Four-echelon Supply Chain Research Articles

The effect of supply chain noise on the financial performance of Kanban and Drum-Buffer-Rope: An agent-based perspective

Managing efficiently the flow of products throughout the supply chain is essential for succeeding in today's marketplace. We consider the Kanban (from Lean Management) and Drum-Buffer-Rope (DBR, from the Theory of Constraints) scheduling mechanisms and evaluate their performance in a four-echelon supply chain operating within a large noise scenario. Through an agent-based system, which is presented as a powerful model-driven decision support system for managers, we show the lower sensitivity against variability and the higher financial performance of DBR, which occurs as this mechanism improves the supply chain robustness due to its bottleneck orientation. Nonetheless, we prove the existence of regions in the decision space where Kanban offers similar performance. This is especially relevant taking into account that Kanban can be implemented at a lower cost, as DBR requires a higher degree of information transparency and a solid contract between partners to align incentives. In this sense, we offer decision makers a methodological approach to reach an agreement when the partners decide to move from Kanban to DBR in a bid to increase the overall net profit in supply chains operating in a challenging noise scenario.

Bi-objective integrated supply chain design with transportation choices: A multi-objective particle swarm optimization

Motivated by observing the importance of logistics cost in the cost structure of some products, this paper aims at multi-objective optimization of integrating supply chain network design with the selection of transportation modes (TMs) for a single-product four-echelon supply chain composed of suppliers, production plants, distribution centers (DCs) and customer zones. The key design decisions are the number, capacity and location of plants and DCs, the flow of products through the network, and the selection of TMs for each flow path. A bi-objective mixed integer linear programming model is first formulated. The two incompatible objectives are minimizing the total cost and maximizing the demand fill rate. The model is validated by applying to the case of the design of fresh apple supply chain. Then, a new metaheuristic, called multi-objective modified particle swarm optimization (MMPSO), is presented to find non-dominated solutions. A new modified binary PSO for updating binary variables along with the adaptive mutation is incorporated into the MMPSO. The MMPSO is compared with a multi-objective basic PSO (MBPSO) and the NSGA-Ⅱ against three small cases and six randomly generated medium and large size problems. The comparative results indicate that the MMPSO is better than the NSGA-Ⅱ and the MBPSO with respect to solution quality and computation efficiency for the problem.

Flexibility evaluation of multiechelon supply chains.

Multiechelon supply chains are complex logistics systems that require flexibility and coordination at a tactical level to cope with environmental uncertainties in an efficient and effective manner. To cope with these challenges, mathematical programming models are developed to evaluate supply chain flexibility. However, under uncertainty, supply chain models become complex and the scope of flexibility analysis is generally reduced. This paper presents a unified approach that can evaluate the flexibility of a four-echelon supply chain via a robust stochastic programming model. The model simultaneously considers the plans of multiple business divisions such as marketing, logistics, manufacturing, and procurement, whose goals are often conflicting. A numerical example with deterministic parameters is presented to introduce the analysis, and then, the model stochastic parameters are considered to evaluate flexibility. The results of the analysis on supply, manufacturing, and distribution flexibility are presented. Tradeoff analysis of demand variability and service levels is also carried out. The proposed approach facilitates the adoption of different management styles, thus improving supply chain resilience. The model can be extended to contexts pertaining to supply chain disruptions; for example, the model can be used to explore operation strategies when subtle events disrupt supply, manufacturing, or distribution.

Integrated production-distribution planning problem in a competition-based four-echelon supply chain

Abstract Integrated production-distribution (P-D) planning reduces costs, enhances the efficiency of the supply chain, and reduces additional proceedings because of its integrated parts and activities. P-D planning facilitates simultaneous planning of supply chain constituents in order to save more time in planning within a supply chain. Therefore, it plays an important role in supply chain management (SCM). Besides, since the introduction of competition into SCM, it has attracted the attention of many researchers and practitioners. The present work was conducted to investigate an integrated P-D planning problem within a four-echelon supply chain with two main objective functions: minimizing total chain cost and maximizing service level. The considered problem is modeled under two circumstances; no competition and competitive market. In the latter, three kinds of competitions are taken into account: Cournot, Stackelberg, and Quality competitions. To tackle the problem, two mixed integer linear programs (MILPs) are developed, which are proved to be unimodular. Moreover, elastic constraint method is used to solve the two multiobjective models. Finally, numerical experiments are conducted. The results showed that although competition improves chain performance in terms of quality of the delivered products, it might also raise the cost of the chain.

Combined Use of Mathematical Optimization and Design of Experiments for the Maximization of Profit in a Four-Echelon Supply Chain

This paper develops a location-allocation model to optimize a four-echelon supply chain network, addressing manufacturing and distribution centers location, supplier selection and flow allocation for raw materials from suppliers to manufacturers, and finished products for end customers, while searching for system profit maximization. A fractional-factorial design of experiments is performed to analyze the effects of capacity, quality, delivery time, and interest rate on profit and system performance. The model is formulated as a mixed-integer linear programming problem and solved by using well-known commercial software. The usage of factorial experiments combined with mathematical optimization is a novel approach to address supply chain network design problems. The application of the proposed model to a case study shows that this combination of techniques yields satisfying results in terms of both its behavior and the obtained managerial insights. An ANOVA analysis is executed to quantify the effects of each factor and their interactions. In the analyzed case study, the transportation cost is the most relevant cost component, and the most relevant opportunity for profit improvement is found in the factor of quality. The proposed combination of methods can be adapted to different problems and industries.

Design of a Logistics Nonlinear System for a Complex, Multiechelon, Supply Chain Network with Uncertain Demands

Industrial systems, such as logistics and supply chain networks, are complex systems because they comprise a big number of interconnected actors and significant nonlinear and stochastic features. This paper analyzes a distribution network design problem for a four‐echelon supply chain. The problem is represented as an inventory‐location model with uncertain demand and a continuous review inventory policy. The decision variables include location at the intermediate levels and product flows between echelons. The related safety and cyclic inventory levels can be computed from these decision variables. The problem is formulated as a mixed integer nonlinear programming model to find the optimal design of the distribution network. A linearization of the nonlinear model based on a piecewise linear approximation is proposed. The objective function and nonlinear constraints are reformulated as linear formulations, transforming the original nonlinear problem into a mixed integer linear programming model. The proposed approach was tested in 50 instances to compare the nonlinear and linear formulations. The results prove that the proposed linearization outperforms the nonlinear formulation achieving convergence to a better local optimum with shorter computational time. This method provides flexibility to the decision‐maker allowing the analysis of scenarios in a shorter time.

Inventory model in a four-echelon integrated supply chain: modeling and optimization

PurposeThe main purpose is to minimize the total inventory cost of chain, whereas the stochastic constraints are satisfied. In other words, the goal is to find optimum agreed stockpiles and period length for products to minimize the total inventory cost of the chain while the stochastic constraints are fulfilled.Design/methodology/approachThis paper designs and optimizes an integrated inventory model in a four-echelon supply chain that contains a supplier, a producer, a wholesaler and multiple retailers. All four levels agree with each other to make an integrated inventory system. Products in this model have a multi-stage production process, and the model is bounded by multiple stochastic constraints. The problem model is nonlinear and large. So, the interior point method as an effective algorithm is used for solving the recent convex nonlinear model. Two numerical examples are solved to demonstrate the application of this methodology and to evaluate the performance of the proposed approach.FindingsThe findings showed the model is applicable for real-world supply chain problems in the cases that echelons are going to do executive external integration. Also, the Interior Point algorithm has a satisfactory performance and a high efficiency in terms of optimum solution for solving nonlinear and large models.Originality/valueThe authors designed and optimized the inventory cost in a four-level integrated supply chain in stochastic conditions. The new decision variables, number of chain levels, multi-products, stochastic constraints and multi-stage products in four-level integrated supply chain are other novelties of this paper. The authors provided an efficient algorithm for solving a large-scale and nonlinear model in this research, too.

Optimal design of a multi-echelon supply chain in a system thinking framework: An integrated financial-operational approach

Abstract The purpose of this study is to design a four-echelon supply chain network in which operational and financial dimensions have been considered by a holistic, comprehensive and rigorous viewpoint within tactical and strategic decision-making levels. In this process, the aforementioned dimensions are extended and integrated in mathematical modeling framework. The research modeling is compared with traditional approaches, which merely rely on operational dimension to optimize profit and due to problems in profit, the research objectives changed to the multiple objectives of corporate value, change in equity and economic value added. Within this framework, a comparison is primarily made between profit and each of corporate value, change in equity and economic value added two by two (in the form of traditional approach (scenario A) and new approach (scenario B)). Then the objectives are simultaneously assessed and a compromise is gained among them through fuzzy goal programming. In this process, the effectiveness and efficiency of the scenario B is analyzed and assessed. Furthermore, a sensitivity analysis is performed on such factors as demand, return of equity rate, tax rate, production capacity and supplier capacity while the impacts of their changes on multi-objective function and satisfaction coefficient are simultaneously calculated. The mathematical model in the study is multi-product and multi-period while the values of parameters are determined under definite circumstances. For modeling and solving the mathematical model, GAMS 24 software and CPLEX solver are employed. To test the model, data of an Iranian petrochemical company are used. Besides highlighting the significance of the financial dimension and its integration with the operational dimension in gaining sustainable competitive advantage, the research results revealed that CVM -in contrast to change in equity and EVA- is much more favorable than the other objectives. Similarly, changes in demand had more favorable effects on multi-objective analysis.

The impact of information sharing on bullwhip effect reduction in a supply chain

In this study, the impact of information sharing on bullwhip effect (BWE) is investigated using a four-echelon supply chain simulation model where each echelon shares some of the customer demand forecast information with a retailer, the lowest echelon. The level of the demand forecast shared at each echelon is represented as information sharing rate (ISR). Four different levels of ISR are considered to evaluate its impact on BWE. A full factorial design with 64 cases is used, followed by statistical analysis. The results show that (1) overall, higher ISR more significantly reduce BWE than lower ISR at all echelons; (2) further, the impact of ISR is not same between echelons. The ISR at an echelon where BWE is measured has the highest impact. However, its impact decreases at downstream echelons; (3) BWE is affected by not only the magnitude but also the balance of ISR’s across echelons, while the former has three times more impact than the latter; (4) lastly, we demonstrate that a highly unbalanced ISR may cause reverse bullwhip effect (RBWE), particularly when the level of unblance at downstream echelons is high and the uppermost echelon where BWE is measured has the highest ISR. Based on this demonstration, we derive a functional relationship between ISR’s and RBWE using regression analysis. We believe that results from this study provide useful implications and insights for better coordination and collaboration in a supply chain.

Arrangement of Inventory Policies across Echelons of the Supply Chain for Minimization of Cost and Variance of Inventory

A model is built to study the impact of the variance of demand on the supply chain inventory and also to identify the best arrangement of the inventory policy across the supply chain. The basic objective of Inventory management by an entity is either to prevent stock out or lock up of working capital. However, benefit of individual entity might not result in value to supply chain. A simulation study was undertaken on the four-echelon supply chain by varying demand at retailer echelon in phases from 10% to 70%. The best arrangement of inventory policy (choosing policy for each echelon from a choice of six familiar policies) across the supply chain is identified for attaining objective of minimization of either cost or variance. It is found that the both the demand flow and moving average policy are dominant inventory policies in attaining these objectives. Contemporary information of demand at retailer echelon drives the costs minimization and variance minimization of the supply chain. Study also identifies the best arrangement for the objective of cost minimization at the each echelon too. The arrangement of inventory policy identified for individual echelon benefit varies from the best arrangement identified for the supply chain benefit. This substantiates that the individual echelon benefit would be deterrent to the value of the supply chain.

Holism versus reductionism in supply chain management: An economic analysis

Since supply chains are increasingly built on complex interdependences, concerns to adopt new managerial approaches based on collaboration have surged. Nonetheless, implementing an efficient collaborative solution is a wide process where several obstacles must be faced. This work explores the key role of experimentation as a model-driven decision support system for managers in the convoluted decision-making process required to evolve from a reductionist approach (where the overall strategy is the sum of individual strategies) to a holistic approach (where global optimization is sought through collaboration). We simulate a four-echelon supply chain within a large noise scenario, while a fractional factorial design of experiments (DoE) with eleven factors was used to explore cause-effect relationships. By providing evidence in a wide range of conditions of the superiority of the holistic approach, supply chain participants can be certain to move away from their natural reductionist behavior. Thereupon, practitioners focus on implementing the solution. The theory of constraints (TOC) defines an appropriate framework, where the Drum–Buffer–Rope (DBR) method integrates supply chain processes and synchronizes decisions. In addition, this work provides evidence of the need for aligning incentives in order to eliminate the risk to deviate. Modeling and simulation, especially agent-based techniques, allows practitioners to develop awareness of complex organizational problems. Hence, these prototypes can be interpreted as forceful laboratories for decision making and business transformation.

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.

A four-echelon supply chain network design with shortage: Mathematical modeling and solution methods

Often, companies deliberately fulfill demands with delay since they can benefit from reducing transportation and setup costs. This paper aims at designing a four-echelon supply chain structure including multiple suppliers, multiple producers, multiple distributors and multiple customers. The objectives are to minimize the total operating costs of all the supply chain elements and to maximize the reliability of the system. A number of transportation systems with different reliability rates are considered. The paper mathematically formulates the problem as a mixed integer linear programming model. In order to solve the large-sized instances of the problem, the paper proposes a novel heuristic algorithm called Comparative Particle Swarm Optimization. This algorithm employs a mechanism in order to compare the generated solutions and to prevent from generating worse solutions. The results of different numerical experiments endorse the effectiveness of the proposed heuristic.

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.

Designing of an intelligent self-adaptive model for supply chain ordering management system

One of the challenging issues in supply chain management is the coordination of ordering processes, especially in dynamic situations. In recent years, reinforcement learning algorithms are considered to be efficient techniques for solving such problems. In this paper, an agent-based simulation technique has been integrated with a reinforcement learning algorithm and has been applied to model a four-echelon supply chain that faces non-stationary customer demands. This approach leads to the development of a novel and intelligent simulation-based optimization framework, which includes a detailed simulation modeling of supply chain behavior. Finally statistical methods, including the Var technique, are used for the risk evaluation and sensitivity analysis have been provided to support the decision making process under uncertainty.

A real-time SPC inventory replenishment system to improve supply chain performances

Inventory replenishment rules contribute significantly to the bullwhip effect and inventory instability in supply chains. Smoothing replenishment rules have been suggested as a mitigation solution for the bullwhip effect but dampening the bullwhip effect might increase inventory instability. This paper evaluates a real-time inventory replenishment system denoted as SPC that utilizes a control chart approach to counteract the bullwhip effect whilst achieving competitive inventory stability. The SPC employs two control charts integrated with a set of decision rules to estimate the expected demand and adjust the inventory position, respectively. The first control chart works as a forecasting mechanism and the second control chart is devoted to control the inventory position variation whilst allowing order smoothing. A simulation analysis has been conducted to evaluate and compare SPC with a generalized (R, S) policy in a four-echelon supply chain, under various operational settings in terms of demand process, lead-time and information sharing. The results show that SPC is superior to the traditional (R, S) and comparable to the smoothing one in terms of bullwhip effect, inventory variance, and service level. Further managerial implications have been obtained from the results.

SPC forecasting system to mitigate the bullwhip effect and inventory variance in supply chains

Demand signal processing contributes significantly to the bullwhip effect and inventory instability in supply chains. Most previous studies have been attempting to evaluate the impact of available traditional forecasting methods on the bullwhip effect. Recently, some researchers have employed SPC control charts for developing forecasting and inventory control systems that can regulate the reaction to short-run fluctuations in demand. This paper evaluates a SPC forecasting system denoted as SPC-FS that utilizes a control chart approach integrated with a set of simple decision rules to counteract the bullwhip effect whilst keeping a competitive inventory performance. The performance of SPC-FS is evaluated and compared with moving average and exponential smoothing in a four-echelon supply chain employs the order-up-to (OUT) inventory policy, through a simulation study. The results show that SPC-FS is superior to the other traditional forecasting methods in terms of bullwhip effect and inventory variance under different operational settings. The results confirm the previous researches that the moving average achieves a lower bullwhip effect than the exponential smoothing, and we further extend this conclusion to the inventory variance.

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.

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.

A model predictive control framework for centralised management of a supply chain dynamical system

In this paper, a centralised model predictive control (MPC) strategy is applied to control inventories in a four-echelon supply chain. The single MPC controller used in this strategy optimises globally and finds an optimal ordering policy for each node. The controller relies on a linear discrete-time state-space model to predict system outputs and the prediction can be approached by either of the two multi-step predictors depending on the measurability of the controller states. The objective function has a quadratic form and thus the resulting optimisation problem can be solved via standard quadratic programming. Simulation results show that a centralised MPC strategy is preferred because it can track customer demand and, in the meantime, maintain a proper inventory position level with reduced bullwhip effect.