Exploitation of Manufacturing Execution System (MES) for production monitoring and product cost estimation in One-of-a-kind production.

In the full evolution of the phenomenon of industry 4.0, digitalization is transforming the landscape of the sector and opening up a massive range of new opportunities. Over 91% of European enterprises invest in digitalizing their processes and are expecting an increase in efficiency due to new technologies, including data analysis solutions, integration of different IT systems and real-time production planning and control. For a company that wants to compete in such a new market, it is necessary to keep up with these emerging technologies. From this perspective, various innovations are being developed to digitalize the production processes. One of the information systems used to manage production data is the Manufacturing Execution System (MES). The new industrial paradigm requires customized, high quality and low-cost products for the customer. The extreme application of this concept is identified as a One-of-a-kind production (OKP). Each product is unique and is not fully defined at the design stage. In this situation, it is particularly critical to estimate, during the design phase, the processing time and the final cost of the product, to propose the right price to the customer. In fact, cost is a central aspect that leads to developing and distributing functional requirements and is critical for product mass costuming success in the product development (PD) chain. To this aim, the thesis addresses the implementation and use of a MES to monitor the production and collect real time data coming from the plant. Particularly, data about the manufacturing processes, the running time of toolpaths, the features of the machines and the components geometric information have been collected. Based on these data, it is possible to compare the initial scheduling of activities on the machines with the current execution of activities, and update the planning in real time. Furthermore, production cost data were used to propose a new feature to allow the MES to estimate also the final product cost. The system has been implemented in an Italian company that produces metal sheet components for prototypes and small series in the automotive sector, which is characterized by a production with high complexity and high mix of products.