Development of a conceptual model for lean supply chain planning in industry 4.0: multidimensional analysis for operations management

被引:71
作者
Reyes, John [1 ,2 ]
Mula, Josefa [1 ]
Diaz-Madronero, Manuel [1 ]
机构
[1] Univ Politecn Valencia, Res Ctr Prod Management & Engn CIGIP, Plaza Ferrandiz & Carbonell 2, Alicante 03801, Spain
[2] Univ Tecn Ambato, Fac Syst Engn, Ambato, Ecuador
关键词
Lean manufacturing; supply chain planning; industry; 4; 0; FUZZY AHP; IMPACT; PERFORMANCE; FRAMEWORK; GREEN;
D O I
10.1080/09537287.2021.1993373
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
A lean supply chain (LSC) is a set of organizations directly linked by upstream and downstream value streams between processes that work collaboratively to reduce costs and waste. Currently, supply chains (SCs) have been put to the test as the world has had to face a series of unprecedented disruptions in demand and supply caused by the COVID-19 pandemic. In this paper, a detailed study of constructs and multistructural components was carried out to develop a conceptual reference model that merges Industry 4.0 (I4.0) digital technologies with lean manufacturing tools to reduce waste and minimize costs in the lean supply chain planning (LSCP) context. The main theoretical contribution of this conceptual proposal is to establish a structured relation among the lean, agile, sustainable, resilient and flexible paradigms to improve SC performance by implementing I4.0 enabling technologies. The proposed conceptual model, dubbed as LSCP 4.0, is applied and validated with a case study in a large footwear company. It can help decision-makers and researchers to improve the planning and management of digital SC production processes, even with unexpected disruptions.
引用
收藏
页码:1209 / 1224
页数:16
相关论文
共 75 条
  • [1] Decision support for collaboration planning in sustainable supply chains
    Allaoui, Hamid
    Guo, Yuhan
    Sarkis, Joseph
    [J]. JOURNAL OF CLEANER PRODUCTION, 2019, 229 : 761 - 774
  • [2] [Anonymous], 2012, IFIP ADV INFORM COMM
  • [3] Supply chain risk modelling and mitigation
    Aqlan, Faisal
    Lam, Sarah S.
    [J]. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2015, 53 (18) : 5640 - 5656
  • [4] Data Analytics for Operational Risk Management
    Araz, Ozgur Merih
    Choi, Tsan-Ming
    Olson, David L.
    Salman, F. Sibel
    [J]. DECISION SCIENCES, 2020, 51 (06) : 1316 - 1319
  • [5] Making connections: a review of supply chain management and sustainability literature
    Ashby, Alison
    Leat, Mike
    Hudson-Smith, Melanie
    [J]. SUPPLY CHAIN MANAGEMENT-AN INTERNATIONAL JOURNAL, 2012, 17 (05) : 497 - 516
  • [6] Carmignani G, 2016, MEAS OPER PERFORM, P203, DOI 10.1007/978-3-319-19995-5_9
  • [7] Carreirao Danielli, 2019, 2018 13 IEEE INT C I
  • [8] Industry 4.0 strategies and technological developments. An exploratory research from Italian manufacturing companies
    Chiarini, Andrea
    Belvedere, Valeria
    Grando, Alberto
    [J]. PRODUCTION PLANNING & CONTROL, 2020, 31 (16) : 1385 - 1398
  • [9] Innovative "Bring-Service-Near-Your-Home" operations under Corona-Virus (COVID-19/SARS-CoV-2) outbreak: Can logistics become the Messiah?
    Choi, Tsan-Ming
    [J]. TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2020, 140
  • [10] When blockchain meets social-media: Will the result benefit social media analytics for supply chain operations management?
    Choi, Tsan-Ming
    Guo, Shu
    Luo, Suyuan
    [J]. TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2020, 135