Product recovery optimization in closed-loop supply chain to improve sustainability in manufacturing

When business practices shift from a traditional open supply chain to a closed loop instead, the environmental and societal issues are efficiently integrated in business development. However, even an efficiently integrated shift introduces a number of trade-offs due to the contradictory goals that emerge from that business’s economical, environmental and social dimensions. In this paper, we propose a multi-objective mixed integer mathematical problem for a generic closed-loop supply chain (CLSC) network to rationalise how a system’s product recovery helps to improve manufacturing sustainability. The CLSC network proposed in this study consists of a hybrid manufacturing facility, warehouse, distribution centres, collection centres and a hybrid recovery facility (HRF). The proposed model determines the best location for the HRF and optimal flow of products, recovered parts and material in the network while it simultaneously maximises profit, saves activity costs, helps to decrease the harmful effects of the manufacturing process and makes a positive impact on societal development. To validate the model, a numerical illustration with the help of a case study from an electrical manufacturing industry is offered. The results authenticate the approach of the model towards the fulfilment of various environmental regulations. A sensitivity analysis, completed on demand, and the return rate also assists decision-makers to manage their decisions with a broader insight towards manufacturing sustainability.     

A heuristic-based approach to integrate the product line selection decision to the supply chain configuration

Product platform development (PPD) as an approach to mass customisation (MC) helps an organisation to reduce costs as well as ensure timely deliveries. Varieties are offered to different market segments by combining and incorporating different modules at different levels. Modules at different levels are essentially features that a customer segment is looking for. It is apparent that overall optimisation would require simultaneous consideration of not only PPD but also other supply chain constraints. In this paper, an attempt has been made to develop a mixed integer linear programming (MILP) model for sourcing, production planning, and PPD decisions. Based on analyses of the model, a heuristic solution procedure has been suggested. The heuristic developed in this paper attempts to decompose the problem and then assimilate the outputs from the simpler parts to obtain the final solution. Finally, a simple example to illustrate the solution procedure is presented.     

A matrix-based Bayesian approach for manufacturing resource allocation planning in supply chain management

Nowadays, the supply chain of manufacturing resources is typically a large complex network, whose management requires network-based resource allocation planning. This paper presents a novel matrix-based Bayesian approach for recommending the optimal resource allocation plan that has the largest probability as the optimal selection within the context specified by the user. A proposed matrix-based representation of the resource allocation plan provides supply chain modelling with a good basis to understand problem complexity, support computer reasoning, facilitate resource re-allocation, and add quantitative information. The proposed Bayesian approach produces the optimal, robust manufacturing resource allocation plan by solving a multi-criteria decision-making problem that addresses not only the ontology-based static manufacturing resource capabilities, but also the statistical nature of the manufacturing supply chain, i.e. probabilities of resource execution and resource interaction execution. A genetic algorithm is employed to solve the multi-criteria decision-making problem efficiently. We use a case study from manufacturing domain to demonstrate the applicability of the proposed approach to optimal manufacturing resource allocation planning.     

A CBFSA approach to resolve the distributed manufacturing process planning problem in a supply chain environment

In this competitive world cost and lead time reduction are of prime concern for manufacturing firms. To achieve this objective manufacturing entities are adopting several management philosophies such as Total Quality Management (TQM), just-in-time (JIT), and theory of constraints (TOC). The present paper addresses the advanced computer-aided process planning (ACAPP) problem in a distributed manufacturing supply chain environment and aims at cost and lead times reduction under several technological constraints. To deal with the complexity of the problem the constraint based fast simulated annealing (CBFSA) algorithm has been explored in this article. Extensive computations have been performed over the well-known benchmarks of advanced planning problems and the results obtained prove the superiority of the proposed algorithm over the prior approaches.     

Selection of biomass materials for bio-oil yield: a hybrid multi-criteria decision making approach

Clean Technologies and Environmental Policy - Determination of the proper physiochemical properties of the biomass sample for pyrolysis process is an important task. Various types of biomass are...     

A portfolio approach to supply chain disruption management

A new, computationally efficient portfolio approach to supplier selection in the presence of supply chain disruption risks is proposed, where the selection of supply portfolios for parts is combined with production scheduling of finished products. Unlike most of reported research on the supply chain risk management which focuses on the risk mitigation decisions taken prior to a disruption, the proposed portfolio approach combines decisions made before, during and after the disruption. The two decision-making approaches are considered: an integrated approach with the perfect information about the future disruption scenarios, and a hierarchical approach with no such information available. In the integrated approach, which accounts for all potential disruption scenarios, the primary supply portfolio that will hedge against all scenarios is determined along with the recovery supply portfolio and production schedule for each scenario. In the hierarchical approach, first the primary supply portfolio is determined, and then, when a primary supplier is hit by a disruption, the recovery supply portfolio is selected. For the integrated and the hierarchical decision-making, mixed integer programming models are developed with the two risk-neutral conflicting objectives that account for both time and cost of recovery: minimising expected cost or maximising expected service level. The findings indicate that for both objectives, the integrated decision-making selects a more diversified primary supply portfolio than the hierarchical approach and when all primary suppliers are shutdown by disruption, a single sourcing recovery portfolio is usually selected.     

A two-way approach to supply chain partner selection

All organisations face partner selection. It is the first step in establishing partnership. Over recent decades, a one-way selection approach has been dominant in the literature. It is assumed that a buyer searches for one or more suppliers from a set of suppliers. Once the best supplier is found and selected, a partnership is formed. However, in reality, not only is the selection not a one-way process as the relationship itself is bilateral, but the existence of other buyers and suppliers also needs to be taken into account. In this study, a two-way partner selection approach is proposed, where not only the buyers evaluate suppliers, but also the suppliers have the opportunity to evaluate buyers. Considering a marketplace where there are several buyers and suppliers, an integrative model is proposed in the form of an assignment model to optimally match all the buyers and suppliers. A generalised model is also formulated to take the suppliers’ capacity and buyers’ demand into account. The proposed models are illustrated using some numerical examples. Finally, conclusions and recommendations for future research are provided.     

A bidding decision model in multiagent supply chain planning

The multiagent environment for supply chain planning application is based on a framework unifying the internal behaviour of agents and coordination among agents. This system presents a formal view of coordination using Contract Net Protocol (CNP) that relies on the basic loop of agent behaviours: order receiving, order announcement, bid calculation, and order scheduling followed by order execution. Among these, bid calculation is most difficult. It needs to determine the quantity, cost and time in which a new order can be implemented. Fuzzy programming has made progress in mathematics since Bellman and Zadeh (1970) first studied decision-making in a fuzzy mathematics programming. Currently there are many valuable works in this field (Zimmermann 1983, 1985, Tanaka and Asai 1984) and fuzzy programming has become an effective tool to deal with the decision-making problems in fuzzy systems. Similarly, stochastic programming is a useful tool to treat decision-making problems in a stochastic system (Kolbin 1977, Kall and Wallace 1994). However, in many practical systems, fuzzy factors and random factors arise concurrently, and this problem has not received the attention it deserves. Therefore, there is a need to develop a new kind of optimization technique to make decisions in a fuzzy random system. In this paper, we build the Bid Calculation model, including a random parameter set, the set of product quantity that will be stored to inventory, a fuzzy parameter set, the Maximum Sales Rates (MSR) set, and we discuss an approach to solve the model, as well as present an implementation procedure with the GA method.     

Traceability as a means to investigate supply chain sustainability: the real case of a leather shoe supply chain

In recent years, the growing attention to environmental challenges has shown that these issues are becoming of more and more interest to both research and industry. Companies are expected to ensure their products are fully traceable and more sustainable, which requires the involvement of all of the actors in the production network. According to this aim, this study proposes a structured approach that uses the traditional traceability concept as a means to identify the main information needed to assess environmental impacts along the whole supply chain (SC). The proposed approach is composed of four main steps: (i) SC modelling to identify all stakeholders and their inter-relations, (ii) data sharing to collect all relevant data, (iii) data elaboration to calculate performance at different levels of detail and (iv) result interpretation to optimise the SC. The distributed implementation of the approach at different SC steps represents a useful means to practically realise a sustainable SC management. A case study involving a leather shoe SC is used to demonstrate the effectiveness of the approach in identifying criticalities, supporting the selection of the most appropriate suppliers and correctly setting a management strategy towards the optimisation of internal and external traceability and environmental sustainability performances.     

A control engineering approach to the assessment of supply chain resilience

There is no consensus on the supply chain management definition of resilience. To aid in evaluating the dynamic behaviour of such systems we need to establish clearly elucidated performance criteria that encapsulate the attributes of resilience. A literature review establishes the latter as readiness, responsiveness and recovery. We also identify robustness as a necessary condition that would complement resilience. We find that the Integral of the Time Absolute Error (ITAE) is an appropriate control engineering measure of resilience when it is applied to inventory levels and shipment rates. We use the ITAE to evaluate an often used benchmark model of make-to-stock supply chains consisting of three decision parameters. We use both linear and nonlinear forms of the model in our evaluation. Our findings suggest that optimum solutions for resilience do not yield a system that is robust to uncertainties in lead-time. Hence supply chains will experience drastic changes in their resilience performance when lead-time changes.     

A mathematical programming approach to supply chain models with fuzzy parameters

Supply chain (SC) models play an important role in supply chain management (SCM) for reducing costs and finding better ways to create and deliver value to customers. An approach to deriving the membership function of the fuzzy minimum total cost of the multi-product, multi-echelon, and multi-period SC model with fuzzy parameters is proposed in this article. On the basis of α-cut representation and the extension principle, a pair of mathematical programs are formulated to calculate the lower and upper bounds of the fuzzy minimum total cost at possibility level α. The membership function of the fuzzy minimum total cost is constructed by enumerating different values of α. To demonstrate the validity of the proposed procedure, a four-echelon five-period SC model with fuzzy parameters is solved successfully. Since the objective value is expressed by membership functions rather than by crisp values, they completely conserve the fuzziness of input information when some of the SC data are ambiguous. Thus the proposed approach can represent SCs with fuzzy parameters more accurately, and more information is provided for designing SCs in real-world applications.     

A hybrid optimisation approach to configure a supply chain for new product diffusion: a case study of multiple-sourcing strategy

Configuring a supply chain for a new product is a challenging task due to the lack of historical demand data and the dynamic/uncertain nature of the new product diffusion process. An integrated supply chain configuration (SCC) and new product diffusion (NPD) model is developed to explicitly account for the impact of demand dynamics during a new product's diffusion on an optimal supply chain configuration. Our hybrid NPD-SCC model allows a manufacturer to source from multiple suppliers, vendors or modes for its supply chain entities. Such a multiple-sourcing approach not only helps the manufacturer to diversify its pool of suppliers and maintain bargaining power, but also builds redundancy into the supply chain to hedge against potential demand surge and supply disruption during the new product life cycle. Through a case study and a comprehensive computational study, we find that although the single-sourcing solution is able to achieve lower unit-manufacturing cost (UMC), the multiple-sourcing approach is superior to single-sourcing on the overall supply chain performance in the environment with random supply disruptions. By building-in redundancy as multiple suppliers and modes, the resultant supply chain has less chance of being disrupted and achieves higher overall profit on average. We also draw several other managerial insights closing the gap between some supply chain operations and marketing strategies.     

An integrated approach to multi-criteria design and control of manufacturing systems using simulation and goal-regression

In this paper we discuss the issue of multi-criteria design and control of manufacturing systems. We also trace the development of domain-independent non-linear planners from the field of artificial intelligence. These planners automate the task of generating a sequence of actions to achieve a specified goal. The problem of conjunctive goal planning is addressed, and a parallel drawn with the design and control of manufacturing systems where multiple criteria are to be satisfied. The inability of existing approaches to facilitate optimization across multiple performance criteria is also highlighted. Finally, to ameliorate the problem an integrated approach based on goal-regression and simulation is proposed. To facilitate conflict-resolution across multiple goals, a new algorithm is developed. The approach is tested on different configurations of test systems and found to perform satisfactorily for manufacturing systems with fairly large sets of defining variables.     

The critical success factors of using social media for supply chain social sustainability in the freight logistics industry

This paper pioneers the investigation of the significant factors that influence corporate decisions on the use of social media for supply chain social sustainability, and it highlights a crucial research area that is currently understudied in supply chain management literature. A theoretical framework was developed in this study based upon the Technology–Organization–Environment (TOE) and Human–Organization–Technology (HOT) theories to obtain the significant critical success factors (CSFs) which influence the use of social media for supply chain social sustainability in freight logistics firms in Nigeria. The Best-Worst Method was applied to analyse and rank the CSFs using their determined relative importance level. The research findings indicate that customer satisfaction, sufficient security and privacy, affordability and competitive pressure are the highest ranked CSFs to achieve supply chain social sustainability using of social media. This research has important implications for policy makers and practitioners to gain perspectives on how to foster the use of social media in the freight logistics sector for supply chain social sustainability.     

A constrained EPSAC approach to inventory control for a benchmark supply chain system

The design of an appropriate inventory control policy for a supply chain (SC) plays an essential role in tempering inventory instability and bullwhip effect. Several constraints are commonly encountered in actual operations so managers are required to take these physical restrictions into account when designing the inventory control policy. Model predictive control (MPC) appears as a promising solution to this issue, due to its capability of finding optimal control actions for a constrained SC system. Therefore, the inventory control problem for a benchmark SC is solved using the extended prediction self-adaptive control approach to MPC. To extend methodologies in our previous work, the control framework relies on generic process model and incorporates the physical constraints arising from practical operations to form the general constrained optimisation problems. The managers can choose from decentralised and centralised control structures according to specific informational and organisational factors of their SCs. The proposed control schemes in this study may be appropriate for industrial practice because the designed policy can bring a reduction of over 30% in operating cost and a significant increase of customer satisfaction level compared with that of the conventional policy.     

An entropy-based approach to identifying vulnerable components in a supply chain

We consider a problem of quantifying risk factors and identifying most informative (or vulnerable) components of the supply chain in terms of the amount of information about the risks and corresponding losses. This knowledge is beneficial for the selection of risk-prevention decisions. Shannon’s entropy is shown to be a powerful tool for risk management in hierarchical supply chains. An efficient algorithm is proposed that permits to reduce the size of the supply chain model without a loss of essential information about the risks and their economic consequences. A case study is presented to demonstrate the validity of the entropy-based approach.     

Monolithic versus hierarchical approach to integrated scheduling in a supply chain

The two approaches, monolithic and hierarchical, with a set of mixed integer programming formulations are proposed and compared for multi-objective integrated scheduling in a customer driven supply chain. The supply chain consists of multiple manufacturers (suppliers) of parts, a single producer of finished products and a set of customers who generate final demand for the products. Each supplier has a number of identical production lines in parallel for the manufacture of parts, and the producer has a flexible assembly line for assembly of products. Given a set of orders, the problem objective is to determine which orders are to be provided with parts by each supplier, find a schedule for the manufacture of parts by each supplier and for the delivery parts from each supplier to the producer, and find a schedule for the assembly of products for each order by the producer, such that a certain performance measure of the supply chain is optimised. The selection of the parts supplier for each order is combined with due date setting for some orders, subject to the suppliers and the producer available capacity. Different objective functions are considered that take into account both customer service level and total manufacturing, delivery and production cost. Numerical examples are presented that are modelled by real-world integrated scheduling in a customer driven supply chain of high-tech products, and some computational results are reported to compare the two approaches.     

Risk analysis of the agri-food supply chain: A multi-method approach

Agri-food supply chains (AFSCs) are becoming more complex in structure, and thus more susceptible to different vulnerabilities and risks. Therefore, to enhance performance, we need to manage the risks in AFSCs effectively and efficiently. This study analyses various AFSC risks using a multi-method approach, including thematic analysis, total interpretive structural modelling (TISM) and fuzzy cross-impact matrix multiplication applied to classification (MICMAC) analysis. Based on the empirical data collected from experienced AFSC practitioners and following thematic analysis, eight categories of risk and 16 risk factors were identified as important. Furthermore, the interrelationships among the identified risks were built using TISM. Finally, the identified risks were classified into various categories according to their dependence and driving power using fuzzy MICMAC analysis. The research results indicate that the weather-related and political risks have the highest driving power and are located at the lowest level in the TISM hierarchy. These risks have a high tendency to disturb the whole flow of AFSC and so should be managed effectively. This study advances existing literature on identifying risk factors, defining interrelations between different AFSC risks, and determining the key risks. The risk analysis results can help AFSC practitioners in AFSC to identify, categorise and analyse the risks.     

A multi-objective and integrated model for supply chain scheduling optimization in a multi-site manufacturing system

In a supply chain, scheduling plays a significant role in coordinating and cooperation. This article considers an integration of supplier and vehicle scheduling problems in terms of vehicle routing determination for transporting raw materials from the suppliers to some manufacturing centres. The aim is to minimize the total tardiness of all assigned orders to the suppliers and simultaneously minimize the total travelled distance of the vehicles. Most manufacturing companies, which have to manage their suppliers as an industrial unit, experience this problem. A new metaheuristic algorithm called the multiple league championship algorithm (MLCA), inspired by championship matches, is proposed to solve this problem. To show the efficiency of MLCA, it is compared with two different algorithms used for the problems in the literature that are closest to this problem and a soccer-based algorithm called golden ball. The experimental results prove that the proposed algorithm has better performance than these algorithms.