Leveraging digital capabilities toward a circular economy: Reinforcing sustainable supply chain management with Industry 4.0 technologies

Computers & Industrial Engineering, Journal Year: 2023, Volume and Issue: 178, P. 109113 - 109113

Published: Feb. 24, 2023

Facing the challenges of globalisation and unpredictable shocks, manufacturers seek novel methods to maintain sustainability their supply chains. Adopting Industry 4.0 (I4.0) technologies facilitates sustainable chain management (SSCM) with precise decision-making activities realisation circular development. However, according bibliometric analysis systematic literature review articles related "SSCM", few frameworks I4.0 are found empower SSCM under economy (CE) logic. Thus, this article proposes a conceptual framework technologies-embedded SSCM, which takes advantage five kinds emerging digital technologies, including cloud services, artificial intelligence (AI), big data analytics (BDA), blockchain technology (BT), internet things (IoT). The CAB2IN is based on mentioned above alongside design, manufacturing, delivering, using, end-of-life stages products services meet requirements reducing material usage, remanufacturing, reusing, recycling. This paper's contribution lies in indicating trends era proposing creatively establish virtual side leverages generated each stage assist decision-making. illuminates several research directions for future studies digitalised perspective CE. case Company S illustrates application healthcare chain. paper also summarises insightful proposed framework.

Language: Английский

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A Sinh Cosh optimizer

Knowledge-Based Systems, Journal Year: 2023, Volume and Issue: 282, P. 111081 - 111081

Published: Oct. 18, 2023

Language: Английский

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The influence of carbon emission reduction instruments on blockchain technology adoption in recycling batteries of the new energy vehicles

International Journal of Production Research, Journal Year: 2023, Volume and Issue: 62(3), P. 891 - 908

Published: March 4, 2023

The new energy vehicle (NEV) is emerging as an important alternative in the automobile industry its potential to alleviate environmental pollution and contribute carbon neutrality. rapid growth of NEVs has been reflected scaling up electric battery production. dramatic increase retired batteries, however, exposes technological limitations current recycling operations, which will ultimately impede sustainable development NEV supply chain. Blockchain technology (BT) adoption provides a solution by contributing construction efficient network. Our research investigates influence reduction instruments on uptake BT. key findings are follows. Under tax system, (1) emission encourages supplier adopt BT; (2) BT increases profits chain stakeholders. cap-and-trade regulations, unit outsourcing fee performance impact investment decision manufacturer; profit third-party enterprise increased introducing both policies, improving efficiency helps upgrade traceability level neutrality

Language: Английский

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Review on ensemble meta-heuristics and reinforcement learning for manufacturing scheduling problems

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 120, P. 109780 - 109780

Published: Oct. 18, 2024

Language: Английский

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A low-carbon, fixed-tour scheduling problem with time windows in a time-dependent traffic environment

International Journal of Production Research, Journal Year: 2022, Volume and Issue: 61(18), P. 6177 - 6196

Published: Dec. 15, 2022

Language: Английский

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Emission reduction and outsourcing remanufacturing: A comparative study under carbon trading

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 227, P. 120317 - 120317

Published: May 5, 2023

Language: Английский

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A review of greenwashing and supply chain management: Challenges ahead

Cleaner Environmental Systems, Journal Year: 2023, Volume and Issue: 11, P. 100136 - 100136

Published: Sept. 10, 2023

As being environmentally responsible is a potential source of competitive advantage, incorporating genuine environmental practices across the supply chain may help firms capitalize on growing demand for corporate accountability and consumer awareness. Therefore, it important to understand what extent are using greenwashing mislead their stakeholders in chain. The purpose this paper review existing literature regarding management (SCM) shed light main thematic groups addressed literature, its challenges develop framework that highlights key drivers companies need tackle prevent chains. For purpose, we have conducted systematic review, following three-stage method. It was possible identify solutions four dimensions SCM: consumers/customers; relationships between focal suppliers; certification programs reporting assessment; leadership. We provide sustainable strategy along This synthesizes face when implementing chain, suggests provides future research avenues.

Language: Английский

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Bobcat Optimization Algorithm: an effective bio-inspired metaheuristic algorithm for solving supply chain optimization problems

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 29, 2024

Supply chain efficiency is a major challenge in today's business environment, where efficient resource allocation and coordination of activities are essential for competitive advantage. Traditional strategies often struggle resources the complex dynamic network. In response, bio-inspired metaheuristic algorithms have emerged as powerful tools to solve these optimization problems. Referring random search nature emphasizing that no algorithm best optimizer all applications, No Free Lunch (NFL) theorem encourages researchers design newer be able provide more effective solutions Motivated by NFL theorem, innovation novelty this paper designing new meta-heuristic called Bobcat Optimization Algorithm (BOA) imitates natural behavior bobcats wild. The basic inspiration BOA derived from hunting strategy during attack towards prey chase process between them. theory stated then mathematically modeled two phases (i) exploration based on simulation bobcat's position change while moving (ii) exploitation simulating catch prey. performance evaluated handle CEC 2017 test suite problem dimensions equal 10, 30, 50, 100, well address 2020. results show has high ability exploration, exploitation, balance them order achieve suitable solution obtained compared with twelve well-known algorithms. findings been successful handling 89.65, 79.31, 93.10, 89.65% functions dimension respectively. Also, 2020 suite, 100% suite. statistical analysis confirms significant superiority competition analyze dealing real world twenty-two constrained problems 2011 four engineering selected. 90.90% CEC2011 addition, SCM applications challenged ten case studies field sustainable lot size optimization. successfully provided superior competitor

Language: Английский

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Designing a sustainable reverse logistics network for used cell phones based on offline and online trading systems

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 354, P. 120417 - 120417

Published: Feb. 21, 2024

Language: Английский

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Machine learning in supply chain management: systematic literature review and future research agenda

International Journal of Production Research, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 30

Published: Feb. 15, 2025

This study conducts a comprehensive systematic literature review of 107 Machine Learning (ML) studies in Supply Chain (SC) Management published from 2019 until 2023. Descriptive analysis (chronological, geographical, publication, ML algorithms) and thematic via iterative theme identification reviewed key themes barriers the SC context. has emerged as disruptive technology, significantly benefiting supply chain planning, execution, control. Yet, no examined its applicability context, especially with advent Generalised Artificial Intelligence (AI) Large Language Models (LLMs). revealed specific gaps discusses 4 major 14 sub-themes SC: (i) Demand forecasting, (ii) procurement, (iii) risk resilience, (iv) network optimisation. Further, uncovered technical (retraining, scalability security), social (resistance to change, ethical), contextual (dependency, regulations) barriers. provides five research propositions. It sets agenda based on 4Vs (Volume, Variety, Variation, Visibility) provide insights for future research, which can be relevant emergence AI LLMs. also technical, social, business implications practitioners.

Language: Английский

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