# Data-Driven Decision Making in Maintenance Service Delivery Process: A Case Study
## 3 The D3M Framework  
This paper wants to present a case study where a manufacturing company re-engineered its maintenance service delivery process in a data-driven fashion following the approach proposed by the D3M framework, first presented in [10]. The novelty of the paper resides in the first complete application of the D3M framework, previously only theoretically discussed. As shown in Figure 1, the D3M framework is made of three main streams. Two of them are parallel (i.e., the service stream and the industrial asset stream), while the third one combines the information collected and elaborated by the previous ones to make decisions related to the maintenance delivery (i.e., the maintenance delivery stream). The Service stream, composed of three stages, is related to the analysis of the maintenance service performance, and aims at:  
1. Supporting the identification of the company's maintenance service offering. Through the process mapping, it is possible to clarify the components of the maintenance service delivery process in terms of actors, activities, decisions, and information flow. 2. Defining a strategy for the service data collection and analysis. Knowing the process, it is possible to define which maintenance service data to collect and analyze to assess performance. 3. Analyzing the data collected to define the service performance. This allows the companies to monitor the strengths and weaknesses of their service process and then favor selecting the maintenance service offering portfolio. The Industrial Asset stream comprises four stages. They guide the company in:  
1. Identifying the most critical components. This phase can be executed following different approaches, some more structured (e.g., the FMECA) and others less (e.g., the Open Review Meetings). When using structured approaches, components criticality can be defined using the Risk Priority Number (RPN), which can be a function of repairing costs, failure frequency, and repairing time and can be kept updated through the Dynamic FMECA [39]. In less structured approaches, criticality may be defined following a discussion between the participants at the meetings. The components' criticality allows (re)defining the list of components necessary to monitor and, thus, updating the data collection list. 2. Defining a strategy for data collection. Given the output of the previous phase, the aim is to define aspects such as the sensor selection, trade-off evaluation, and selection of the algorithms for the analyses. It is performed by selecting, among the algorithms considered, the one that performs better according to the company's interests. 3. Collecting the data and categorizing them to favor the analysis. This is a continuous activity performed daily while the asset is working. 4. Analyzing the data to identify the health status of the asset. Based on the results of the analyses, maintenance-related decisions (e.g., maintenance required, wait) can be made. The two streams merge into the Maintenance Delivery stream, composed of three stages aimed at:  
1. Cross analyzing the data incoming from the previous streams as well as the data related to the available service resources (e.g., technicians, schedules, skills) and the customer (e.g., location, contractual clauses, skills). This step allows matching the service requests with the resources, proposing, at the same time, a tentative schedule for the service execution. 2. Making decisions related to service delivery. Based on the resolution proposal defined in the previous phase, the actors decide on the service delivery. Figure 1: The D3M framework [10]. 3. Collecting data during the service execution that will be analyzed and used to capture deviations from the expected trends in terms of failures or service execution and, thus, as an input for improvement activities related to the Industrial Asset or the Service.