Disruption response strategy models for supplier selection and order allocation in customised logistics service supply chain

A fuzzy multiobjective team decision model for CODP and supplier selection in customized logistics service supply chain

The key factor in realising competitiveness of a logistics service supply chain (LSSC) is its ability to meet customised requirements with the cost of mass service. This paper introduces the concepts of mass customisation and customer order decoupling point (CODP) into the field of logistics service to solve the problem of CODP when a logistics service integrator (LSI) in an LSSC operates under mass customisation. Two models of CODP decision, respectively, on single and multiple customer demands, are developed. Both process constraints and lead time constraints from the customised logistics service orders are considered. Unlike CODP positioning models within the manufacturing supply chain, the objective of this paper is to exclusively consider the cost of order transferring and order waiting at CODP to minimise total cost of the LSI. The applications of the two models are also discussed by two real cases, and three major conclusions are drawn. First, introducing mass customisation into an LSSC and minimising total cost of order processing of the LSI by selecting an optimised CODP is feasible. Second, total cost will decrease monotonously along with the movement of CODP toward the last service procedure under the precondition of satisfying lead time constraints. Third, parameters about the customers’ logistics service orders will bring about great influence on the selection of CODP. For instance, the lead time of orders will influence the number of feasible CODPs and the position of optimal CODP. Also, decreasing cost of order transferring and order waiting will not cause the change of optimal CODP, while increasing cost of order transferring and order waiting will lead to the switch of optimal CODP.

PurposeOn one side, the purpose of this paper is to numerically analyze the emergency order allocation mechanism and help managers to understand the relationship between the emergency coefficient, uncertainty and emergency cost in two‐echelon logistics service supply chain. On the other side, the purpose of this paper is to help managers understand how to deal with the problem of order allocation in the two‐echelon logistics service supply chain better in the case of emergency.Design/methodology/approachThe paper presents a multi‐objective planning model for emergency order allocation and then uses numerical methods with LINGO 8.0 software to identify the model's properties. The application of the order allocation model is then presented by means of a case study.FindingsWith the augment of uncertainty, the general cost of logistics service integrator (LSI) is increasing, while the total satisfaction of all functional logistics service providers (FLSPs) is decreasing, as well as the capacity reliability; at the same time the emergency cost coefficient is closely correlative with the satisfaction and general penalty intensity of FLSPs; finally, the larger the emergency cost coefficient is, the more satisfaction of FLSPs, but the capacity reliability goes up first and down later.Research limitations/implicationsManagement should note that it is not better when emergency cost coefficient is bigger. The general satisfaction degree of FLSP increases with the augment of emergency cost coefficient, but there is an upper limit of the value, i.e. it will not increase indefinitely with the augment of emergency cost coefficient. This paper also has some limitations. The optional emergency cost coefficient only adopted a group of data to analyze while the trend of the reliability of logistics capacity needs to be further discussed. In addition, the algorithm of emergency order allocation model in the case of multi‐objective remains to be solved.Practical implicationsUnder emergency conditions, LSIs can adopt this kind of model to manage their FLSPs to obtain the higher logistics performance. But LSIs should be careful selecting emergency cost coefficient. In accordance with different degrees of emergency logistics demand, LSIs can determine reasonable emergency cost coefficient, but not the bigger, the better, on the premise that LSIs acquire maximum capacity guarantee degree and overall satisfaction degree of FLSPs. FLSPs can make contract bargaining of reasonable emergency coefficient with LSIs to make both sides get the best returns and realize the benefit balance.Originality/valueMany studies have emphasized the capacity allocation of manufactures, order allocation of manufacturing supply chain and scheduling model of emergency resources without monographic study of supply chain order allocation of logistics service. Because the satisfaction degree of FLSPs the cost of integrators needs to be considered in the process of order allocation, and the inventory cost of capacity does not exist, it is different from the issue of capacity allocation planning of manufacture supply chain. Meanwhile, the match of different kinds of logistics service capacity must be considered for the reason of the integrated feature of logistics service. Additionally, cost is not the most important decision objective because of the characteristics of demand uncertainty and weak economy. Accordingly, this paper considers these issues.

Integrated linguistic entropy weight method and multi-objective programming model for supplier selection and order allocation in a circular economy: A case study

Sustainability consciousness among food manufacturers or transformers is one of the principal drivers for a sustainable food supply chain (SC) and it creates a possibility to enforce sustainability across all tiers of suppliers. This paper focuses on addressing sustainable supplier selection and optimal order allocation problem in agri-food SC. For this, an integrated bi-objective optimization model is developed in the study for configuring the SC network. It facilitates the selection of sustainable suppliers, allocates optimum order quantities among the selected suppliers, and determines flow of units across all the echelons of the SC network. The central focus is on enhancing the sustainable impact of the SC while keeping the SC costs at the minimum. The model developed in the study contains a three-phase comprehensive approach. The primary phase utilizes Best–Worst Method for evaluating and selecting sustainable suppliers based on economic, environmental, and sustainability criteria; the secondary phase involves formulation of a mixed-integer programming model for configuring the SC network and addressing the supplier selection and order allocation problem while aiming to achieve a compromised solution between the fuzzy objectives of minimization of cost and maximization of the sustainable value of purchase. In the final phase, fuzzy programming is effectively utilized for obtaining a compromised solution to the fuzzy bi-objective model utilizing real data of a case study from flour milling industry in India.

Two important decisions in supply chains and logistics systems design are the supplier selection and order allocation (SS&OA) problem and the vehicle routing problem (VRP). Supply disruption may reduce the capacity of suppliers, and the transportation network disruption may decrease the number of vehicles in the fleet and disrupt some routes. Also, increasing environmental regulations and environmental awareness makes companies pay more attention to green supply chain management (GSCM). In this paper, we integrate green and resilient supplier selection and order allocation decisions with vehicle routing decisions under disruption. We present a multiproduct two-stage risk-averse mixed-integer stochastic linear programming for the green and resilient supplier selection and order allocation integrated with vehicle routing (G&RSS&OA-V) problem. We consider resilient strategies before disruption, including multiple sourcing, supplier fortification, prepositioned inventory at the protected supplier, and contract with third-party logistics providers (3PLs). The objective function is to minimize the total mean-risk cost and the cost of greenhouse emissions. We use conditional value at risk (CVaR) as a risk measure to control the risk of worst-case cost. The most significant decisions of this model are the strategic decisions of determining the optimal suppliers and the operational decisions of vehicles routing under disruption simultaneously. Other decisions include determining which suppliers should be fortified, the amount to be transported to the hybrid manufacturing-distribution (HMD) center through the supplier or prepositioned emergency inventory, and the amount of lost sales. In order to validate the proposed model and its features, several numerical examples along with sensitivity analysis are performed by GAMS software, which shows the efficiency and application of the developed model, and some managerial insights are reported. The results of the sensitivity analysis show that as α increases from 0.1 to 0.9, the mean-CVaR objective function cost increases to 13.2%. As λ increases from 0.1 to 0.9, the mean-CVaR objective function cost increases to 35.6%. The increase of these two risk factors makes the proposed model more risk-averse. As the expected shortage cost increases by 150%, the mean-CVaR objective function cost increases to 36% while the amount of expected shortage decreases by 56%.

An integrated GBWM-PROMETHEE-CLOUD & MCGP model for green supplier selection and order allocation (GSSOA) in an oil refinery

An integrated decision-making process for sustainable supplier selection and order allocation in the automotive industry

Global supply chains have become increasingly complex, interconnected and interdependent, thus, managing this complexity and the associated supply chain risks is the key for a firm to gain and maintain competitive advantage. Nowadays, sustainability has introduced another layer to supply chain complexity, and addressing supply chain risks and sustainability issues together are the two greatest concerns for supply chain executives. Thus, this dissertation explores three important strategic issues in global supply chains. The first strategic issue deals with the design of contracts to mitigate the exchange rate volatility risk between a buyer and a supplier in global supply chains during outsourcing. Two types of currency exchange rate flexibility contracts are proposed to explore the characteristics of exchange rate risk mitigation policies for the buyer and the supplier. Furthermore, the effects of contract structures on the optimal order quantity, as well as the expected profits of both supply chain members are investigated. The second major issue addressed focuses on the trade-off between supply chain sustainability-related risks and cost in the supplier selection and order allocation problem, often encountered in outsourcing manufacturing. An integrated model that utilizes principal component analysis-based approach for evaluating supplier's sustainability performance and a bi-objective mixed integer programming model for supplier selection and order quantity allocation is developed. Then, an illustrative example of outsourcing contract manufacturers in the US apparel industry is presented to demonstrate the applicability of the proposed framework in practice. The final strategic issue examined in this dissertation is the design of an integrated production-distribution network to reduce environmental risk, which focuses on the minimization of carbon emissions from transportation activities in the entire supply chain. A mixed integer linear programming model is developed for a three-echelon supply chain network design problem, in a deterministic, multi-period, multi-product environment with a finite planning horizon. This model minimizes the total supply chain cost and solves strategic, tactical, and operational decision-making problems which include: locating distribution centers, the quantity of each product to produce, frequency of flow of delivery vehicles, as well as the allocation of inventory to each echelon of the supply chain. A numerical study is conducted through illustrative examples in order to demonstrate the applicability of this model and gain some managerial insights.

PurposeBy introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.Design/methodology/approachThis paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.Findings(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.Originality/valueThe originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.

In mass customization logistics service, reasonable scheduling of the logistics service supply chain (LSSC), especially time scheduling, is benefit to increase its competitiveness. Therefore, the effect of a customer order decoupling point (CODP) on the time scheduling performance should be considered. To minimize the total order operation cost of the LSSC, minimize the difference between the expected and actual time of completing the service orders, and maximize the satisfaction of functional logistics service providers, this study establishes an LSSC time scheduling model based on the CODP. Matlab 7.8 software is used in the numerical analysis for a specific example. Results show that the order completion time of the LSSC can be delayed or be ahead of schedule but cannot be infinitely advanced or infinitely delayed. Obtaining the optimal comprehensive performance can be effective if the expected order completion time is appropriately delayed. The increase in supply chain comprehensive performance caused by the increase in the relationship coefficient of logistics service integrator (LSI) is limited. The relative concern degree of LSI on cost and service delivery punctuality leads to not only changes in CODP but also to those in the scheduling performance of the LSSC.

Mass customization logistics service mode provides a new way to maintain the sustainable cooperative relationship between customers and integrators. One of the key factors to maintain the sustainable development of logistics service supply chain under MC mode is to locate a suitable customer order decoupling point (CODP) location. This paper investigates the problem of CODP in the logistics service supply chain based on the fuzzy set theory under the mass customization mode. With the help of a fuzzy QFD method and a new service quality function that we constructed, this paper quantifies the quality of a logistics service when the LSI selects a different CODP. Then, the fuzzy set of the high-quality logistics service and the fuzzy set of the satisfactory delivery time are built. Based on those two new fuzzy sets, this paper builds a new fuzzy programming model on CODP positioning. The solving methods of this model under different conditions are given. Finally, the influence of some important parameters on the optimal CODP position is studied by sensitivity analysis on a specific numerical case.

In this paper, we propose (a) fuzzy multiple objective linear programming models for the Supplier Selection and Order Allocation (SSOA) problem under fuzzy demand and volume/quantity discount environments, and (b) an analysis of how to select the suitable aggregation operator based on the risk preferences of decision makers. The aggregation operators under consideration are additive, maximin, and augmented operators while the risk preferences are classified as risk-averse, risk-taking, and risk-neutral ones. The suitabilities of aggregation operators and risk preferences of decision makers are analyzed by a statistical technique, considering the average and the lowest satisfaction levels of the supplier selection criteria, based on numerical examples. Analysis results reveal that decision makers with different risk preferences will prefer only some aggregation operators and models. Moreover, a particular aggregation operator and model may generate a dominated solution for some situations. Thus, it should be applied with caution.

Data‐driven distributionally robust supplier selection and order allocation problems considering carbon emissions

Supply chain and logistics management is of tremendous importance for multinational organizations. Logistics Service Providers (LSPs) provide logistics services and smooth logistics operations between suppliers, manufacturers, distributors, and customers. This paper uses a Systematic Literature Review (SLR) to identify the current trends and future developments of LSPs and the underlying (smart) logistics operations connected to the concept of lifecycle management. An SLR review was conducted to identify relevant research papers in the areas of LSPs and logistics lifecycle management. Out of 288 papers analyzed, 81 were identified as highly appropriate for in-depth analysis. The LSP Lifecycle Model (LSLM) was then developed by combining logistics service characteristics and the lifecycle management concept, including Product Lifecycle Management (PLM), Service Lifecycle Management (SLM), and Product Service System (PSS). The LSLM consists of three phases: The Beginning of Life (BOL), the Middle of Life (MOL), and the End of Life (EOL). The LSLM is characterized by three phases, eight criteria, and seventeen sub-criteria. This paper aims to fulfil customer requirements through a product or service in the whole lifecycle of the logistics service provider. The findings further present an adaptable LSLM by focusing on various logistics services and integrating sustainability factors to meet market trends. Logistics cost factors can also be used to evaluate logistics services in the MOL stage. The EOL shows the trend of risk management, evaluation, and decomposition, which is determined by new or re-designed logistics products and services.