# Data-Driven Decision Making in Maintenance Service Delivery Process: A Case Study
## 2 Literature Background  
The literature on PSS, Industry 4.0, maintenance, and decision-making shows that these themes are strongly connected if analyzed from a service-oriented perspective. The transition towards economically sustainable PSS offerings requires the definition of processes able to fulfill customer requests and, contemporarily, to manage company resources [11] to be efficient and avoid economic losses [12; 13]. To improve efficiency in service delivery, companies must rethink their internal processes and manage them throughout the whole PSS lifecycle [14], building them on strengths and tackling weaknesses [15] (e.g., by exploiting collaboration networks [8]). The fourth industrial revolution contributes significantly to improving service delivery [16] and, as asserted by [17], the digitalization of services will gain importance over the next years. Indeed, the possibility of gathering and processing data generated by the asset during its Middle-of-Life (MoL) stage [14] contributes to (i) generating new knowledge [18] on the asset [19], (ii) monitoring the asset's health status [20; 21], (iii) better scheduling the maintenance activities to prevent failures and downtimes [22; 23], and (iv) improving the sustainability of the PSS offering [24]. In this sense, data processing becomes a value-added activity for a company only when data are converted into usable knowledge. In any case, data processing is not a trivial activity and requires structured processes for data collection, management, and analysis [25; 26]. For this purpose, it is important to establish well-defined goals and strategies [27] and pursue them properly. Authors like [PERSON] et al. [28] clarify that errors during data collection influence the following analyses and may lead to wrong decisions affecting maintenance outcomes. Besides issues in data collection, customers' willingness to share operational data with the maintenance service provider also emerges as critical for generating new knowledge and value for the stakeholders [29]. The main benefit of gathering asset MoL data is that it can feed data-driven decision-making tools and methods, representing a growing research field in maintenance service delivery [30; 31]. [PERSON], [PERSON], and [PERSON] [32] affirm that the presence of a Decision-Support System (DSS) in a manufacturing environment is fundamental to support operations and activities along the product lifecycle. Despite this, in manufacturing companies, decisions are predominantly made by humans grounded on their expertise rather than on field data [33]. To support companies in introducing DSS, [PERSON] et al. [34] discuss the importance of designing suitable DSS to allow \"_dynamic and near real-time actions_\". Some authorsproposed classifications for DSSs to facilitate their selection [35; 36] in support of human decision-making. As [PERSON] and [PERSON] [35] affirm, it is necessary to distinguish between specific DSS, built to handle specific decisions and with high reliability in their domain of competence, from the more general ones, which can be applied to different domains at the cost of lower reliability. In this perspective, the improvement of PSS performance and, specifically, the maintenance service delivery processes are strongly linked to how decision-support and data-processing methods are selected and integrated inside companies [37], as well as how companies decide to study and reuse knowledge previously generated [38; 39]. This link becomes even more evident when the operational perspective is adopted, where data availability and suitable decision-making instruments influence daily performance [40]. The availability of such instruments, able to manage the integration between service and asset-related information and handle domain-specific data, is questioned by authors like [PERSON] et al. [41], [PERSON] et al. [33], and [PERSON] et al. [7], who clarify the necessity for such tools to improve the PSS and service-related decision-making.