# Data-Driven Decision Making in Maintenance Service Delivery Process: A Case Study
## 1 Introduction  
In recent decades, manufacturing companies have been experiencing a critical transformation due to the spread of new business models based on the offering of bundles of products and services--namely, Product Service Systems (PSS) [1]--and the introduction of Industry 4.0 technologies, which has contributed to the progressive digitalization of production and management processes [2, 3]. Digitalization allows companies to collect increasing quantities of data to support decision-making [4] in both product and service domains.  
The introduction of new service-oriented business models and digital technologies is not trivial for manufacturing companies, since they need to change their relationships with customers (traditionally short-termed and based on products sale) [5] and, consequently, the way processes are managed and services delivered (e.g., new situations to handle or new technologies, resulting in increased complexity and risks) [6]. Complexity further increases when dealing with services since they require facing all the stakeholders' peculiar characteristics [7], such as customers' preferences, behaviors, and attitudes, the way stakeholders interact, the skills available, and others [3, 8]. Over the past few years, researchers and practitioners have started investigating the benefits achievable by combining PSS with Industry 4.0 technologies in the manufacturing companies' processes. Maintenance is, among others, the service that can benefit the most from digitalization, since the acquisition and processing of data from the field can substantially improve the way maintenance service is delivered [9]. Leveraging the case of an Italian manufacturing company, thispaper contributes to the understanding of how a maintenance service delivery process can be re-engineered in a data-driven fashion thanks to the guidance of the Dual-perspective, Data-based, Decision-making process for Maintenance service delivery (D3M) framework proposed by [10]. Based on the D3M framework, this paper shows how the company has identified weaknesses in its original process and has coped with them by selecting and customizing activities and methods to support the newer-engineered, data-driven, maintenance service delivery process. It is important to clarify that the D3M framework should not be intended as a novel IT architecture, but rather as a framework to support companies in understanding how their decision-making processes can be improved by combining new activities related to data collection and processing (e.g., improved collection of data during maintenance service delivery and following analysis, analysis of fault trends to identify the necessity for redesign components), shifting from pure experience-based decision-making to a mix of data and experience.  
Thus, this paper seeks to answer the research question: \"How can a manufacturing company re-engineer its maintenance service delivery process in a data-driven fashion through the D3M framework?\"  
The paper is structured as follows. Section 2 provides a literature background. Section 3 briefly presents the D3M framework. Section 4 describes the research methodology. Section 5 describes the case study, showing how the transformation of Company Beta took place. Section 6 discusses the results, while Section 7 concludes the paper by delineating future developments.