scholarly journals Enablers to supply chain performance on the basis of digitization technologies

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Himanshu Gupta ◽  
Sarangdhar Kumar ◽  
Simonov Kusi-Sarpong ◽  
Charbel Jose Chiappetta Jabbour ◽  
Martin Agyemang
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Information Technologies ◽  
Data Science ◽  
Content Type ◽  
Big Data Technologies ◽  
Fierce Competition ◽  
Practical Implications ◽  
Improve Supply Chain

PurposeThe aim of this study is to identify and prioritize a list of key digitization enablers that can improve supply chain management (SCM). SCM is an important driver for organization's competitive advantage. The fierce competition in the market has forced companies to look the past conventional decision-making process, which is based on intuition and previous experience. The swift evolution of information technologies (ITs) and digitization tools has changed the scenario for many industries, including those involved in SCM.Design/methodology/approachThe Best Worst Method (BWM) has been applied to evaluate, rank and prioritize the key digitization and IT enablers beneficial for the improvement of SC performance. The study also used additive value function to rank the organizations on their SC performance with respect to digitization enablers.FindingsThe total of 25 key enablers have been identified and ranked. The results revealed that “big data/data science skills”, “tracking and localization of products” and “appropriate and feasibility study for aiding the selection and adoption of big data technologies and techniques ” are the top three digitization and IT enablers that organizations need to focus much in order to improve their SC performance. The study also ranked the SC performance of the organizations based on digitization enablers.Practical implicationsThe findings of this study will help the organizations to focus on certain digitization technologies in order to improve their SC performance. This study also provides an original framework for organizations to rank the key digitization enablers according to enablers relevant in their context and also to compare their performance with their counterparts.Originality/valueThis study seems to be the first of its kind in which 25 digitization enablers categorized in four main categories are ranked using a multi-criteria decision-making (MCDM) tool. This study is also first of its kind in ranking the organizations in their SC performance based on weights/ranks of digitization enablers.

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.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

The influence of big data analytics management capabilities on supply chain preparedness, alertness and agility

2019 ◽  
Vol 32 (2) ◽  
pp. 297-318 ◽  
Author(s):  
Santanu Mandal
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Investment Decision ◽  
Big Data Analytics ◽  
Content Type ◽  
Investment Decision Making ◽  
Management Capabilities ◽  
The Impact

Purpose The importance of big data analytics (BDA) on the development of supply chain (SC) resilience is not clearly understood. To address this, the purpose of this paper is to explore the impact of BDA management capabilities, namely, BDA planning, BDA investment decision making, BDA coordination and BDA control on SC resilience dimensions, namely, SC preparedness, SC alertness and SC agility. Design/methodology/approach The study relied on perceptual measures to test the proposed associations. Using extant measures, the scales for all the constructs were contextualized based on expert feedback. Using online survey, 249 complete responses were collected and were analyzed using partial least squares in SmartPLS 2.0.M3. The study targeted professionals with sufficient experience in analytics in different industry sectors for survey participation. Findings Results indicate BDA planning, BDA coordination and BDA control are critical enablers of SC preparedness, SC alertness and SC agility. BDA investment decision making did not have any prominent influence on any of the SC resilience dimensions. Originality/value The study is important as it addresses the contribution of BDA capabilities on the development of SC resilience, an important gap in the extant literature.

Information and reformation in KM systems: big data and strategic decision-making

2017 ◽  
Vol 21 (1) ◽  
pp. 71-91 ◽  
Author(s):  
Ali Intezari ◽  
Simone Gressel
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Strategic Decisions ◽  
Content Type ◽  
For Profit ◽  
Not For Profit ◽  
Advanced Analytics ◽  
Practical Implications

Purpose The purpose of this paper is to provide a theoretical framework of how knowledge management (KM) systems can facilitate the incorporation of big data into strategic decisions. Advanced analytics are becoming increasingly critical in making strategic decisions in any organization from the private to public sectors and from for-profit companies to not-for-profit organizations. Despite the growing importance of capturing, sharing and implementing people’s knowledge in organizations, it is still unclear how big data and the need for advanced analytics can inform and, if necessary, reform the design and implementation of KM systems. Design/methodology/approach To address this gap, a combined approach has been applied. The KM and data analysis systems implemented by companies were analyzed, and the analysis was complemented by a review of the extant literature. Findings Four types of data-based decisions and a set of ground rules are identified toward enabling KM systems to handle big data and advanced analytics. Practical implications The paper proposes a practical framework that takes into account the diverse combinations of data-based decisions. Suggestions are provided about how KM systems can be reformed to facilitate the incorporation of big data and advanced analytics into organizations’ strategic decision-making. Originality/value This is the first typology of data-based decision-making considering advanced analytics.

How big data can enhance business strategies

2020 ◽  
Vol 36 (9) ◽  
pp. 41-44
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Value Creation ◽  
Design Methodology ◽  
Reading Time ◽  
Business Strategies ◽  
Content Type ◽  
Pertinent Information ◽  
Operational Activities ◽  
Practical Implications

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings To date, big data has been largely deployed to enhance operational activities. Its effectiveness has inspired growing recognition of its potential in relation to different strategic areas that include supply chain, co-creation, planning and value creation. Originality/value The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

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.

Dave Snowden on KM and big data/analytics: interview with David J. Pauleen

2017 ◽  
Vol 21 (1) ◽  
pp. 12-17 ◽  
Author(s):  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Theory And Practice ◽  
Content Type ◽  
Face To Face ◽  
The Relationship ◽  
A Company ◽  
Practical Implications ◽  
Scientific Officer

Purpose Dave Snowden has been an important voice in knowledge management over the years. As the founder and chief scientific officer of Cognitive Edge, a company focused on the development of the theory and practice of social complexity, he offers informative views on the relationship between big data/analytics and KM. Design/methodology/approach A face-to-face interview was held with Dave Snowden in May 2015 in Auckland, New Zealand. Findings According to Snowden, analytics in the form of algorithms are imperfect and can only to a small extent capture the reasoning and analytical capabilities of people. For this reason, while big data/analytics can be useful, they are limited and must be used in conjunction with human knowledge and reasoning. Practical implications Snowden offers his views on big data/analytics and how they can be used effectively in real world situations in combination with human reasoning and input, for example in fields from resource management to individual health care. Originality/value Snowden is an innovative thinker. He combines knowledge and experience from many fields and offers original views and understanding of big data/analytics, knowledge and management.

Has Lean improved organizational decision making?

2016 ◽  
Vol 29 (5) ◽  
pp. 536-549 ◽  
Author(s):  
Pascale Simons ◽  
Jos Benders ◽  
Jochen Bergs ◽  
Wim Marneffe ◽  
Dominique Vandijck
Keyword(s):  
Decision Making ◽  
Design Methodology ◽  
Lean Management ◽  
Structured Interviews ◽  
Organizational Decision Making ◽  
Content Type ◽  
Organizational Contexts ◽  
Sustainable Improvement ◽  
Care Processes ◽  
Practical Implications

Purpose – Sustainable improvement is likely to be hampered by ambiguous objectives and uncertain cause-effect relations in care processes (the organization’s decision-making context). Lean management can improve implementation results because it decreases ambiguity and uncertainties. But does it succeed? Many quality improvement (QI) initiatives are appropriate improvement strategies in organizational contexts characterized by low ambiguity and uncertainty. However, most care settings do not fit this context. The purpose of this paper is to investigate whether a Lean-inspired change program changed the organization’s decision-making context, making it more amenable for QI initiatives. Design/methodology/approach – In 2014, 12 professionals from a Dutch radiotherapy institute were interviewed regarding their perceptions of a Lean program in their organization and the perceived ambiguous objectives and uncertain cause-effect relations in their clinical processes. A survey (25 questions), addressing the same concepts, was conducted among the interviewees in 2011 and 2014. The structured interviews were analyzed using a deductive approach. Quantitative data were analyzed using appropriate statistics. Findings – Interviewees experienced improved shared visions and the number of uncertain cause-effect relations decreased. Overall, more positive (99) than negative Lean effects (18) were expressed. The surveys revealed enhanced process predictability and standardization, and improved shared visions. Practical implications – Lean implementation has shown to lead to greater transparency and increased shared visions. Originality/value – Lean management decreased ambiguous objectives and reduced uncertainties in clinical process cause-effect relations. Therefore, decision making benefitted from Lean increasing QI’s sustainability.

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