An analysis of quantitative techniques for lean production assessment
Keywords:
Produção enxuta, Avaliação, Revisão de literaturaAbstract
The implementation of lean production occurs through a continuous improvement process that requires constant monitoring. For this, assessment methods are used, and they are usually composed of quantitative techniques responsible for providing indicators that allow assessing the implementation stage of lean production. Given the diversity of techniques that have emerged in the literature, this article aims to analyze the quantitative techniques that have been applied, contributing to new research with a critical and up-to-date overview of the subject. For this purpose, we conducted a systematic literature review that selected 79 articles on the topic. From the analysis of the selected articles, ten techniques were identified, which were analyzed according to assessment requirements extracted from the same sample of articles. The results of the analysis showed that no technique was able to meet all requirements. However, the analysis allowed comparing the techniques in terms of fundamentals, the information they provide, and limitations. Thus, researchers and practitioners will benefit from a theoretical basis for choosing techniques, as well as they can obtain a sufficient foundation to outline future work.
References
Agarwal, A., Shankar, R., & Tiwari, M. K. (2006). Modeling the metrics of lean, agile and leagile supply chain: an ANP-based approach. European Journal of Operational Research, 173(1), 211–225.
Akyuz, E., & Celik, E. (2015). A fuzzy DEMATEL method to evaluate critical operational hazards during gas freeing process in crude oil tankers. Journal of Loss Prevention in the Process Industries, 38, 243–253.
Almeida, A. T. (2013). Processo de decisão nas organizações: construindo modelos de decisão multicritério. São Paulo: Atlas.
Amid, A., Ghodsypour, S. H., & O’Brien, C. (2006). Fuzzy multiobjective linear model for supplier selection in a supply chain. International Journal of Production Economics, 104(2), 394–407.
Amid, A., Ghodsypour, S. H., & O’Brien, C. (2011). A weighted max-min model for fuzzy multi-objective supplier selection in a supply chain. International Journal of Production Economics, 131(2), 139–145.
Anvari, A., Zulkifli, N., Sorooshian, S., & Boyerhassani, O. (2014). An integrated design methodology based on the use of group AHP-DEA approach for measuring lean tools efficiency with undesirable output. International Journal of Advanced Manufacturing Technology, 70(9–12), 2169–2186.
Anvari, A., Zulkifli, N., & Yusuff, R. M. (2013). A dynamic modeling to measure lean performance within lean attributes. International Journal of Advanced Manufacturing Technology, 66(5–8), 663–677.
Attri, Grover, S., & Dev, N. (2014). A graph theoretic approach to evaluate the intensity of barriers in the implementation of total productive maintenance (TPM). International Journal of Production Research, 52(10), 3032–3051.
Attri, R., Dev, N., & Sharma, V. (2013). Interpretive Structural Modelling (ISM) approach: an overview. Research Journal of Management Sciences, 2(2), 3–8.
Azadeh, A., Zarrin, M., Abdollahi, M., Noury, S., & Farahmand, S. (2015). Leanness assessment and optimization by fuzzy cognitive map and multivariate analysis. Expert Systems with Applications, 42(15–16), 6050–6064.
Belton, V., & Stewart, T. J. (2002). Multiple criteria decision analysis: an integrated approach. New York: Springer.
Ben Ruben, R., Asokan, P., & Vinodh, S. (2017). Performance evaluation of lean sustainable systems using adaptive neuro fuzzy inference system: a case study. International Journal of Sustainable Engineering, 10(3), 158–175.
Bhasin, S. (2011). Measuring the leanness of an organisation. International Journal of Lean Six Sigma, 2(1), 55–74.
Chang, B., Chang, C. W., & Wu, C. H. (2011). Fuzzy DEMATEL method for developing supplier selection criteria. Expert Systems with Applications, 38(3), 1850–1858.
Chaple, A. P., Narkhede, B. E., Akarte, M. M., & Raut, R. (2018). Interpretive framework for analyzing lean implementation using ISM and IRP modeling. Benchmarking, 25(9), 3406–3442.
Cho, K. T. (2003). Multicriteria decision methods: an attempt to evaluate and unify. Mathematical and Computer Modelling, 37, 1099–1119.
Cil, I., & Turkan, Y. S. (2013). An ANP-based assessment model for lean enterprise transformation. International Journal of Advanced Manufacturing Technology, 64(5–8), 1113–1130.
Cocca, P., Marciano, F., Alberti, M., & Schiavini, D. (2019). Leanness measurement methods in manufacturing organisations: a systematic review. International Journal of Production Research, 57(15–16), 5103–5118.
Danese, P., Manfè, V., & Romano, P. (2018). A systematic literature review on recent lean research: state-of-the-art and future directions. International Journal of Management Reviews, 20(2), 579–605.
Doumpos, M., & Zopounidis, C. (2004). Multicritteria decision aid classification methods. New York: Kluwer Academic Publishers.
Dyer, J. S., Fishburn, P. C., Steuer, R. E., & Wallenius, J. (2013). Multiple criteria decision making, multiattribute utility theory: the next ten years. Management Science, 38(5), 645–654.
Furlan, A., Vinelli, A., & Pont, G. D. (2011). Complementarity and lean manufacturing bundles: An empirical analysis. International Journal of Operations and Production Management, 31(8), 835–850.
Greco, S., Ehrgott, M., & Figueira, J. R. (2005). Multiple criteria decision analysis state of the art surveys. New York: Springer.
Gurumurthy, A., Mazumdar, P., & Muthusubramanian, S. (2013). Graph theoretic approach for analysing the readiness of an organisation for adapting lean thinking: a case study. International Journal of Organizational Analysis, 21(3), 396–427.
Haykin, S. (1999). Redes neurais: princípios e prática. Porto Alegre: Bookman.
Heinrich, G., Basson, L., Cohen, B., Howells, M., & Petrie, J. (2007). Ranking and selection of power expansion alternatives for multiple objectives under uncertainty. Energy, 32(12), 2350–2369.
Jansen, S. J. T., Coolen, H., & Goetgeluk, R. (2011). The measurement and analysis of housing preference and choice. New York: Springer.
Ji, Y., & Lee, C. (2010). Data envelopment analysis. The Stata Journal, 10(2), 267–280.
Kamruzzaman, J., Begg, R. K., & Sarker, R. A. (2006). Artificial neural networks in finance and manufacturing. Hershey: Idea Group Publishing.
Karlsson, C., & Åhlström, P. (1996). Assessing changes towards lean. International Journal of Operations & Production Management, 16(2), 24–41.
Lacerda, R. T. de O., Ensslin, L., & Ensslin, S. R. (2012). Uma análise bibliométrica da literatura sobre estratégia e avaliação de desempenho. Gestão & Produção, 19(1), 59–78.
Lucato, W. C., Calarge, F. A., Loureiro Junior, M., & Calado, R. D. (2014). Performance evaluation of lean manufacturing implementation in Brazil. International Journal of Productivity and Performance Management, 63(5), 529-549.
Madeira, A. G., Cardoso, M. M. H., Belderrain, M. C. N., Correia, A. R., & Schwanz, S. H. (2012). Multicriteria and multivariate analysis for port performance evaluation. International Journal of Production Economics, 140(1), 450–456.
Marodin, G. A., & Saurin, T. A. (2013). Implementing lean production systems: research areas and opportunities for future studies. International Journal of Production Research, 51(22), 6663–6680.
Matawale, C. R., Datta, S., & Mahapatra, S. S. (2014). Lean metric appraisement: exploration of grey numbers set theory. Grey Systems: Theory and Application, 4(3), 400–425.
Mhatre, T. N., Thakkar, J. J., & Maiti, J. (2017). Modelling critical risk factors for Indian construction project using interpretive ranking process (IRP) and system dynamics (SD). International Journal of Quality and Reliability Management, 34(9), 1451–1473.
Narayanamurthy, G., & Gurumurthy, A. (2016a). Leanness assessment: a literature review. International Journal of Operations & Production Management, 36(10), 1115–1160.
Narayanamurthy, G., & Gurumurthy, A. (2016b). Systemic leanness: an index for facilitating continuous improvement of lean implementation. Journal of Manufacturing Technology Management, 27(8), 1014–1053.
Nasab, H. H., Bioki, T. A., & Zare, H. K. (2012). Finding a probabilistic approach to analyze lean manufacturing. Journal of Cleaner Production, 29, 73–81.
Opricovic, S., & Tzeng, G.-H. (2003). Defuzzification within a multicriteria decision model. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11(5), 635–652.
Poduval, P. S., Pramod, V. R., & Jagathy Raj, V. P. (2015). Interpretive structural modeling (ISM) and its application in analyzing factors inhibiting implementation of total productive maintenance (TPM). International Journal of Quality and Reliability Management, 32(3), 308–331.
Saaty, T. L. (2008). The analytic network process. Iranian Journal of Operations Research, 1(1), 1–27.
Saaty, T. L., & Vargas, L. G. (2006). Decision making with the analytic network process: economic, political, social and technological applications with benefits, opportunities, costs and risks. New York: Springer.
Sabharwal, S., & Garg, S. (2013). Determining cost effectiveness index of remanufacturing: a graph theoretic approach. International Journal of Production Economics, 144(2), 521–532.
Saleeshya, P. G., & Binu, M. (2019). A neuro-fuzzy hybrid model for assessing leanness of manufacturing systems. International Journal of Lean Six Sigma, 10(1), 473–499.
Saurin, T. A., Marodin, G. A., & Ribeiro, J. L. D. (2011). A framework for assessing the use of lean production practices in manufacturing cells. International Journal of Production Research, 49(11), 3211–3230.
Scherrer-Rathje, M., Boyle, T. A., & Deflorin, P. (2009). Lean, take two! Reflections from the second attempt at lean implementation. Business Horizons, 52(1), 79–88.
Seyedhosseini, S. M. Taleghani, A. E., Bakhsha, A., & Partovi, S. (2011). Extracting leanness criteria by employing the concept of Balanced Scorecard. Expert Systems with Applications, 38(8), 10454–10461.
Shah, R., & Ward, P. T. (2003). Lean manufacturing: context, practice bundles, and performance. Journal of Operations Management, 21(2), 129–149.
Singh, M., Sachdeva, A., & Bhardwaj, A. (2014). An interpretive structural modelling approach for analysing barriers in total productive maintenance implementation. International Journal of Industrial and Systems Engineering, 16(4), 433.
Sushil. (2012). Interpreting the interpretive structural model. Global Journal of Flexible Systems Management, 13(2), 87-106.
Sushil. (2009). Interpretive ranking process. Global Journal of Flexible Systems Management, 10(4), 1-10.
Susilawati, A., Tan, J., Bell, D., & Sarwar, M. (2015). Fuzzy logic based method to measure degree of lean activity in manufacturing industry. Journal of Manufacturing Systems, 34, 1–11.
Tekez, E. K., & Taşdeviren, G. (2020). Measuring the influence values of lean criteria on leanness. Journal of Manufacturing Technology Management, 31(7), 1391–1416.
Thakkar, J., Deshmukh, S. G., Gupta, A. D., & Shankar, R. (2006). Development of a balanced scorecard: an integrated approach of Interpretive Structural Modeling (ISM) and Analytic Network Process (ANP). International Journal of Productivity and Performance Management, 56(1), 25–59.
Thakkar, J., Kanda, A., & Deshmukh, S. (2008). Interpretive structural modeling (ISM) of IT-enablers for Indian manufacturing SMEs. Information Management & Computer Security, 16(2), 113–136.
Thomas, T., Saleeshya, P. G., & Kumar, P. H. (2017). A combined AHP and ISM-based model to assess the leanness of a manufacturing company. International Journal of Business Performance Management, 18(4), 403–426.
Tiwari, R. K., & Tiwari, J. K. (2018). Evaluating lean performance of indian small and medium sized enterprises in automotive sector. Open Engineering, 8(1), 293–306.
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222.
Vaidya, O. S., & Kumar, S. (2006). Analytic hierarchy process: an overview of applications. European Journal of Operational Research, 169(1), 1–29.
Vargas, L. G. (1990). An overview of the analytic hierarchy process and its applications. European Journal of Operational Research, 48(1), 2–8.
Vimal, K. E. K., & Vinodh, S. (2013). Application of artificial neural network for fuzzy logic based leanness assessment. Journal of Manufacturing Technology Management, 24(2), 274–292.
Vinodh, S., & Chintha, S. K. (2011). Leanness assessment using multi-grade fuzzy approach. International Journal of Production Research, 49(2), 431–445.
Vinodh, S., & Kumar, C. D. (2012). Development of computerized decision support system for leanness assessment using multi grade fuzzy approach. Journal of Manufacturing Technology Management, 23(4), 503–516.
Vinodh, S., Shivraman, K. R., & Viswesh, S. (2011). AHP-based lean concept selection in a manufacturing organization. Journal of Manufacturing Technology Management, 23(1), 124–136.
Vinodh, S., & Vimal, K. E. K. (2012). Thirty criteria based leanness assessment using fuzzy logic approach. International Journal of Advanced Manufacturing Technology, 60(9–12), 1185–1195.
Wan, H. D., & Chen, F. F. (2008). A leanness measure of manufacturing systems for quantifying impacts of lean initiatives. International Journal of Production Research, 46(23), 6567–6584.
Watson, R. H. (1978). Interpretive structural modeling - a tool for technology assessment? Technological Forecasting and Social Change, 11(2), 165–185.
Webster, J., & Watson, R. (2002). Analyzing the past to prepare for the future: writing a literature review. MIS Quarterly - Management Information Systems, 26(2), 13-23.
Wong, W. P., Ignatius, J., & Soh, K. L. (2014). What is the leanness level of your organisation in lean transformation implementation? An integrated lean index using ANP approach. Production Planning and Control, 25(4), 273–287.
Wu, W. W., & Lee, Y. T. (2007). Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Systems with Applications, 32(2), 499–507.
Yadav, V., Khandelwal, G., Jain, R., & Mittal, M. L. (2019). Development of leanness index for SMEs. International Journal of Lean Six Sigma, 10(1), 397–410.
Zalatar, W. F., & Siriban-Manalang, A. B. D. (2018). Development of a composite lean index to measure lean implementation in Philippine manufacturing companies. DLSU Business and Economics Review, 28(1), 176–188.
Zhou, Q., Huang, W., & Zhang, Y. (2011). Identifying critical success factors in emergency management using a fuzzy DEMATEL method. Safety Science, 49(2), 243–252.
Downloads
Published
Issue
Section
License
The Journal reserves the right to make normative, spelling and grammatical changes in the published article, aiming at maintaining the proper standard of the language, while respecting the personal style of the authors.
Author (s) are fully responsible for the published article. Journal of Lean Systems is not responsible for any violations of Copyright.
Journal of Lean Systems allows the author to retain the copyright of articles accepted for publication without restriction.
Plagiarism constitutes unethical behavior and is inconceivable. This journal reserves the right to use any methods of detecting plagiarism to analyze the submitted works.
This journal is licensed under a Creative Commons License.
