Big data has the potential to revolutionize the art of management. Despite the high operational and strategic impacts, there is a paucity of empirical research to assess the business value of big data. Drawing on a systematic review and case study findings, this paper presents an interpretive framework that analyzes the definitional perspectives and the applications of big data. The paper also provides a general taxonomy that helps broaden the understanding of big data and its role in capturing business value. The synthesis of the diverse concepts within the literature on big data provides deeper insights into achieving value through big data strategy and implementation.
The amount of data produced and communicated over the Internet is significantly increasing, thereby creating challenges for the organizations that would like to reap the benefits from analyzing this massive influx of big data. This is because big data can provide unique insights into, inter alia, market trends, customer buying patterns, and maintenance cycles, as well as into ways of lowering costs and enabling more targeted business decisions. Realizing the importance of big data business analytics (BDBA), we review and classify the literature on the application of BDBA on logistics and supply chain management (LSCM) – that we define as supply chain analytics (SCA), based on the nature of analytics (descriptive, predictive, prescriptive) and the focus of the LSCM (strategy and operations). To assess the extent to which SCA is applied within LSCM, we propose a maturity framework of SCA, based on four capability levels, that is, functional, process-based, collaborative, agile SCA, and sustainable SCA. We highlight the role of SCA in LSCM and denote the use of methodologies and techniques to collect, disseminate, analyze, and use big data driven information. Furthermore, we stress the need for managers to understand BDBA and SCA as strategic assets that should be integrated across business activities to enable integrated enterprise business analytics. Finally, we outline the limitations of our study and future research directions.
The emergent field of green supply chain management has been rapidly evolving with a geometric growth in the number of academic publications in this field. A number of literature reviews have been published focusing on specific aspects of green supply chain management such as performance measurement, supplier selection/evaluation, analytical modeling efforts, and some others with broader areas of focus. This paper presents a thorough bibliometric and network analysis that provides insights not previously fully grasped or evaluated by other reviews on this topic. The analysis begins by identifying over 1000 published studies, which are then distilled down to works of proven influence and those authored by influential investigators. Using rigorous bibliometric tools, established and emergent research clusters are identified for topological analysis, identification of key research topics, interrelations, and collaboration patterns. This systematic mapping of the field helps graphically illustrate the publications evolution over time and identify areas of current research interests and potential directions for future research. The findings provide a robust roadmap for further investigation in this field.
We review the literature on sustainable supply chains during the last decade; 2000–2010. We analyze the literature from different perspectives. We then provide frameworks for sustainable supply chain management and performance measures. We also provide a case study to illustrate the experience of a utility supply chain in setting performance indicators.
Green supply chain management (GSCM) has gained increasing attention within both academia and industry. As the literature grows, finding new directions by critically evaluating the research and identifying future directions becomes important in advancing knowledge for the field. Using organizational theories to help categorize the literature provides opportunities to address both the objectives of understanding where the field currently stands and identifying research opportunities and directions. After providing a background discussion on GSCM, we categorize and review recent GSCM literature under nine broad organizational theories, with a special emphasis on investigation of adoption, diffusion and outcomes of GSCM practices. Within this review framework, we also identify GSCM research questions that are worthy of investigation. Additional organizational theories which are considered valuable for future GSCM research are also identified with a conclusion for this review.
Today׳s supply chain professionals are inundated with data, motivating new ways of thinking about how data are produced, organized, and analyzed. This has provided an impetus for organizations to adopt and perfect data analytic functions (e.g. data science, predictive analytics, and big data) in order to enhance supply chain processes and, ultimately, performance. However, management decisions informed by the use of these data analytic methods are only as good as the data on which they are based. In this paper, we introduce the data quality problem in the context of supply chain management (SCM) and propose methods for monitoring and controlling data quality. In addition to advocating for the importance of addressing data quality in supply chain research and practice, we also highlight interdisciplinary research topics based on complementary theory.
Additive manufacturing (AM), colloquially known as 3D printing, is currently being promoted as the spark of a new industrial revolution. The technology allows one to make customized products without incurring any cost penalties in manufacturing as neither tools nor molds are required. Moreover, AM enables the production of complex and integrated functional designs in a one-step process, thereby also potentially reducing the need for assembly work. In this article, we discuss the impact of AM technology at both firm and industry level. Our intention is to discern how market structures will be affected from an operations management perspective. Based on an analysis of established economic models, we first identify the economic and technological characteristics of AM and distill four key principles relevant to manufacturers at firm level. We then critically assess the effects of AM at industry level by analyzing the validity of earlier assumptions in the models when these four principles apply. In so doing, we derive a set of seven propositions which provide impetus for future research. In particular, we propose that in a monopoly, the adoption of AM allows a firm to increase profits by capturing consumer surplus when flexibly producing customized products. Meanwhile in competitive markets, competition is spurred as AM may lower barriers to market entry and offers the ability to serve multiple markets at once. This should ultimately result in lower prices for consumers.
Sustainable business development has received much attention over the past decade owing to the significant attention given by governments and both profit and not-for-profit organizations to environmental, social and corporate responsibility. The emergence of a changing economic order has also made companies around the world seriously think about manufacturing and service sustainability. Global markets and operations have prompted companies to revisit their corporate, business and functional strategies in addition to focusing on outsourcing, virtual enterprise and supply chain management. Sustainability research on supply management has received limited attention. Nevertheless, considering the physically disbursed enterprise environment, supply management is critical for organizational competitiveness. Realizing the importance of sustainability in supply management, an attempt has been made to develop a theoretical framework and then to study the framework by means of an empirical study using perceptions and practices of selected French companies. Finally, a summary of findings and conclusions are reported.
Sustainable Supply Chain Management (SSCM) and Dynamic Capabilities (DCs) are both relatively young research fields examining dynamically changing corporate environments and industries. The food industry is an example of such a dynamic environment. Customers have high expectations for food safety and a growing demand for sustainably produced food. Companies fulfilling these demands target a customer base with high awareness of all three dimensions of sustainability, i.e., the economical, ecological, and social, circumstances in which food is produced and offered. This paper aims at describing how SSCM practices allow companies to maintain control over their supply chain and achieve a competitive advantage with the implementation of dynamic capabilities. Previously identified practices in SSCM are related to DC theory by identifying them as basic routines that form specific DCs. We conduct a literature review, including content analysis, examining publications (52 articles) on sustainable food supply chains published in English, peer-reviewed journals. We form the link between SSCM and DCs by integrating them into the same conceptual context. Specific DCs in the supply chain of a sustainability-oriented industry are also identified, such as knowledge sharing and re-conceptualizing the supply chain. Thereafter, we scrutinize the food industry according to SSCM and DC criteria and offer insights into the strategies used in that business market. The results show that sustainability practices and DCs in the supply chain are used among others to enhance traceability and tracking and to fulfill customer demands. Further research is needed to extend the operationalization of the existing conceptual frameworks.
Increase in environmental concerns together with legislations are forcing industries to take a fresh look at the impact of their supply chain operations on the environment. This paper introduces a mixed-integer linear programming based framework for sustainable supply chain design that considers life cycle assessment (LCA) principles in addition to the traditional material balance constraints at each node in the supply chain. Indeed, the framework distinguishes between solid and liquid wastes, as well as gaseous emissions due to various production processes and transportation systems. The framework is used to evaluate the tradeoffs between economic and environmental objectives under various cost and operating strategies in the aluminum industry. The results suggest that current legislation and Emission Trading Schemes (ETS) must be strengthened and harmonized at the global level in order to drive a meaningful environmental strategy. Moreover, the model demonstrates that efficient carbon management strategies will help decision makers to achieve sustainability objectives in a cost-effective manner.
Radio frequency identification (RFID) has been widely used in supporting the logistics management on manufacturing shopfloors where production resources attached with RFID facilities are converted into smart manufacturing objects (SMOs) which are able to sense, interact, and reason to create a ubiquitous environment. Within such environment, enormous data could be collected and used for supporting further decision-makings such as logistics planning and scheduling. This paper proposes a holistic Big Data approach to excavate frequent trajectory from massive RFID-enabled shopfloor logistics data with several innovations highlighted. Firstly, RFID-Cuboids are creatively introduced to establish a data warehouse so that the RFID-enabled logistics data could be highly integrated in terms of tuples, logic, and operations. Secondly, a Map Table is used for linking various cuboids so that information granularity could be enhanced and dataset volume could be reduced. Thirdly, spatio-temporal sequential logistics trajectory is defined and excavated so that the logistics operators and machines could be evaluated quantitatively. Finally, key findings from the experimental results and insights from the observations are summarized as managerial implications, which are able to guide end-users to carry out associated decisions.
Line balancing belongs to a class of intensively studied combinatorial optimization problems known to be NP-hard in general. For several decades, the core problem originally introduced for manual assembly has been extended to suit robotic, machining and disassembly contexts. However, despite various industrial environments and line configurations, often quite similar or even identical mathematical models have been developed. The objective of this survey is to analyze recent research on balancing flow lines within many different industrial contexts in order to classify and compare the means for input data modelling, constraints and objective functions used. This survey covers about 300 studies on line balancing problems. Particular attention is paid to recent publications that have appeared in 2007–2012 to focus on new advances in the state-of-the-art.
The recent interest in big data has led many companies to develop big data analytics capability (BDAC) in order to enhance firm performance (FPER). However, BDAC pays off for some companies but not for others. It appears that very few have achieved a big impact through big data. To address this challenge, this study proposes a BDAC model drawing on the resource-based theory (RBT) and the entanglement view of sociomaterialism. The findings show BDAC as a hierarchical model, which consists of three primary dimensions (i.e., management, technology, and talent capability) and 11 subdimensions (i.e., planning, investment, coordination, control, connectivity, compatibility, modularity, technology management knowledge, technical knowledge, business knowledge and relational knowledge). The findings from two Delphi studies and 152 online surveys of business analysts in the U.S. confirm the value of the entanglement conceptualization of the higher-order BDAC model and its impact on FPER. The results also illuminate the significant moderating impact of analytics capability–business strategy alignment on the BDAC–FPER relationship.
Increasing environmental, legislative, and social concerns are forcing companies to take a fresh view of the impact of supply chain operations on environment and society when designing a sustainable supply chain. A challenging task in today's food industry is distributing high quality perishable foods throughout the food supply chain. This paper proposes a multi-objective optimization model by integrating sustainability in decision-making, on distribution in a perishable food supply chain network (SCN). It introduces a two-echelon location–routing problem with time-windows (2E-LRPTW) for sustainable SCN design and optimizing economical and environmental objectives in a perishable food SCN. The goal of 2E-LRPTW is to determine the number and location facilities and to optimize the amount of products delivered to lower stages and routes at each level. It also aims to reduce costs caused by carbon footprint and greenhouse gas emissions throughout the network. The proposed method includes a novel multi-objective hybrid approach called MHPV, a hybrid of two known multi-objective algorithms: namely, multi-objective particle swarm optimization (MOPSO) and adapted multi-objective variable neighborhood search (AMOVNS). MHPV features two strategies for leader selection procedures (LSP), (i.e. Grids) and crowding distance is compared to common genetic algorithms based on metaheuristics (i.e. MOGA, NRGA and NSGA-II). Results indicate that the hybrid approach achieves better solutions compared to others, and that crowding distance method for LSP outperforms the former Grids method.
The aim of the paper is to test the impacts of supplier relationship management (SRM) and total quality management (TQM) on environmental performance under the influence of leadership and the moderation effect of institutional pressures (IP). The study investigates these effects using a pre-tested structured questionnaire. Data was collected using a split survey method using a modified version of total design method with 187 and 174 complete and usable responses for the respective parts. We performed non-response bias before checking assumptions such as constant variance and normality. We further checked reliability and construct validity using confirmatory factor analysis and hierarchical regression analyses for hypothesis testing. We find that constructs and indicators of our theoretical framework meet the criteria, and find them to be a good fit based on confirmatory factor analysis and fit indices output. The hierarchical regression analyses outputs suggest that all our hypotheses are supported, which further supports the extant literature. Our present study is unique in terms of scope and contribution to SCM and OM theory and practice. The study has tested empirically the research calls of various researchers and extended them to green supply chain networks. Our findings support institutional theory.
Line balancing belongs to a class of intensively studied combinatorial optimization problems known to be NP-hard in general. For several decades, the core problem originally introduced for manual assembly has been extended to suit robotic, machining and disassembly contexts. However, despite various industrial environments and line configurations, often quite similar or even identical mathematical models have been developed. The objective of this survey is to analyze recent research on balancing flow lines within many different industrial contexts in order to classify and compare the means for input data modelling, constraints and objective functions used. This survey covers about 300 studies on line balancing problems. Particular attention is paid to recent publications that have appeared in 2007-2012 to focus on new advances in the state-of-the-art. (C) 2012 Elsevier B.V. All rights reserved.
As mass production has migrated to developing countries, European and US companies are forced to rapidly switch towards low volume production of more innovative, customised and sustainable products with high added value. To compete in this turbulent environment, manufacturers have sought new fabrication techniques to provide the necessary tools to support the need for increased flexibility and enable economic low volume production. One such emerging technique is Additive Manufacturing (AM). AM is a method of manufacture which involves the joining of materials, usually layer-upon-layer, to create objects from 3D model data. The benefits of this methodology include new design freedom, removal of tooling requirements, and economic low volumes. AM consists of various technologies to process versatile materials, and for many years its dominant application has been the manufacture of prototypes, or Rapid Prototyping. However, the recent growth in applications for direct part manufacture, or Rapid Manufacturing, has resulted in much research effort focusing on development of new processes and materials. This study focuses on the implementation process of AM and is motivated by the lack of socio-technical studies in this area. It addresses the need for existing and potential future AM project managers to have an implementation framework to guide their efforts in adopting this new and potentially disruptive technology class to produce high value products and generate new business opportunities. Based on a review of prior works and through qualitative case study analysis, we construct and test a normative structural model of implementation factors related to AM technology, supply chain, organisation, operations and strategy.
Supply chain integration is widely considered by both practitioners and researchers a vital contributor to supply chain performance. The two key flows in such relationships are material and information. Previous studies have addressed information integration and material (logistics) integration in separate studies. In this paper, we investigate the integrations of both information and material flows between supply chain partners and their effect on operational performance. Specifically, we examine the role of long-term supplier relationship as the driver of the integration. Using data from 232 Australian firms, we find that logistics integration has a significant effect on operations performance. Information technology capabilities and information sharing both have significant effects on logistics integration. Furthermore, long-term supplier relationships have both direct and indirect significant effects on performance; the indirect effect via the effect on information integration and logistics integration. ► Strategic supplier relationship has a positive effect on information integration. ► Information integration consists of information technology and information sharing. ► Increasing information integration has a positive effect on material integration. ► Material integration has a positive effect on operational performance. ► Strategic supplier relationship has direct and indirect effects on performance.
The purpose of this paper is to investigate the research development in supply chain risk management (SCRM), which has shown an increasing global attention in recent years. Literature survey and citation/co-citation analysis are used to fulfil the research task. Literature survey has undertaken a thorough search of articles on selected journals relevant to supply chain operations management. Meanwhile, citation/co-citation analysis uses Web of Sciences database to disclose SCRM development between 1995 and 2009. Both the approaches show similar trends of rising publications over the past 15 years. This review has piloted us to identify and classify the potential risk associated with different flows, namely material, cash and information flows. Consequently, we identify some research gaps. Even though there is a pressing need and awareness of SCRM from industrial aspect, quantitative models in the field are relatively lacking and information flow risk has received less attention. It is also interesting to observe the evolutions and advancements of SCRM discipline. One finding is that the intellectual structure of the field made statistically significant increase during 2000–2005 and evolved from passively reacting to vague general issues of disruptions towards more proactively managing supply chain risk from system perspectives.
Manufacturing industries started adopting the green concept in their supply chain management recently to focus on environmental issues. But, industries still struggle to identify barriers hindering green supply chain management implementation. This work focuses on identifying barriers to the implementation of a green supply chain management (Green SCM) based on procurement effectiveness. A total of 47 barriers were identified, both through detailed literature and discussion with industrial experts and through a questionnaire-based survey from various industrial sectors. Essential barriers/priorities are identified through recourse to analytic hierarchy process. Finally, a sensitivity analysis investigates priority ranking stability.