# Data-Driven Decision Making in Maintenance Service Delivery Process: A Case Study
## 6 Discussion
### Benefits of the D3M Framework Introduction  
While the D3M framework and the methods are mainly focused on the exploitation of data to support operational decision-making, the aggregated information could be used to make decisions at the strategical (e.g., dimensioning the service resources) and tactical (e.g., update the maintenance policies) levels, thus shifting the perspective from short-term towards medium- and long-term impact. Indeed, the D3M framework aims at favoring dialogue among the departments involved in the PSS delivery (e.g., service and asset design) to create new knowledge that Beta used to improve the asset and the service, change the maintenance policy previously associated with them, deciding to acquire new internal skills (e.g., both in terms of maintenance execution and data management for  
Figure 4: Company Beta pre- (red rectangles) and post- (green rectangles) D3M implementation.  
decision-making), defining new spare parts refilling policies, and proposing new services tailored to the customer needs (e.g., training).  
In terms of assets, the new knowledge allowed Beta to improve management of the asset stream (e.g., to design or redesign components, update maintenance policies through the dynamic FMECA), strengthen the preventive maintenance offering, and reduce the corrective interventions.  
Another aspect that could be considered is the increased awareness that the company had of its capability to manage and respond promptly to maintenance requests, which was used to define the details of the maintenance contracts with the customers. Following the D3M framework implementation, Beta gained knowledge on the number, length, content, and frequency of the interventions, and this, in combination with the adoption of the optimization model, led to an improved schedule of the maintenance activities. The analysis of the intervention executed also contributed to better defining the instruments required for the execution of each intervention typology as well as, in the future, defining the most efficient and effective countermeasures for each failure in terms of execution time and procedure. By analyzing the maintenance execution procedures and timing, skill-based improvement plans for the technicians were defined, allowing them to cover their gaps and making them more efficient.  
Regarding the intervention planning, in the medium term, Beta expects benefits in terms of the time required to schedule an intervention between 10-15%. Such reduction would affect the downtime (DT) of the asset (10-15%) and the Mean Time To Repair (MTTR) (10-15%), which can benefit from both a prompt allocation of the request and from the selection of skilled technicians to execute it.  
Such improvements would also be connected to reducing the number of corrective interventions (also due to the update of the maintenance policies) and reducing penalties paid for contractual clauses not respected (e.g., interventions executed after the contractual due date) by around 5-10%. This will also positively impact the number of preventive contracts subscribed by customers, availability of the assets, and others.  
The analysis of the spare part used during the intervention contributed to better defining the stocking policies as well as their consumption rates, which, in the future, will allow one to also achieve benefits in terms of sustainability (both in economic and environmental terms).  
Furthermore, the data analysis opens new business opportunities (e.g., offering tailored services). In this case, the analysis of data from previous interventions allowed the company to offer tailored maintenance programs and contracts to customers, as well as training programs allowing them to use the assets better increasing not only productivity but also reducing unexpected failures.