Supply Chain Management Problems Research Articles

Applying nonlinear MODM model to supply chain management with quantity discount policy under complex fuzzy environment

Purpose: The aim of this paper is to deal with the supply chain management (SCM) with quantity discount policy under the complex fuzzy environment, which is characterized as the bifuzzy variables. By taking into account the strategy and the process of decision making, a bifuzzy nonlinear multiple objective decision making (MODM) model is presented to solve the proposed problem. Design/methodology/approach: The bi-fuzzy variables in the MODM model are transformed into the trapezoidal fuzzy variables by the DMs's degree of optimism α 1 and α 2 , which are de-fuzzified by the expected value index subsequently. For solving the complex nonlinear model, a multi-objective adaptive particle swarm optimization algorithm (MO-APSO) is designed as the solution method. Findings: The proposed model and algorithm are applied to a typical example of SCM problem to illustrate the effectiveness. Based on the sensitivity analysis of the results, the bifuzzy nonlinear MODM SCM model is proved to be sensitive to the possibility level α 1 .

A memetic algorithm for the capacitated location-routing problem with mixed backhauls

The design of distribution networks is one of the most important problems in supply chain and logistics management. The main elements in designing a distribution network are location and routing decisions. As these elements are interdependent in many distribution networks, the overall system cost can decrease if location and routing decisions are simultaneously tackled. In this paper, we consider a Capacitated Location-Routing Problem with Mixed Backhauls (CLRPMB) which is a general case of the capacitated location-routing problem. CLRPMB is defined as finding locations of the depots and designing vehicle routes in such a way that pickup and delivery demands of each customer must be performed with the same vehicle and the overall cost is minimized. Since CLRPMB is an NP-hard problem, we propose a memetic algorithm to solve the problem. To evaluate the performance of the proposed approach, we conduct an experimental study and compare its results with the lower bounds obtained by the branch-and-cut algorithm on a set of instances derived from the literature. Computational results indicate that the proposed approach is able to find optimal or very good quality solutions in a reasonable computation time.

A Hybrid Particle Swarm Optimization for Solving Vehicle Routing Problem with Stochastic Demands

As one of the most popular supply chain management problems, the Vehicle Routing Problem (VRP) has been thoroughly studied in the last decades, most of these studies focus on deterministic problem where the customer demands are known in advance. But the Vehicle Routing Problem with Stochastic Demands (VRPSD) has not received enough consideration. In the VRPSD, the vehicle does not know the customer demands until the vehicle arrive to them. This paper use a hybrid algorithm for solving VRPSD, the hybrid algorithm based on Particle Swarm Optimization (PSO) Algorithm, combines a Greedy Randomized Adaptive Search Procedure (GRASP) algorithm, and Variable Neighborhood Search (VNS) algorithm. A real number encoding method is designed to build a suitable mapping between solutions of problem and particles in PSO. A number of computational studies, along with comparisons with other existing algorithms, showed that the proposed hybrid algorithm is a feasible and effective approach for Vehicle Routing Problem with Stochastic Demands.

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 multi-echelon inventory system with supplier selection and order allocation under stochastic demand

This paper investigates an integrated supplier selection and inventory control problems in supply chain management by developing a mathematical model for a multi-echelon system. In particular, a buyer firm that consists of one warehouse and N identical retailers procures a type of product from a group of potential suppliers, which may have different prices, ordering costs, lead times and have restriction on minimum and maximum total order size, to satisfy stochastic demand. A continuous review system that implements the order quantity, reorder point (Q, R) inventory policy is considered in the proposed model. The objective is to select suppliers and to determine the optimal inventory policy that coordinates stock levels between each echelon of the systems while properly allocating orders among selected suppliers to maximize the expected profit. The model is solved by decomposing the mixed integer nonlinear programming model into two sub-models. Numerical experiments are conducted to evaluate the model and some managerial insights are obtained with sensitivity analysis.

A reactive adaptive memory metaheuristic for solving HFFVRP

Product distribution is one of the most important problems in supply chain management. The heterogeneous fixed fleet vehicle routing problem (HFFVRP) is a variant of VRP that aims to determine the routes for a fleet of vehicles while satisfying demand and minimizing cost. The fleet composition is fixed and it consists of various types of vehicles. These vehicles differ according to their variable cost. In this paper, a reactive adaptive memory metaheuristic was developed which made use of the Tabu Search as an improvement procedure (ReAMTS) in order to solve the HFFVRP. The method was implemented and evaluated on two sets of test problems, which are used in the literature. In addition, its performance was compared with an exact method and a set of tractable instances. The computational results demonstrated high-quality results within a reasonable time.

Simulation and modeling efforts to support decision making in healthcare supply chain management.

Recently, most healthcare organizations focus their attention on reducing the cost of their supply chain management (SCM) by improving the decision making pertaining processes' efficiencies. The availability of products through healthcare SCM is often a matter of life or death to the patient; therefore, trial and error approaches are not an option in this environment. Simulation and modeling (SM) has been presented as an alternative approach for supply chain managers in healthcare organizations to test solutions and to support decision making processes associated with various SCM problems. This paper presents and analyzes past SM efforts to support decision making in healthcare SCM and identifies the key challenges associated with healthcare SCM modeling. We also present and discuss emerging technologies to meet these challenges.

Service supply chain management: generalised and applied framework perspective

Despite service supply chain is emerging as a new research hot topic, the systematical theoretical framework of service supply chain management still remains unexplored. The intent of this research is to examine the research progress of service supply chain management, address some key problems of service supply chain management and propose new thinking and new ideas about service supply chain management research. Considering the feature of SSCM, a generalised conceptual framework model of service supply chain management (SSCM) based on multi-discipline theoretical perspective is first developed, The framework integrates service operations and service marketing activities, reflects the customer-oriented value creation based on four components (information flow, customer flow, cash flow and work flow) and four processes (service sourcing, service innovation and design, service network configuration and service delivery). Key activities of implementing SSCM derived from the framework model are discussed. As an application of the generalised framework, applied framework of tourism supply chain management (TSCM) is further studied. Research agenda, methods of TSCM are proposed. Finally, future research directors of SSCM are presented. The systematical managerial ideas and methods described in this paper will be very helpful for boosting the progress of service supply chain research.

An application of RFID in supply chain management to reduce inventory estimation error

Article history: Received September 10, 2013 Received in revised format 10 December 2013 Accepted January 12 2014 Available online January 18 2014 Radio-frequency identification (RFID) is the wireless non-contact implementation of radiofrequency electromagnetic fields to transfer necessary data to identify and to track tags attached to objects, automatically. RFID has been successfully used for supply chain management (SCM) problems in order to reduce errors associated with inventory. This paper uses RFID technique in five different scenarios of labeling, errors in stolen parts, waste, wrong replacement and delivery. The study uses simulation technique based on Enterprise Dynamics to analyze three parts in an auto industry. The study considers different criteria including inventory error estimation, profitability, profitability, customer satisfaction and SCM performance. The preliminary results indicate that the proposed method is capable of optimizing most objectives, significantly. © 2014 Growing Science Ltd. All rights reserved.

Fatal and Serious Anaphylaxis Following Peginesatide Administration

BackgroundIn March 2012, the Food and Drug Administration (FDA) approved peginesatide as anemia therapy for chronic kidney disease patients. In December, the manufacturer warned that a large dialysis organization identified eight patients with peginesatide-associated anaphylaxis. In February 2013, the organization again reported five anaphylaxis events, including three deaths, and discontinued administering peginesatide. After a manufacturer's review identified five peginesatide-associated anaphylaxis deaths, the manufacturer discontinued marketing the drug. MethodsWe reviewed peginesatide-associated anaphylaxis in FDA's Adverse Event Reporting System (FAERS). Clinical findings and product lot numbers were analyzed. Inclusion criteria included peginesatide administration and toxicity within thirty minutes of administration. ResultsBetween July 2012 and February 2013, 38 CKD patients developed anaphylaxis within a median of five minutes following peginesatide administration (range, zero to 20 minutes). Findings included dyspnea (25 patients), chest pain (10), hypotension (18), diaphoresis (9), pruritis (11), rash (3), angioedema (5), and vomiting (9). Five deaths from cardio-respiratory arrests occurred. Exposure-adjusted incidence was 1.5 cases of anaphylaxis per 1,000 peginesatide-treated patients. Of five FAERS reports for anaphylaxis occurring in February 2013, three included the product lot number C18881 while two did not have lot numbers. Of 33 FAERS anaphylaxis events reported prior to December 4, 2012; 18 included the lot number C18685, two reports included the lot number C18696, one included the lot number C18881, and lot numbers were not identified for the remaining 12 reports. ConclusionAnaphylaxis can occur within minutes of initiating peginesatide therapy. Production or supply chain management problems may be the underlying etiology. Disclosures:Macdougall: Affymax and Takeda: Consultancy, Research Funding. Mikhail: Affymax and Takeda: Consultancy, Research Funding. Goldsmith: Affymax and Takeda: Consultancy, Research Funding.

Biological System Behaviours and Natural-Inspired Methods and their Applications to Supply Chain Management

People have learnt from biological system behaviours and structures to design and develop a number of different kinds of optimisation algorithms that have been widely used in both theoretical study and practical applications in engineering and business management. An efficient supply chain is very important for companies to survive in global competitive market. An effective SCM (supply chain management) is the key for implement an efficient supply chain. Though there have been considerable amount of study of SCM, there have been very limited publications of applying the findings from the biological system study into SCM. In this paper, through systematic literature review, various SCM issues and requirements are discussed and some typical biological system behaviours and natural-inspired algorithms are evaluated for the purpose of SCM. Then the principle and possibility are presented on how to learn the biological systems' behaviours and natural-inspired algorithms for SCM and a framework is proposed as a guide line for users to apply the knowledge learnt from the biological systems for SCM. In the framework, a number of the procedures have been presented for using XML to represent both SCM requirement and bio-inspiration data. To demonstrate the proposed framework, a case study has been presented for users to find the bio-inspirations for some particular SCM problems in automotive industry.

Design and Implementation of Supply Chain Integrated System Based on Internet of Things

In recent years, the technology of the Internet of Things has attracted highly attention of academia, enterprise and news media. This paper made a brief introduction of the connotation and application prospect of Internet of Things, analysed the supply chain management problems in Chinese enterprises, stated the effects of Internet of Things on supply chain management. We designed the supply chain architecture based on EPC Net work, proposed the supply chain integrated system application model based on Internet of Things, designed and implemented the drug supply chain logistics business process system based on Internet of Things.

Purchase offering quality

Purpose – This paper aims to explore the supply chain management problem of understanding the response of suppliers to buyer behaviours and characteristics. Design/methodology/approach – The paper reviews the relevant literature in search of references to critically assesses the effects of applying the mirror images of the concepts of customer value, satisfaction and product or service quality, developed in the field of marketing, to the field of operations management. Findings – The study suggests that the new concept of purchase offering quality is worthy of further development in the supply chain management area and that supplier value and supplier satisfaction are useful concepts for understanding supplier behavioural intentions. Practical implications – The study will help organisations to improve supply chain performance through an enhanced ability of organisations-as-buyers to influence supplier behaviour by modifying aspects of their own behaviour. It should be of particular interest to all organisations in their dealings with recalcitrant and unresponsive suppliers, or buyers with very low levels of power trying to improve the responsiveness of more powerful suppliers. Originality/value – The paper introduces two new concepts: “purchase offering quality” that describes supplier perceptions of the characteristics and behaviours of buyers offering supplier benefits, and “supplier behavioural intentions” that is a term referring to the responses of suppliers to buyer characteristics and behaviours. This work can form the foundations of an extensive area of new study into organisational supplying behaviour.

Supply chain management using an optimization driven simulation approach

In this work, we propose a hybrid simulation‐based optimization framework to solve the supply chain management problem. The hybrid approach combines a mathematical programming model with an agent‐based simulation model and uses them in an iterative framework. The optimization model is used to guide the decisions toward an optimal allocation of resources given the realistic supply chain representation given by the simulation. Thus, the proposed approach provides a more realistic solution compared to a stand‐alone optimization model, often a simplified representation of the actual system, by making use of the simulation model, which captures the detailed dynamic behavior of the system. A multiobjective problem has been formulated by taking into consideration the environmental impact of supply chain operations. The proposed framework has been applied to small‐scale case studies to study the effectiveness of the approach for such problems. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4612–4626, 2013

Key Factor Extraction of Supply Chain Performance Based on Heterogeneous Selective Ensemble PCA

It is valuable for supply chain management to analyze and evaluate the operation performance of supply chain, and use timely and accurate operation state feedback to adjust the system to keep smooth running. Aiming at the complexity and uncertainty of supply chain, this thesis put forward a heterogeneous selective ensemble principle component analysis (PCA) algorithm based on fuzzy integral via Bagging ensemble learning. Besides, by using dimension reduction on high-dimensional data set, it realizes to extract the key factors of supply chain performance and breaks the bottleneck that problems in supply chain management cannot be recognized rapidly or analyzed by traditional performance evaluation method. According to the empirical research on survey data of supply chain performance at C Group, the algorithm is proved be effective.

Transformation of optimization problems in revenue management, queueing system, and supply chain management

For revenue optimization problems in the literature on revenue management, supply chain management, and queueing systems, some assumptions (such as concavity of revenue functions or increasing generalized failure rate) are often needed to ensure the problems to be analytically tractable. We show that these assumptions are not necessary. For this, we present and study a parametric revenue maximization problem to unify some problems in the literature. Without the usual assumptions, we transform the problem into an equivalent one where the revenue function is increasing, continuous and concave. We then apply the transformation method to a continuous time revenue management problem and conclude that the monotone results are robust to demand function and allowable price set. Also, we apply the transformation method to study a parametric cost minimization problem. We further apply our method to two optimal control problems in queueing systems and an inventory control problem in a supply chain with price-only contract.

A hybrid vendor managed inventory and redundancy allocation optimization problem in supply chain management: An NSGA-II with tuned parameters

In this research, a bi-objective vendor managed inventory model in a supply chain with one vendor (producer) and several retailers is developed, in which determination of the optimal numbers of different machines that work in series to produce a single item is considered. While the demand rates of the retailers are deterministic and known, the constraints are the total budget, required storage space, vendor's total replenishment frequencies, and average inventory. In addition to production and holding costs of the vendor along with the ordering and holding costs of the retailers, the transportation cost of delivering the item to the retailers is also considered in the total chain cost. The aim is to find the order size, the replenishment frequency of the retailers, the optimal traveling tour from the vendor to retailers, and the number of machines so as the total chain cost is minimized while the system reliability of producing the item is maximized. Since the developed model of the problem is NP-hard, the multi-objective meta-heuristic optimization algorithm of non-dominated sorting genetic algorithm-II (NSGA-II) is proposed to solve the problem. Besides, since no benchmark is available in the literature to verify and validate the results obtained, a non-dominated ranking genetic algorithm (NRGA) is suggested to solve the problem as well. The parameters of both algorithms are first calibrated using the Taguchi approach. Then, the performances of the two algorithms are compared in terms of some multi-objective performance measures. Moreover, a local searcher, named simulated annealing (SA), is used to improve NSGA-II. For further validation, the Pareto fronts are compared to lower and upper bounds obtained using a genetic algorithm employed to solve two single-objective problems separately.

A tri-level programming model based on Conditional Value-at-Risk for three-stage supply chain management

For the problem of supply chain management, the existing literature mainly focuses on the research of the single-stage supply chain or the two-stage supply chain that consists of a manufacturer and a retailer. To our best knowledge, little attention has been paid to the study of a more extensive supply chain that consists of a material supplier, a manufacturer and a retailer, which is a more practical and interesting case. Therefore, based on the Conditional Value-at-Risk (CVaR) measure of risk management, this paper proposes a tri-level programming model for the three-stage supply chain management. In this model, the material supplier and the manufacturer maximize their own profit while the retailer maximize his/her CVaR of expected profit. Further, we show that the proposed tri-level programming model can be transferred into a bilevel programming model, which can be solved by the existing methods. Numerical results show that the proposed model is efficient for improving the risk management of the three-stage supply chain.

IMPACT OF E PROCUREMENT AND LOGISTICS ON EFFECTIVENESS OF SUPPLY CHAIN MANAGEMENT: A STUDY OF AVIATION INDUSTRY IN PAKISTAN

The principal objective of this study is to identify and evaluate the potential solutions to the problems in the traditional supply chain management in the Aviation Industry of Pakistan. The role of technology with Supply Chain Management is also discussed in this study. The data was collected from various Airline companies in Pakistan. The results indicate that e-procurement and logistics are vital for supply chain effectiveness in aviation industry of Pakistan. Specific recommendations and implications are also discussed.

A product bundle determination model for multi-product supplier selection

Supplier selection an important problem in supply chain management (SCM), since the performances of suppliers determine the competitiveness of the SCM. It is practical for the purchasing company to procure multiple products simultaneously. Synergy effect may exist between products in multi-product supplier selection process, and affect the final choice of suppliers. It is advantageous for the purchasing company to take into account the synergy effect between products to reduce cost and improve efficiency. On the other hand, suppliers need to consider the synergy effect between products to increase the chance of winning bids. Nevertheless, supplier selection involves complex decision making processes regarding supplier evaluation, selection and order fulfillment, it is difficult to fully incorporate the synergy effect between products in the case of multi-product supplier selection. Most of existing research only deal with supplier selection involving the acquisition of one product, or they assume that the multiple required products are independent. They are not sufficient to incorporate the synergy effect between products for multi-product supplier selection. This paper presents a product bundle determination model which can determine the synergy effect between products, group products based on the synergy effect between products, and determine the preferred product bundles of the purchasing company. This model can help the decision maker to make right decision in multi-product supplier selection process. Illustrative examples are conducted to demonstrate the functioning of the proposed product bundle determination model and its application in multi-product supplier selection process.