The optimal management of manufacturing processes can be achieved through a set of optimal decisions, which must be made to choose the best method to follow every time the process planner reaches a point when several potential manufacturing paths branch off. A dedicated method, namely the Holistic Optimization Method (HOM), has already been developed for this purpose and has been validated in several studies based on artificial- and real-instance databases. The HOM consists of two algorithms: (i) the causal identification of a manufacturing process and (ii) a comparative assessment with similar already-assessed manufacturing cases recorded in an instance database. The two algorithms can be used to estimate the values of the different performance indicators of manufacturing processes. Their application for processing cost estimation in the case of the manufacturing processes of bearing components has already shown good results. In this paper, the HOM is presented as a solution for predicting the timespan needed for grinding roller bearing rings. The specific algorithms of the HOM were applied, grounded in the use of a database with data collected from the industrial environment. The cause variables selected to describe the grinding process of the roller bearing rings were the inner and outer diameter of the ring, its width and weight, the machined surface roughness, the grinding stone rotation speed, the cutting speed, the feed rate, and the cutting depth, while the effect variable to be used by the process planner as the decision criterion was the timespan.

The optimal management of the manufacturing processes is achieved through a set of optimal decisions, which must be made for choosing the best way to follow, every time we find ourselves in a point from which several potential manufacturing paths start. A dedicated method, namely the Holistic Optimization Method has been already developed in this purpose, and validated in a number of studies based on artificial and real instances databases. In the current papers that approach the optimal management of the manufacturing processes, in order to estimate the consequences of a decision, are used known methods, such as: NN modeling, big data analysis, statistics, etc. In all these cases, the database size plays an essential role in terms of estimation quality. The present study aims to prove the feasibility of applying the Holistic Optimization Method when the decision-maker does not dispose of a consistent database. This can be a significant advantage relative to the other methods. The study is performed using an artificially generated instances database in the case of a turning process, and the results obtained are promising.

In today's globalization of businesses, the behavior of the industrial consumers registers a change regarding the products available on market. The industrial consumers become increasingly selective when they decide which components are to be integrated into their final products. The trend observed in business processes is to accommodate the former production processes with the sustainability requirements and recommendations. By considering the markets requests, the manufacturers start to develop sustainable products and associated processes considering the entire life cycle of the product. One of the main jobs of the designers is to integrate in all development and production phases the applicable requirements and finally to come in front of customers with a product which satisfies the technical requirements, on one side, and the sustainability requirements, on the other side, without affecting the capability of the product to be used in a safe way. Safety should be considered in an integrative approach related to producer and user, environmental impact and business efficiency.