DEVELOPMENT OF A MANUFACTURING STRATEGY OF SPARE PARTS: A CASE STUDY
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Published
Oct 4, 2023
Khaled M. S. Gad El Mola
Abstract
Managing supplementary parts for plants and machines plays a major role in accomplishing the needed system availability in a cost-effective manner. Today’s industrial demands require mature technology that is strong in terms of capital and bulk-production oriented. However, the idleness of these machines and equipment due to unavailability of spare parts is a major problem and barrier to effective systems availability. This study aims to develop an effective manufacturing strategy for spare parts. A case study was conducted at a General Water Desalination Establishment in Saudi Arabia to select the right strategy to reduce the total downtime and total maintenance costs of equipment. The results showed the importance of creating a production plan that suits an organization’s ability to manage the supply chain for customers and ensure the company remains competitive within its market. Identification of critical spare parts of equipment for maintenance operations is one of the key decision-making activities to obtain lower downtime for equipment and inventory costs. Therefore, decision-makers should apply the best method and use accurate criteria to analyze and rank the spare parts based on criticality. The strategies proposed in the present study assure that these important parts are available for maintenance and repair of the plant and machinery when required at an optimum cost.
How to Cite
El Mola, K. M. S. G. (2023). DEVELOPMENT OF A MANUFACTURING STRATEGY OF SPARE PARTS: A CASE STUDY. International Journal of the Analytic Hierarchy Process, 15(2). https://doi.org/10.13033/ijahp.v15i2.1000
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Keywords
control characteristics, logistic characteristics, criticality, manufacturing strategy, spare parts, multi-criteria decision making, analytic hierarchy process
References
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Aguarón, J., Escobar, M. T., Moreno-Jiménez, J. M., & Turón, A. (2022). Geometric Compatibility indexes in a local AHP-group decision making context: A framework for reducing incompatibility. Mathematics, 10(2), 278. Doi: https://doi.org/10.3390/math10020278
Ali, H. (2022). A systematic bibliometric analysis of the analytic hierarchy process from 1980 to 2020. International Journal of Bibliometrics in Business and Management, 2(2), 148-169. Doi: https://doi.org/10.1504/IJBBM.2022.125988
Arts, J.J. (2014). Spare parts planning and control for maintenance operations. Second International Conference on Railway Technology: Research, Development, and Maintenance (Railways 2014), (301-306). Ajaccio, France: Civil-Comp Press.
Bacchetti, A. & Saccani, N. (2012). Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice. Omega, 40(6), 722-737. Doi: https://doi.org/10.1016/j.omega.2011.06.008
Baig, M.B., Alotibi, Y., Straquadine, G.S. & Alataway, A. (2020). Water resources in the Kingdom of Saudi Arabia: Challenges and strategies for improvement. In S. Zekri (Ed.) Water Policies in MENA Countries (pp. 135-160). Springer International Publishing. Doi: https://doi.org/10.1007/978-3-030-29274-4_7
Baryannis, G., Dani, S., & Antoniou, G. (2019). Predicting supply chain risks using machine learning: The trade-off between performance and interpretability. Future Generation Computer Systems, 101, 993-1004. Doi: https://doi.org/10.1016/j.future.2019.07.059
Bounou, O., El Barkany, A. & El Biyaali, A. (2017). Inventory models for spare parts management: a review. International Journal of Engineering Research in Africa, 28, 182-198. Doi: https://doi.org/10.4028/www.scientific.net/JERA.28.182
Chaudhuri, A., Gerlich, H. A., Jayaram, J., Ghadge, A., Shack, J., Brix, B. H., & Ulriksen, N. (2021). Selecting spare parts suitable for additive manufacturing: a design science approach. Production Planning & Control, 32(8), 670-687. Doi: https://doi.org/10.1080/09537287.2020.1751890
Chekurov, S., Metsä-Kortelainen, S., Salmi, M., Roda, I., & Jussila, A. (2018). The perceived value of additively manufactured digital spare parts in the industry: An empirical ‘investigation.’ International Journal of Production Economics, 205, 87-97. Doi: https://doi.org/10.1016/j.ijpe.2018.09.008
Das, A., Chatham, C.A., Fallon, J.J., Zawaski, C.E., Gilmer, E.L., Williams, C.B. & Bortner, M.J. (2020). Current understanding and challenges in high temperature additive manufacturing of engineering thermoplastic polymers. Additive Manufacturing, 34, 101218. Doi: https://doi.org/10.1016/j.addma.2020.101218
Dekker, R., Pinçe, Ç., Zuidwijk, R. & Jalil, M.N. (2013). On the use of installed base information for spare parts logistics: A review of ideas and industry practice. International Journal of Production Economics, 143(2), 536-545. Doi: https://doi.org/10.1016/j.ijpe.2011.11.025
Driessen, M., Arts, J., van Houtum, G.J., Rustenburg, J.W. & Huisman, B. (2015). Maintenance spare parts planning and control: a framework for control and agenda for future research. Production Planning & Control, 26(5), 407-426. Doi: https://doi.org/10.1080/09537287.2014.907586
Duran, O., Roda, I. & Macchi, M. (2016). Linking the spare parts management with the total costs of ownership: An agenda for future research. Journal of Industrial Engineering and Management, 9, 991-1002. Doi: https://doi.org/10.3926/jiem.2083
Heinen, J.J. & Hoberg, K. (2019). Assessing the potential of additive manufacturing for the provision of spare parts. Journal of Operational Management, 65(8), 810-826. Doi: https://doi.org/10.1002/joom.1054
Helo, P., & Hao, Y. (2021). Artificial intelligence in operations management and supply chain management: an exploratory case study. Production Planning & Control, 33(16), 1573-1590. Doi: https://doi.org/10.1080/09537287.2021.1882690
Hettiarachchi, B. D., Brandenburg, M., & Seuring, S. (2022). Connecting additive manufacturing to circular economy implementation strategies: Links, contingencies and causal loops. International Journal of Production Economics, 246, 108414. Doi: https://doi.org/10.1016/j.ijpe.2022.108414
Hu, Q., Boylan, J. E., Chen, H., & Labib, A. (2018). OR in spare parts management: A review. European Journal of Operational Research, 266(2), 395-414. Doi: https://doi.org/10.1016/j.ejor.2017.07.058
Huiskonen, J. (2001). Maintenance spare parts logistics: Special characteristics and strategic choices. International Journal of Production Economics, 71(1-3), 125-133. Doi: https://doi.org/10.1016/s0925-5273(00)00112-2
Kahraman, C., Oztaysi, B., & Cevik Onar, S. (2020). Single & interval-valued neutrosophic AHP methods: Performance analysis of outsourcing law firms. Journal of Intelligent & Fuzzy Systems 38(1), 749-759. Doi: http://doi.org/10.3233/JIFS-179446
Kim, B. & Park, S. (2008). Optimal pricing, EOL (end of life) warranty, and spare parts manufacturing strategy amid product transition. European Journal of Operational Research, 188(3), 723-745. Doi: https://doi.org/10.1016/j.ejor.2007.04.036
Krejčí, J. & Stoklasa, J. (2018). Aggregation in the analytic hierarchy process: Why weighted geometric mean should be used instead of weighted arithmetic mean, Expert Systems with Applications, 114(30), 97-106. Doi: https://doi.org/10.1016/j.eswa.2018.06.060
Kuzu, Ö. H. (2020). Strategy selection in the universities via fuzzy AHP method: A case study. International Journal of Higher Education, 9(2), 107-117. Doi: https://doi.org/10.5430/ijhe.v9n2p107
Lami, I. M., & Todella, E, (2023). A multi-methodological combination of the strategic choice approach and the analytic network process: From facts to values and vice versa. European Journal of Operational Research, 307(2), 802-812. Doi: https://doi.org/10.1016/j.ejor.2022.10.029
Molenaers, A., Baets, H., Pintelon, L. & Waeyenbergh, G. (2012). Criticality classification of spare parts: A case study. International Journal of Production Economics, 140(2), 570-578. Doi: https://doi.org/10.1016/j.ijpe.2011.08.013.
Obstfeld, M., & Rogoff, K. S. (2005). Global current account imbalances and exchange rate adjustments. Brookings Papers on Economic Activity, 2005(1), 67-146. Doi: https://doi.org/10.1353/eca.2005.0020
Ott, K., Pascher, H., & Sihn, W. (2019). Improving sustainability and cost efficiency for spare part allocation strategies by utilisation of additive manufacturing technologies. Procedia Manufacturing, 33, 123-130. Doi: https://doi.org/10.1016/j.promfg.2019.05.001
Peres, F. & Noyes, D. (2006). Envisioning e-logistics developments: making spare parts in situ and on demand: state of the art and guidelines for future developments. Computers in Industry, 57(6), 490-503. Doi: https://doi.org/10.1016/j.compind.2006.02.010
Rawal, N. (2021). An approach for ranking of hospitals based on waste management practices by Analytical Hierarchy Process (AHP) methodology. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 192, 671-676. Doi: https://doi.org/10.1007/s40010-021-00760-x
Rosita, K. K. M., & Young, M. N. (2020). Optimizing maintenance spare parts re-ordering process using computerized maintenance management system. 2020 7th International Conference on Frontiers of Industrial Engineering (ICFIE) (104-108). IEEE. Doi: https://doi.org/10.1109/icfie50845.2020.9266717
Saaty, T.L. (2016). The analytic hierarchy and analytic network processes for the measurement of intangible criteria and for decision-making. In S. Greco, M. Erghott, J. Rui Figueira (Eds.) Multiple criteria decision analysis (pp. 363-419). Springer. Doi: https://doi.org/10.1007/978-1-4939-3094-4_10
Sarmah, S.P. & Moharana, U.C. (2015). Multi-criteria classification of spare parts inventories–a web-based approach. Journal of Quality in Maintenance Engineering, 21(4), 456-477. Doi: https://doi.org/10.1108/jqme-04-2012-0017
Sgarbossa, F., Peron, M., Lolli, F., & Balugani, E. (2021). Conventional or additive manufacturing for spare parts management: An extensive comparison for Poisson demand. International Journal of Production Economics, 233, 107993. Doi: https://doi.org/10.1016/j.ijpe.2020.107993
Stoll, J., Kopf, R., Schneider, J. &Lanza, G. (2015). Criticality analysis of spare parts management: a multi-criteria classification regarding a cross-plant central warehouse strategy. Production Engineering, 9(2), 225-235. Doi: https://doi.org/10.1007/s11740-015-0602-2
Traneva, V., & Tranev, S. (2022). Intuitionistic fuzzy model for franchisee selection. In C. Kahraman, A Cagri Tolga, S. Cevik Onar, S. Cebi, B. Oztaysi, & I. Ucar Sali (Eds.) Intelligent and Fuzzy Systems: Digital Acceleration and The New Normal-Proceedings of the INFUSE 2022 Conference, Volume 1 (pp. 632-640). Cham: Springer International Publishing. Doi: https://doi.org/10.1007/978-3-031-09173-5_73
Turrini, L. & Meissner, J. (2019). Spare parts inventory management: New evidence from distribution fitting. European Journal of Operational Research, 273(1), 118-130. Doi: https://doi.org/10.1016/j.ejor.2017.09.039
Tusar, M. I. H., & Sarker, B. R. (2022). Spare parts control strategies for offshore wind farms: a critical review and comparative study. Wind Engineering, 46(5). Doi: https://doi.org/10.1177/0309524x221095258
Van Houtum, G.J. & Kranenburg, B. (2015). Spare parts inventory control under system availability constraints, International Series in Operations Research and Management Sciences. Springer. Doi: https://doi.org/10.1007/978-1-4899-7609-3
Venkataraman, K. (2007) Maintenance engineering and management 1st edition. Prentice-Hall of India Pvt. Ltd.
Wu, M.C. & Hsu, Y.K. (2008). Design of BOM configuration for reducing spare parts logistic costs. Expert Systems with Applications, 34(4), 2417-2423. Doi: https://doi.org/10.1016/j.eswa.2007.04.001
Yang, Y., Liu, W., Zeng, T., Guo, L., Qin, Y., & Wang, X. (2022). An improved stacking model for equipment spare parts demand forecasting based on scenario analysis. Scientific Programming, 2022, 1-15 Doi: https://doi.org/10.1155/2022/5415702
Yuen, K. K. F. (2022). Decision models for information systems planning using primitive cognitive network process: comparisons with analytic hierarchy process. Operational Research, 22(3), 1759-1785. Doi: https://doi.org/10.1007/s12351-021-00628-3
Zeng, Y.R., Wang, L. & He, J. (2012). A novel approach for evaluating control criticality of spare parts using fuzzy comprehensive evaluation and GRA. International Journal of Fuzzy Systems, 14(3), 392-401 Doi: https://doi.org/10.1109/iccias.2006.294083
Zhang, X. & Zeng, J. (2017). Joint optimization of condition-based opportunistic maintenance and spare parts provisioning policy in multiunit systems. European Journal of Operational Research, 262(2), 479-498. Doi: https://doi.org/10.1016/j.ejor.2017.03.019
Zhang, Y., Jedeck, S., Yang, L., & Bai, L. (2018). Modeling and analysis of the on-demand spare parts supply using additive manufacturing. Rapid Prototyping Journal, 25(3), 473-487. Doi: https://doi.org/10.1108/rpj-01-2018-0027
Achillas, C., Aidonis, D., Iakovou, E., Thymianidis, M., & Tzetzis, D. (2015). A methodological framework for the inclusion of modern additive manufacturing into the product portfolio of a focused factory. Journal of Manufacturing Systems, 37(1), 328-339. Doi: https://doi.org/10.1016/j.jmsy.2014.07.014
Aguarón, J., Escobar, M. T., Moreno-Jiménez, J. M., & Turón, A. (2022). Geometric Compatibility indexes in a local AHP-group decision making context: A framework for reducing incompatibility. Mathematics, 10(2), 278. Doi: https://doi.org/10.3390/math10020278
Ali, H. (2022). A systematic bibliometric analysis of the analytic hierarchy process from 1980 to 2020. International Journal of Bibliometrics in Business and Management, 2(2), 148-169. Doi: https://doi.org/10.1504/IJBBM.2022.125988
Arts, J.J. (2014). Spare parts planning and control for maintenance operations. Second International Conference on Railway Technology: Research, Development, and Maintenance (Railways 2014), (301-306). Ajaccio, France: Civil-Comp Press.
Bacchetti, A. & Saccani, N. (2012). Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice. Omega, 40(6), 722-737. Doi: https://doi.org/10.1016/j.omega.2011.06.008
Baig, M.B., Alotibi, Y., Straquadine, G.S. & Alataway, A. (2020). Water resources in the Kingdom of Saudi Arabia: Challenges and strategies for improvement. In S. Zekri (Ed.) Water Policies in MENA Countries (pp. 135-160). Springer International Publishing. Doi: https://doi.org/10.1007/978-3-030-29274-4_7
Baryannis, G., Dani, S., & Antoniou, G. (2019). Predicting supply chain risks using machine learning: The trade-off between performance and interpretability. Future Generation Computer Systems, 101, 993-1004. Doi: https://doi.org/10.1016/j.future.2019.07.059
Bounou, O., El Barkany, A. & El Biyaali, A. (2017). Inventory models for spare parts management: a review. International Journal of Engineering Research in Africa, 28, 182-198. Doi: https://doi.org/10.4028/www.scientific.net/JERA.28.182
Chaudhuri, A., Gerlich, H. A., Jayaram, J., Ghadge, A., Shack, J., Brix, B. H., & Ulriksen, N. (2021). Selecting spare parts suitable for additive manufacturing: a design science approach. Production Planning & Control, 32(8), 670-687. Doi: https://doi.org/10.1080/09537287.2020.1751890
Chekurov, S., Metsä-Kortelainen, S., Salmi, M., Roda, I., & Jussila, A. (2018). The perceived value of additively manufactured digital spare parts in the industry: An empirical ‘investigation.’ International Journal of Production Economics, 205, 87-97. Doi: https://doi.org/10.1016/j.ijpe.2018.09.008
Das, A., Chatham, C.A., Fallon, J.J., Zawaski, C.E., Gilmer, E.L., Williams, C.B. & Bortner, M.J. (2020). Current understanding and challenges in high temperature additive manufacturing of engineering thermoplastic polymers. Additive Manufacturing, 34, 101218. Doi: https://doi.org/10.1016/j.addma.2020.101218
Dekker, R., Pinçe, Ç., Zuidwijk, R. & Jalil, M.N. (2013). On the use of installed base information for spare parts logistics: A review of ideas and industry practice. International Journal of Production Economics, 143(2), 536-545. Doi: https://doi.org/10.1016/j.ijpe.2011.11.025
Driessen, M., Arts, J., van Houtum, G.J., Rustenburg, J.W. & Huisman, B. (2015). Maintenance spare parts planning and control: a framework for control and agenda for future research. Production Planning & Control, 26(5), 407-426. Doi: https://doi.org/10.1080/09537287.2014.907586
Duran, O., Roda, I. & Macchi, M. (2016). Linking the spare parts management with the total costs of ownership: An agenda for future research. Journal of Industrial Engineering and Management, 9, 991-1002. Doi: https://doi.org/10.3926/jiem.2083
Heinen, J.J. & Hoberg, K. (2019). Assessing the potential of additive manufacturing for the provision of spare parts. Journal of Operational Management, 65(8), 810-826. Doi: https://doi.org/10.1002/joom.1054
Helo, P., & Hao, Y. (2021). Artificial intelligence in operations management and supply chain management: an exploratory case study. Production Planning & Control, 33(16), 1573-1590. Doi: https://doi.org/10.1080/09537287.2021.1882690
Hettiarachchi, B. D., Brandenburg, M., & Seuring, S. (2022). Connecting additive manufacturing to circular economy implementation strategies: Links, contingencies and causal loops. International Journal of Production Economics, 246, 108414. Doi: https://doi.org/10.1016/j.ijpe.2022.108414
Hu, Q., Boylan, J. E., Chen, H., & Labib, A. (2018). OR in spare parts management: A review. European Journal of Operational Research, 266(2), 395-414. Doi: https://doi.org/10.1016/j.ejor.2017.07.058
Huiskonen, J. (2001). Maintenance spare parts logistics: Special characteristics and strategic choices. International Journal of Production Economics, 71(1-3), 125-133. Doi: https://doi.org/10.1016/s0925-5273(00)00112-2
Kahraman, C., Oztaysi, B., & Cevik Onar, S. (2020). Single & interval-valued neutrosophic AHP methods: Performance analysis of outsourcing law firms. Journal of Intelligent & Fuzzy Systems 38(1), 749-759. Doi: http://doi.org/10.3233/JIFS-179446
Kim, B. & Park, S. (2008). Optimal pricing, EOL (end of life) warranty, and spare parts manufacturing strategy amid product transition. European Journal of Operational Research, 188(3), 723-745. Doi: https://doi.org/10.1016/j.ejor.2007.04.036
Krejčí, J. & Stoklasa, J. (2018). Aggregation in the analytic hierarchy process: Why weighted geometric mean should be used instead of weighted arithmetic mean, Expert Systems with Applications, 114(30), 97-106. Doi: https://doi.org/10.1016/j.eswa.2018.06.060
Kuzu, Ö. H. (2020). Strategy selection in the universities via fuzzy AHP method: A case study. International Journal of Higher Education, 9(2), 107-117. Doi: https://doi.org/10.5430/ijhe.v9n2p107
Lami, I. M., & Todella, E, (2023). A multi-methodological combination of the strategic choice approach and the analytic network process: From facts to values and vice versa. European Journal of Operational Research, 307(2), 802-812. Doi: https://doi.org/10.1016/j.ejor.2022.10.029
Molenaers, A., Baets, H., Pintelon, L. & Waeyenbergh, G. (2012). Criticality classification of spare parts: A case study. International Journal of Production Economics, 140(2), 570-578. Doi: https://doi.org/10.1016/j.ijpe.2011.08.013.
Obstfeld, M., & Rogoff, K. S. (2005). Global current account imbalances and exchange rate adjustments. Brookings Papers on Economic Activity, 2005(1), 67-146. Doi: https://doi.org/10.1353/eca.2005.0020
Ott, K., Pascher, H., & Sihn, W. (2019). Improving sustainability and cost efficiency for spare part allocation strategies by utilisation of additive manufacturing technologies. Procedia Manufacturing, 33, 123-130. Doi: https://doi.org/10.1016/j.promfg.2019.05.001
Peres, F. & Noyes, D. (2006). Envisioning e-logistics developments: making spare parts in situ and on demand: state of the art and guidelines for future developments. Computers in Industry, 57(6), 490-503. Doi: https://doi.org/10.1016/j.compind.2006.02.010
Rawal, N. (2021). An approach for ranking of hospitals based on waste management practices by Analytical Hierarchy Process (AHP) methodology. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 192, 671-676. Doi: https://doi.org/10.1007/s40010-021-00760-x
Rosita, K. K. M., & Young, M. N. (2020). Optimizing maintenance spare parts re-ordering process using computerized maintenance management system. 2020 7th International Conference on Frontiers of Industrial Engineering (ICFIE) (104-108). IEEE. Doi: https://doi.org/10.1109/icfie50845.2020.9266717
Saaty, T.L. (2016). The analytic hierarchy and analytic network processes for the measurement of intangible criteria and for decision-making. In S. Greco, M. Erghott, J. Rui Figueira (Eds.) Multiple criteria decision analysis (pp. 363-419). Springer. Doi: https://doi.org/10.1007/978-1-4939-3094-4_10
Sarmah, S.P. & Moharana, U.C. (2015). Multi-criteria classification of spare parts inventories–a web-based approach. Journal of Quality in Maintenance Engineering, 21(4), 456-477. Doi: https://doi.org/10.1108/jqme-04-2012-0017
Sgarbossa, F., Peron, M., Lolli, F., & Balugani, E. (2021). Conventional or additive manufacturing for spare parts management: An extensive comparison for Poisson demand. International Journal of Production Economics, 233, 107993. Doi: https://doi.org/10.1016/j.ijpe.2020.107993
Stoll, J., Kopf, R., Schneider, J. &Lanza, G. (2015). Criticality analysis of spare parts management: a multi-criteria classification regarding a cross-plant central warehouse strategy. Production Engineering, 9(2), 225-235. Doi: https://doi.org/10.1007/s11740-015-0602-2
Traneva, V., & Tranev, S. (2022). Intuitionistic fuzzy model for franchisee selection. In C. Kahraman, A Cagri Tolga, S. Cevik Onar, S. Cebi, B. Oztaysi, & I. Ucar Sali (Eds.) Intelligent and Fuzzy Systems: Digital Acceleration and The New Normal-Proceedings of the INFUSE 2022 Conference, Volume 1 (pp. 632-640). Cham: Springer International Publishing. Doi: https://doi.org/10.1007/978-3-031-09173-5_73
Turrini, L. & Meissner, J. (2019). Spare parts inventory management: New evidence from distribution fitting. European Journal of Operational Research, 273(1), 118-130. Doi: https://doi.org/10.1016/j.ejor.2017.09.039
Tusar, M. I. H., & Sarker, B. R. (2022). Spare parts control strategies for offshore wind farms: a critical review and comparative study. Wind Engineering, 46(5). Doi: https://doi.org/10.1177/0309524x221095258
Van Houtum, G.J. & Kranenburg, B. (2015). Spare parts inventory control under system availability constraints, International Series in Operations Research and Management Sciences. Springer. Doi: https://doi.org/10.1007/978-1-4899-7609-3
Venkataraman, K. (2007) Maintenance engineering and management 1st edition. Prentice-Hall of India Pvt. Ltd.
Wu, M.C. & Hsu, Y.K. (2008). Design of BOM configuration for reducing spare parts logistic costs. Expert Systems with Applications, 34(4), 2417-2423. Doi: https://doi.org/10.1016/j.eswa.2007.04.001
Yang, Y., Liu, W., Zeng, T., Guo, L., Qin, Y., & Wang, X. (2022). An improved stacking model for equipment spare parts demand forecasting based on scenario analysis. Scientific Programming, 2022, 1-15 Doi: https://doi.org/10.1155/2022/5415702
Yuen, K. K. F. (2022). Decision models for information systems planning using primitive cognitive network process: comparisons with analytic hierarchy process. Operational Research, 22(3), 1759-1785. Doi: https://doi.org/10.1007/s12351-021-00628-3
Zeng, Y.R., Wang, L. & He, J. (2012). A novel approach for evaluating control criticality of spare parts using fuzzy comprehensive evaluation and GRA. International Journal of Fuzzy Systems, 14(3), 392-401 Doi: https://doi.org/10.1109/iccias.2006.294083
Zhang, X. & Zeng, J. (2017). Joint optimization of condition-based opportunistic maintenance and spare parts provisioning policy in multiunit systems. European Journal of Operational Research, 262(2), 479-498. Doi: https://doi.org/10.1016/j.ejor.2017.03.019
Zhang, Y., Jedeck, S., Yang, L., & Bai, L. (2018). Modeling and analysis of the on-demand spare parts supply using additive manufacturing. Rapid Prototyping Journal, 25(3), 473-487. Doi: https://doi.org/10.1108/rpj-01-2018-0027
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