Digital Supply Chain Twins: Managing the Ripple Effect, Resilience, and Disruption Risks by Data-Driven Optimization, Simulation, and Visibility

2019 ◽  
pp. 309-332 ◽  
Author(s):  
Dmitry Ivanov ◽  
Alexandre Dolgui ◽  
Ajay Das ◽  
Boris Sokolov
Keyword(s):  
Supply Chain ◽  
Data Driven ◽  
Ripple Effect ◽  
Optimization Simulation ◽  
Disruption Risks

Analysis of the Supply Chain Disruption Risks in the Malaysian Automotive Remanufacturing Industry-a Case Study

2020 ◽  
Vol 12 (05) ◽  
Author(s):  
Nuramilawahida Mat Ropi ◽  
◽  
Hawa Hishamuddin ◽  
Dzuraidah Abd Wahab ◽  
◽  
...  
Keyword(s):  
Supply Chain ◽  
Supply Chain Disruption ◽  
Disruption Risks

Data-driven operations and supply chain management: established research clusters from 2000 to early 2020

2021 ◽  
pp. 1-25
Author(s):  
Duy Tan Nguyen ◽  
Yossiri Adulyasak ◽  
Jean-François Cordeau ◽  
Silvia I. Ponce
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Data Driven ◽  
Chain Management

Big data-driven supply chain and performance: a resource-based view

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shampy Kamboj ◽  
Shruti Rana
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Small And Medium Enterprises ◽  
Data Driven ◽  
Equation Modeling ◽  
Firm Age ◽  
Content Type ◽  
Sustainable Performance ◽  
Medium Enterprises

PurposeThe main objective of this paper is to study the role of supply chain performance (SCP) as a mediator between big data-driven supply chain (BDDSC) and firm sustainable performance. In addition, the role of firm age as a moderator between BDDSC and SCP as well as between SCP and firm sustainable performance has also been explored.Design/methodology/approachThe 200 managers of medium or senior level positions in micro, small and medium enterprises (MSMEs) located at Delhi-NCR have been contacted. Further, collected data have been confirmed with confirmatory factor analysis (CFA). In this paper, structure equation modeling (SEM) has been employed to empirically check the proposed hypotheses and their relationships.FindingsThe findings confirmed that SCP mediates the link between BDDSC and firm sustainable performance. Additionally, firm age moderates the association between BDDSC and SCP as well as between SCP and firm sustainable performance.Research limitations/implicationsThe role of SCP and firm age between BDDSC and sustainable performance have been examined in the context of MSMEs in Delhi-NCR and thereby limit the generalization of results to other industries and country contexts.Originality/valueThe present study adds to the existing literature via recognizing the blackbox using SCP and firm age to comprehend BDDSC and firm sustainable performance relationship.

Reinforcing the significance of human factor in achieving quality performance in data-driven supply chain management

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sanjana Mondal ◽  
Kaushik Samaddar
Keyword(s):  
Supply Chain ◽  
Organizational Performance ◽  
Industry 4.0 ◽  
Management Practices ◽  
Human Factor ◽  
Research Work ◽  
Data Driven ◽  
Theory Approach ◽  
Quality Performance ◽  
Content Type

PurposeThe paper aims to explore the various dimensions of human factor relevant for integrating data-driven supply chain quality management practices (DDSCQMPs) with organizational performance. Keeping the transition phase from “Industry 4.0” to “Industry 5.0” in mind, the paper reinforces the role of the human factor and critically discusses the issues and challenges in the present organizational setup.Design/methodology/approachFollowing the grounded theory approach, the study arranged in-depth interviews and focus group sessions with industry experts from various service-oriented firms in India. Dimensions of human factor identified from there were grouped together through a morphological analysis (MA), and interlinkages between them were explored through a cross-consistency matrix.FindingsThis research work identified 20 critical dimensions of human factor and have grouped them under five important categories, namely, cohesive force, motivating force, regulating force, supporting force and functional force that drive quality performance in the supply chain domain.Originality/valueIn line with the requirements of the present “Industry 4.0” and the forthcoming “Industry 5.0”, where the need to collaborate human factor with smart system gets priority, the paper made a novel attempt in presenting the critical human factors and categorizing them under important driving forces. The research also contributed in linking DDSCQMPs with organizational performance. The proposed framework can guide the future researchers in expanding the theoretical constructs through initiating further cross-cultural studies across industries.

scholarly journals Big Data Driven Agricultural Products Supply Chain Management: A Trustworthy Scheduling Optimization Approach

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 49990-50002 ◽  
Author(s):  
Qian Tao ◽  
Chunqin Gu ◽  
Zhenyu Wang ◽  
Joseph Rocchio ◽  
Weiwen Hu ◽  
...  
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Agricultural Products ◽  
Data Driven ◽  
Optimization Approach ◽  
Scheduling Optimization ◽  
Chain Management

Data-driven quality improvement approach to reducing waste in manufacturing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rose Clancy ◽  
Dominic O'Sullivan ◽  
Ken Bruton
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Quality Management ◽  
Process Improvement ◽  
Six Sigma ◽  
Data Driven ◽  
Manufacturing Processes ◽  
Management Performance ◽  
Chain Management ◽  
Content Type

PurposeData-driven quality management systems, brought about by the implementation of digitisation and digital technologies, is an integral part of improving supply chain management performance. The purpose of this study is to determine a methodology to aid the implementation of digital technologies and digitisation of the supply chain to enable data-driven quality management and the reduction of waste from manufacturing processes.Design/methodology/approachMethodologies from both the quality management and data science disciplines were implemented together to test their effectiveness in digitalising a manufacturing process to improve supply chain management performance. The hybrid digitisation approach to process improvement (HyDAPI) methodology was developed using findings from the industrial use case.FindingsUpon assessment of the existing methodologies, Six Sigma and CRISP-DM were found to be the most suitable process improvement and data mining methodologies, respectively. The case study revealed gaps in the implementation of both the Six Sigma and CRISP-DM methodologies in relation to digitisation of the manufacturing process.Practical implicationsValuable practical learnings borne out of the implementation of these methodologies were used to develop the HyDAPI methodology. This methodology offers a pragmatic step by step approach for industrial practitioners to digitally transform their traditional manufacturing processes to enable data-driven quality management and improved supply chain management performance.Originality/valueThis study proposes the HyDAPI methodology that utilises key elements of the Six Sigma DMAIC and the CRISP-DM methodologies along with additions proposed by the author, to aid with the digitisation of manufacturing processes leading to data-driven quality management of operations within the supply chain.

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