Risk management plays a vital role in effectively operating supply chains in the presence of a variety of uncertainties. Over the years, many researchers have focused on supply chain risk management (SCRM) by contributing in the areas of defining, operationalising and mitigating risks. In this paper, we review and synthesise the extant literature in SCRM in the past decade in a comprehensive manner. The purpose of this paper is threefold. First, we present and categorise SCRM research appearing between 2003 and 2013. Second, we undertake a detailed review associated with research developments in supply chain risk definitions, risk types, risk factors and risk management/mitigation strategies. Third, we analyse the SCRM literature in exploring potential gaps.
Big data analytics have become an increasingly important component for firms across advanced economies. This paper examines the quality dynamics in big data environment that are linked with enhancing business value and firm performance (FPER). The study identifies that system quality (i.e. system reliability, accessibility, adaptability, integration, response time and privacy) and information quality (i.e. completeness, accuracy, format and currency) are key to enhance business value and FPER in a big data environment. The study also proposes that the relationship between quality and FPER is mediated by business value of big data. Drawing on the resource-based theory and the information systems success literature, this study extends knowledge in this domain by linking system quality, information quality, business value and FPER.
Rapid advances in industrialisation and informatisation methods have spurred tremendous progress in developing the next generation of manufacturing technology. Today, we are on the cusp of the Fourth Industrial Revolution. In 2013, amongst one of 10 'Future Projects' identified by the German government as part of its High-Tech Strategy 2020 Action Plan, the Industry 4.0 project is considered to be a major endeavour for Germany to establish itself as a leader of integrated industry. In 2014, China's State Council unveiled their ten-year national plan, Made-in-China 2025, which was designed to transform China from the world's workshop into a world manufacturing power. Made-in-China 2025 is an initiative to comprehensively upgrade China's industry including the manufacturing sector. In Industry 4.0 and Made-in-China 2025, many applications require a combination of recently emerging new technologies, which is giving rise to the emergence of Industry 4.0. Such technologies originate from different disciplines including cyber-physical Systems, IoT, cloud computing, Industrial Integration, Enterprise Architecture, SOA, Business Process Management, Industrial Information Integration and others. At this present moment, the lack of powerful tools still poses a major obstacle for exploiting the full potential of Industry 4.0. In particular, formal methods and systems methods are crucial for realising Industry 4.0, which poses unique challenges. In this paper, we briefly survey the state of the art in the area of Industry 4.0 as it relates to industries.
Smart factories Industry 4.0 on the basis of collaborative cyber-physical systems represents a future form of industrial networks. Supply chains in such networks have dynamic structures which evolve over time. In these settings, short-term supply chain scheduling in smart factories Industry 4.0 is challenged by temporal machine structures, different processing speed at parallel machines and dynamic job arrivals. In this study, for the first time, a dynamic model and algorithm for short-term supply chain scheduling in smart factories Industry 4.0 is presented. The peculiarity of the considered problem is the simultaneous consideration of both machine structure selection and job assignments. The scheduling approach is based on a dynamic non-stationary interpretation of the execution of the jobs and a temporal decomposition of the scheduling problem. The algorithmic realisation is based on a modified form of the continuous maximum principle blended with mathematical optimisation. A detailed theoretical analysis of the temporal decomposition and computational complexity is performed. The optimality conditions as well as the structural properties of the model and the algorithm are investigated. Advantages and limitations of the proposed approach are discussed.
In an ever-more interconnected world (social, technological and environmental), no organisation can retain a competitive position and survive disruptions as an independent entity. This article provides a review of resilience literature in its widest context and later its application at an organisational level context. The origins of the concept are reported and consequently, the various fields of research are analysed. The concept is shown to remain essentially constant regardless of its field of enquiry and has much to inform the fields of organisation theory, strategy and operations management. This article identifies a number of areas for advancing resilience research, in particular: the relationship between human and organisational resilience; understanding interfaces between organisational and infrastructural resilience.
Physical Internet (PI, π) has been widely used for transforming and upgrading the logistics and supply chain management worldwide. This study extends the PI concept into manufacturing shop floors where typical logistics resources are converted into smart manufacturing objects (SMOs) using Internet of Things (IoT) and wireless technologies to create a RFID-enabled intelligent shop floor environment. In such PI-based environment, enormous RFID data could be captured and collected. This study introduces a Big Data Analytics for RFID logistics data by defining different behaviours of SMOs. Several findings are significant. It is observed that task weight is primarily considered in the logistics decision-making in this case. Additionally, the highest residence time occurs in a buffer with the value of 12.17 (unit of time) which is 40.57% of the total delivery time. That implies the high work-in-progress inventory level in this buffer. Key findings and observations are generated into managerial implications, which are useful for various users to make logistics decisions under PI-enabled intelligent shop floors.
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.
Recent research underlines the crucial role of disruption events and recovery policies in supply chains. Despite a wealth of literature on supply chain design with disruption considerations, to the best of our knowledge there is no survey on supply chain with disruptions and recovery considerations. We analyse state-of-the-art research streams on supply chain design and planning with both disruptions and recovery considerations with the aim of relating the existing quantitative methods to empirical research. The paper structures and classifies existing research streams and application areas of different quantitative methods subject to different disruption risks and recovery measures. We identify gaps in current research and delineate future research avenues. The results of this study are twofold: operations and supply chain managers can observe which quantitative tools are available for different application areas; on the other hand, limitations and future research needs for decision-support methods in supply chain risk management domains can be identified.
Environmental pressures have caused green supply chain management (GSCM) to emerge as an important corporate environmental strategy for manufacturing enterprises. For manufacturers to fully realise the performance potentials of GSCM, they need to integrate internal GSCM practices emphasising functional coordination with external GSCM practices such as cooperation with suppliers and customers in the implementation. Using coordination theory, this article examines three models used to evaluate the mediation relationships between the external and internal practices of GSCM with respect to environmental, economic, and operational performance. We posit that the strategic stance of manufacturing enterprises in improving their overall performance and competitive position requires a joint coordination of internal and external GSCM practices. Survey data collected from 396 Chinese manufacturing enterprises are used to validate our arguments by testing the mediation effects of two categories of GSCM practices. Our empirical results show support for the mediation effects, which indicates the importance for manufacturers to coordinate between the internal and external aspects of implementing GSCM practices to reap the performance benefits. Coordinating internal and external GSCM practices to seek performance improvements is an important aspect of the manufacturing operations strategy. The dynamics of implementing GSCM practices and the performance contingencies are worthwhile topics to pursue in future research.
The purpose of this paper is to perform the analysis of literature review of lean production (LP). The analysis involved studying 546 research articles published from 1988 to 2011 in selected 24 operations research journals. The articles are classified by: time distribution of articles, research methodology, research stream, authorship patterns, sector-wise focus, popular elements in lean literature, focus of the articles on lean waste, and implementation status and performance measurement of various existing frameworks/models. Then, the suggestions for the future scope of research possibilities and development are identified. The study provides a taxonomical and integrated review of articles, puts up perspective into the conceptualisation and various critical parameters for research. The findings include: increase in empirical approach research articles, need of applying lean principles in the field of product development and enterprise level areas, need of more interregional research collaborations, need of lean elements as group instead of individual element, need of avoiding seven lean wastes instead of specific waste and lack of testing and validation of the proposed frameworks/models by researchers. The study results shall help researchers, academicians and professionals to focus on the growth, pertinence and research developments in the LP system field.
There has been considerable academic interest in recent years in supply chain resilience (SCRES). This paper presents a timely review of the available literature on SCRES based on a three-stage systematic search that identified 91 articles/sources. We provide a comprehensive definition of SCRES before strategies proposed for improving resilience are identified and the contributions to the literature are critiqued, e.g. in terms of research method and use of theory. We take stock of the field and identify the most important future research directions. A wide range of strategies for improving resilience are identified, but most attention has been on increasing flexibility, creating redundancy, forming collaborative supply chain relationships and improving supply chain agility. We also find that only limited research has been conducted into choosing and implementing an appropriate set of strategies for improving SCRES. Much of the literature is conceptual, theoretical and normative; the few available empirical studies are mainly cross-sectional and confined to a large firm, developed country context; and, there has been limited use of theory frames to improve understanding. We propose Complex Adaptive Systems (CAS) theory as an appropriate lens for studying SCRES. We demonstrate that SCRES mirrors many characteristics of a CAS - including adaptation and coevolution, non-linearity, self-organisation and emergence - with implications for the direction of both future research and practice.
Order picking (OP) activities, essential to logistics operations, are laborious and time-intensive. Humans are central actors in the OP process and determine both OP effectiveness and efficiency. Many researchers have developed models for planning OP activities and increasing the efficiencies of such systems by suggesting different warehouse layouts, OP routes or storage assignments. These studies have, however, ignored workers' characteristics, or human factors, suggesting that they cannot be substantiated, which led to only partially realistic results. This paper proposes a conceptual framework for integrating human factors into planning models of OP activities and hypothesises that doing so improves the performance of an OP system and workers' welfare. The framework is based on a systematic literature review that synthesises findings documented in the OP and human factors literature. The results of the paper may assist researchers and practitioners in designing OP systems by developing planning models that help in enhancing performance and reducing long-term costs caused by work-related inefficiencies.
Evidence suggests that lean methods and tools have helped manufacturing organisations to improve their operations and processes. However, the real effect of these methods and tools on contemporary measures of operational performance, i.e. cost, speed, dependability, quality and flexibility, is still unclear. This paper investigates the impact of five essential lean methods, i.e. JIT, autonomation, kaizen, total productive maintenance (TPM) and value stream mapping (VSM), on these measures. A linear regression analysis modelled the correlation and impact of these lean practices on the operational performance of 140 manufacturing organisations around the world. In addition, structural equation modelling (SME) was used to cross verify the findings of the regression and correlation analyses. The results indicate that JIT and automation have the strongest significance on operational performance while kaizen, TPM and VSM seem to have a lesser, or even negative, effect on it. This paper provides further evidence regarding the effects that lean practices have on the performance of organisations and thus the research offers companies, and their managers, a better understanding of the relationship between the lean strategy and the performance of their operations.
Future manufacturing systems need to be more flexible, to embrace tougher and constantly changing market demands. They need to make better use of plant data, ideally utilising all data from the entire plant. Low-level data should be refined to real-time information for decision-making, to facilitate competitiveness through informed and timely decisions. The Line Information System Architecture (LISA), is presented in this paper. It is an event-driven architecture featuring loose coupling, a prototype-oriented information model and formalised transformation services. LISA is designed to enable flexible factory integration and data utilisation. The focus of LISA is on integration of devices and services on all levels, simplifying hardware changes and integration of new smart services as well as supporting continuous improvements on information visualisation and control. The architecture has been evaluated on both real industrial data and industrial demonstrators and it is also being installed at a large automotive company. This article is an extended and revised version of the paper presented at the 2015 IFAC Symposium on Information Control in Manufacturing (INCOM 2015). The paper has been restructured in regards to the order and title of the chapters, and additional information about the integration between devices and services aspects have been added. The introduction and the general structure of the paper now better highlight the contributions of the paper and the uniqueness of the framework.
The European Union Emissions Trading System (EU-ETS) is considered one of the main legislative systems that are set up to reduce emissions and protect the environment. Most of the works in the literature approach this system from a legislation and/or global point of view. Little has been done to examine this system from the perspective of the user. This work is believed to be the first to consider the EU-ETS system in a supply chain and operations management context. A two-level (vendor-buyer) supply chain model with a coordination mechanism is presented while accounting for greenhouse gas (GHG) emissions from manufacturing processes. Different emissions trading schemes are considered, and possible combinations between these schemes are presented. The developed model could be found useful by mangers who wish to jointly minimise the inventory-related and GHG emissions costs of their supply chains when penalties for exceeding emissions limits are considered. Numerical examples are presented, and results are discussed.
Dynamics of structures and processes is one of the underlying challenges in supply chain management, where multiple dimensions of economic efficiency, risk management and sustainability are interconnected. One of the substantiated issues in supply chain dynamics is resilience. Resilience has a number of intersections with supply chain sustainability. This paper aims at analysing disruption propagation in the supply chain with consideration of sustainability factors in order to design resilient supply chain structure in regard to ripple effect mitigation and sustainability increase. Ripple effect in the supply chain occurs if a disruption at a supplier cannot be localised and cascades downstream impacting supply chain performance. This simulation-based study helps to identify what sustainability factors mitigate the ripple effect in the supply chain and what sustainability factors enhance this effect. The results indicate that (i) sustainable single sourcing enhances the ripple effect; (ii) facility fortification at major employers in regions mitigates the ripple effect and enhances sustainability; and (iii) a reduction in storage facilities in the supply chain downstream of a disruption-risky facility increases sustainability but causes the ripple effect.
For the past eight years, cloud manufacturing as a new manufacturing paradigm has attracted a large amount of research interest worldwide. The aim of cloud manufacturing is to deliver on-demand manufacturing services to consumers over the Internet. Scheduling is one of the critical means for achieving the aim of cloud manufacturing. Thus far, about 158 articles have been published on scheduling in cloud manufacturing. However, research on scheduling in cloud manufacturing faces numerous challenges. Thus, there is an urgent need to ascertain the current status and identify issues and challenges to be addressed in the future. Covering articles published on the subject over the past eight years, this article aims to provide a state-of-the-art literature survey on scheduling issues in cloud manufacturing. A detailed statistical analysis of the literature is provided based on the data gathered from the Elsevier's Scopus abstract and citation database. Typical characteristics of scheduling issues in cloud manufacturing are systematically summarised. A comparative analysis of scheduling issues in cloud manufacturing and other scheduling issues such as cloud computing scheduling, workshop scheduling and supply chain scheduling is also carried out. Finally, future research issues and challenges are identified.
Manufacturing has evolved and become more automated, computerised and complex. In this paper, the origin, current status and the future developments in manufacturing are disused. Smart manufacturing is an emerging form of production integrating manufacturing assets of today and tomorrow with sensors, computing platforms, communication technology, control, simulation, data intensive modelling and predictive engineering. It utilises the concepts of cyber-physical systems spearheaded by the internet of things, cloud computing, service-oriented computing, artificial intelligence and data science. Once implemented, these concepts and technologies would make smart manufacturing the hallmark of the next industrial revolution. The essence of smart manufacturing is captured in six pillars, manufacturing technology and processes, materials, data, predictive engineering, sustainability and resource sharing and networking. Material handling and supply chains have been an integral part of manufacturing. The anticipated developments in material handling and transportation and their integration with manufacturing driven by sustainability, shared services and service quality and are outlined. The future trends in smart manufacturing are captured in ten conjectures ranging from manufacturing digitisation and material-product-process phenomenon to enterprise dichotomy and standardisation.
The rapid prototyping has been developed from the 1980s to produce models and prototypes until the technologies evolution today. Nowadays, these technologies have other names such as 3D printing or additive manufacturing, and so forth, but they all have the same origins from rapid prototyping. The design and manufacturing process stood the same until new requirements such as a better integration on production line, a largest series of manufacturing or the reduce weight of products due to heavy costs of machines and materials. The ability to produce complex geometries allows proposing of design and manufacturing solutions in the industrial field in order to be ever more effective. The additive manufacturing (AM) technology develops rapidly with news solutions and markets which sometimes need to demonstrate their reliability. The community needs to survey some evolutions such as the new exchange format, the faster 3D printing systems, the advanced numerical simulation or the emergence of new use. This review is addressed to persons who wish have a global view on the AM and improve their understanding. We propose to review the different AM technologies and the new trends to get a global overview through the engineering and manufacturing process. This article describes the engineering and manufacturing cycle with the 3D model management and the most recent technologies from the evolution of additive manufacturing. Finally, the use of AM resulted in new trends that are exposed below with the description of some new economic activities.
This study aims at presenting the Ripple effect in supply chains. It develops different dimensions of the Ripple effect and summarises recent developments in the field of supply chain (SC) disruption management from a multi-disciplinary perspective. It structures and classifies existing research streams and applications areas of different quantitative methods to the Ripple effect analysis as well as identifying gaps in current research and delineating future research avenues. The analysis shows that different frameworks already exist implicitly for tackling the Ripple effect in the SC dynamics, control and disruption management domain. However, quantitative analysis tools are still rarely applied in praxis. We conclude that the Ripple effect can be the phenomenon that is able to consolidate research in SC disruption management and recovery similar to the bullwhip effect regarding demand and lead time fluctuations. This may build the agenda for future research on SC dynamics, control, continuity and disruption management, making supply chains more robust, adaptable and profitable.