IIoT-enabled and Data-driven Sustainability Evaluation Framework for Textile Supply Chain

2021 ◽  
Author(s):  
Tan Wei Chit ◽  
Liu Ning ◽  
Noel Antony Paliath ◽  
Yuan Miao Long ◽  
Humza Akhtar ◽  
...  
Keyword(s):  
Supply Chain ◽  
Evaluation Framework ◽  
Data Driven ◽  
Sustainability Evaluation ◽  
Textile Supply Chain

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.

Benchmarking the risk assessment in green supply chain using fuzzy approach to FMEA

2018 ◽  
Vol 25 (8) ◽  
pp. 2660-2687 ◽  
Author(s):  
Sachin Kumar Mangla ◽  
Sunil Luthra ◽  
Suresh Jakhar
Keyword(s):  
Risk Assessment ◽  
Supply Chain ◽  
Failure Mode ◽  
Failure Modes ◽  
Research Work ◽  
Evaluation Framework ◽  
Green Supply Chain ◽  
Content Type ◽  
Efficient Management ◽  
Fmea Analysis

PurposeThe purpose of this paper is to facilitate green supply chain (GSC) managers and planners to model and access GSC risks and probable failures. This paper proposes to use the fuzzy failure mode and effects analysis (FMEA) approach for assessing the risks associated with GSC for benchmarking the performance in terms of effective GSC management adoption and sustainable production.Design/methodology/approachInitially, different failure modes are defined using FMEA analysis, and in order to decide the risk priority, the risk priority number (RPN) is determined. Such priority numbers are typically acquired from the judgment decisions of experts that could contain the element of vagueness and imperfection due to human biases, and it may lead to inaccuracy in the process of risk assessment in GSC. In this study, fuzzy logic is applied to conventional FMEA to overcome the issues in assigning RPNs. A plastic manufacturer GSC case exemplar of the proposed model is illustrated to present the authenticity of this method of risk assessment.FindingsResults indicate that the failure modes, given as improper green operating procedure, i.e. process, operations, etc. (R6), and green issues while closing the loop of GSC (R14) hold the highest RPN and FRPN scores in classical as well as fuzzy FMEA analysis.Originality/valueThe present research work attempts to propose an evaluation framework for risk assessment in GSC. This paper explores both sustainable developments and risks related to efficient management of GSC initiatives in a plastic industry supply chain context. From a managerial perspective, suggestions are also provided with respect to each failure mode.

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

A quantitative framework for lean and green assessment of supply chain performance

2018 ◽  
Vol 67 (2) ◽  
pp. 366-400 ◽  
Author(s):  
Shashank Thanki ◽  
Jitesh Thakkar
Keyword(s):  
Supply Chain ◽  
Performance Measures ◽  
Value Chain ◽  
Evaluation Framework ◽  
Fuzzy Decision Making ◽  
Causal Relationships ◽  
Assessment Framework ◽  
Content Type ◽  
Green Performance ◽  
Lean And Green

Purpose The purpose of this paper is to propose a balanced scorecard (BSC)- and strategy map-based quantitative framework for assessing the lean and green performance of the supply chain (SC). As the SC competitiveness demands efficient and effective utilization of resources throughout the value chain, not only adoption of lean and green SC paradigms but simultaneously its performance evaluation is also vital. Design/methodology/approach The lean and green SC performance measures are classified into four categories of BSC. A fuzzy decision-making trial and evaluation laboratory (DEMATEL) methodology combined with analytical network process is proposed for examining the causal relationships between BSC perspectives and respective assessment criteria. The application of the proposed assessment framework is demonstrated for the case of Indian textile SC. Findings The research delivers a quantitative assessment framework for evaluating lean and green performance of the SC. The results obtained for a typical case of Indian textile SC revealed that “delivery performance,” “profitability” and “operational cost” are the most crucial performance measures. The perspective of “internal processes” is the most significant of all BSC perspectives while “learning and growth” perspective acts as the driving force to improve lean and green SC performance. Originality/value The paper makes two contributions in the domain of lean and green assessment of SC performance. First, it proposes an evaluation framework to investigate into the causal relationships among the BSC perspectives and related factors. Second, it undertakes an empirical investigation for Indian textile SC to develop key managerial insights and provide policy-related recommendations.

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