Modeling big data enablers for operations and supply chain management

2018 ◽  
Vol 29 (2) ◽  
pp. 629-658 ◽  
Author(s):  
Kuldeep Lamba ◽  
Surya Prakash Singh
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Science ◽  
Structural Modeling ◽  
Security And Privacy ◽  
Interpretive Structural Modeling ◽  
Chain Management ◽  
Content Type

Purpose The purpose of this paper is to identify and analyse the interactions among various enablers which are critical to the success of big data initiatives in operations and supply chain management (OSCM). Design/methodology/approach Fourteen enablers of big data in OSCM have been selected from literature and consequent deliberations with experts from industry. Three different multi criteria decision-making (MCDM) techniques, namely, interpretive structural modeling (ISM), fuzzy total interpretive structural modeling (fuzzy-TISM) and decision-making trial and evaluation laboratory (DEMATEL) have been used to identify driving enablers. Further, common enablers from each technique, their hierarchies and inter-relationships have been established. Findings The enabler modelings using ISM, Fuzzy-TISM and DEMATEL shows that the top management commitment, financial support for big data initiatives, big data/data science skills, organizational structure and change management program are the most influential/driving enablers. Across all three different techniques, these five different enablers has been identified as the most promising ones to implement big data in OSCM. On the other hand, interpretability of analysis, big data quality management, data capture and storage and data security and privacy have been commonly identified across all three different modeling techniques as the most dependent big data enablers for OSCM. Research limitations/implications The MCDM models of big data enablers have been formulated based on the inputs from few domain experts and may not reflect the opinion of whole practitioners community. Practical implications The findings enable the decision makers to appropriately choose the desired and drop undesired enablers in implementing the big data initiatives to improve the performance of OSCM. The most common driving big data enablers can be given high priority over others and can significantly enhance the performance of OSCM. Originality/value MCDM-based hierarchical models and causal diagram for big data enablers depicting contextual inter-relationships has been proposed which is a new effort for implementation of big data in OSCM.

Establishment of Green Supply Chain Management in Steel Industries Using Interpretive Structural Modeling

2019 ◽  
Vol 18 (4) ◽  
pp. 363-374
Author(s):  
Rajesh Kumar ◽  
Shiena Shekhar
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Structural Modeling ◽  
Green Supply Chain Management ◽  
Green Supply Chain ◽  
Multi Criteria Decision Making ◽  
Interpretive Structural Modeling ◽  
Chain Management ◽  
Steel Industries

Abstract The state of Chhattisgarh in India has a very large number of steel plants causing pollution in the region. The effect of this pollution exceeds the geographical territory of a unit, and goes much beyond it, so it becomes essential to find the reasons for the pollution and the enablers for the green supply chain management, which in turn will help in providing a cleaner environment. In this study Multi-Criteria Decision Making (MCDM) tools like Interpretive Structural Modeling and MICMAC analysis have been used.

Barriers to sustainable supply chain management implementation in Egyptian industries: an interpretive structural modeling (ISM) approach

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Esraa Osama Zayed ◽  
Ehab A. Yaseen
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Structural Modeling ◽  
Equation Modeling ◽  
Sustainable Supply Chain ◽  
Interpretive Structural Modeling ◽  
Structured Model ◽  
Chain Management ◽  
Content Type ◽  
Sustainable Supply Chain Management

PurposeRecently, sustainability aspects are gaining importance among supply chain management (SCM) research field, hence this study aims to explore barriers to sustainable supply chain management (SSCM) implementation in Egyptian industries and the interrelationships among these barriers to provide a structured detailed model for barriers and suggest recommendations to deal with these barriers.Design/methodology/approachThe paper is an empirical study with a descriptive research approach using qualitative methodology. Data were collected through interviewing experts involved in sustainability implementation within supply chain functions. Afterward interpretive structural modeling (ISM) for barriers was conducted to develop a structured model representing possible interrelationships between barriers.FindingsFindings have reported slight differences among barriers to SSCM implementation in Egyptian industries other than those stated previously. ISM analysis helped in shaping barriers into a detailed structured model where interrelationships among barriers can be clearly defined. Additionally, based on the data collected and the ISM model, this study managed to offer recommendations to deal with these barriers.Research limitations/implicationsFuture researches might consider developing ISM analysis for a smaller number of barriers, or focus on each of internal and external barriers individually to minimize ISM analysis complexity and enhance its accuracy. As ISM analysis technique is highly dependent on experts' opinions and experience, validation is highly recommended either by structural equation modeling (SEM) or linear structural relationship approach.Practical implicationsThis study provides insights for managers about internal and external barriers to SSCM implementation in Egyptian industries, a detailed structured model for interrelationships among these barriers and recommendations to deal with these barriers.Originality/valueThis study is one of the very first studies to implement ISM for barriers to SSCM on data collected from Egyptian industries. Consequently, it will direct further research focusing on developing strategies or recommendations to overcome these barriers.

An interpretive structural modeling of drivers and barriers of sustainable supply chain management

2020 ◽  
Vol 31 (5) ◽  
pp. 1071-1090 ◽  
Author(s):  
Gunjan Soni ◽  
Surya Prakash ◽  
Himanshu Kumar ◽  
Surya Prakash Singh ◽  
Vipul Jain ◽  
...  
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Management Practices ◽  
Structural Modeling ◽  
Sustainable Supply Chain ◽  
Interpretive Structural Modeling ◽  
Chain Management ◽  
Content Type ◽  
Sustainable Supply Chain Management ◽  
Stone Industry

PurposeThe Indian marble and stone industry has got the potential to contribute well to the development of the emerging economy. However, unlike the other Indian industries, stone and marble industries are highly underrated sectors, which may become a critical factor for development. This paper analyses the sustainability factors in supply chain management practices.Design/methodology/approachA literature review is used to identify the barriers and drivers in sustainable supply chain management practices. Interpretive structural modeling has been used to obtain a hierarchy of barriers and drivers along with driving power and dependence power analysis. Further, MICMAC analysis is used for segregating the barriers and drivers in terms of their impact on sustainability.FindingsThe findings of the work of this research are that the attention of society, government, and commercial banks should be more toward the unorganized condition of stone and marble sector. There should be an increase in the commitment of stakeholders to reduce pollution and install safety, by enforcing more relevant laws and regulations and creating the importance of environmental awareness.Originality/valueThe main contribution of this research is to identify the barriers and drivers of sustainable supply chain management in a stone and marble industry. The paper proposes a sound mathematical model to prioritize the critical factors for responsible production and consumption of resources from sustainability perspectives of stone industry.

Sustainable supply chain management under big data: a bibliometric analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xinyi Zhang ◽  
Yanni Yu ◽  
Ning Zhang
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Bibliometric Analysis ◽  
Data Science ◽  
Sustainable Supply Chain ◽  
Chain Management ◽  
Content Type ◽  
Sustainable Supply Chain Management ◽  
Interdisciplinary Field

PurposeThis study aims to provide a literature review and bibliometric analysis of sustainable supply chain management using big data. We reviewed the literature on sustainable supply chain management under big data from 2012 to 2019 and extracted 777 articles.Design/methodology/approachWe conducted quantitative analysis and data network visualization of the chosen literature, including authors, journals, countries, research institutions and citations.FindingsWe discovered that the development of this interdisciplinary field has gained increasing popularity among researchers around the world, such as China and the US publishing the most articles and Western states having more cooperation, which indicates this research topic is growing in significance globally.Originality/valueScientific and technological revolutions such as big data have been incorporated in various industries. Modern supply chain management has also been combined with the advances in data science to achieve sustainability goals. No studies have reviewed the sustainable supply chain management based on big data. This study fills this gap.

scholarly journals Understanding the value of big data in supply chain management and its business processes

2018 ◽  
Vol 38 (7) ◽  
pp. 1589-1614 ◽  
Author(s):  
Morten Brinch
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Business Process ◽  
Business Processes ◽  
Value Theory ◽  
Value Capture ◽  
Chain Management ◽  
Content Type ◽  
Process Perspective

Purpose The value of big data in supply chain management (SCM) is typically motivated by the improvement of business processes and decision-making practices. However, the aspect of value associated with big data in SCM is not well understood. The purpose of this paper is to mitigate the weakly understood nature of big data concerning big data’s value in SCM from a business process perspective. Design/methodology/approach A content-analysis-based literature review has been completed, in which an inductive and three-level coding procedure has been applied on 72 articles. Findings By identifying and defining constructs, a big data SCM framework is offered using business process theory and value theory as lenses. Value discovery, value creation and value capture represent different value dimensions and bring a multifaceted view on how to understand and realize the value of big data. Research limitations/implications This study further elucidates big data and SCM literature by adding additional insights to how the value of big data in SCM can be conceptualized. As a limitation, the constructs and assimilated measures need further empirical evidence. Practical implications Practitioners could adopt the findings for conceptualization of strategies and educational purposes. Furthermore, the findings give guidance on how to discover, create and capture the value of big data. Originality/value Extant SCM theory has provided various views to big data. This study synthesizes big data and brings a multifaceted view on its value from a business process perspective. Construct definitions, measures and research propositions are introduced as an important step to guide future studies and research designs.

Sustainable Supply Chain Management

2019 ◽  
pp. 103-114
Author(s):  
Vimal K. E. K. ◽  
Nishal M. ◽  
Jayakrishna K.
Keyword(s):  
Sustainable Development ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Environmental Economic ◽  
Environmental Performance ◽  
High Volume ◽  
Structural Modeling ◽  
Sustainable Supply Chain ◽  
Interpretive Structural Modeling ◽  
Chain Management

The integration of sustainable development concepts with the traditional supply chain improves the environmental performance and green image among its stakeholders. During adoption of sustainability concepts in traditional supply chain management, some hurdles can be anticipated. These hurdles are called barriers, and industries must equip themselves to remove them. The difficulties associated with removal of barriers are identification and analysis for selection significant barriers. In this chapter, the significant barriers for incorporating sustainability in supply chain of high volume manufacturing are consolidated from the literature and categorized into seven groups: people, strategic, environmental, economic, societal, regulatory, and functional. The widely used evaluation methods are interpretive structural modeling and DEMATEL for which the procedure and guidance to infer the results are detailed. The chapter is expected to support the practicing engineers involved in implementation of sustainable concepts in supply chain.

Big Data Analytics in Supply Chain Management

2021 ◽  
pp. 2443-2457
Author(s):  
Nenad Stefanovic
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Current Approach ◽  
Data Systems ◽  
Chain Management ◽  
Big Data Systems

The current approach to supply chain intelligence has some fundamental challenges when confronted with the scale and characteristics of big data. In this chapter, applications, challenges and new trends in supply chain big data analytics are discussed and background research of big data initiatives related to supply chain management is provided. The methodology and the unified model for supply chain big data analytics which comprises the whole business intelligence (data science) lifecycle is described. It enables creation of the next-generation cloud-based big data systems that can create strategic value and improve performance of supply chains. Finally, example of supply chain big data solution that illustrates applicability and effectiveness of the model is presented.

scholarly journals Understanding supply chain analytics capabilities and agility for data-rich environments

2019 ◽  
Vol 39 (6/7/8) ◽  
pp. 887-912 ◽  
Author(s):  
Samuel Fosso Wamba ◽  
Shahriar Akter
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Structural Equation ◽  
Equation Modeling ◽  
Future Research ◽  
Chain Management ◽  
Content Type ◽  
Supply Chain Agility ◽  
The Usa

Purpose Big data-driven supply chain analytics capability (SCAC) is now emerging as the next frontier of supply chain transformation. Yet, very few studies have been directed to identify its dimensions, subdimensions and model their holistic impact on supply chain agility (SCAG) and firm performance (FPER). Therefore, to fill this gap, the purpose of this paper is to develop and validate a dynamic SCAC model and assess both its direct and indirect impact on FPER using analytics-driven SCAG as a mediator. Design/methodology/approach The study draws on the emerging literature on big data, the resource-based view and the dynamic capability theory to develop a multi-dimensional, hierarchical SCAC model. Then, the model is tested using data collected from supply chain analytics professionals, managers and mid-level manager in the USA. The study uses the partial least squares-based structural equation modeling to prove the research model. Findings The findings of the study identify supply chain management (i.e. planning, investment, coordination and control), supply chain technology (i.e. connectivity, compatibility and modularity) and supply chain talent (i.e. technology management knowledge, technical knowledge, relational knowledge and business knowledge) as the significant antecedents of a dynamic SCAC model. The study also identifies analytics-driven SCAG as the significant mediator between overall SCAC and FPER. Based on these key findings, the paper discusses their implications for theory, methods and practice. Finally, limitations and future research directions are presented. Originality/value The study fills an important gap in supply chain management research by estimating the significance of various dimensions and subdimensions of a dynamic SCAC model and their overall effects on SCAG and FPER.

Sign in / Sign up

Export Citation Format

Share Document