## 1 Introduction
Particularly, this identification and reduction of losses, which are considered as activities that do not add value, but add cost to the product that customers are not willing to pay, allows the reduction in manufacturing costs ([PERSON] et al., 2020). The three Mus, Muda, Mura, and Muri, are some of the techniques to detect wastes. Defects, overproduction, waiting, transportation, inventory, motion, and over-processing are seven types of wastes covered by Muda, while Muri refers to any action related to tangible or intangible stress conditions, and Mura identifies irregular machine or person use ([PERSON] et al., 2020). Regarding losses, the six big losses concept developed by [PERSON] is currently used in production processes that involve machining processes, and it has been chosen to establish our new KPI based on cost losses. These losses were categorized by [PERSON] into three big blocks, i.e., time, speed, and quality loss, described as follows (see Figure 1) ([PERSON] et al., 2020):  
* Breakdowns or equipment failures: time losses. * Set-up and adjustments. * Minor stoppages during production. * Reduced speed. * Reworks: products with minor defects that can be reworked. * Rejects: defective products that cannot be reworked. These six big losses were used by [PERSON] to develop an indicator, known as Overall Equipment Effectiveness (OEE). This OEE measures the productivity of individual equipment in a manufacturing environment. It is calculated by multiplying three indicators: availability, performance, and quality ([PERSON] et al., 2020). Although these three indicators are key elements of performance, other relevant factors, e.g., efficient use of raw materials, their quality, or costs are not taken into account in the OEE calculation [23]. Over the years, the literature scarcely considered any references to the economic aspects associated with the impact of the losses indicated by [PERSON] [24]. Although some criticisms are related to its non-connection with costs [25], [PERSON] and [PERSON] [22] considered that the translation of loss effectiveness in terms of cost should be explored and [PERSON] and [PERSON] [26] considered cost measure as part of the internal effectiveness for their total maintenance effectiveness calculation. These six big losses had been used to develop other KPIs, e.g., ECL (Energy Consumption Loses), which takes part of our previous work and shows the energy losses that are involved in a machining process [27]. In order to compare our new KPI with them, both KPIs, OEE, and ECL, have been implemented in our CPS. KPIs and manufacturing targets, such as production cost, are indispensable for efficient operation of a manufacturing enterprise. Therefore, the design, production, and manufacturing decision variables need to be identified, modeled and compared against them [28]. [PERSON] and [PERSON] [29] remarked how a company that wants to maintain a competitive position must be able to combine high quality product and/or services with fine-tuned delivery times and the lowest possible costs. In order to minimize costs, exact information about those costs must be available [30]. Regarding cost, the identification of KPIs, which allows enterprises to control and reduce costs, is crucial. According to [PERSON] et al. [31] the selection, evaluation and configuration of systems should be based on economic decisions. [PERSON] and [PERSON] [32] talked about the disadvantage of having excessive production costs in markets characterized by predatory competition. Cost can be defined in terms of financial outlay or as an effort made to achieve an objective [33], and its plan, control, prediction, and reduction in operating processes allow companies to achieve revenue streams and to assess the financial implications [34]. Industry 4.0 has remodeled the data acquisition in enterprises. Not only allow the development of CPS enterprises to collect real-time data, but they can develop KPIs in real-time too.