Abstract
Maintenance service delivery constitutes one of the most problematic tasks for companies offering such service. Besides dealing with customers expecting to be served as soon as possible, companies must consider the penalties they are incurring if the service is delivered later than the deadline, especially if the service suppliers want to establish long and lasting relationships with customers. Despite being advisable to use appropriate tools to schedule such activity, in many companies, planners rely only on simple tools (e.g., Excel sheets) to schedule maintenance interventions. Frequently, this results in a suboptimal allocation of the interventions, which causes customer satisfaction problems. This paper, contextualised in the Balance Systems case study, proposes an optimisation model that can be used by planners to perform the intervention allocation. The optimisation model has been developed in the context of the Dual-perspective, Data-based, Decision-making process for Maintenance service delivery (D3M) framework, which aims to improve the maintenance service delivery by making a proper use of real-time and historical data related to the asset status and the service resources available. The proposed model tries to cope with the current problems present in the company’s service delivery process by proposing the introduction of a mathematical instrument in support of the planner. Being strongly influenced by the contextual setting, the model discussed in this paper originates from the D3M framework logic and is adapted to the company necessities.
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Acknowledgements
This research is supported by MADE Competence Center in the project “PRocessi, strumEnti e dAti a supporto delle deciSiOni di MaNutenzione 4.0 (REASON4.0)”.
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Sala, R., Pirola, F., Pezzotta, G., Vernieri, M. (2021). Improving Maintenance Service Delivery Through Data and Skill-Based Task Allocation. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 631. Springer, Cham. https://doi.org/10.1007/978-3-030-85902-2_22
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DOI: https://doi.org/10.1007/978-3-030-85902-2_22
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