Abstract
Maintenance has traditionally been seen as a costly and unwanted evil. Nowadays, the view of maintenance has changed, as new maintenance technologies and Smart Maintenance have proven to give competitive advantages for industrialists. Additionally, increased global competition and need for improving manufacturing performance has resulted in more focus on company’s maintenance function. Another field of interest, is how modern value chains can support the maintenance function in a company. Research on the field of value chain and maintenance has mainly been carried out independently, and research on how these two fields can be combined and utilized to strengthen each other seem to have just launched the start line. This is especially true when it comes to the area of improving maintenance programs, which traditionally have been static, inflexible, and inefficient. Thus, the aim of this paper is therefore to investigate on how maintenance programs can benefit from including a value chain perspective, and, additionally, present a concept for balanced maintenance programs in asset intensive industrial plants.
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References
Fordal, J.M., Rødseth, H., Schjølberg, P.: Initiating industrie 4.0 by implementing sensor management – improving operational availability. In: International Workshop of Advanced Manufacturing 2018, Advanced Manufacturing and Automation VIII. Lecture Notes in Electrical Engineering, vol. 484, pp. 200–207. Springer, Changzhou (2019)
Chan, F.T.S., Lau, H.C.W., Ip, R.W.L., Chan, H.K., Kong, S.: Implementation of total productive maintenance: a case study. Int. J. Prod. Econ. 95, 71–94 (2005)
Kagermann, H., Wahlster, W., Helbig, J.: Recommendations for implementing the strategic initiative INDUSTRIE 4.0 (2013)
DIN: German Standardization Roadmap - Industry 4.0 (2018)
Frank, A.G., Dalenogare, L.S., Ayala, N.F.: Industry 4.0 technologies: implementation patterns in manufacturing companies. Int. J. Prod. Econ. 210, 15–26 (2019)
Schuh, G., Anderi, R., Gausemeier, J., Ten Hompel, M., Washlster, W.: Industrie 4.0 maturity index. Managing the Digital Transformation of Companies (Acatech STUDY) (2017)
Rødseth, H., Schjølberg, P., Marhaug, A.: Deep digital maintenance. Adv. Manuf. 5, 299–310 (2017)
Staufen: Industry 4.0 - German Industry 4.0 Index 2018: A study from Staufen AG and Staufen Digital Neonex GmbH (2018)
Nur Adi, T., Wahid, N., Sutrisnowati, R., Choi, Y., Bae, H., Seo, C.S., Jeong, S.H., Seo, T.Y.: Cloud-based predictive maintenance framework for sensor data analytics (2018)
Standards Norway: Risk based maintenance and consequence classification Z-008, pp. 14. Standards Norway (2017)
Biteus, J., Lindgren, T.: Planning flexible maintenance for heavy trucks using machine learning models, constraint programming, and route optimization. SAE Int. J. Mater. Manuf. 10, 306–315 (2017)
Lindgren, T., Biteus, J.: Expert guided adaptive maintenance. In: European Conference of the Prognostics and Health Management Society, Nantes, France, 8th–10th July (2014)
Lindgren, T., Warnquist, H., Eineborg, M.: Improving the maintenance planning of heavy trucks using constraint programming. In: ModRef 2013: The Twelfth International Workshop on Constraint Modelling and Reformulation, Uppsala, Sweden, 16th September 2013, pp. 74–90. Université Laval (2013)
Prytz, R., Nowaczyk, S., Rögnvaldsson, T., Byttner, S.: Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data. Eng. Appl. Artif. Intell. 41, 139–150 (2015)
Cholasuke, C., Bhardwa, R., Antony, J.: The status of maintenance management in UK manufacturing organisations: results from a pilot survey. J. Qual. Maint. Eng. 10, 5–15 (2004)
Basak, D.: Integrating maintenance activities and quality assurance in a research and development (R&D) system. Qual. Assur. J. 10, 249–254 (2006)
Susto, G.A., Schirru, A., Pampuri, S., McLoone, S., Beghi, A.: Machine learning for predictive maintenance: a multiple classifier approach. IEEE Trans. Ind. Inform. 11, 812–820 (2015)
Al-Mudimigh, A.S., Zairi, M., Ahmed, A.M.M.: Extending the concept of supply chain: the effective management of value chains. Int. J. Prod. Econ. 87, 309–320 (2004)
Porter, M.E.: Competitive Advantage - Creating and Sustaining Superior Performance. Free Press, New York (1985)
Walters, D., Lancaster, G.: Implementing value strategy through the value chain. Manag. Decis. 38, 160–178 (2000)
Ambrosini, V., Bowman, C.: How value is created, captured and destroyed. Eur. Bus. Rev. 22, 479–495 (2010)
Alsyouf, I.: The role of maintenance in improving companies’ productivity and profitability. Int. J. Prod. Econ. 105, 70–78 (2007)
Maletič, D., Maletič, M., Al-Najjar, B., Gomišček, B.: The role of maintenance in improving company’s competitiveness and profitability: a case study in a textile company. J. Manuf. Technol. Manag. 25, 441–456 (2014)
Rødseth, H., Fordal, J.M., Schjølberg, P.: The journey towards world class maintenance with profit loss indicator. In: International Workshop of Advanced Manufacturing 2018, Advanced Manufacturing and Automation VIII. Lecture Notes in Electrical Engineering, vol. 484, pp. 192–199. Springer (2019)
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Fordal, J.M., Bernhardsen, T.I., Rødseth, H., Schjølberg, P. (2020). Balanced Maintenance Program with a Value Chain Perspective. In: Wang, Y., Martinsen, K., Yu, T., Wang, K. (eds) Advanced Manufacturing and Automation IX. IWAMA 2019. Lecture Notes in Electrical Engineering, vol 634. Springer, Singapore. https://doi.org/10.1007/978-981-15-2341-0_39
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