Identifying and Prioritizing the Key Factors Affecting the Efficient Maintenance Management in the Agricultural Industry Based on Empirical Studies

Document Type : Research Article

Authors

• H. Soltanali ¹
• M. Khojastehpour ²

¹ Postdoctoral Researcher, Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

² Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

https://doi.org/10.22067/jam.2022.76333.1104

Abstract

Introduction: With the emergence of new automation and mechanized technologies in the production and processing of agricultural products in Iran, which aim to accelerate the food supply process, adopting appropriate management models in the field of maintenance becomes inevitable. This is crucial to maintain and enhance the operational reliability of agricultural machinery, tools, and equipment. Furthermore, proper management of various physical assets in the agricultural industry, including operation and maintenance, is one of the most important requirements. This is due to their crucial role in ensuring readiness and high availability during the seasons of planting, cultivating, and harvesting agricultural products. These needs differ from that of other continuous production processes.
 Materials and Methods: To achieve an efficient model in the field of maintenance, the following steps have been investigated:

a) Reviewing and identifying the most important criteria and sub-criteria driving the maintenance management. This is based on the previous literature and the experts’ opinion.
b) Evaluating and prioritizing the main criteria and the interactions between their sub-criteria using the Best-Worst Method (BWM).
c) Providing improved solutions for maintenance management of Iranian agro-industries.

We decided to employ BWM because, compared to similar methods, it (i) provides more reliable pairwise comparisons, (ii) reduces the possible anchoring bias that may occur during the weighting process by respondents, (iii) is the most data-efficient method, and (iv) provides multiple optimal solutions which increase flexibility when accessing the best weight point. The process of weighting by BWM is summarized in five steps:
1) Determine a set of evaluation criteria identified by the experts or decision-makers.
2) Identify the most important (Best) and the least important (Worst) criteria according to the experts or decision-makers, each of which may have their own Best and Worst.
3) Determine the preference of the Best criterion over all the other criteria using a number from 1 to 9 (where 1 represents equal importance and 9 represents extremely more important).
4) Determine the preference of all the decision criteria over the Worst criterion.
5) Compute optimal weights.
 Results and Discussion: According to the preliminary surveys, the most important criteria in the excellence maintenance model were identified as “organizational management”, “human-related factors”, and “organizational aspects”, respectively. The results of the BWM revealed that sub-criteria such as "top management support," "fund allocation and inventory resource management," and "appropriate maintenance strategies" had the greatest impact on maintenance management in agro-industries, with global weights of 0.108, 0.075, and 0.067, respectively. Additionally, these findings were compared to previous research conducted in the field of agricultural and production system maintenance models.
 Conclusion: The findings of this study could assist managers in revising and developing maintenance management models in the agro-industries. Future studies could consider calculating the interactions among the criteria that were omitted in this study to simplify the evaluation process which might improve the accuracy of weighing criteria. This can be achieved through the combination of the Decision Making Trial and Evaluation Laboratory (DEMATEL) and structural equation modeling.

Keywords

• Agro-industry
• Best-Worst method
• Machinery
• Maintenance
• Mechanization

20.1001.1.22286829.1402.13.4.3.6

Main Subjects

• Modeling

©2023 The author(s). This article is licensed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

References

Ab Wahid, R., & Corner, J. (2009). Critical success factors and problems in ISO 9000 maintenance. International Journal of Quality & Reliability Management, 26(9), 881-893. https://doi.org/10.1108/02656710910995073

Adebiyi, K. A., Ojediran, J. O., & Oyenuga, O. A. (2004). An appraisal of maintenance practice in food industries in Nigeria. Journal of Food Engineering, 62(2), 131-133 (in Persian). https://doi.org/10.1016/s0260-8774(03)00201-2

Afsharnia, F., Asoodar, M., & Abdeshahi, A. (2014). Regression Analysis and Modeling of Failure Rate and its Effective Factors on Tractors in Some Cities of Khuzestan Province. Journal of Agricultural Engineering, 36(2), 49-58. (in Persian).

Al-Turki, U. M., Ayar, T., Yilbas, B. S., & Sahin, A. Z. (2014). Integrated maintenance planning. In Integrated Maintenance Planning in Manufacturing Systems (pp. 25-57). Springer, Cham. https://doi.org/10.1007/978-3-319-06290-7_3

Amrani, M. A., Alhomdi, M., Ghaleb, A. M., Al-Qubati, M., & Shameeri, M. (2020). Implementing an integrated maintenance management system for monitoring production lines: a case study for biscuit industry. Journal of Quality in Maintenance Engineering, 28(1), 180-196. https://doi.org/10.1108/jqme-06-2020-0049

Anthony, R. N. (1965). Planning and control systems: a framework for analysis. Division of Research, Graduate School of Business Administration, Harvard University.

Arslankaya, S., & Atay, H. (2015). Maintenance management and lean manufacturing practices in a firm which produces dairy products. Procedia-Social and Behavioral Sciences, 207, 214-224. https://doi.org/10.1016/j.sbspro.2015.10.090

Barberá, L., Crespo, A., Viveros, P., & Stegmaier, R. (2012). Advanced model for maintenance management in a continuous improvement cycle: integration into the business strategy. International Journal of System Assurance Engineering and Management, 3(1), 47-63. https://doi.org/10.1007/s13198-012-0092-y

Bekar, E. T., Nyqvist, P., & Skoogh, A. (2020). An intelligent approach for data pre-processing and analysis in predictive maintenance with an industrial case study. Advances in Mechanical Engineering, 12(5), 1687814020919207. https://doi.org/10.1177/1687814020919207.

Bengtsson, M., & Salonen, A. (2009, June). On the need for research on holistic maintenance. In Proceedings of the 22nd international congress on Condition Monitoring and Diagnostic Engineering Management (COMADEM) (pp. 9-11).

Bokrantz, J. (2019). Smart Maintenance:-Maintenance in Digitalised Manufacturing. Department of Industrial and Materials Science. Chalmers University of Technology. Gothenburg, Sweden. ISBN 978-91-7905-191-4.

Bottani, E., Ferretti, G., Montanari, R., & Vignali, G. (2014). An empirical study on the relationships between maintenance policies and approaches among Italian companies. Journal of Quality in Maintenance Engineering, 20(2), 135-162. https://doi.org/10.1108/jqme-11-2012-0039

Branská, L., Pecinová, Z., Paták, M., Stankova, M., & Kholová, P. (2016). Maintenance management systems in the Czech enterprises of chemical and food industries. Trends Economics and Management, 10(27), 20-29. https://doi.org/10.13164/trends.2016.27.20

Fountas, S., Sorensen, C. G., Tsiropoulos, Z., Cavalaris, C., Liakos, V., & Gemtos, T. (2015). Farm machinery management information system. Computers and Electronics in Agriculture, 110, 131-138. https://doi.org/10.1016/j.compag.2014.11.011

Fredriksson, G., & Larsson, H. (2012). An analysis of maintenance strategies and development of a model for strategy formulation. Master of science thesis in production engineering. Department of product and production development. Chalmers University of Technology. Göteborg, Sweden.

Gandhare, B. S., & Akarte, M. M. (2020). Benchmarking maintenance performance in select agro-based industry. Journal of Quality in Maintenance Engineering, 28(2), 296-326. https://doi.org/10.1108/jqme-02-2019-0018

Gandhare, B. S., Akarte, M. M., & Patil, P. P. (2018). Maintenance performance measurement–a case of the sugar industry. Journal of Quality in Maintenance Engineering, 24(1), 79-100. https://doi.org/10.1108/jqme-07-2016-0031

Gandhi, A., Purwani, D. R., Susanti, Y. D., & Prasetya, Y. (2021). The Status of Maintenance Management in Indonesia: Result from a Pilot Survey Food Snack MSMEs. Proceedings of the 11^th Annual International Conference on Industrial Engineering and Operations Management. Singapore

Garg, A., & Deshmukh, S. G. (2006). Maintenance management: literature review and directions. Journal of Quality in Maintenance Engineering, 12(3), 205-238. https://doi.org/10.1108/13552510610685075

Gomes, C. F., Yasin, M. M., & Simões, J. M. (2020). The emerging organizational role of the maintenance function: a strategic perspective. Journal of Quality in Maintenance Engineering, 27(1). https://doi.org/10.1108/jqme-03-2017-0012

Gopalakrishnan, M. (2018). Data-driven Decision Support for Maintenance Prioritisation: Connecting Maintenance to Productivity. Chalmers Tekniska Hogskola (Sweden).

Hassanain, M. A. (2002). Integrated systems for maintenance management (Doctoral dissertation, University of British Columbia). http://hdl.handle.net/2429/13109

Holweg, M., Davies, J., De Meyer, A., Lawson, B., & Schmenner, R. W. (2018). Process theory: The principles of operations management. Oxford University Press.

Horyovy, V. P. (2007). Developing the production and maintenance support of enterprises of the AIC [agro-industrial complex of Ukraine]. Visnyk Ahrarnoyi Nauky (Ukraine).

Jandali, D., & Sweis, R. (2018). Factors affecting maintenance management in hospital buildings: Perceptions from the public and private sector. International Journal of Building Pathology and Adaptation, 37(1). https://doi.org/10.1108/ijbpa-12-2017-0064

Johannes, K., Theodorus Voordijk, J., Marias Adriaanse, A., & Aranda-Mena, G. (2021). Identifying Maturity Dimensions for Smart Maintenance Management of Constructed Assets: A Multiple Case Study. Journal of Construction Engineering and Management, 147(9), 05021007. https://doi.org/10.1061/(asce)co.1943-7862.0002112

Jonsson, P. (2000). Towards a holistic understanding of disruptions in Operations Management. Journal of Operations Management, 18(6), 701-718. https://doi.org/10.1016/s0272-6963(00)00040-1

Karia, N., Asaari, M. H. A. H., & Saleh, H. (2014, January). Exploring Maintenance Management in Service Sector: A Case Study. In Proceedings of the 2014 International Conference on Industrial Engineering and Operations Management Bali, Indonesia.

Kheybari, S., Rezaie, F. M., Naji, S. A., Javdanmehr, M., & Rezaei, J. (2020). Evaluation of factors contributing to the failure of information systems in public universities: The case of Iran. Information Systems, 92, 101534. https://doi.org/10.1016/j.is.2020.101534

Kim, E. J., Jeong, W. I., Lee, Y. K., & Lim, C. S. (2014). Development of evaluation indicators for maintenance and preservation of agriculture and rural heritage. Journal of Agricultural Extension & Community Development, 21(4), 1191-1226. https://doi.org/10.12653/jecd.2014.21.4.1191

Kumar, A., Shankar, R., & Thakur, L. S. (2018). A big data driven sustainable manufacturing framework for condition-based maintenance prediction. Journal of Computational Science, 27, 428-439. https://doi.org/10.1016/j.jocs.2017.06.006

Li, D., Zheng, Y., & Zhao, W. (2019). Fault analysis system for agricultural machinery based on big data. IEEE Access, 7, 99136-99151. https://doi.org/10.1109/access.2019.2928973

Liang, F., Brunelli, M., & Rezaei, J. (2019). Consistency issues in the best worst method: Measurements and thresholds. Omega, 102175. https://doi.org/10.1016/j.omega.2019.102175

Liu, Y., Hu, Y., Wen, J., & Tang, Y. (2018, July). An overview on smart maintenance service scheduling system and theoretical basis for agricultural machinery. In 2018 IEEE International Conference on Internet of Things (I Things) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) (pp. 766-771). IEEE. https://doi.org/10.1109/cybermatics_2018.2018.00151

Macchi, M., & Fumagalli, L. (2013). A maintenance maturity assessment method for the manufacturing industry. Journal of Quality in Maintenance Engineering, 19(3), 295-315. https://doi.org/10.1108/jqme-05-2013-0027

Marquez, A. C., & Gupta, J. N. (2004). Modern maintenance management for enhancing organizational efficiency. In Intelligent Enterprises of the 21st Century (pp. 321-332). IGI Global. https://doi.org/10.4018/978-1-59140-160-5.ch019

Meixner, O., Pöchtrager, S., & Haas, R. (2001). Determining the success factors for the introduction and maintenance of quality management in the Austrian food industry using the Analytic Hierarchy Process. In Proceedings of the 6^th International Symposium on the Analytic Hierarchic Process, ISAHP (pp. 2-4). https://doi.org/10.13033/isahp.y2001.032

Milana, M., Khan, M. K., & Munive-Hernandez, J. E. (2017). Design and development of knowledge-based system for integrated maintenance strategy and operations. Concurrent Engineering, 25(1), 5-18. https://doi.org/10.1177/1063293x16665662

Moktadir, M. A., Kumar, A., Ali, S. M., Paul, S. K., Sultana, R., & Rezaei, J. (2020). Critical success factors for a circular economy: Implications for business strategy and the environment. Business Strategy and the Environment, 29(8), 3611-3635. https://doi.org/10.1002/bse.2600

Mousavipour, A., Sheikh Davoodi, M. J., Ghanian, M., & Saeedi, S. N. (2012). Economic comparison of two common emergency methods and oil status monitoring maintenance and repair of sugarcane harvesting machines. Journal of Agricultural Engineering, 36(2).

Najafi, P., Asoodar, M. A., Marzban, A., & Hormozi, M. A. (2015). Reliability evaluation and analysis of sugarcane 7000 series harvesters in sugarcane harvesting. Journal of Agricultual Machinery, 5(2), 446-455. (in Persian). https://doi.org/10.22067/jam.v5i2.28447

Naji, A., Oumami, M. E., Bouksour, O., & Beidouri, Z. (2019). A Mixed methods research toward a framework of a maintenance management model: A survey in Moroccan industries. Journal of Quality in Maintenance Engineering, 26(2), 260-289. https://doi.org/10.1108/jqme-10-2018-0079

Oliveira, M. A., & Lopes, I. (2019). Evaluation and improvement of maintenance management performance using a maturity model. International Journal of Productivity and Performance Management, 69(3), 559-58. https://doi.org/10.1108/ijppm-07-2018-0247

Pinjala, S. K., Pintelon, L., & Vereecke, A. (2006). An empirical investigation on the relationship between business and maintenance strategies. International Journal of Production Economics, 104(1), 214-229. https://doi.org/10.1016/j.ijpe.2004.12.024

Pintelon, L. M., & Gelders, L. F. (1992). Maintenance management decision making. European Journal of Operational Research, 58(3), 301-317. https://doi.org/10.1016/0377-2217(92)90062-e

Pintelon, L., & Parodi-Herz, A. (2008). Maintenance: an evolutionary perspective. In Complex system maintenance handbook (pp. 21-48). Springer, London. https://doi.org/10.1007/978-1-84800-011-7_2

Priyantha, J. (2021). Literature Review: The Role of organizational factors in maintenance organizations affecting their manufacturing performance, from Sri Lankan cultural perspective. International Journal of Research and Innovation in Social Science (IJRISS), 4(5).

Reis, L. F., & Alves, F. J. D. C. (2020). Brazilian sugarcane agro-industry human resources’ management: strategies to increase work intensity. Gestão & Produção, 27. https://doi.org/10.1590/0104-530x5147-20

Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57. https://doi.org/10.1016/j.omega.2014.11.009

Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126-130. https://doi.org/10.1016/j.omega.2015.12.001

Roda, I., & Macchi, M. (2021). Maintenance concepts evolution: a comparative review towards Advanced Maintenance conceptualization. Computers in Industry, 133, 103531. https://doi.org/10.1016/j.compind.2021.103531

Rohani, A., Ranjbar, I., Ajabshir, Y., Abbaspour-Fard, M. H., & Valizadeh, M. (2009). Prediction of two-wheel drive tractor repair and maintenance costs using artificial neural network in comparing with regression. Journal of Agricultural Engineering Research, 16(1), 225-235.

Salonen, A., (2008). Strategic Factors for Maintenance in Manufacturing Industry, in the proceedings of 21^st International Congress and Exhibition COMADEM – 2008: Prague, The Czech Republic.

Salonen, A. (2012). Formulation of maintenance strategies: A simplified process. International Journal of COMADEM, 15(3), 9-18.

Sarbini, N. N., Ibrahim, I. S., Abidin, N. I., Yahaya, F. M., & Azizan, N. Z. N. (2021). Review on maintenance issues toward building maintenance management best practices. Journal of Building Engineering, 44, 102985. https://doi.org/10.1016/j.jobe.2021.102985

Sharma, S. K. (2013). Maintenance reengineering framework: a case study. Journal of Quality in Maintenance Engineering, 19(2), 96-113. https://doi.org/10.1108/13552511311315922

Soltanali, H., & Rohani, A. (2016). Evaluation of reliability of production systems in food industry. 10^th National Congress of Agricultural Machinery Engineering (Biosystems) and Mechanization of Iran, Ferdowsi University of Mashhad, Mashhad, Iran.

Soltanali, H., Garmabaki, A. H. S., Thaduri, A., Parida, A., Kumar, U., & Rohani, A. (2019). Sustainable production process: An application of reliability, availability, and maintainability methodologies in automotive manufacturing. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 233(4), 682-697. https://doi.org/10.1177/1748006x18818266

Soltanali, H., Khojastehpour, M., & Farinha, J. T. (2020). Measuring the production performance indicators for food processing industry. Measurement, 173, 108394. https://doi.org/10.1016/j.measurement.2020.108394

Sørensen, C. G., & Bochtis, D. D. (2010). Conceptual model of fleet management in agriculture. Biosystems Engineering, 105(1), 41-50. https://doi.org/10.1016/j.biosystemseng.2009.09.009

Tan, Y., Shen, L., Langston, C., Lu, W., & Yam, M. C. (2014). Critical success factors for building maintenance business: a Hong Kong case study. Facilities. https://doi.org/10.1108/f-08-2012-0062

Terminology, M. (2010). CEN (European Committee for Standardization). European Standard EN, 13306, 2010.

Thomas, S. J. (2005). Improving maintenance and reliability through cultural change. Industrial Press Inc.

Tsang, A. H. (2002). Strategic dimensions of maintenance management. Journal of Quality in maintenance Engineering.

Tubis, A., & Werbińska-Wojciechowska, S. (2015). Concept of controlling for maintenance management performance: a case study of passenger transportation company. In Safety and reliability of complex engineered systems proceedings of the 25^th European Safety and Reliability Conference, ESREL (pp. 1055-1063).

Van de Kaa, G., Fens, T., & Rezaei, J. (2019). Residential grid storage technology battles: a multi-criteria analysis using BWM. Technology Analysis & Strategic Management, 31(1), 40-52. https://doi.org/10.1080/09537325.2018.1484441

Van Horenbeek, A., & Pintelon, L. (2014). Development of a maintenance performance measurement framework-using the analytic network process (ANP) for maintenance performance indicator selection. Omega, 42(1), 33-46. https://doi.org/10.1016/j.omega.2013.02.006

Vasudevan, A., & Duan, X. (2021). A systematic data science approach towards predictive maintenance application in manufacturing industry. Examensarbete för masterexamen. Master Thesis. https://hdl.handle.net/20.500.12380/302632

Wen, Q., Zhang, J. P., Hu, Z. Z., Xiang, X. S., & Shi, T. (2019). A data-driven approach to improve the operation and maintenance management of large public buildings. IEEE Access, 7, 176127-176140. https://doi.org/10.1109/access.2019.2958140

Wireman, T. (2010) Benchmarking Best Practices in Maintenance Management (2nd edition). [Electronic] New York: Industrial Press Inc.

Yavuz, O., Doğan, E., Carus, E., & Görgülü, A. (2019). Reliability Centered Maintenance Practices in Food Industry. Procedia Computer Science, 158, 227-234. https://doi.org/10.1016/j.procs.2019.09.046

Zhang, W., Yang, D., & Wang, H. (2019). Data-driven methods for predictive maintenance of industrial equipment: A survey. IEEE Systems Journal, 13(3), 2213-2227. https://doi.org/10.1109/jsyst.2019.2905565

Zhou, Q., Jiang, J., Zhao, Z., Zhong, J., Pan, B., Jin, X., & Sun, Y. (2017, August). Research on the Internet of Things Platform Design for Agricultural Machinery Operation and Operation Management. In International Conference on Computer and Computing Technologies in Agriculture (pp. 400-410). Springer, Cham. https://doi.org/10.1007/978-3-030-06179-1_40