# A Solution for Predicting the Timespan Needed for Grinding Roller Bearing Rings
## 1 Introduction  
In the current economic environment, industrial manufacturers have defined and implemented different development strategies based on cost-efficiency principles, considering, on one hand, resource availability and rational consumption and, on the other, production parameter improvement through a lead time reduction under conditions of keeping the same quality of the produced and delivered goods. Customer behavior has become focused on finding and qualifying suppliers, which are more suited to providing a quick reply to a customer's request, even in terms of a quotation, but more importantly, after the confirmation of a firm order, it has become a trend to predict the production cost and lead time using the data collected on formerly closed orders [1]. In recent years, companies have sought to increase their profits by investing resources in both the creation of new products and the discovery of new methods of manufacturing and delivering existing products [2]. The use of past knowledge collected during the product production stages (namely order acceptance, production planning, product design, process planning, product processing, and product delivery) is considered a key factor for improving manufacturing performance in the modern manufacturing industry [3]. In the quotation phase, the supplier should be able to predict with good accuracy the final answer in order to respond to diversified customer needs [4]. For this purpose, both the input data from the potential customer (i.e., the requested quantity of goods, the requested delivery date, and the quality of the goods) and the production lead time, considering the production flow and necessary resources, must be considered. In a real-life manufacturing processes, such as the grinding of some bearing components, the accumulation of sufficient data [5] for making timespan predictions may take a long time, during which the initial conditions (production mix, orders, process parameters, and so forth) may change. The processing of the collected data by some empirical methods involves a great deal of time and the results may be affected by potential errors; meanwhile, a certain delay in answering customers could occur. In order to eliminate as much as possible the mentioned weakness related to time measurement and data processing, suppliers should focus more on the development of programs or dedicated applications that are able to predict the correlation between different factors of the production process (timespan, production range, technical specification for the products, machine availability, and productivity) and the order delivery deadline, based on a database which contains information gathered from previous closed orders and by using a real-world manufacturing process [4]. Under these circumstances, companies that use knowledge collection based on recorded and processed data concerning their products and ongoing processes, and which can apply it effectively and efficiently, will be successful at achieving competitive advantages [6]. The economy of machining operations plays a key role in competitiveness in the market. The authors addressed the minimization of the production time of a cutting process in [7]. They determined the optimal processing parameters in the case of processing a continuous profile in relation to the production time. The lead time is a central element of the economic evaluation of a newly ordered or only quoted product [8]. A cost breakdown helps the process engineers understand the main cost components and also observe the main influences of the process parameters, such as the timespan, cutting speed, feed rate, and cutting depth, on the final cost of the product during the quotation phase [9]. The manufacturing process between ordering and product delivery involves two successive stages that are time-consuming and have an impact on the results--the production cost and the delivery time.