## 4 Case Study  
### Problem Setting  
This case study provides an in-depth analysis of a company previously analyzed in [4], with the goal of tailoring effective recommendations on specific measures to be implemented within the main logistic strategies suggested in the previous work. The company operates in the renewable energy sector, specializing in the production and marketing of high-quality solar panels. The manufacturing facility employs cutting-edge technologies, including ribbon-less technology, aiming at surpassing traditional manufacturing methods. This innovative approach allows the company to enhance process efficiency while ensuring greater reliability in the product portfolio. Apart from technological advancements, a significant commitment to meeting market demands emerges by analyzing the adaptable approach to product development. Indeed, continuously assessing industry trends and customer preferences makes it easier for the company to tailor the product offer accordingly. This customer-centric focus has been crucial in establishing and maintaining an effective competitive edge. Moreover, the company invested significantly in refining its traditional approaches to production and logistics. By prioritizing the use of superior-grade materials and maintaining a heavily automated manufacturing plant, high-quality products need to be consistently delivered within designated timelines. However, logistical challenges can arise unexpected edly, requiring the implementation of proactive measures. Consequently, the company invests significant resources in fine-tuning its logistical strategies to ensure a consistent and adaptable flow of modules over time. This can be achieved by planning and coordinating to manage inventory effectively as well as responding promptly to fluctuations in demand. Several strategies were analyzed in [4], aiming at optimizing data-driven supply chain processes. Among these, two key strategies emerged: route optimization and warehouse management. Route optimization refers to analyzing extensive datasets, including traffic patterns, weather conditions, and delivery schedules. By processing these data, transportation fleets optimize their routes, therefore reducing transportation costs and enhancing delivery efficiency. Essentially, this strategy aims to streamline the transportation process, ensuring goods reach their destination in a timely and cost-effective manner. On the other hand, warehouse management mainly focuses on evaluating warehouse layout and order volumes. By assessing these factors, the company optimizes various aspects of warehouse operations, including storage, picking, and packing processes. This optimization leads to improved management of procedures within the warehouse, resulting in reduced costs and smoother operations overall. It appears evident that prioritizing route optimization and warehouse management can significantly enhance logistic operations on the whole, benefiting the company's bottom line and customer satisfaction. In the present case study, we propose a roadmap for the gradual adoption of these two strategies through the prioritization of related proactive data-driven measures. This prioritization process will be grounded in meaningful criteria derived from aspects considered as pertinent to the business sector of reference. These criteria were established through a comprehensive analysis of existing literature, resulting in the identification of ten key aspects, comprehensively described in Table 1. As specified before, this research is a considerable extension of a conference paper [4]. While the previous work analyzed seven out of ten criteria outlined in Table 1, our current research has substantially developed upon this foundation. We have updated the nomenclature and descriptions of these criteria but have also led a more exhaustive literature review tailored to our specific application domain.