For an in-house scenario what are characteristics of materials requirements planning in classic erp

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 Harris 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].

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. 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 networksthe 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. The scenarios vary depending on the procurement order quantity and on the procurement shortages from suppliers. The final goal of the developed system is to provide adaptability and flexibility mechanisms to industrial organizations by determining a differentiated strategy based on the supplier’s procurement behavior. This is pursued to provide managers with a tool to select the procurement order quantity needed to meet required service levels while optimizing inventory costs.

The hypothesis is that EOQ models need to be considered together with the procurement shortage risks, along with the service and cost goals, in order to determine the best-fit procurement order quantity for a specific supplier’s behavior.

As a result, the system contributes to improving management capabilities within the company to act early and appropriately as well as optimizing information systems to be a crucial support for processes and decision making in volatile environments resulting from a lack of transparency regarding planning and control of production systems as one of the greatest weaknesses of ERP supply chain management (SCM) systems—as they are insufficiently integrated with each other to ensure holistic planning and control across all networks. The consequences of this problem include: unrealistic delivery dates and consequent inadequate compliance with delivery dates for customers [24]. In this context, the problem is delimited to the management of production and supplies and more related to the determination of supply needs based on the master production program.

Companies are faced with the challenge of having to master an increasing number of individualized product solutions and short delivery times at the lowest possible cost with high market dynamics leading to fluctuating customer demand and increasing short-term order changes. To successfully produce despite increasing external volatility, flexible and adaptable production systems are required that can be adapted to new requirements in a short period of time [25,26].

The novelty of the research is that it can support decision making by complementing MRP planning with a simulation of scenarios with different procurement order quantity policies, enabling prediction of the results in cases of procurement shortages based on unexpected non-reliable behavior of suppliers. It also supports preparing preventive planning, applying, and verifying lessons learned, providing an easy-to-use decision-making and continuous improvement tool for managers to direct their decisions in the best way possible.

The vision of this work is to provide a conceptual model that provides a framework on how to design procurement policies based on the regulation of the order quantities to face procurement shortage scenarios, thus enabling management levels to decide and select the optimal order quantity based on the specific scenario and characteristics of the company and its goals. As a result, the conceptual model seeks to serve as a basis for decision making for future procurement improvement projects capable of increasing service levels towards end-customers and reducing operational costs to secure the long-term viability of their organizations.

According to the “VDI-Richtlinie”, simulation is the reproduction of a system with its dynamic processes in an experimental model capable of acquiring knowledge that can be transferred to reality. In particular, processes which develop over time [43]. In contrast to analytical models that offer the possibility of reaching an optimal solution, the main problem with simulation models is that these models do not provide a closed set of solutions [44]. Simulation models are used mainly to support decision making as they reveal a system’s dynamic behavior [45]. On the other hand, simulations are the only practical way to test models, because our mental models are dynamically deficient, omitting evaluations, lags, accumulations, and nonlinearities [46].

With the help of technical simulation models, results can be transferred to reality. The objectives of a simulation are, among others, the assurance of the methodology, as well as the planning, management, and control of the flows of people, materials, energy, and information [47]. Simulation models are increasingly used in logistics, which have been actively applied in the supply chain context [44]. On the other hand, this allows us to evaluate the possible scenarios before implementing a plan and to make better planning decisions with additional information provided by the simulation. Therefore, the development of supply chain management simulation models has become a necessity [48] to gain competitiveness.

The simulation is carried out through the Microsoft Office package, specifically in Excel since it includes additional programmable functionalities with standard tools and allows the storage and interconnection of databases for their calculation and processing and for the management of manufacturing needs and supplies.

In this section, the simulation results for the simulation model for the three scenarios with different POQ and the same demand are presented in the following tables (Table 1, Table 2 and Table 3).

As shown in Table 1, five different POQ are evaluated for the first scenario. As this scenario represents the ideal or theoretical planning that reproduces the supply as expected, the simulation model provides a 100% service level without backlogs for all values of the POQ. In addition, inventories are lower as the POQ reduces, being almost 10 times lower for a POQ of 4 units than for a POQ of 96 units. On the other hand, the number of procurement orders placed follows a contrasting trend with 54 orders for a POQ of 4 and 4 orders for a POQ of 96 units. Moreover, in relation to cost, it can be seen how the minimum inventory costs and also procurement management costs are for a POQ of 24 units, which represents the economic order quantity (EOQ) for the input parameters.

As can be seen in Table 2, five different POQ are also evaluated for the second scenario. As this scenario represents planning that reproduces a reality in which the supply reliability is affected, there are deviations that imply delays reducing the service levels for the different POQ options. Service levels increase, as does the POQ, however, a significant increase in the service levels in the number of products is realized up to POQ that equals the EOQ. In addition, inventories are also lower as the POQ reduces—being almost 10 times lower for a POQ of 4 units than for a POQ of 96 units. Moreover, in relation to cost, it can be seen how the minimum inventory costs and procurement management costs are for a POQ of 96 units. However, the relevant cost reduction occurs in the change from a POQ of 4 units to a POQ of 24 units, almost USD 9000, while the change from 24 to 96 units represents less than USD 4000.

As can be seen in Table 3, five different POQ are also evaluated for the third scenario. As this scenario represents planning that reproduces a reality in which the supply reliability is affected, there are deviations that imply delays, reducing the service levels for the different POQ options. Service levels increase as does the POQ, however, a significant increase in the service levels in the number of products is realized up to a POQ that equals the maximum POQ level, which represents a difference with scenario 2. In addition, inventories are also lower as the POQ reduces—being almost 15 times lower for a POQ of 4 units than for a POQ of 96 units. Moreover, in relation to cost, it can be seen how the minimum inventory costs and procurement management costs are for a POQ of 96 units. In this case, on the contrary, as for the second scenario, the relevant cost reduction occurs in the change from a POQ of 24 units to a POQ of 96 units, almost USD 30,000, while the change from 4 to 24 units represents less than USD 20,000. This can be explained by the fact that the service level in days for a POQ equal to the EOQ is almost half than the service level with a POQ of 96 units. This implies that the delivery reliability of the plant is low and therefore the stock-out costs are a relevant factor in the scenario. This scenario presents the lower levels of inventories for all POQ due to the fact that the raw materials are not in the warehouse for long lead times as the backlog needs to be recovered and as soon as the raw materials are available, they are assembled and sent to the client.

Being able to select the most appropriate procurement order quantity can lead to relevant implications on service and cost performances. However, as syndicated with the simulation results, the most suitable procurement order quantity for the same demand depends on the supply reliability, i.e., the supply behavior. As a result, for the same demand and for the same product, there should be different procurement order quantities based on the procurement reliability of the supplier. By doing so, a manager can secure operations in the plant under their responsibility with complete planning that includes the supply deviations.

Based on the need to consider the supplier’s behavior, in the sense of their procurement reliability (in quantity and time) to generate robust procurement plans, the selection of differentiating procurement order quantities represents a key factor for logistics managers to design, optimize, and secure operations. To provide a framework for managers on how to select the most suitable POQ, Figure 9 presents a methodological sequence for this purpose while considering a given master production schedule and organizational strategy. With this information, five steps are to be followed to enable a continuous improvement process in the selection of the POQ that implies a global optimization of the supply chain. The first step consists of executing the planning—assuming a reliable supply. Then, in the second step, the expected supplier´s behavior is to be considered to simulate what-if scenarios. With the results and the probability of different kinds of behaviors and reliability levels, in the third step, the selection of the suitable POQ can be performed. Afterward, in the fourth step, the execution in the planning time horizon, providing actual indicators, can be compared with the target indicators. Finally, after the comparison, the deviations with target goals influence the continuous improvement process based on the organizational strategy, which will imply, for instance, the need for more or less safety stocks in the next procurement planning cycle.

Based on the current global situation, in which supply chain disruptions and shortages, production stoppages, and stock-outs are becoming increasingly common and are developing into a global supply crisis, the trend for procurement order quantities and related factors is expected to be as follows:

• One expected strategy is to increase procurement order quantities as producers will try to secure operations considering the supply uncertainty, thus leading to increased ordering to cover the potential future lack of supplies;

• Another strategy is to maintain procurement order quantities with orders being placed in advance as producers expect supply delivery delays;

• A third strategy might be to order the same or more procurement order quantities distributed in lower order quantities assigned to different suppliers while considering the supply risk of each supplier and potential order cancellations.

All three strategies lead—if orders are delivered according to planned dates—to an increase in the average stock levels. In an uncertain context, an increase in stocks up to a certain level will be beneficial when considering the opportunity costs of stock-outs that might arise. However, if there is no collaboration regarding transparency among supply chain partners, it could lead to the generation of a bullwhip effect that could then develop into a supply crisis. In this situation, suppliers and producers will give priorities depending on product margins or strategic factors as they might have a higher quantity in their order books than they are able to process. This prioritization leads to the supply of goods with differentiated strategies depending on the product margin, product characteristics, sector, geographical location, etc., which may imply there will be some products or sectors suffering more from the lack of supplies than others.

To balance this situation for producers, the proposed methodology provides a reliable method to balance stocks and stock-out consequences based on the selection of appropriate procurement order quantities based on the supplier´s behavior. Moreover, it should be complemented with transparency among supply chain partners and with alignment to the organizational strategy. As a result, and based on the potential supply crisis and related risks, this methodology is key to securing operations, balancing stock levels and opportunity costs, and building trust in the relationships among supply chain partners.

This section is divided into theoretical, managerial, and empirical conclusions, and explains the limitations of the research as well as describing the potential research work derived.

Theoretical conclusions: This research provides syndication of the need for models and systems to respond to the increasing global risks of supply that are creating major disruptions in international supply chains. Moreover, it provides a contribution via the methodology and developed system to deal with this challenge:

• The current challenges of procurement methodologies and supply planning systems were described;

• A new methodology for assessing and improving procurement order quantities was developed;

• A system enabling the simulation of scenarios for determining the best-fit procurement order quantity depending on the supply risk pattern was developed.

• The methodology can support managers in the management and distribution of functions in production and procurement planning departments;

• The system for materials requirements planning with simulation capabilities can provide managers with short-term procurement insights to deal with the pressure of managerial decision making;

• Steps for assessing, selecting, and improving the procurement strategy were described in the discussion section.

Empirical conclusions: To prove the utility of the new concept a simulation of a component manufacturer´s materials requirement planning was performed. In the simulation, planning data are reliable, with the exception of the procurement risk of the supplier responsible to supply a material needed to produce the final product. The simulated scenarios analyze the procurement order quantity for different supply disruption or shortages scenarios. The benefits of the simulation—and its results—are:

• The results provide evidence that suppliers’ behavior regarding supply risks is relevant for target indicators as a key factor when defining the procurement strategy of any producer;

• When suppliers’ behavior, in terms of the delivery date, is reliable, the best set of target indicators are obtained for a procurement order quantity equal to the economic order quantity (EOQ);

• When suppliers’ behavior, in terms of the delivery date, is not reliable the best set of target indicators are obtained for a procurement order quantity not equal to the economic order quantity (EOQ) and that will depend on the organization’s goals concerning the service level to the customer and total inventory costs;

• When suppliers’ behavior in terms of the delivery date is not reliable, a procurement order quantity lower than the economic order quantity (EOQ) provides higher backlogs and lower service levels with end-customers;

• When suppliers’ behavior, in terms of the delivery date, is not reliable, a procurement order quantity higher than the economic order quantity (EOQ) provides lower backlogs and higher service levels with end-customers with a small increase in average inventory levels.

Limitations of this research work include:

• A product with two-component levels and with no great complexity;

• Factors as demand, replenishment times set as constant to isolate the factors in consideration in the research work;

• Lack of organizations working with this methodology;

• The tool developed does not include the connection with other related data such as demand planning or distribution management;

• The model does not consider its integration into a system landscape;

• The selection of the most appropriate procurement order quantity depends on the user, as the user decides on the different scenarios to be compared;

• The selection of the procurement order quantity does not consider other factors such as organizational strategy and other planning methods and horizons.

Future research: The potential research derived from this paper is:

• Combine or consider other procurement factors and policies;

• Apply in other product structures and production characteristics as case studies;

• Add the influence of intelligent capabilities based on data analytics;

• Integrate the methodology in a planning system with greater functionalities.

In summary, the research shows the potential benefits of differentiated order quantity strategies depending on suppliers’ procurement behavior and their associated supply risk. As a result, the proposed methodology provides a useful tool for organizations and managers to align organizational strategies to the procurement performance and reliability to ensure greater flexibility and adaptability to secure supplies in procurement shortages and disruptions.

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Figure 1. Evolution of planning models and systems: From the EOQ model to ERP systems (own Elaboration based on [4,5,6]).

Figure 1. Evolution of planning models and systems: From the EOQ model to ERP systems (own Elaboration based on [4,5,6]).

Figure 2. Target parameter system (own elaboration).

Figure 2. Target parameter system (own elaboration).

Figure 3. Parameter system target (own elaboration).

Figure 3. Parameter system target (own elaboration).

Figure 4. Procurement order quantity planning based on deviation and compensation factors in three incremental adaptability phases (own elaboration).

Figure 4. Procurement order quantity planning based on deviation and compensation factors in three incremental adaptability phases (own elaboration).

Figure 5. Production flow of the simulation (own elaboration).

Figure 5. Production flow of the simulation (own elaboration).

Figure 6. Input–output diagram of the simulation model (own elaboration).

Figure 6. Input–output diagram of the simulation model (own elaboration).

Figure 7. Sub-models areas of the simulation model (own elaboration).

Figure 7. Sub-models areas of the simulation model (own elaboration).

Figure 8. Simulation scenarios based on supply reliability and disruption level (own elaboration).

Figure 8. Simulation scenarios based on supply reliability and disruption level (own elaboration).

Figure 9. Methodological sequence for the selection of the most suitable procurement order quantity based on the supplier’s behavior (own elaboration).

Figure 9. Methodological sequence for the selection of the most suitable procurement order quantity based on the supplier’s behavior (own elaboration).

Table 1. Simulation results for scenario 1: A reliable supply behavior.

Table 1. Simulation results for scenario 1: A reliable supply behavior.

Table 2. Simulation results for scenario 2: Non-reliable supply behavior—week disruptions.

Table 2. Simulation results for scenario 2: Non-reliable supply behavior—week disruptions.

Table 3. Simulation results for scenario 3: Non-reliable supply behavior (two weeks disruptions).

Table 3. Simulation results for scenario 3: Non-reliable supply behavior (two weeks disruptions).