Firm productivity, profit and business goal satisfaction: an assessment of maintenance decision effects on small and medium scale enterprises (SME’s)




maintenance decision making, funds availability, business goals satisfaction


This study was carried out to identify which factors are most relevant to managers of SMEs in maintenance decision making, and to investigate how these factors influence the realization of business goals satisfactorily, using structural equation modelling, partial least square design (PLS-SEM) to establish significant relationships between manifest and latent variables. A study of maintenance cost vis a vis the number of maintenance works carried out and profits realized was conducted to ascertain correlations and identify which factors played key roles in profit maximization. Results showed that with increasing level of maintenance for SMEs, profit margins reduced significantly. Also, an R2 value of 0.83 showed that the latent variable, business goal satisfaction was explained to a high degree (83%) by the manifest variables. Rentals of equipment from third parties (0.27), halting production (0.11) and outsourcing (0.39) were less considered for business sustainability per correlation coefficients than funds (0.79), and the possibilities to carry out both corrective (0.64) and preventive (0.58) maintenance works.  F-square value greater than zero was realized (0.387) and this showed reliability of the both inner and outer models. These findings can be used in building a decision tool or framework that will best suit SMEs with high financial budget constraints.


Download data is not yet available.

Author Biographies

Daniel Owusu-Mensah, Jiangsu University

School of Mechanical Engineering

Evans K. Quaye, Accra Institute of Technology

School of Advanced Technology engineering and sciences

Lydia Brako, Jiangnan University

School of Medicine


Al-Tabbaa, O., Ankrah, S. (2016). Social capital to facilitate ‘engineered’university–industry collaboration for technology transfer: A dynamic perspective. Technological Forecasting and Social Change, 104, 1-15.

Alarcón, D., Sánchez, J.A., Pablo de Olavide, U. (2015). Assessing convergent and discriminant validity in the ADHD-R IV rating scale: User-written commands for Average Variance Extracted (AVE), Composite Reliability (CR), and HeterotraitMonotrait ratio of correlations (HTMT). In Spanish STATA Meeting (pp. 1-39). Universidad Pablo de Olavide.

Barone, G., Frangopol, D.M. (2014). Life-cycle maintenance of deteriorating structures by multi-objective optimization involving reliability, risk, availability, hazard and cost. Structural Safety, 48, 40-50.

Bertolini, M., Bevilacqua, M. (2006). A combined goal programming—AHP approach to maintenance selection problem. Reliability Engineering & System Safety, 91(7), 839-848.

Hair, Jr, Joseph, F., Tomas, G., Hult, M., Ringle, C., Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.

Jiang, R., Murthy, D.N.P. (2008). Maintenance: Decision Models for Management. Science press, Beijing, China.

Joo, S-J. (2009). Scheduling preventive maintenance for modular designed components: A dynamic approach. European Journal of Operational Research, 192(2), 512-520.

Lee, H. (2005). A cost/benefit model for investments in inventory and preventive maintenance in an imperfect production system. Computers and Industrial Engineering, 48(1), 55-68.

Liu, X., Wang, W., Peng, R. (2015). An integrated production: inventory and preventive maintenance model for a multiproduct production system. Reliab Eng Syst Safety, 137(2), 76-86.

Liu, X., Zheng, J., Fu, J., Ji, J., Chen, G. (2017). Multi-level optimization of maintenance plan for natural gas pipeline systems subject to external corrosion. Journal of Natural Gas Science and Engineering, 50, 64-73.

Ma, J., Cheng, L., Li, D. (2018). Road Maintenance Optimization Model Based on Dynamic Programming in Urban Traffic Network. Journal of Advanced Transportation. Article ID 4539324, 11 pages.

Marquez, A.C., Gupta, J.N.D. (2006). Contemporary maintenance management: process, framework and supporting pillars. Omega, 34(3), 313-326.

Nourelfath, M., Nahas, N. & Ben-Daya, M. (2015). Integrated preventive maintenance and production decisions for imperfect processes. Reliab Eng Syst Safety, 148, 21-31.

Olivotti D., Passlick J., Dreyer S., Lebek B., Breitner M.H. (2018) Maintenance Planning Using Condition Monitoring Data. In: Kliewer N., Ehmke J., Borndörfer R.(eds) Operations Research Proceedings 2017.

Pallant, J. (2007). SPSS survival manual, 3rd. Edition. McGrath Hill.

Parida, A., Kumar, U. (2016). Applications and Case Studies. Maintenance performance measurement (MPM): issues and challenges. Journal of Quality in Maintenance Engineering, 12(3), 239-251.

Qiu, Q., Cui, L., Shen, J., Yang, L. (2017). Optimal maintenance policy considering maintenance errors for systems operating under performance-based contracts. Comput Industr Eng., 112, 147-155.

Ruschel, E., Santos, E.A.P. & Loures, E.D.F.R. (2017). Industrial maintenance decision-making: a systematic literature review. J Manuf Syst., 45, 180-194.

Shayesteh, E., Yu, J., Hilber, P. (2018). Maintenance optimization of power systems with renewable energy sources integrated. Energy, 149, 577-586.

Shen, J., Zhu, K. (2017). An uncertain single machine scheduling problem with periodic maintenance. Knowledge-Based Systems, 144, 32-41.

Stebbins, R. A. (2001). Exploratory research in the social sciences (Vol. 48). Sage.

Van, P.D., Bérenguer, C. (2012). Condition-based maintenance with imperfect preventive repairs for a deteriorating production system. Qual Reliab Eng., 28(6), 624-633.

Verbert, K., Schutter, B.D., Babuska, R. (2017). Timely condition-based maintenance planning for multi-component systems. Reliab Eng Syst Safety, 159, 310-321.

Yang, L., Ma, X., Zhao, Y. (2017). A condition-based maintenance model for a three-state system subject to degradation and environmental shocks. Comput Industr Eng., 105, 210-222.