The Application of Monte Carlo Simulation on Cement Sales of Al-burj Cement Plant in Zliten For The Purpose of Quantifying And Estimating The Market Demand

Nowadays, there is a need to improve the production of cement in Libya to meet high levels of market demand and eliminate the most possible levels of waste. In this study, the technique of Monte Carlo (MC) Simulation is applied using the cement monthly sales quantities record in all 2018 as obtained from the marketing department of Al-burj Plant. The technique is used via quantifying the amounts of cement in metric tons demanded by the market on monthly basis, to estimate the market daily and monthly need for cement to participate in reducing the waste and satisfying the consumer. The results of both monthly and yearly demand are simulated to be: 199,539.801 and 2,394,477.608 metric tons of cement respectively. This technique is applied to reduce, if not to eliminate any cost or waste as a result of shortage or overproduction within this continuous massive flow process of producing cement, from the lean manufacturing perspective. The annual designed capacity of the plant is partially neglected, because, it is most likely, the plant actual production does not reach the annual designed capacity, where, the waste percentage is very huge between the designed and actual capacity of the plant as posted in (2016 - 2018), due to many reasons. Also, the market demand of cement always fluctuates up and down along days and months of the year. Unless "the majeure force" is underway, the technique should be applied at any cement plant in Libya to avoid shortage or overproduction and satisfy the consumer.


INTRODUCTION
One of the main challenges that face the cement industry in Libya is the high levels of market demand in most of the time, including, the high levels of waste that take a place throughout the manufacturing operations of cement. Most of the industrial organizations work very hard to increase their productivity, produce good quality as well as to stay competitive https://doi.org/10.59743/jauas.7.1. 6 globally. Lean Manufacturing is one of the main factors that can lead any organization to achieve that. The Lean Manufacturing may be defined as a complete integrated system that deals with all aspects of production operations [1]. It relies upon the principles of eliminating or minimizing wastes at all levels throughout the manufacturing system within the organization [1]. Lean can also be defined as a production practice that considers the expenditure of resources for any goal other than the creation of value for the end customer, to be wasteful, and thus target for elimination [2]. Therefore, the application of Monte Carlo Simulation can be considered as one of the tools or techniques that participate in implementing the lean manufacturing system within this industrial organization of producing cement, since, it focuses on estimating the market demand to satisfy the end consumer. Many companies use Monte Carlo Simulation as an important tool for decision-making, like General Motors, Sears, Wall Street firms and others [3]. Consequently, Al-burj plant is a good case for conducting the MC technique to eliminate as much waste and cost as possible.
Furthermore, MC Simulation is a mathematical modeling that can be used to many types of production management situations [4]. It is often applied to production and manufacturing problems such as production scheduling [4], where, the cement manufacturing is a typical example for that. Simulation does not normally provide a direct solution, but instead it provides informative figures that is used to make a decision in regard and for that, it is used as one of the operational decision making tools since it enables us to module situations that present uncertainty and play them hundreds if not thousands of times on a computer [3][4].
One research paper in June 2010 discussed the need to investigate Supply Chain Management (SCM) strategies that will enable the cement manufacturing industry of Libya to move towards an increase in cement production and reduce its cost, where, Witness Simulation Software has been used to model the manufacturing operations at a specific cement plant in Libya [5]. Another conference paper in August 2014 used the Witness Simulation package as well to support the implementation of management systems in SCM and the introduction of Just-In-Time systems (J.I.T). The developed Simulation model is based on Libyan Cement industry, exploring SCM and JIT system from start to end of the processes [6].
In January 2016 a conference paper used the Monte Carlo Simulation for the purpose of optimizing the cement raw material blending. The Monte Carlo Simulation was used to cope with challenges associated with uncertainties in raw material compositions [7]. But, in this research paper, the Monte Carlo Simulation is applied using Microsoft Excel 2010 to Simulate supply and demand of cement in particular, by the Libyan consumer for Al-burj Cement Plant in Libya, which is the most modern with the highest production capacity among all other cement plants in Libya by far. This paper focuses specifically more on satisfying the end customer or consumer than the previous papers above, in addition to participating in reducing waste and cost within the cement organization, from lean manufacturing perspective as mentioned. However, most previous simulation research topics reflect the major concerns of the cement industry like cement production stages, operations, material, management and sustainability, but there are limitations of using the MC technique such as "the majeure force" which has taken a place in the industry field in Libya within the last ten years or so.

MC SIMULATION APPLICATION & ANALYSIS
Monte Carlo Methods are now extensively used in all industries and government to study the behavior of complex systems of all sorts and many of these applications are performed with software programs, like the Excel solver model [ 8 ]. As a stochastic model, MC Simulation are abroad of computational algorithms that rely on repeated random sampling to obtain numerical results. The underlying concept is to use randomness to solve problems that might be deterministic in principle [9][10].
The year of 2018 is deliberately used in this study among the other two years: 2016 and 2017 as shown in Table ( 1). Because, the annual actual production in 2018 is the closest to the plant designed capacity, where, 2,215,137.660 metric tons is the closest to the plant designed capacity, which is about 2.9 million metric tons/year, among the other two annual actual capacities of 2016 and 2017. The demand by the consumer or market for each month of the year is based on the assumption of the sales record as shown in Table (2). The total cement amounts sold on daily basis are accumulated to end up with the assumed total of one month production as assigned accordingly to every month in 2018 as shown in Table (2). Thus, the daily market demand is the essential key that indicates the accumulative quantity of cement to be sold monthly, from which the monthly % of demand (frequency %) is calculated as shown in Table (2), where, these frequencies of occurrence are required for conducting the MC Simulation model.
The question will arise, why is the MC Simulation used? where demand for cement in Libya is higher than supply and the designed production capacity of Al-burj plant is known? The answer to the question is, MC Simulation can satisfy the consumer by avoiding any shortage cost, including, avoiding any overproduction cost, regardless of knowing the plant designed capacity. From lean manufacturing perspective, as shortage in cement is considered to be waste, the over production in cement is classified to be waste as well and target for elimination, since it includes inventory cost and time.
Besides, there are cases from time to another, where, demand is less than supply for cement. In this case of surplus, the overproduction material of cement should be handled to avoid any buffer inventory costs that may take a place, as per lean production perspective [1]. Figure Table (1), including the fluctuations and decrease in cement sales in the summer time of 2018, are all due to: shortage in blasting material to be used for getting raw materials, shortage in heavy oil, as well as, the most influential factor of all which is power blackouts that used to occur frequently for very long durations, as informed by individual engineers at Al-burj Cement Plant in regard.    Table (2), regardless of any other external or internal factors that may cause increase or decrease in demand or supply of cement. Any probability(P) lies between (0 < P < 1). Therefore, the frequency of demand of the 12 months from Al-burj Cement Plant in 2018 are obtained as mentioned above. These frequency percentages of demand are by default the demand probability distributions within 2018 required for conducting and applying the MC Simulation technique. For example, the demand percentage assigned to the first month in 2018 ensures that, the demand of 252,212.860 tons of cement occurs 11.39% of the time. In another word, there is a 11.39% probability that the demand of 252,212.860 tons of cement will occur in the first month of the year and so on with the other monthly percentage demands in 2018.

‫األسوريت‬ ‫الجاهعت‬ ‫هجلت‬
The MC Simulation Demand Model of Al-burj Cement Plant is constructed and executed using Microsoft Excel 2010 [3]. The Model is consisted of three papers on Microsoft Excel Sheets as shown in Figure (2). These sheets contains four tables: Table(3), Table(4), Table (5) and Table(6) as shown and followed clearly in sequence throughout these procedures: Up to 5000 random numbers are generated by copying the function from C3 to C5002 on the Microsoft Excel Sheet. These numbers represent % of occurrence (probability) for every monthly amount of cement sold and the mean of these numbers is always around 0.5 as shown in Table ( 3-Generating 5000 trials as shown in Table (3), of 12 monthly demands of the year 2018. These 12 month demands are also shown in Table (2).

1-
The cutoff figures as shown in Table ( That yielded 0.1184 which is 11.84% This is the result of fraction of occurrence (%) assigned to the demand of the first month among the 5000 random numbers. Then, this fraction of occurrence is multiplied by its month of demand "252,212.860" tons to get the simulated cement amount for the first month as shown in Table (5) under the column of "Amounts" by using: =(A17*B17) Likewise, to determine the monthly simulated demand for the second month of February in 2018, where, the cutoff is 0.1139, the COUNTIF function is used to add all fractions of random numbers that lie between (0.1139 and 0.1843) as assigned to the second month demand of "155,915.100" tons at random among the 5000 numbers previously generated and they are divided by the total number of rows: That yielded 0.0676 which is 6.76% Then, this fraction of occurrence is multiplied by the its month demand "155,915.100" tons to get the simulated cement amount for the second month as shown in Table ( The same procedures are carried out for every month demand individually in 2018. After that, the monthly cement amounts obtained for the 12 months as shown in Table (5) are accumulated to end up with the MC monthly simulated demand result. Then this total is multiplied by 12 to get the MC annual simulated demand as well. Both of these monthly and annual simulated results are shown in Table (6).

RESULTS & DISCUSSION
The basic notion in probability theory is that of a random experiment; an experiment whose outcome cannot be determined in advance [11]. MC Simulation is used by many manufacturing firms as an important tool leading to decision making as mentioned, where in this study it is applied to determine and estimate the amounts of cement in metric tons to be sold on every month of the year from Al-burj Plant, in order to avoid any shortage cost or overproduction cost. However, the shortage is the most common case at Al-burj Cement Plant and at most of the other cement plants in Libya than the overproduction situations (surplus). From lean manufacturing point of view, both cases are classified to be waste for as far as customers' satisfaction and inventory cost are concerned [1].
Many of the previous simulation research topics reflect the major concerns of the cement industry like cement production stages, operations, material, management and sustainability as stated in the introduction. In one of these previous studies the MC technique is used to cope with challenges associated with uncertainty in raw material composition, but in this research paper the technique of Monte Carlo reflects the requirement of reducing, if not eliminating the waste in terms of satisfying the consumer or meeting the market demand, as well as reducing any other kind of cost in money or material within all production stages of the cement manufacturing plant. This study by using the MC technique focuses on estimating the market demand, the fact that, not satisfying the consumer due to any shortage in cement or any other reason is classified as waste, as far as the lean logic is concerned.
Market demand is the total quantity demanded across all consumers in a market for a given good [12]. So that, the market demand of cement in this study, include the accumulative amounts of cement demanded by individual consumers in one day to end up to the total of one month demand as shown in Table (2) and Table (4) for every month in 2018. Since the market demand plays a critical role in economic policy analyses, the monthly simulated demand of 199,539.801 metric tons of cement as shown in Table (6), may be executed or implemented as figured out by the technique to comply with the lean production logic ,but one of the constraints that face the implementation technique is the fluctuations of the market demand as shown very clearly in the year of 2018 in Figure (1). This huge fluctuation requires a good study to be conducted to make it stable as much as possible. However, nowadays, the market demand is high due to many reasons, like: (the major force) from time to another or other reasons. On the other side, the monthly simulated demand leads to the total simulated demand from Al-burj Cement Plant for the whole year to be around 2,394,477.608 metric tons of cement as shown in Table (6). As far as the two (monthly & yearly) simulated amounts are concerned and figured out precisely by the simulation technique, many operational decisions can be undertaken by individual managers and top management of the plant as required.

Journal of Alasmarya University: Basic and Applied Sciences
By pressing F9 (means: repeat the Simulation) on the keyboard of the computer, every time the whole 5000 random numbers will change which leads to change of the final results of the monthly and annual simulated demand of cement a little up or down.

CONCLUSION & RECOMMENDATION
Based on the frequencies that are calculated in 2018 by default as required for Simulating the 12 months of demand from Al-burj Cement Plant, the followings are concluded:  The monthly Simulated demand is calculated to be around 199,539.801 metric tons of cement in order to comply with the lean production logic in terms of: avoiding shortage cost, avoiding overproduction cost and satisfying the consumer. That leads to the total cement demand from Al-burj Cement Plant for the whole year simulated to be around 2,394,477.608 metric tons.  Having precisely known the monthly simulated demand of cement from Al-burj Cement Plant, a decision can be reached between the production department and marketing department about the amount of cement in tons to be sold on daily basis and consequently on monthly basis in order to avoid any costs due to shortage (run out of stock) or due to overproduction (surplus) which leads to inventory cost  The application of MC Simulation may be more adequate and convenient when supply is higher than demand for the substance of cement.  On the other hand, however, if demand is higher than supply, the use of the MC Simulation technique is very helpful in executing many operational management decisions within the industrial organization as required.  Accurate results may be obtained from the MC Simulation technique for monthly and yearly demand, if a good data record with the frequency of occurrence (probability distributions) of every individual month for many years of previous sales is maintained and used for this technique.  The fact that, there is high levels of demand for cement, whereas, the cement industry is accompanied with high levels of waste, it's recommended that, the future studies should be conducted in implementing the technique of the MC Simulation within the cement industry in all cement plants in Libya.  Furthermore, when demand of cement in a certain time cannot be covered as figured out from the Simulation Model, a decision may be undertaken in advance by top management to cover any shortage via importing business operations, and likewise via exporting business operations for surplus cases.