## 1 Introduction  
Humans have managed inventory problems since we began utilizing the planet's resources [1]. The first mathematical approach to inventory planning was the economic lot size or economic order quantity (EOQ) model, carried out by [PERSON] in 1913, who developed the EOQ model, where the bases for integrating storage and other costs when determining the size of the batches to be produced [2]. Although the EOQ model was developed and initiated a development process that can be seen in Figure 1, it was not until the 1950s and 1960s that other important advances were made to meet logistics needs, such as consideration of surpluses, shortages, and order costs, to determine inventory parameters [1]. Furthermore, computerized forecasting and inventory control began in the 1960s and 1970s [3]. Since the 1980s, great effort has been made to develop inventory planning software solutions with the goal of reducing operating costs and capital employed due to stock levels in storage. However, in the day-to-day life of most manufacturers in the process industries, inventory planning continues to be driven by databases of \"manual\" spreadsheets [7]. Furthermore, the EOQ model after a century is still analyzed and expanded by researchers and is used in practice [1]. MRP II (manufacturing resources planning) emerged in the 1980s and was aimed at identifying problems of productive capacity, considering the availability of resources and production orders in such a way that the planner was able to correct possible deviations. However, it was not until the late 1980s that an attempt was made to integrate support software for automatic decision-making that suggested corrective actions. In the 90s, ERP (enterprise resources planning) emerged--given the need to integrate different areas of the company. In this context, in supply management, several authors have developed various models based on the EOQ model, as well as other models [8]. Most of the models developed consider the demand for an item as a parameter with static product specifications, that is, they do not change over time [8]. In contrast, as markets have become more complex, inventory management is also now more complex [9] and market conditions have changed more rapidly than the rate at which researchers could respond [1]. Moreover, managers in practice have been able to provide effective and efficient solutions for the global supply chain. In this way, conventional planning methods based on average values are highly extended in practice [10]. An update of the planning parameters is carried out with long intervals of time [11]. In this context, the business success of the actors in a supply chain will be determined in the future by the ability to identify disturbances early and to compensate for them with adequate planning models that ensure a high quality of planning [12]. World logistics flows have increased dramatically [13] with an international competition in meeting the service level in terms of the delivery date, delivery reliability, and nature of delivery, which increases pressure on supply flexibility [14]. Furthermore, demand volatility in almost all industry sectors appears to be higher than in the past due to shorter product and technology life cycles, sales promotions, rearranged quantities, and unplanned outages [15]. The conventional response to this challenge when dealing with uncertainty is to increase the safety stock of products in order to ensure the expected level of service [16]. Figure 1: Evolution of planning models and systems: From the EOQ model to ERP systems (own Elaboration based on [4; 5; 6]). In this context, being able to cope with the changing needs of customers, the volatility of demand, and new product launches, it is becoming increasingly important to gain a competitive advantage [17]. The need to respond to the uncertainty of demand is a necessity.