# Data-Driven Decision Making in Maintenance Service Delivery Process: A Case Study
## 5 Case Study
### The Service Stream
#### 5.1.2 Definition of the Strategy for Service Data Collection and Analysis  
As mentioned above, Beta was already collecting data while providing maintenance. Despite this, the data were usually unstructured (i.e., free text), incomplete (i.e., reports were seldomly fully completed), challenging to interpret and, thus, time-consuming to analyze. For Beta, this was one of the main limitations preventing it from improving its maintenance performance. Therefore, Beta decided to update the content of the service report to cope with the gaps identified and allow a more valuable data analysis. As part of the reengineering strategy, Beta decided to upgrade the original service report with new features to guide the filling phase instead of introducing a completely new instrument. This decision followed the intention of not revolutionizing the workflow of the technicians used to deal with Excel-based reports. Beta decided to simplify its filling (e.g., introducing drop-down menus, showing warnings in case of empty fields) so that the technicians would have been able to handle the changes immediately, without the need for specific training sessions. Among the changes, one can highlight the presence of:  
* New fields (e.g., software version) are important to describe problems. * Drop-down menus allow one to select the failed component and also guarantee uniformity in the vocabulary. * The possibility to include pictures of the failure to integrate the textual description. * The creation of a link between the spare part used, the failure, and the asset requiring the part. All these improvements were introduced in the scope of favoring the data extraction and automating the analyses using Microsoft Excel(r) macros or external means (e.g., Python code). After the improvements, Beta was able to extract information related to the failure typology, frequency, geographical distribution, spare parts consumption, and others. Additionally, Beta decided to adopt Natural Language Processing (NLP) to analyze the free-text fields in the reports. The knowledge reported in the free-text fields could be used to infer correlations between a failure and its cause (e.g., the bad behavior of component X may cause the failure of component Y), as well as depict important hints for its resolution. With the introduction of NLP on top of the traditional analyses (extended by the new data collected), Beta can have a better overview of the service performance, understand the most effective resolution approaches (e.g., in terms of resolution time), the technicians most skilled for an intervention, and other information connected to maintenance execution. Figures 2 and 3 show a comparison between the original service report and the new one. Beta decided to tackle the problem related to the unstructured trial-and-error attempts carried out by technicians to identify the failure, using the knowledge acquired during previous maintenance interventions to define a list of issues and possible solutions. Furthermore, to address the several negotiation cycles at the end of the maintenance delivery process due to the requests for discounts made by customers, Beta, on the one hand, introduced a checklist in the service report to share with the customer all the activities performed, and, on the other hand, it decided to introduce maintenance contracts to allow the customer to know in advance the price of the intervention. The maintenance contracts offered by Beta consisted of pre-established checks and interventions defined in advance and executed at time-fixed intervals. Two types of intervention were provided with the contract. The first typology focused on the general check of the asset(s) status, while the second one required a more profound analysis and a prolonged stop of the asset(s) to fix all the identifiable problems and put in place preventive measures. Figure 2: The original Service Report. The figure is elaborated by the authors. Figure 3: The new service report. Changes are highlighted in green rectangles. The figure is elaborated by the authors.