A Modified Bayesian Model for Sustainable Production System Effectiveness Measurement under Competitive Environment
Subject Areas : Production SystemsBuliaminu Kareem 1 , Jimoh Anakobe Yakubu 2 , Basil Olufemi Akinnul 3
1 -
2 -
3 -
Keywords: Productivity Challenges, System Effectiveness, Sustainable Decision, Competition,
Abstract :
The need to determine the sustainability of the established industries demands the development of a model at resolving sustainable productivity challenges. The attributes (internal and external) of industrial failure were identified from the literature and the responses of the interviewed industrial experts. System Effectiveness (PSE) factors (availability, performance and quality) were determined using both traditional and Modified Bayesian (MBA) models in order to arrive at manageable decision-making criteria under certainty and uncertainty conditions. Initial measurements of PSE were based on the identified internal factors (manpower, machine, material, energy, management, information / communication, money and marketing), while sustainability decisions were determined using external factors (sustainability trend, globally acceptable standards, industrial revolution class, and competition level). The model was tested using weighted and normal data from the five selected companies to determine their sustainability performances, while paired t-test statistic was used to test the levels of significant difference between weighted and normal PSE at 5 %. The results indicated varying optimum decisions which were influenced by the nature/types of competition, uncertainty and standards of measurement. Statistical result showed that there was a significant difference between the normal and weighted PSE; p (0.007 < 0.05). However, the differences had little or no effect on sustainable decision making in all companies investigated
[1] Majer, J. M., Henscher H. A., Fischer-Kreer D., and Fischer D. (2022). The Effects of Visual Sustainability Labels on Consumer Perception and Behavior: A systematic review of the empirical literature. Journal of Cleaner Production, 33, 1-14, https://doi.org/10.1016/j.spc.2022.06.012.
[2] Wudhikarn, R and Manopiniwes W. (2010). Autonomous maintenance using total productive maintenance approach: A case study of synthetic wood plank factory, Technology Innovation & Industrial Management Conference, TIIM2010, Pattaya, Thailand, in press.
[3] Kareem, B., Alabi A. S., Ogedengbe T. I., Akinnuli B. O., Aderoba O. A. and Idris M. O.(2020). Development of OEE Error-Proof (OEE-EP) Model for Production Process Improvement. The Journal of Engineering Research (TJER), 17(2)59-74.
[4] Dejan, M.,Bahman R., Svetlana N. and Marinko M. (2021). Forecasting hierarchical time series in supply chains: an empirical investigation. Inter. Journal of Production Research, https://doi.org/10.1080/00207543.2021.1896817.
[5] Adenikinju, A. F. (2005). African Imperatives in the New World Order: Country Case Study of the Manufacturing Sector in Nigeria, in O.E. Ogunkola A. and Bankole (eds.), Nigeria’sImperatives in the New World Trade Order. Nairobi. African Economic Research Consortium and Ibadan: Trade Policy Research and Training Programme.
[6] World Bank (2006). Investment Climate Survey Data. Washington, DC: World Bank.
[7] NPC (2009). Nigeria Vision 20:2020: Economic Transformation Blueprint. National Planning Commission, Abuja.
[8] International Institute for Sustainable Development [IISD] (1992). Business Strategy for Sustainable Development: Leadership and Accountability for the 90s, in conjunction with Deloitte &Touche and the World Business Council for Sustainable Development.
[9] Jianjun H., Yao Y., Hameed J. Kamran H. W. Nawaz M. A., Aqdas R., and Patwary A. K. (2021). The Role of Artificial and Non-artificial Intelligence in the New Product Success with Moderating Role of New Product Innovation: A Case of Manufacturing Companies in China. Complexity, 2021(8891298) 1-14. https://doiorg/10.1155/2021/8891298.
[10] World Bank (2012). World Development Indicators. Washington, DC: World Bank.
[11] Akinnuli, B. O. Yakubu A. J. and Adeyemi A. A. (2015). Computer Aided System for Uni-functional Job Shop Machine Selection Based on Production Cost and Technology Advancement. Advances in Research 5(3) 1-12, https://doi.org/10.9734/air/2015/15691.
[12] Mohammed, S.A. (2002). Nigerian Steel Industry-historical Development. African Iron and Steel Development Association, Abuja, Nigeria.
[13] Yakubu, A. J., Kareem B. and Akinnuli B. O. (2018). Computer Aided System for Crankshafts Failure Rate of Automobile Based on Distance Travel and Age, Open Access Library Journal, 5, 1-14.
[14] Almeanazel, O.T. R. (2010). Total Productive Maintenance Review and Overall Equipment Effectiveness Measurement, Jordan Journal of Mechanical and Industrial Engineering (JJMIE), 4(4) 517 – 522. www.jjmie.hu.edu.jo.
[15] Aminuddin, N. A. B., Garza-Reyes I. A., Kumar V. (2015). An analysis of managerial factors affecting the implementation and use of overall equipment effectiveness, International Journal of Production Research, doi:10.1080/00207543.2015.1055849
[16] Felsberger, A., Qaiser F., Choudhary A., and Reiner, G. (2020). The impact of Industry 4.0 on the reconciliation of dynamic capabilities: Evidence from the European manufacturing industries, Production. Planning. & Control: The management Operations 33 (2-3), 277-300
[17] Fayomi, O. S., Akande I. G., Processo R. T., and Ongbali S. O. (2019). Sustainable need in Manufacturing Industry in Nigeria toward Quality, Policy and Planning. Cite as: AIP Conference Proceedings 2123, 020072 (2019); https://doi.org/10.1063/1.5116999.
[18] Dal, B., Tugwell P., and Greatbanks R. (2000): Overall equipment effectiveness as a measure of operational improvement, International Journal of Operations &Production Management, 20(12) 1488–1520.
[19] Vinceta, S. (2014). An Impact and Challenges of Sustainable Development in Global Era. Journal of Economics and Development Studies, 2(2) 327-337.
[20] Kareem, B. and Yakubu A. J. (2017). Modelling Failure Rate of Automobile Crankshafts based on Distance Travelled and Age. International Journal of Advance Industrial Engineering, 5(4) 210-217, http://inpressco.com/category/ijaie/
[21] Shahbazi, S., Salloum M, Kurdve M, and Wiktorsson M. (2017). Material Efficiency Measurement: Empirical Investigation of Manufacturing Industry. Procedia Manufacturing, 8, 112–120.
[22] Virbahu, N. J. and Devesh M. (2018). Blockchain for Supply Chain and Manufacturing Industries and Future It Holds, Int. J. Eng. Res., 7(9), 32-39, doi:10.17577/ijertv7is090020
[23] Micic, V. and Jankovic N. (2017). Investment in the manufacturing industry of Serbia, Bankarstvo, 46(4) 52–73.
[24] Singh, B. N, (2018). Role of Automation in Steel Industry, no. May.
[25] Landry, J. and Ahmed S. A, (2016). Adoption of Leanness in the Manufacturing Industry,” Int. J. Eng. Res. V7, Univers. J. Manag., 4(1) 1–4.
[26] Fallah, J. M. (2020). Efficiency, effectiveness and productivity of personnel’s health in petrochemical companies, Journal of Research in Industrial Engineering, 7 (3), 280–286.
[27] Saputra, Y., Putra F. E. and Hidayat T. (2022). Energy Effectiveness and Conservation of Pipeline Construction Industry Using Iso 50001 Energy Management System, Journal of Applied Research on Industrial Engineering, 9 (1), 108-114.
[28] Lotfi, F. H. and Jahanbakhsh M. (2015): Assess the efficiency and effectiveness simultaneously in a three-stage process, by using a unified model, Internal journal of Research in Industrial Engineering, 4 (1-4), 15-23.
[29] Jatwa, M. and Sukhwani V. K. (2022). Fuzzy FMEA model: a case study to identify rejection and losses in fibre industry, Journal of Fuzzy Extension and Applications, 3 (1), 19-30.
[30] Li, P., Edalatpanah S. A., Sorourkhah A., Yaman S. and Kausar N. (2023). An Integrated Fuzzy Structured Methodology for Performance Evaluation of High Schools in a Group Decision-Making Problem, Systems: Special Issue on Preference and Consensus Modeling in Group Decision Making under Complex Contexts , 11(3), 159.
[31] Dui, H., Lu Y., and Wu S. (2024). Competing risks-based resilience approach for multi-state systems under multiple shocks, Reliability Engineering & System Safety, 242, 109773.
[32] Zu, X., and Cong Y. (2022). Sustainable Production and Consumption, an Empirical Examination of the Effectiveness and Sustainability of Operational-level Environmental Management Practices in U.S. industry. Journal Science. 33(11-12), 1-14.
[33] Atkins J., Doni F., Gasperini A., Artuso S., La-Torre I., and Sorrentino L. (2022): Exploring the Effectiveness of Sustainability Measurement: Which ESG Metrics Will Survive COVID‑19? Journal of Business Ethics, 1-18, https://doi.org/10.1007/s10551-022-05183-1
[34] Baumer‑Cardoso, M. I., Ashton W. S., and Campos L. M. S. (2023). Measuring the Adoption of Circular Economy in Manufacturing Companies: The Proposal of the Overall Circularity Effectiveness (OCE) Index. Circular Economy and Sustainability 3(1), 511-534, https://doi.org/10.1007/s43615-022-00188-4
[35] Hannola, L.; Richter A.; Richter S., and Stocker A (2018). Empowering production workers with digitally facilitated knowledge processes—A conceptual framework. Inter Journal. Production Research, 56, 4729–4743.
[36] Ichter, A., Heinrich P., Stocker A, and Schwabe G. (2018). Digital work Design. Business. Information System. Engineering. 60, 259–264.
[37] European Commission Horizon [ECH] (2020). Work Programme 2018–2020. Available online: https://ec.europa.eu/commission/presscorner/detail/en/MEMO_17_4123 (accessed on 12 August 2020).
[38] Culot, G., Nassimbeni G., Orzes G. and Sartor M. (2020). The future of manufacturing: A Delphi-based scenario analysis on Industry, Technological Forecast and Social Change. 157, 120092. https://doi.org/10.1016/j.techfore.2020.120092
[39] Garrido-Hidalgo, C., Hortelano, D., Roda-Sanchez, L., Olivares, T., Ruiz, M. C., and Lopez, V. (2018). IoT Heterogeneous Mesh Network Deployment for Human-in-the-Loop challenges towards a social and sustainable Industry. IEEE Access, 6, 28417–28437.
[40] Pournader, M., Shi Y., Seuring S., and Koh, S.L. (2020). Block-chain Applications in Supply Chains, Transport and logistics: A Systematic review of the literature. International Journal Production Research. 58, 2063–2081.
[41] Zheng. F., Wang Z., Zhang E. and Liu M. (2022). K-adaptability in robust container vessel sequencing problem with week. International Journal of Production Research, 60(9), 2787-2801, https://doi.org/10.1080/00207543.2021.1902014.
[42] Carlos, H. S., Jose A. M., Jose A. Q., Rafael C. M. and Fabiano L. (2021). Decision support in productive processes through DES and ABS in the Digital Twin era: a systematic literature review. International Journal of Production Research. 60(8) 2662-2681
[43] Bevan, D., Collier P., and Gunning J. W. (1999). The Political Economy of Poverty, Equity, and Growth: Nigeria and Indonesia. Oxford: Oxford University Press and World Bank.
[44] McKone, K. E., Schroeder R. G and Cua K. O. (2001). The impact of Total Productive Maintenance Practices on Manufacturing Performance. Journal of Operations Management, 19(1), 39–58.
[45] Lesshammar, M. (1999). Evaluation and Improvement of Manufacturing Performance Measurement Systems. The role of OEE, International Journal of Operations and Production Management, 19(1) 55-78.
[46] Frendall, L. D. J, Patterson J. W and Kneedy W. J. (1997). Maintenance modeling its strategic impact, Journal of Managerial Issues, 9(4) 440-448.
[47] Raouf, A. (1994). Improving Capital Productivity through Maintenance, International Journal of Operations and Production Management, 14(7), 44–52.
[48] Dohale, V., Gunasekaran A., Akarte M., and Verma P. (2021). An integrated Delphi-MCDM-Bayesian Network framework for production system selection, International Journal of Production Economics, 242, 108296.
[49] Barron, F. H. and Barrett B. E (1996): Decision Quality using Ranked Attribute Weights. Management Science, 42(11)1515-1522.
[50] Kwon, O and Lee H. (2004). Calculation Methodology for Contributive Managerial Effect by OEE as a Result of TPMActivities.Journal of Quality in Maintenance Engineering, vol. 10, no. 4, pp.263-272, 2004.
[51] Wudhikarn, R., Smithikul C., and Manopiniwes W. (2009). Developing Overall Equipment Cost Loss Indicator, in Proc.6th Conf. of Digital Enterprise Technology, DET2009, Hong Kong, Hong Kong, pp. 557-567.
[52] Ljungberg, O. (1998). Measurement of overall equipment effectiveness as a basis for TPM activities, International Journal of Operations & Production Management, 18(5) 495-507.
[53] Gupta, H., Kharub M., Shreshth K., Kumar A., Huisingh D., and Kumar A. (2023). Evaluation of strategies to manage risks in smart, sustainable agri‐logistics sector: A Bayesian‐based group decision‐making approach, Business Strategy and the Environment, 32 (7), 4335- 4359.
[54] Debnath, B., Shakur M. S., Bari A.B.M., and Karmaker C. L. (2023). A Bayesian Best–Worst approach for assessing the critical success factors in sustainable lean manufacturing, Decision Analytics Journal, 6, 100157.
[55] Xiang, F., Zhang Y., Zhang S., Wang Z., Qiu L.,and Choi J-H. (2024). Bayesian gated-transformer model for risk-aware prediction of aero-engine remaining useful life, Expert Systems with Applications, 238, Part B, 121859.
[56] Tasias, K. A. (2022). Integrated Quality, Maintenance and Production model for multivariate processes: A Bayesian Approach, Journal of Manufacturing Systems, 63, 35-51.
[57] Chien, C. F., Nguyen T. H. V., Li Y. C., and Chen Y. J. (2023). Bayesian decision analysis for optimizing in-line metrology and defect inspection strategy for sustainable semiconductor manufacturing and an empirical study, Computers & Industrial Engineering, 182, 109421. Doi: 10.1016/j.cie.2023.109421
[58] Khan, I., Khan D. M., Noor-ul-Amin M., Khalil U., Alshanbari H. M., and Ahmad Z. (2023). Hybrid EWMA Control Chart under Bayesian Approach Using Ranked Set Sampling Schemes with Applications to Hard-Bake Process, Applied Science, 13(5), 2837.
[59] Ma, Y., Wang J., and Tu Y. (2024). Concurrent optimization of parameter and tolerance design based on the two-stage Bayesian sampling method, Quality Technology and Quantitative Management, 21 (1) 88-110.
[60] Punyamurthula, S., and Badurdeen F. (2018). Assessing Production Line Risk using Bayesian Belief Networks and System Dynamics, Procedia Manufacturing, 26, 76-86.
[61] Mim, T. I., Fowzia Tasnim F., Shamrat B. A. R., and Xames M. D. (2022). Performance Prediction of Green Supply Chain Using Bayesian Belief Network: Case Study of a Textile Industry, International Journal of Research in Industrial Engineering, 11 (4), 327-348.
[62] Uflaz, E., Sezer S. I., Tunçel A. L., Aydin M., Akyuz E., and Arslan O. (2024). Quantifying potential cyber-attack risks in maritime transportation under Dempster–Shafer theory FMECA and rule-based Bayesian network modelling, Reliability Engineering & System Safety, 243, 109825.
[63] Zhu, Q., Dhavale D. G., Sarkis, J., Wang, X. (2023). Formalizing organizational product deletion through strategic cross-functional evaluation: A Bayesian Analysis Approach, International Journal of Production Economics, 262, 108894.
[64] Dongfeng, C., Yan W. (2018). Bayesian Evaluation Method for Energy Efficiency of Manufacturing System Based on Combined Weights, Journal of System Simulation, 30(11) 4313-4322.
[65] Alruqi, M., and Sharma P. (2023). Biomethane Production from the Mixture of Sugarcane Vinasse, Solid Waste and Spent Tea Waste: A Bayesian Approach for Hyperparameter Optimization for Gaussian Process Regression, Fermentation: Special Issue Progress in Microbial Treatment of Wastewater, Solid Wastes and Waste Gases, 9 (2) 120. https://doi.org/10.3390/fermentation9020120
[66] Barnes, B., Parsa M., Giannini F., and Ramsey D. (2023). Analytical Bayesian approach for the design of surveillance and control programs to assess pest-eradication success, Theoretical Population Biology, 149, 1-11.
[67] Puli, V. K., and Huang B. (2023). Variational Bayesian Approach to Nonstationary and Oscillatory Slow Feature Analysis With Applications in Soft Sensing and Process Monitoring, IEEE Transactions on Control Systems Technology, 31 (4), 1708 – 1719.
[68] Chen, W., Gao S., Chen, W, and Du J. (2022). Optimizing resource allocation in service systems via simulation: A Bayesian formulation, https://doi.org/10.1111/poms.13825
[69] Bader, J., Lehmann F., Thamsen L, Leser U., and Kao O. (2024). Lotaru: Locally predicting workflow task runtimes for resource management on heterogeneous infrastructures, Future Generation Computer Systems, 150, 171-185.
[70] Hu, Z., Dang C., Wang L., and Beer M. (2024). Parallel Bayesian probabilistic integration for structural reliability analysis with small failure probabilities, Structural Safety, 106, 102409.
[71] Tohidi, H., Jabbari, M.M., (2012). “Measuring organizational learning capability”. Procedia-social and behavioral sciences, 31, 428-432. https://doi.org/10.1016/j.sbspro.2011.12.079
[72] Tohidi, H., Jabbari, M.M., (2012). “Important factors in determination of innovation type”. Procedia Technology, 1, 570-573. https:// doi: 10.1016/j.protcy.2012.02.124
[73] Jabbari, M.M., Tohidi, H., (2012). “Providing a Framework for Measuring Innovation withinCompanies”. Procedia Technology, 1, 583-585. https:// doi: 10.1016/j.protcy.2012.02.127
[74] Dardeno, T.A., Worden K., Dervilis N., Mills R.S., Bull L.A. (2024). On the hierarchical Bayesian modelling of frequency response functions, Mechanical Systems and Signal Processing, 208, 111072.
[75] Hamdan, B., and Wang P. (2024). Multi-fidelity Bayesian learning for offshore production well reliability analysis, Applied Mathematical Modeling, 125, Part A, 555-570.
[76] Song, J, Cui Y., Wei P., Valdebenito M. A., Zhang W. (2024). Constrained Bayesian optimization algorithms for estimating design points in structural reliability analysis, Reliability Engineering & System Safety, 241, 109613.
[77] Wang, R., Cheung, C., Zang, Y., Wang, C., and Liu, C. (2024). Material Removal Rate Optimization with Bayesian Optimized Differential Evolution Based on Deep Learning in Robotic Polishing, https://ssrn.com/abstract=4690513.
[78] Tong, Q., and Gernay T. (2023). Resilience assessment of process industry facilities using dynamic Bayesian networks, Process Safety and Environmental Protection, 169, 547-563.
[79] Verma, A. P. (2013). Operations Research, 6th Edition, S.K. Kataria & Sons, New Delhi, 1183p.
