A quantitative approach to resilience in manufacturing systems

被引:37
作者
Alexopoulos, Kosmas [1 ]
Anagiannis, Ioannis [1 ]
Nikolakis, Nikolaos [1 ]
Chryssolouris, George [1 ]
机构
[1] Univ Patras, Dept Mech Engn & Aeronaut, Lab Mfg Syst & Automat LMS, Patras 26504, Greece
关键词
Manufacturing plant; COVID-19; Resilience; 3D printing; Additive manufacturing; Injection moulding; DEFINITIONS; PERFORMANCE;
D O I
10.1080/00207543.2021.2018519
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Resilience is one of the key characteristics that manufacturing systems should have as it offers the ability to withstand difficult situations and be able to accommodate disruptions without the incurrence of significant additional costs. The main contribution of this study is the presentation of a method for quantifying resilience in manufacturing systems based on calculating the penalty of possible changes. The method is applied to an industrially-relevant scenario to estimate the resilience of two production systems when COVID-19 disrupts their production. The first system uses additive manufacturing (3D printing), and the second uses injection moulding. Several scenarios, related to the systems' operational environment, are presented on the basis of pandemic-related possible events. The validation of the proposed resilience measure demonstrates the method's suitability and reliability to be considered in industrial practice, in support of decision-making. The resilience measure can be used by managers to assess, compare and improve their production systems, and decide on strategic investment costs to improve systems' resilience. It can be applied for several disruption scenarios or variations of the same disruption scenario with different disruption characteristics, such as duration, recovery time and impact on the production system.
引用
收藏
页码:7178 / 7193
页数:16
相关论文
共 38 条
  • [11] De Vet J.M., 2021, IMPACTS COVID 19 PAN
  • [12] Resilience engineering of industrial processes: Principles and contributing factors
    Dinh, Linh T. T.
    Pasman, Hans
    Gao, Xiaodan
    Mannan, M. Sam
    [J]. JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES, 2012, 25 (02) : 233 - 241
  • [13] Garsten, 2020, FORBES MAGAZINE
  • [14] Manufacturing System Design for Resilience
    Gu, Xi
    Jin, Xiaoning
    Ni, Jun
    Koren, Yoram
    [J]. CIRP 25TH DESIGN CONFERENCE INNOVATIVE PRODUCT CREATION, 2015, 36 : 135 - 140
  • [15] Evaluation mechanism for structural robustness of supply chain considering disruption propagation
    Han, Jihee
    Shin, KwangSup
    [J]. INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2016, 54 (01) : 135 - 151
  • [16] HOSSEINI S, 2020, IEEE T ENG MANAGEMEN
  • [17] Review of quantitative methods for supply chain resilience analysis
    Hosseini, Seyedmohsen
    Ivanov, Dmitry
    Dolgui, Alexandre
    [J]. TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2019, 125 : 285 - 307
  • [18] A general framework for assessing system resilience using Bayesian networks: A case study of sulfuric acid manufacturer
    Hosseini, Seyedmohsen
    Al Khaled, Abdullah
    Sarder, M. D.
    [J]. JOURNAL OF MANUFACTURING SYSTEMS, 2016, 41 : 211 - 227
  • [19] A review of definitions and measures of system resilience
    Hosseini, Seyedmohsen
    Barker, Kash
    Ramirez-Marquez, Jose E.
    [J]. RELIABILITY ENGINEERING & SYSTEM SAFETY, 2016, 145 : 47 - 61
  • [20] Is the COVID-19 vaccine effective on the US financial market?
    Khalfaoui, R.
    Nammouri, H.
    Labidi, O.
    Ben Jabeur, S.
    [J]. PUBLIC HEALTH, 2021, 198 : 177 - 179