From costs to prices through a data-driven approach: the NLMK Verona S.P.A case

NLMK Verona SPA is a manufacturing company operating in the metalworking sector, specializing in the production of large volumes of steel annually. In this context, a company's competitiveness is measured by its ability to strike the right balance between profitability and competitive selling prices. To maximize company margins, monitoring and managing variable costs is crucial. Effective and efficient management allows for attractive selling prices. In this project it is conducted a historical analysis of variable costs by product line, aimed at creating a customized price list for specific orders. The assumptions underlying the analysis are the following: a limited time interval, i.e., 2017-2024; evaluation of only the "standard" production months, thus excluding August and December due to plant downtime for maintenance; and, finally, harmonization of all variable production costs, which consists of excluding cost variations due to energy and cost variations related to inflation. The analysis is conducted by attempting to estimate the true variable production cost, eliminating exogenous variables as much as possible and attempting to understand in which period the company was able to produce while limiting costs and why, through logarithmic regression analysis. Once the trend is understood, the conclusions are applied to the current situation to forecast the variable cost based on a given monthly production or a future order. Once the analysis is complete, a variable production cost forecast is obtained based on a given company production. This study generates a price list that allows the selling price to be reduced in exchange for purchasing greater volumes and, at the same time, generates a reasonable margin for the company. The price list is dynamic and allows the projected production volumes to be adjusted from time to time based on the situation. The support tools for carrying out the analysis are SAP management software, for extracting data for each period, and Microsoft Excel for data collection, preparation, and processing. Furthermore, specific, custom-built graphs allow for quicker and more immediate visualization and comparison.