalanced business processes. This means that some particular DMUs on the supply chain have a superiority which maintains efficiency. That is why the other DMUs on the supply chain need to operate in unfavorable conditions. As a result, their operations badly affect the total efficiency of the supply chain. Therefore, the proposed method introduces an adjustment variable to calculate the optimum operation of the supply chain. The utility and effectiveness of the proposed method are shown by numerical experiments.     

Reducing the negative effects of sales promotions in supply chains using genetic algorithms

This paper considers the detrimental effect of promotions on the supply chain (SC), one of the main causes of the bullwhip effect. A genetic algorithm (GA) is proposed to reduce these negative effects. In order to validate the GA, it is used to determine the optimal ordering policy in an online version of the MIT beer distribution game. Subsequently, the GA is applied in a number of experiments involving deterministic and random demand and lead times combined with sales promotions. It is shown how GAs can be used to dampen the impact of the bullwhip effect and can be used to assist supply managers in predicting reorder quantities along the supply chain.     

Coordination of the supply chain of seasonal products

Because of the demand uncertainty of seasonal products, the retailer prefers to place an order as late as possible, so that he can have enough time to collect more information, which is helpful to reduce demand forecast error. On the other hand, the manufacturer has limited production capacity in general cases. The late ordering would result in insufficient production times, thus increasing the production cost. Such a conflict exists universally in a supply chain, especially in the seasonal products' supply chain. As a result, coordination between the retailer and the manufacturer becomes very important. In the paper, based on the traditional operating system, an improved operating system is introduced whose impact to both bodies of a supply chain is examined under the condition of information symmetry. The result shows that the manufacturer may not be better off or well off, although the retailer's performance is improved. Then, some profit compensation plans are designed so as to make the operating system Pareto improve     

Coordinating a two-level supply chain with delay in payments and profit sharing

Achieving effective coordination among suppliers and retailers has become a pertinent research issue in supply chain management. Channel coordination is a joint decision policy achieved by a supplier(s) and a retailer(s) characterized by an agreement on the order quantity and the trade credit scenario (e.g., quantity discounts, delay in payments). This paper proposes a centralized model where players in a two-level (supplier–retailer) supply chain coordinate their orders to minimize their local costs and that of the chain. In the proposed supply chain model the permissible delay in payments is considered as a decision variable and it is adopted as a trade credit scenario to coordinate the order quantity between the two-levels. Computational results indicate that with coordination, the retailer orders in larger quantities than its economic order quantity, with savings to either both players, or to one in the supply chain. Moreover, a profit-sharing scenario for the distribution of generated net savings among the players in the supply chain is presented. Analytical and experimental results are presented and discussed to demonstrate the effectiveness of the proposed model.     

Two inventory review policies on supply chain configuration problem

With increasing business competition and complexity, supply chain provides opportunity to increase business competitiveness. Supply chain configuration is an important strategy to enhance business advantage. It is a vital approach to develop new products and manage dynamic supply chain. In this paper, two inventory review policies, continuous replenishment and periodic replenishment, are modeled in the supply chain configuration problem. Harmony search is used to solve the problem. Numerical example is given to illustrate how the models work. Using three different scenarios of various average on hand inventory rate and work in process rate, both review policies are tested. The proposed model shows that the average on hand inventory plays a more significant role when compared with the work in process.     

Location and transportation planning in supply chains under uncertainty and congestion by using an improved electromagnetism-like algorithm

Supply chain decision makers are constantly trying to improve the customer demand fulfillment process and reduce the associated costs via decision making models and techniques. As two of the most important parameters in a supply chain, supply and demand quantities are subject to uncertainty in many real-world situations. In addition, in recent decades, there is a trend to think of the impacts of supply chain design and strategies on society and environment. Especially, transportation of goods not only imposes costs to businesses but also has socioeconomic influences. In this paper, a fuzzy nonlinear programming model for supply chain design and planning under supply/demand uncertainty and traffic congestion is proposed and a hybrid meta-heuristic algorithm, based on electromagnetism-like algorithm and simulated annealing concepts, is designed to solve the model. The merit of this paper is presenting a realistic model of current issues in supply chain design and an efficient solution method to the problem. These are significant findings of this research which can be interesting to both researchers and practitioners. Several numerical examples are provided to justify the model and the proposed solution approach.     

Optimizing supply chain network for perishable products using improved bacteria foraging algorithm

In a supply chain environment, time delay has a significant impact on the success of perishable products. A major concern is therefore aimed at development of a holistic optimized approach in a supply chain environment for perishable products. Thus, integration of production, inventory and, distribution of perishable products in a supply chain environment are the challenging tasks for practitioners and researchers. In general, the standard optimal supply chain model cannot work for perishable products. There is therefore, a need for a holistic model that focuses on the consolidation of the processes. Shorter product shelf-life, temperature control, requirement of strict tractability, large number of product variants, and a large volume of goods handled are the major challenges in a supply chain environment for perishable products. The present work focuses on the development of a holistic model which uses improved bacteria forging algorithm (IBFA) for solving the formulated model. We have proposed and analyzed some general properties of the model and, finally applied it to a three-stage supply chain problem using an IBFA. Two case studies have been considered for support and demonstration of the integrated perishable supply chain network problem. Results obtained from IBFA reveal that the proposed model is more useful for decision makers while considering optimal supply chain network for perishable products. Finally, validation of results has been carried out using bacteria forging algorithm (BFA). The computational performance of the proposed algorithm proves that IBFA is instrumental in effectively handling the proposed approach.     

Investing in lead-time variability reduction in a collaborative vendor–buyer supply chain model with stochastic lead time

This study deals with investing in lead-time variability reduction problems for the integrated vendor–buyer supply chain system with partial backlogging under stochastic lead time. We consider that lead time variability can be reduced through further investment; more specifically, a logarithmic investment function is used that allows investment to be made to reduce lead-time variability. By using the proposed supply chain model, considerable savings can be achieved to increase the competitive edge. The objective is to derive the optimal production/ordering strategy, and the best investment policy to minimize joint total cost. A computer code using the software, Mathematica, is developed to derive the optimal solution. Furthermore, we discuss the sensitivity of the optimal solution together with the changes of the values of the parameters associated with the model for decision-making. Various numerical examples are given to illustrate the results.     

Coordinating orders in a two echelon supply chain with controllable lead time and ordering cost using the credit period

In this research, a coordination mechanism based on a credit period in a two echelon supply chain with one buyer and one supplier, is designed. The buyer is faced with uncertain demand by coping with normal distribution. Both lead time and ordering cost for receiving his order can be reduced at an added cost; in other words, they are controllable. The optimization models with and without integration are proposed. Then a way to coordinate orders in supply chain based on the credit period so that the total cost of supply chain would be minimized is designed. By using this mechanism we also discuss how the credit period is to be determined in order to achieve channel coordination and a win-win outcome. Finally, numerical examples are solved to illustrate the theoretical results and obtain the managerial insights.