Determination of Production System Effectiveness Based on Sustainable Global Standards

Yakubu Anakobe; B Kareem; Basil Olufemi Akinnuli

doi:10.38032/jea.2023.02.002

Authors

Yakubu Anakobe Department of Civil Engineering, Federal Polytechnic Ile-Oluji, Nigeria
B Kareem Department of Industrial and Production Engineering, Federal University of Technology, Akure, Nigeria
Basil Olufemi Akinnuli Department of Industrial and Production Engineering, Federal University of Technology, Akure, Nigeria

DOI:

https://doi.org/10.38032/jea.2023.02.002

Keywords:

Golabal, Standard, Production, Effectiveness, Sustainability

Abstract

Production system effectiveness determine to measure the sustainability of the established industries demands the development of a model for resolving global sustainable productivity challenges. The attributes (internal and external) of industrial failure were determined using questionnaire administration and oral interviews of industry experts in five (5) selected production companies in Nigeria: (Company A); (Company B); (Company C); (Company D) and (Company E). Production System Effectiveness (PSE) factors: Availability A, Performance P, and Quality Q were determined to arrive at manageable decision-making criteria under uncertainty, risk, or competition. Initial measures of PSE were based on the input internal factors (manpower, machine, material, energy, management, information/communication, money, and marketing), while sustainability decisions were determined using globally acceptable standards. The model was tested using data (weighted and normal) from the stated companies to determine their sustainability performances, while paired t-test statistic was used to test the levels of significant difference between weighted (WPSE) and normal (PSE) at 5%. The results indicated varying optimum decisions which were influenced by the nature/types of competition, risk, and standard of measure. The statistical result showed that there was a significant difference between the PSE and WPSE. These differences had little or no effect on optimum decision-making in all companies investigated.

References

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, pp.1-18 DOI: https://doi.org/10.1007/s10551-022-05183-1

Wudhikarn, R. and Manopiniwes, W., 2010, June. Autonomous maintenance using total productive maintenance approach: A case study of synthetic wood plank factory. In Proc. Technology Innovation & Industrial Management Conf.

Kareem, B., Alabi, A.S., Ogedengbe, T.I., Akinnuli, B.O. and Aderoba, O.A., 2020. Development of OEE Error-Proof (OEE-EP) Model for Production Process Improvement. The Journal of Engineering Research [TJER], 17(2), pp.59-74.

Agbu, O., 2007. The iron and steel industry and Nigeria's Industrialization: Exploring cooperation with Japan. (No Title). Agbu, O. (2007).

World Bank, 2006. Investment Climate Survey Data. Washington, DC: World Bank, 1(1), pp.1-21.

Fayomi, O.S.I., Akande, I.G., Loto, R.T. and Ongbali, S.O., 2019, July. Sustainable need in manufacturing industry in Nigeria toward quality, policy and planning. In AIP Conference Proceedings (Vol. 2123, No. 1, p. 020072). AIP Publishing LLC. DOI: https://doi.org/10.1063/1.5116999

Dal, B., Tugwell, P. and Greatbanks, R., 2000. Overall equipment effectiveness as a measure of operational improvement–a practical analysis. International Journal of Operations & Production Management, 20(12), pp.1488-1502. DOI: https://doi.org/10.1108/01443570010355750

Singh, V., 2014. An impact and challenges of sustainable development in global era. Journal of Economics and Development Studies, 2(2), pp.327-337.

Kareem, B. and Jimoh, Y.A., 2017. Modelling Failure Rate of Automobile Crankshafts based on Distance Travelled and Age.

Shahbazi, S., Salloum, M., Kurdve, M. and Wiktorsson, M., 2017. Material efficiency measurement: empirical investigation of manufacturing industry. Procedia Manufacturing, 8, pp.112-120. DOI: https://doi.org/10.1016/j.promfg.2017.02.014

Silaipillayarputhur, K., 2018. Process safety management in manufacturing industries a review. International Journal of Engineering & Technology, 7(2), pp.540-543. DOI: https://doi.org/10.14419/ijet.v7i2.9252

Jain, V.N. and Mishra, D., 2018. Blockchain for supply chain and manufacturing industries and future it holds. International Journal of Engineering Research, 7(9), pp.32-39. DOI: https://doi.org/10.17577/IJERTV7IS090020

Mićić, V. and Janković, N., 2017. Investment in the manufacturing industry of Serbia. Bankarstvo, 46(4), pp.52-73. DOI: https://doi.org/10.5937/bankarstvo1704052M

Singh, B.N., 2018. Role of Automation in Steel Industry. no. May.

Landry, J. and Ahmed, S.A., 2016. Adoption of leanness in the manufacturing industry. Univers. J. Manag, 4(1), pp.1-4. DOI: https://doi.org/10.13189/ujm.2016.040101

Momodu, A.S., Aransiola, E.F., Okunade, I.D., Ogunlusi, G.O., Awokoya, K.N., Ogundari, I.O., Falope, O.T., Makinde, O.W. and Akinbami, J.F.K., 2019, May. Greening Nigeria's economy for industrial and environmental sustainability: Polyurethane production as a test case. In Natural Resources Forum (Vol. 43, No. 2, pp. 73-81). Oxford, UK: Blackwell Publishing Ltd. DOI: https://doi.org/10.1111/1477-8947.12173

Felsberger, A., Qaiser, F.H., Choudhary, A. and Reiner, G., 2022. The impact of Industry 4.0 on the reconciliation of dynamic capabilities: Evidence from the European manufacturing industries. Production Planning & Control, 33(2-3), pp.277-300. DOI: https://doi.org/10.1080/09537287.2020.1810765

European Commission Horizon [ECH], 2020. Work Programme 2018–2020. Available online:https://ec.europa.eu/commission/presscorner/detail/en/MEMO, 17(8), pp.1-14.

Culot, G., Orzes, G., Sartor, M. and Nassimbeni, G., 2020. The future of manufacturing: A Delphi-based scenario analysis on Industry 4.0. Technological forecasting and social change, 157, p.120092. DOI: https://doi.org/10.1016/j.techfore.2020.120092

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 4.0. Ieee Access, 6, pp.28417-28437. DOI: https://doi.org/10.1109/ACCESS.2018.2836677

Pournader, M., Shi, Y., Seuring, S. and Koh, S.L., 2020. Blockchain applications in supply chains, transport and logistics: a systematic review of the literature. International Journal of Production Research, 58(7), pp.2063-2081. DOI: https://doi.org/10.1080/00207543.2019.1650976

Gan, T.S., Steffan, M., Grunow, M. and Akkerman, R., 2022. Concurrent design of product and supply chain architectures for modularity and flexibility: process, methods, and application. International Journal of Production Research, 60(7), pp.2292-2311. DOI: https://doi.org/10.1080/00207543.2021.1886370

Zheng, F., Wang, Z., Zhang, E. and Liu, M., 2022. K-adaptability in robust container vessel sequencing problem with week-dependent demands of a service route. International Journal of Production Research, 60(9), pp.2787-2801. DOI: https://doi.org/10.1080/00207543.2021.1902014

Shajalal, M., Hajek, P. and Abedin, M.Z., 2023. Product backorder prediction using deep neural network on imbalanced data. International Journal of Production Research, 61(1), pp.302-319. DOI: https://doi.org/10.1080/00207543.2021.1901153

Li, R., Yi, H. and Cao, H., 2022. Towards understanding dynamic design change propagation in complex product development via complex network approach. International Journal of Production Research, 60(9), pp.2733-2752. DOI: https://doi.org/10.1080/00207543.2021.1901155

dos Santos, C.H., Montevechi, J.A.B., de Queiroz, J.A., de Carvalho Miranda, R. and Leal, F., 2022. 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), pp.2662-2681. DOI: https://doi.org/10.1080/00207543.2021.1898691

Bevan, D., Collier, P. and Gunning, J.W., 1999. The political economy of poverty, equity and growth: Nigeria and Indonesia. Oxford University Press.

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), pp.39-58. DOI: https://doi.org/10.1016/S0272-6963(00)00030-9

Zu, X. and Cong, Y., 2022. Green at source: an empirical examination of the effectiveness and sustainability of operational-level environmental management practices in US industry. Total Quality Management & Business Excellence, 33(11-12), pp.1213-1232. DOI: https://doi.org/10.1080/14783363.2021.1944084

Majer, J.M., Henscher, H.A., Reuber, P., 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. Sustainable Production and Consumption. DOI: https://doi.org/10.1016/j.spc.2022.06.012

Baumer-Cardoso, M.I., Ashton, W.S. and Campos, L.M., 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), pp.511-534. DOI: https://doi.org/10.1007/s43615-022-00188-4

Jonsson, P. and Lesshammar, M., 1999. Evaluation and improvement of manufacturing performance measurement systems‐the role of OEE. International Journal of Operations & Production Management. DOI: https://doi.org/10.1108/01443579910244223

Fredendall, L.D., Patterson, J.W., Kennedy, W.J. and Griffin, T., 1997. Maintenance: modeling its strategic impact. Journal of managerial issues, pp.440-453.

Raouf, A., 1994. Improving capital productivity through maintenance. International Journal of Operations & Production Management. DOI: https://doi.org/10.1108/01443579410062167

Wudhikarn, R., Smithikul, C. and Manopiniwes, W., 2010. Developing overall equipment cost loss indicator. In Proceedings of the 6th CIRP-Sponsored International Conference on Digital Enterprise Technology (pp. 557-567). Springer Berlin Heidelberg. DOI: https://doi.org/10.1007/978-3-642-10430-5_43

Almeanazel, O.T.R., 2010. Total productive maintenance review and overall equipment effectiveness measurement. Jordan Journal of Mechanical and Industrial Engineering, 4(4).

Binti Aminuddin, N.A., Garza-Reyes, J.A., Kumar, V., Antony, J. and Rocha-Lona, L., 2016. An analysis of managerial factors affecting the implementation and use of overall equipment effectiveness. International journal of production research, 54(15), pp.4430-4447. DOI: https://doi.org/10.1080/00207543.2015.1055849

Mohammed, S.A., 2008, March. Privatization of the iron and steel industry in Africa. In 8th International Arab Iron and Steel Conference, held at Doha, Qatar 17th–19th march.