## 1 Introduction
The current situation, characterized by epidemic crises, product changes, and deviations in replenishment time, can lead to stockouts and a high level of inventory required to provide a given level of service. People affected by the potential lack of supplies are at great risk [18], as experienced with the global COVID-19 crisis when companies speculated on the supply of demand--reducing supply to increase margins due to client demand. Therefore, theoretical developments are required with methods that determine inventory levels based on measures other than cost [1]. The COVID-19 pandemic has had significant global economic consequences [18]. This situation demonstrated the future risks and current fragility [19] of supply chains. The consequences have included production stoppages due to a lack of supply of raw materials, while other producers had to decrease or interrupt production because finished products could not be shipped abroad. In addition, there can be an increase in obsolete products and materials when demand is low or products cannot be shipped, as has occurred during the COVID-19 pandemic, causing risks associated with oversizing of stock levels and causing an increase in waste. Therefore, there is an urgent practical need to increase the adaptability of manufacturing and procurement systems in order to react to unforeseen events, such as COVID-19 [20]. Moreover, new technologies have led to increasingly recurring changes in product specifications, which has led to high volatility in demand for products in a finite life cycle [1]. Therefore, as product life cycles are shortened, the randomness of demand increases [21]. As a result, logistics managers and supply planners must make decisions with a higher level of uncertainty about demand. It is at this point that new models and concepts should contribute to providing solutions to this challenge [20]. In this context of fluctuating demand, long lead times, inaccurate forecasts, the large variety of products, and the impact of complex networks the current methods of production planning and inventory control have shortcomings. MRP, as a production control system, is appropriate for the deterministic environment. Therefore, in a stochastic condition, research has proposed a modified MRP to anticipate the demands and lead time with demand-driven material requirements planning for the MRP to adapt to the variability of demands and supplies. However, there are few studies on the benefit of this type of approach [4]. Companies that have had experience with MRP have chronic problems and the risk of large variations, overstocks and shortages for customer supply. These conditions affect three main factors: inventory performance, service level performance, and higher levels of expenses and waste [4]. In the current global system, procurement planning in the process and assembly industries acquire vital importance to provide companies with the productive flexibility necessary to adapt quickly to changes through efficient management of stocks and supply costs. Scenario simulation can help to solve manufacturing and inventory planning challenges [22] combined with classical materials requirements planning. To date, many models have been developed to deal with uncertainty and determine the optimal inventory policy [23]. However, most inventory models are related to the classical cost analysis approach with uncertain input data precision [1]. The cause of the uncertainty has several reasons [23] such as the procurement risks of a certain supplier that can produce stock-outs and production losses. Therefore, how to balance the procurement strategy and cost optimization while considering potential procurement shortages is a key decision for managers. In this context, this research aims to develop a methodology for materials requirement planning by means of scenario simulations in order to improve the future procurement order quantities based on the impacts of target indicators.