Lean versus agile production: flexibility trade-offs within the automotive supply chain

Given the recent dynamics of the automotive industry in the UK, the ability for a firm to be flexible has often taken priority over other performance indicators. Using the notion of distinct business models and trade-offs as our theoretical lens, the purpose of this study was to: (1) Distinguish lean and agile firms based upon production methods; and (2) Compare lean and agile levels of external flexibility (EF) and supply chain flexibility (SCF). Data was obtained from 140 automotive firms in the Midlands (UK) via a survey which was sent by emails. Findings supported the theoretical notion of trade-offs, as firms implementing agile production methods were found to be more flexible in comparison with firms implementing lean production methods. More importantly, the agile firms that possessed high EF levels and SCF levels were predominantly positioned at the lower end of the automotive supply chain, whereas the lean firms were largely found to be operating at the top of the supply chain. First, we provide an innovative way in which lean and agile firms can be conceptualised. Second, as flexibility levels were assessed on actual numerical values, as opposed to using opinion based Likert Scale questions, a methodological contribution is made. Third, as flexibility is in its infancy stage of theoretical development we make an empirical contribution by developing a taxonomy that distinguishes each production concept. Finally, given the supply chain position to where lean and agile firms were found, we invoked a power perspective better understand this phenomenon.     

Pressure analysis for green supply chain management implementation in Indian industries using analytic hierarchy process

Due to growing awareness about environment protection, firms are forced to implement environmental practices to enhance their green image. In recent times, academicians and practitioners have shown interest in green marketing and green supply chain management (GSCM). Fields of green branding and sustainability have seen special interest from different business disciplines including information management, marketing, supply chain management, etc. Due to economical and ecological impact, there is a growing concern for the environment and related critical issues. Pressure on the environment is dynamic and diverse, and demands new levels of accountability, financial commitment and supply chain capabilities. Indian manufacturing industries have started adopting green concepts in their supply chain management giving special attention to environmental issues based on pressures from different directions, e.g. customer pressure, government regulations etc. Yet, industries struggle hard to identify essential pressures for implementation of GSCM. This work focuses mainly on identifying such pressures for implementation of GSCM. Initially 65 pressures were identified through detailed literature and categorised into six groups. Then common acceptable pressures were identified through a questionnaire survey from different industrial sectors in Phase 1. Finally, essential pressures are prioritised with the help of analytic hierarchy process in Phase 2.     

New hybrid COPRAS-G MADM Model for improving and selecting suppliers in green supply chain management

Greening the supply chain is an increasingly important concern for many business enterprises and a challenge for logistics management. Critical functions within green supply chain management are internal improvements and selection of green suppliers. This study proposes a novel, hybrid model that addresses dependent relationships between various criteria and the vague information coming from decision-makers. The Decision-making Trial and Evaluation Laboratory (DEMATEL) technique structures the relationships among criteria, thereby constructing an influential network relationship map (INRM). Meanwhile the DEMATEL-based, analytical network process (ANP) method aids in obtaining influential weights of the criteria. Decision-makers may hold diverse opinions and preferences due to incomplete information, differences in knowledge or simply conflicts that are inherent between various departments. This can make it difficult to judge the performance of alternatives. One remedy is to apply a modified COmplex PRoportional ASsessment of alternatives with Grey relations. Next, this is applied to improve each criterion for integration of the performance values obtained in closing the aspiration level from different expert opinions based on INRM. An empirical example using data from a Taiwanese electronics company is provided to demonstrate our proposed method. The results can provide firms with a knowledge-based understanding of the source of some problems, thus reducing the performance gaps and closing the aspiration levels. Finally, there is a discussion on certain managerial implications.     

Leagile supply chain: design drivers and business performance implications

In the context of a dynamic and hypercompetitive business environment, effective supply chain design helps organisations to align resources for improved flow of products and services and satisfy customers’ diverse needs. Scholars have proposed several mutually exclusive supply chain designs such as efficient versus responsive, and lean versus agile. Quantitative testing has revealed that supply chain designs of many firms do not match with what was conceptually expected. To address this mismatch, in this study, a new approach to supply chain leagility is introduced and the impact of uncertainty as the key design driver of supply chains on leagility is investigated. The partial least squares (PLS) was employed to analyse data collected from 299 Australian firms by administering a structured questionnaire. Results indicate that higher performance is achievable on minimising the deviation from a balanced supply chain in which aspects of both leanness and agility are equally embedded. Further, the level of uncertainty directly and positively affects the Deviation from Leagility (DFL) index.     

Agile supply chain strategy and supply chain performance: complementary roles of supply chain practices and information systems capability for agility

The paper examines the mediating effect of supply chain (SC) practices on the relationship between agile supply chain (ASC) strategy and SC performance. It further examines the moderating effect of information systems (IS) capability for agility on this mediated relationship. Using the theoretical lenses of complementarity and the information processing view of the supply chain, we hypothesise that strategic supplier partnership, customer relationship, postponement and lean practices, mediate the relationship between ASC strategy and SC performance. We further hypothesise that IS capability for agility moderates each of these mediated relationships. We empirically test the hypotheses using survey data from members of senior and executive management in the logistics/supply chain functions of 205 firms. The paper contributes to the literature on ASCs by theoretically explaining and empirically demonstrating how SC practices and IS capability for agility act together to effect a positive relationship between ASC strategy and supply chain performance.     

Information technology strategy for a patient-oriented,lean, and agile integration of hospital pharmacy and medical equipment supply chains

Both public and private hospitals are increasingly under pressure to reduce costs while improving patient care across all medical disciplines and departments. Hospitals must become patient-oriented, lean, and agile in order to properly realign and integrate health care processes, helping to reconcile efficiency imperatives with patient needs and hospital mission. One of the highest potential for improvement can be found in supply chain management (SCM) practices for medical supplies, which often represent more than 40% of a hospital’s operating budget. We report on 3 case studies of business process management and reengineering projects, relying on advanced information technology, focused on the supply chains of two major urban hospitals, involving $2 million in minimum stocks for drug inventory. Case study 1 deals with an in-depth analysis of SCM practices around a key medical asset in pharmaceutical supply, i.e. infusion pumps. Case study 2 builds upon the findings of case 1, and proposes an radio-frequency identification solution to support a new hospital-wide asset location process and system, aiming for just-in-time availability of infusion pumps for critical drugs administration. Case study 3 complements cases 1 and 2 by analysing the feasibility of integrating the various components of the hospital pharmacy inventories, which in turn could be integrated to asset location systems. Our 3 case studies lead us to a number of conclusions on how hospitals can develop a patient-oriented, agile, and lean perspectives and practices, as well as ensure the proper integration of patient needs within optimised supply chains.     

Two-period vs. multi-period model for supply chain disruption management

A novel two-period modelling approach is developed for supply chain disruption mitigation and recovery and compared with a multi-period approach. For the two-period model, planning horizon is divided into two aggregate periods: before disruption and after disruption. The corresponding mitigation and recovery decisions are: (1) primary supply and demand portfolios and production before a disruption, and (2) recovery supply, transshipment and demand portfolios and production after the disruption. In the multi-period model, a multi-period planning horizon is applied to account for a detailed timing of supplies and production. The primary and recovery portfolios are determined simultaneously and for both approaches the integrated decision-making, stochastic mixed integer programming models are developed. While the simplified two-period setting may overestimate (for best-case capacity constraints) or underestimate (for worst-case capacity constraints) the available production capacity, it can be easily applied in practice for a fast, rough-cut evaluation of disruption mitigation and recovery policy. The findings indicate that for both two- and multi-period setting, the developed multi-portfolio approach leads to computationally efficient mixed integer programming models with an embedded network flow structure resulting in a very strong linear programming relaxation.     

A hybrid multi-stage predictive model for supply chain network collapse recovery analysis: a practical framework for effective supply chain network continuity management

In this paper, we present a multi-stage hybrid model for analysing a supply chain network (SCN) collapse recovery possibility. In the first stage of the model, we analyse the ability of an SCN to fulfil its customers required due date (RDD). As soon as the SCN defaults to timely fulfil their customers RDD, the second stage of the model is triggered to measure the collapse recovery possibility (CRP) of the SCN. Then, we calculate the final collapse recovery possibility (FCRP) of the SCN. Since the operation times, customer demand and external supply of raw material are uncertain, we use fuzzy triangular numbers to estimate the value of foregoing parameters. Consequently, we employ fuzzy program evaluation and review technique (FPERT) to calculate the completion time of SCN operations. In the third stage, for the critical elements of the SCN obtained from FPERT, the SC simulator is developed to provide a dynamic view of the SC and assesses the impact of decisions recommended by the SC fuzzy models on SC performance. Moreover, an empirical example is presented to validate the effectiveness of the proposed model. Finally, we conduct sensitivity analysis on the parameters employed in the model to analyse the behaviour of each parameter.     

Review of supply chain configuration and design decision-making for new product

The nature of competitive markets continuously pushes manufacturers to develop new products to meet the increasingly diversified customer demands. Manufacturers thus have to handle the complexities generated during the total life cycles of various product types, from product design to procurement, production, marketing and recycling. Though some management practices in mass customisation help to improve the performance of manufacturing systems, there are still some fundamental problems not covered. Joint decision-making of product and supply chain design, for example, is one of them. The existing industrial practice tends to treat these two problems separately. Decoupling these two problems decrease the design complexity but may lead to suboptimal decision outcome. To enhance understanding of the interconnected decisions for supply chain management and product design, this review collects related literature on this topic and focuses on the analysis of existing papers from an operation research perspective.     

A green supply chain is a requirement for profitability

Many companies are not dramatically changing to more sustainable environmental practices despite pressure from the investment community, the government and consumers. This study explores a simple model that companies can use to understand and improve supply chain sustainability practices. It applies this model in two case studies, Coca-Cola, a leader in global sustainability, and Apple, a company that has only recently started to develop a sustainability strategy. The model was developed through a review of existing research and an application of supply chain principles. The results of this study demonstrate that following this model to eliminate waste throughout the supply chain will make the supply chain more profitable. The outcomes from this study highlight the importance for every company to do so in order to stay competitive. This study is unique in the relative simplicity of its model, combined with the supporting evidence that a sustainable supply chain is the same as a supply chain that is using best practices to reduce waste.     

A multi-agent architecture for reverse logistics in a green supply chain

Green supply chain issues have attracted a lot of attention in recent years with growing awareness of environmental concerns. This has drawn the considerable world-wide attention of academics and practitioners. Therefore, recycling has now become an integral component of the supply chain. Recycling of used products and the related logistics management pose a significant challenge to manufacturing industries. In order to resolve the complexity of the task, this study proposes a multi-agent architecture to handle recycling and reverse logistics issues, which have so far been neglected. It addresses the different aspects of recycling such as waste classification, recycling, logistics and reuse of products. Additionally, it also discusses how the agent communicates and acts autonomously to facilitate the efficient logistics of materials between different units. The proposed agent architecture can assist manufacturing industries in efficiently managing their green supply chain system and complex logistics issues.     

Information and digital technologies of Industry 4.0 and Lean supply chain management: a systematic literature review

The purpose of this paper is to provide an overview of the current state of research and the key aspects and implications of the relationships between Information and Digital Technologies (IDT) of Industry 4.0 and Lean Supply Chain Management (LSCM), with the identification of the lines of research developed and an analysis of the main findings. A Systematic Literature Review methodology has been used for the identification, selection, and evaluation of the published research. A set of 78 papers deduced from the most relevant scientific databases, including Web of Science, Scopus, and ABI/Inform, from 1996 to December 2019, has been analyzed and synthesized. The analysis and evaluation of these papers has enabled a new classification of the literature to be offered that identifies four lines of research based on the Life Cycle of Technology: obsolete IDT in LSCM; mature IDT in LSCM; emerging IDT in LSCM; and an Information Systems and IDT general approach in LSCM. The paper goes on to discuss the gaps found in the literature and proposes new opportunities and challenges for future research. A series of implications are presented intended to be useful from not only an academic point-of-view but also from a management focus, including recommendations for industrial managers and policymakers.     

A multi-agent system for chemical supply chain simulation and management support

Modern chemical production is customer-driven and the desired delivery time for the products is often shorter than their campaign length. In addition, the raw materials supplying time is often long. These features make it desirable to provide tools to support collaborative supply chain decision making, preferably over the Internet, and where there are conflicts, compromise decisions can be quickly reached and the effects of the decisions can be quantitatively simulated. This paper des cribes such a multi-agent system (MAS) that can be used to simulate the dynamic behaviour and support the management of chemical supply chains over the Internet. Geographically distributed retailers, logistics, warehouses, plants and raw material suppliers are modelled as an open and re-configurable network of co-operative agents, each performing one or more supply chain functions. Communication between agents is made through the common agent communication language KQML (knowledge query message language). A t the simulation layer, the MAS allows distributed simulation of the chain behaviour dynamically, so that compromise decisions can be rapidly and quantitatively evaluated. Because in a chemical supply chain the scheduling of the plant often dominates the chain performance, an optimum scheduling system for batch plants is integrated into the MAS. The functions of the system are illustrated by reference to a case study for the supply and manufacture using a multi-purpose batch plant of paints and coatings.     

A multi-agent protocol for multilateral negotiations in supply chain management

Within a supply chain network, it is common for companies to engage in negotiations to resolve conflicts in task allocation and order fulfilment problems. This kind of supply chain negotiation is usually regarded as buyer–seller negotiation. In recent years, multi-agent systems have been established to automate buyer–seller negotiations. However, most have limitations in handling complex negotiation scenarios such as multilateral negotiations and multi-issue negotiations. This paper presents ECNPro (the Extended Contract-Net-like multilateral Protocol), which is a new multi-agent protocol for handling buyer–seller negotiations in supply chain management. ECNPro is designed to handle agent bargaining and interactions in complex multilateral and multi-issue negotiations in which the buyer has to negotiate with many suppliers. The multi-attribute utility theory (MAUT) approach is adopted to establish the utility functions for a set of negotiation issues in the bargaining process. It adopts a multi-threaded approach to allow the buyer to bargain concurrently with multiple suppliers. ECNPro is able to split an order to more than one supplier to achieve a better negotiation payoff. In addition, mobile agents are employed in ECNPro, the buyer sends mobile agents to sites of the sellers to conduct bargaining locally. This approach improves the negotiation efficiency significantly.     

A GIS-based green supply chain model for assessing the effects of carbon price uncertainty on plastic recycling

Recycling plastic can abate the environmental pollution as well as CO₂ emissions by saving the carbon-intensive feedstock input. The uncertain carbon price places significant effects on the establishment and operation of the whole supply chain. This study develops a green supply chain model combined with geographic information system (GIS) to account for carbon price uncertainty and evaluate its effects on the closed-loop supply chain (CLSC) of plastic recycling. A two-stage stochastic programming model is constructed, in which the stochastic variable, CO₂ price is modelled as a geometric Brownian motion process. Six scenarios are designed with respect to price expectation and volatility. A case study is performed with the GIS information of the plastic supply chain in Zhejiang province, China. The results illustrate that triggering the establishment of reverse logistics requires a carbon price threshold significantly beyond the current level. Lower price volatility would facilitate the decision-making of investment into the reverse logistics. Mechanisms to alleviate the market variation shall be introduced. A sound market condition is desired to obtain the optimal balance that encourages the CLSC without creating extra pressure on the firms. The proposed modelling framework can be easily applied to other sectors with similar characteristics.     

Integration of production planning,project management and logistics systems for supply chain management

The objective of this research was to develop an integrated framework to handle application modules in enterprise resource planning (ERP) and supply chain management (SCM) systems that traditionally needs to be interfaced on a case-by-case basis. This paper presents a framework integrating various modules in both systems, for planning, control and execution of materials, resources and operations. The framework enables planning and execution over a range of areas, from flow to project-based manufacturing and distribution from suppliers to customers. This document presents the conceptual framework development along with algorithms for scheduling paths, and illustrated numerical examples in the supply chain environment. The numerical evaluation of scheduling paths and planning of components, in the integrated networks, shows that the developed framework could cater for ERP/SCM application modules. The framework also facilitates simultaneous planning of many components, and eliminates the need for separate modules in ERPSCM systems. It enhances planning, control and execution, and simplifies the vendor follow-up system by integrating supplier components into the integrated network. The framework therefore becomes a new production planning (PP) module in integrated ERP/SCM systems and can provide links to other manufacturing philosophies, such as just-in-time (JIT).     

A multi-objective model for risk mitigating in supply chain design

The goal of this study is to recognise various factors for responsive SCs that affect supply risk and model their impact on SC design and operation. We propose a conceptual model for SC responsiveness that encompasses practices such as flexibility, agility, internal integration, and visibility. This conceptual model is utilised to build up a multi-objective, multi-period SC design and operation model. A heuristic algorithm is developed to find the supplier, product, period, and production rate for the numerical problem. The improved genetic algorithm (GA) produces solutions with more accuracy in considerably less time than a traditional GA. Finally, an approach to prioritise the objective functions is developed that allows managers to focus on specific objective functions more than optimum values. This approach provides risk-averse, responsiveness-oriented, cost-effective managers the capability to set priorities based on their policies.     

A three-stage model for closed-loop supply chain configuration under uncertainty

In this paper, a general closed-loop supply chain (CLSC) network is configured which consists of multiple customers, parts, products, suppliers, remanufacturing subcontractors, and refurbishing sites. We propose a three-stage model including evaluation, network configuration, and selection and order allocation. In the first stage, suppliers, remanufacturing subcontractors, and refurbishing sites are evaluated based on a new quality function deployment (QFD) model. The proposed QFD model determines the relationship between customer requirements, part requirements, and process requirements. In addition, the fuzzy sets theory is utilised to overcome the uncertainty in the decision-making process. In the second stage, the closed-loop supply chain network is configured by a stochastic mixed-integer nonlinear programming model. It is supposed that demand is an uncertain parameter. Finally in the third stage, suppliers, remanufacturing subcontractors, and refurbishing sites are selected and order allocation is determined. To this end, a multi-objective mixed-integer linear programming model is presented. An illustrative example is conducted to show the process. The main novel innovation of the proposed model is to consider the CLSC network configuration and selection process simultaneously, under uncertain demand and in an uncertain decision-making environment.     

A reliable decision support system for fresh food supply chain management

The paper proposes a decision support system (DSS) for the supply chain of packaged fresh and highly perishable products. The DSS combines a unique tool for sales forecasting with order planning which includes an individual model selection system equipped with ARIMA, ARIMAX and transfer function forecasting model families, the latter two accounting for the impact of prices. Forecasting model parameters are chosen via two alternative tuning algorithms: a two-step statistical analysis, and a sequential parameter optimisation framework for automatic parameter tuning. The DSS selects the model to apply according to user-defined performance criteria. Then, it considers sales forecasting as a proxy of expected demand and uses it as input for a multi-objective optimisation algorithm that defines a set of non-dominated order proposals with respect to outdating, shortage, freshness of products and residual stock. A set of real data and a benchmark – based on the methods already in use – are employed to evaluate the performance of the proposed DSS. The analysis of different configurations shows that the DSS is suitable for the problem under investigation; in particular, the DSS ensures acceptable forecasting errors and proper computational effort, providing order plans with associated satisfactory performances.     

A supply chain network equilibrium model for operational and opportunism risk mitigation

Risk management holds a crucial role in ensuring efficiency, predictability, and coherency in supply chain operations of an enterprise. Risks are associated with every member of a supply chain network. Thus, an end-to-end risk management approach is essential to fortify the entire supply chain network. In this paper, we consider a supply chain network consisting of suppliers, manufacturers, distributors and retailers, as the representative stakeholders. In particular, we take supply chain operational, and opportunism risks into account, and investigate the roles of flexibility, and social relationship, respectively, as a mitigation approach. We develop a multi-period network equilibrium model by considering the stakeholders’ objectives of maximising profit and minimising risk. Further, the finite-dimensional variational inequality formulations are derived for the underlying network optimisation problem. An algorithm, with nice features for computations, is then applied to three simulated examples in order to illustrate the model and computational procedure as well as the types of interventions that can help the strategic decision-makers to explore quantitatively the associated profits and incurred risks in an entire supply chain network.