Assessment of Lean Six Sigma Readiness (LESIRE) for manufacturing industries using fuzzy logic

被引:45
|
作者
Sreedharan, Raja, V [1 ]
Raju, R. [2 ]
Sunder, Vijaya M. [3 ,4 ]
Antony, Jiju [5 ]
机构
[1] Amrita Vishwa Vidyapeetham, Dept Management, Kochi Campus, Kochi, Kerala, India
[2] Anna Univ, Dept Ind Engn, Chennai, Tamil Nadu, India
[3] Indian Inst Technol Madras, Dept Management Studies, Chennai, Tamil Nadu, India
[4] World Bank, Business Proc Excellence, Chennai, Tamil Nadu, India
[5] Heriot Watt Univ, Sch Management & Languages, Edinburgh, Midlothian, Scotland
关键词
Fuzzy logic; Lean Six Sigma (LSS); FLSS; FPII; CRITICAL SUCCESS FACTORS; SUPPLY CHAIN; QUALITY IMPROVEMENT; AGILITY EVALUATION; DECISION-MAKING; RISK-ASSESSMENT; IMPLEMENTATION; ENTERPRISES; PERFORMANCE; MANAGEMENT;
D O I
10.1108/IJQRM-09-2017-0181
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
Purpose Many organizations have reported significant benefits after the implementation of Lean Six Sigma (LSS). Embracing LSS requires asking some important questions: How Lean Six Sigma Readiness (LESIRE) can be measured? How can an organization identify the barriers for LESIRE? Answers to these questions are critical to both academicians and practitioners. The paper aims to discuss this issue. Design/methodology/approach This study illustrates the development process of a Lean Six Sigma Readiness (LESIRE) evaluation model to assess an organization's readiness for LSS deployment using the fuzzy approach. The model was developed from 4 enablers, 16 criteria and 46 attributes of LSS, identified through a literature review. Findings To demonstrate the efficiency of the model, this study testing the LESIRE evaluation model in three Indian SMEs. Using experts' ratings and weight, the researchers calculated the Fuzzy Lean Six Sigma index (FLSS) which indicates the LESIRE level of an organization and the Fuzzy Performance Importance Index (FPII) that helps to identify the barriers for LESIRE. Research limitations/ implications - The main limitations of this study are that it did not consider the failure factors of LSS for model development and the LESIRE was only tested in manufacturing industries. Thus, future researchers could focus on developing a model with failure factors. The results obtained from the SMEs show that LESIRE is capable of assessing LESIRE in an industrial scenario and helps practitioners to measure LESIRE for the future decision making process. Practical implications - The LESIRE model is easy to understand and use without much computation complexity. This simplicity makes the LESIRE evaluation model unique from other LSS models. Further, LESIRE was tested in three different SMEs, and it aided them to identify and improve their weak areas, thereby readying them for LSS deployment. Originality/ value - The main contribution of this study it proposes a LESIRE model that evaluates the organization for FLSS and FPII for LESIRE, which is essential for the organization embarking on an LSS journey. Further, it improves the readiness of the organization that is already practicing LSS.
引用
收藏
页码:137 / 161
页数:25
相关论文
共 50 条
  • [41] Variation modeling of lean manufacturing performance using fuzzy logic based quantitative lean index
    Oleghe, Omogbai
    Salonitis, Konstantinos
    [J]. RESEARCH AND INNOVATION IN MANUFACTURING: KEY ENABLING TECHNOLOGIES FOR THE FACTORIES OF THE FUTURE - PROCEEDINGS OF THE 48TH CIRP CONFERENCE ON MANUFACTURING SYSTEMS, 2016, 41 : 608 - 613
  • [42] Assessment of Lean Manufacturing and Six Sigma operation with Decision Making Based on the Analytic Hierarchy Process
    Alhuraish, I
    Robledo, C.
    Kobi, A.
    [J]. IFAC PAPERSONLINE, 2016, 49 (12): : 59 - 64
  • [43] Interpretive structural modeling of lean six sigma critical success factors in perspective of industry 4.0 for Indian manufacturing industries
    Kumar, Pramod
    Bhamu, Jaiprakash
    Goel, Sunkulp
    Singh, Dharmendra
    [J]. INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT, 2024, 15 (08) : 3776 - 3793
  • [44] Using Lean Six Sigma implied assumptions
    Kane, Victor E. E.
    [J]. TQM JOURNAL, 2020, 32 (06): : 1561 - 1575
  • [45] Using Logic Concepts on Six Sigma
    Kirilo, Caique Z.
    Abe, Jair M.
    Lozano, Luiz
    Parreira, Renato H.
    Dacorso, Eduardo P.
    [J]. ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: INITIATIVES FOR A SUSTAINABLE WORLD, 2016, 488 : 36 - 42
  • [46] Evaluating Sustainable Lean Six Sigma enablers using fuzzy DEMATEL: A case of an Indian
    Parmar, Pranay Sureshbhai
    Desai, Tushar N.
    [J]. JOURNAL OF CLEANER PRODUCTION, 2020, 265 (265)
  • [47] An analysis of drivers for the adoption of integrated sustainable-green-lean-six sigma-agile manufacturing system (ISGLSAMS) in Indian manufacturing industries
    Hariyani, Dharmendra
    Mishra, Sanjeev
    [J]. BENCHMARKING-AN INTERNATIONAL JOURNAL, 2023, 30 (04) : 1073 - 1109
  • [48] An assessment of barriers to integrate lean six sigma and industry 4.0 in manufacturing environment: case based approach
    Rajak, Sonu
    Kumar, Prakash
    Modi, Aayush
    Swarnakar, Vikas
    Antony, Jiju
    Sony, Michael
    [J]. INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, 2024,
  • [49] Lean Six Sigma education in manufacturing companies: the case of transitioning markets
    Kavcic, Klemen
    Gosnik, Dusan
    [J]. KYBERNETES, 2016, 45 (09) : 1421 - 1436
  • [50] Lean Six Sigma in manufacturing process: a bibliometric study and research agenda
    Sordan, Juliano Endrigo
    Oprime, Pedro Carlos
    Pimenta, Marcio Lopes
    Chiabert, Paolo
    Lombardi, Franco
    [J]. TQM JOURNAL, 2020, 32 (03): : 381 - 399