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Car Assembly and Distribution Supply Chain
Introduction
This paper uses a Monte Carlos simulation technique to evaluate car assembly and distribution supply chain. It aims at identifying the likely supply quantities that will be delivered in car assembly company and the number of completed vehicles that will be delivered to retailers and distributors. Usually, events in the entire production process of a car can affect its completion time, which, in turn, affects the number of vehicles delivered to customers.
Reflection
The automotive industry is the largest single manufacturing activity in the world. Accordingly, it is among the largest employers in the world. As of 2008, this sector consumed fifteen percent of the world’s steel, 25 percent of its glass, and 40 percent of the oil output in the world. This industry enjoyed a high growth rate of about 5.9% annually from 1951 to 1972. Unfortunately, after the 1973 first oil shock, the industry’s growth rate slowed to just about 1% until the year 2002 (Suthikarnnarunai, 2008). Although the oil shock was the first cause of the decline in the growth rate of the automotive industry, saturation of the developed economies market is the primary cause of the current slowdown in this sector. This situation is aggravated by the low demand for vehicles in underdeveloped economies due to their average low-income levels.
At the moment, although developed nations have a lower population than underdeveloped countries, more than 70 percent of vehicles are sold in these countries. Therefore, unless there are adequate incomes in developing economies, these nations will not have as many cars like those that are already in developed nations (Suthikarnnarunai, 2008). The increased competition in the automotive industry, coupled with an increased production capacity by major manufacturers, increased wage rates, and a sluggish global demand for vehicles can result in poor performance of most companies (Jacobs & Chase, 2017). This situation leads to significant financial constraints in automotive companies, and the increased need for the establishment of efficient production and distribution networks.
Strategies
Currently, companies in the automotive industry are modifying their supply chain systems for them to be more efficient. These companies have established a new practice in which every business is tied to a forecast. Additionally, all vehicle manufacturers avoid excess inventories by matching supplies with demands at all levels of production. They typically use systems such as the just-in-time procurement method (Sarkar, 2017). The variation in demand due to forecasting and the capability and responsiveness of the supply chain leads to a bullwhip effect.
Most efficient companies in automotive industry also use the build-to-forecast and build-to-deliver systems (Bateman & Snell, 2016). The build-to-forecast concept applies the dealers’ sales forecasts to schedule its production program. This method is based on a bottom-up approach. The production program is simply the automotive company’s determination of the production capacity for each factory/assembly line. In the build-to-deliver approach, the company aims at ensuring that all its vehicles are able to satisfy the demands of the economy. Simply, this concept aims at ensuring the firm manufactures the type of vehicle that customers want. Accordingly, a company normally asks dealers of the number of cars that they want and the specifications (internal and external) for each.
In a typical automotive production system, many intricate parts are used in each vehicle. The manufacture of cars always occurs in stages, which are usually 3-5 stages. As a result, any adverse effect at any stage can affect the entire supply chain process. The main risks in the automotive assembly are usually: quality of parts, part stock-out, a possible shutdown of a supplier, delays in the production process, machine breakdown, and strikes in one section of the assembly line or the entire factory (Bateman & Snell, 2016). The most common methods to avoid these challenges are adding a factory’s capacity and the number of suppliers of parts, shifting to low-cost foreign manufacturing countries to reduce the occurrence of strikes, and increasing the inventories (Myerson, 2012). Unfortunately, these solutions come with the additional risk of increasing the inefficiency of a company.
Monte Carlo Simulation
The Monte Carlos simulation on this paper examines the number of tires, engines, chassis, front and back windshield, and batteries that the company can manufacture per day by considering possible eventualities that may happen in the production process. It will also simulate the number of cars that can be assembled, delivered to distributor 1, 2, and 3, and to retailer Alwaraqa, Jumera, Qarhood, Alrams, Alsee, Theet, Albateem Khalifa-a, and Mashraf.
The possible factors that may affect the number of tires manufactured per day are labor strikes, material defects, not meeting standard, poor transportation, and factory system failure, and good delivery. The engine production, on the other hand, is affected by: obsolete parts, material defects, not meeting standards, poor transportation, untrained labor, and good delivery. In the manufacture of chassis, the following are the factors that may affect the number of complete parts: factory blackout, material defect, not meeting standards, poor transportation, shortage of labor, and good delivery. The manufacture of front and back windshield is affected by the following: poor quality, material defects, untrained labor, poor transportation, downtime of factory systems, and good delivery. The manufacture of batteries is affected by material defect, poor quality, poor transportation, shortage of manpower, downtime of factory, and good delivery.
The number of units delivered in the assembly process is affected by broken machines, blackout of factory, poor transportation, labor strike, downtime of factory systems, and good delivery. The number of units delivered to distributor 1, 2, and 3 is determined by not meeting standards, the blackout of factory, poor transportation, labor strikes, downtime of factory systems, and good delivery. The number of cars delivered to retailer Alwaraqa, Jumera, Qarhood, Albateem Khalifa-a, and Mashraf are affected by the following: failure to meet specifications, technical issues, lack of transportation, downtime of factory systems, the requirement of different options, and good delivery. Finally, the number of cars delivered to retail Alrams, Alsee, and Theet are not meeting the specifications, technical issues, lack of transportation, shortage of staff, the requirement of different options, and good delivery.
Progress Report
The total number of tires delivered in the 52 weeks were 12480, which was an average of 240 units daily. Therefore, there were no delivery shortages.2450 engines were delivered in the 52 weeks. This delivery was an average of 47.115 engines per week. There was a shortage in delivery of 46 engines. The chassis delivered during the 52 weeks were 2460, which was an average of 47.3077 per week. There was a shortage of 36 engines per week. The front and back windshields completed in 52 weeks were 4902, which was a shortage of 94.27 per week. There was a shortage of 90 front and back windscreens. 2481 batteries were delivered during the 52 weeks, which was an average of 47.7115 batteries per week. There was a shortage of 15 batteries in the year.
There were 2462 cars delivered from the assembly section, which resulted in an average of 47.34615 units per week. This output resulted in a shortage of 34 units in the year. 1236 cars were delivered to distributor 1. As a result, the average weekly production was 23.769 units per week. Since the optimal demand units was 24, there was a shortage of 12 units. 459 cars were delivered to distributor 2, which was an average of 8.826 units per day. The shortage in the number of units delivered was 9 cars. 780 vehicles were delivered to distributor 3, which was an average of 15 units per week. There was no shortage of the delivery.  The company delivered 413 units to Alwarq, which is an average of 7.9423 units per day. As a result, there was a shortage of 3 units in the year’s delivery. 414 cars were delivered to Jumera throughout the year. The delivery was an average of 7.962 units per day. The company has a shortage of 2 units in the delivery.
The company supplied 414 units to Qarhood in 52 weeks. The delivery was an average of 7.962 units per day. The company has a shortage of 2 units in the delivery. The company delivered 155 units to Alrams during the year. This delivery was an average of 2.9807 units per day. The company had a shortage of only 1 car. 151 units were delivered to Alseeh during the year, which was an average of 2.90 cars per week. Therefore, the company delivered 5 vehicles less than required. The company delivered 154 cars to Theet, which is an average of 2.96 units per year. Accordingly, it had a shortage of only 2 cars in the year. 256 vehicles were delivered to Albateen in 52 week, which was an average of 4.92 cars per week. Therefore, the company delivered 4 vehicles less than required. 259 cars were delivered to Khalifa-a during the year, which is an average of 4.98 vehicles per week. The company had a shortage in delivery by only 1 unit. Finally, 257 vehicles were delivered to Mashraf in the year, which was an average of 4.9423 cars per week. Therefore, the company had a shortage in delivery by 3 cars.
Methodology
Supplier
The company has five suppliers for its main components, which are tires, engine, chassis, front and back windshield, and batteries.
Tires
Six factors determine the number of tires supplied: labor strikes, material defects, not meeting standards, poor transportation, factory system failure, and good delivery. Where there are labor strikes, the company can only deliver 90 tires weekly. In case of a material defect, the supplier delivers 200 tires weekly. If the materials do not meet the required standards, the supplier delivers 220 tires weekly. If there is poor transportation, only 140 tires are supplied weekly. 190 tires are supplied per week if there is a factory system failure. Finally, 240 tries are supplied if there is no eventuality “good delivery.” The frequency represents the probability of each event occurring. Labor strikes, material defects, not meeting standards, poor transportation, and factory system failure have a probability of 1 percent, which is represented by a frequency of 1. There is a 95 percent chance of a good delivery, which is shown by a frequency of 95. The cumulative frequency is derived by adding an events frequency to the cumulative frequency of the previous eventualities in the table. The random numbers were generated using the Excel Workbook (random number) RAND function with the intervals (1,100). The formula used was RAND()*(100-1)+1.
Engine
The number of engines supplied is determined by the following: obsolete spares, material defects, not meeting standards, poor transportation, untrained labor, and good delivery. The supplier is only able to deliver 40 engines if there are obsolete spares. He/she supplies 28 engines if there are material defects. He/she supplies 42 engines if there is a problem in meeting standards. The supplier delivers 39 engines if he/she is affected by poor transport. The supplier delivers 15 engines if he/she has a shortage of laborers. Finally, the supplier delivers 48 engines if no eventuality occurs “good delivery.” The frequency represents the probability of each event occurring. Obsolete spares, material defects, not meeting standards, poor transportation, and untrained labor have a probability of 1 percent each, which are shown using a frequency of 1. Good delivery, on the other hand, has a probability of 95 percent, which is shown by a frequency of 95. The cumulative frequency was derived by adding the events frequency to the cumulative frequency of the previous eventualities in the table. The random numbers were generated using the Excel Workbook (random number) RAND function with the intervals (1,100). The formula used was RAND()*(100-1)+1.
Chassis
The factors that affect the supplier’s ability to deliver a chassis order are: factory blackout, material defects, not meeting standards, poor transportation, shortage of labor, and good delivery. The supplier is only able to supply 20 chassis if he/she has a blackout in the factory. He/she is expected to supply 40 chassis if he/she experiences some material defects. The supplier delivers only 42 chassis if he/she has a challenge in meeting standards. He/she delivers 30 chassis if he/she experiences challenges due to poor transport. The supplier only delivers 45 units if he/she has a shortage of labor. Finally he/she supplies 48 units if he/she experiences no eventuality “good delivery.” The likelihood of a factory blackout, material defects, not meeting standards, poor transportation, and shortage of labor is only one percent for each, which is represented with a frequency of 1. The probability of a “good delivery” is 95 percent, which is shown with a frequency of 95. The cumulative frequency was derived by adding the events frequency to the cumulative frequency of the previous eventualities in the table. The random numbers were generated using the Excel Workbook (random number) RAND function with the intervals (1,100). The formula used was RAND()*(100-1)+1.
Front and back windshield
The factors that affect the supplier’s ability to deliver front and back windshields are: poor quality, material defects, untrained labors, poor transportation, downtime of factory systems, and good delivery. The supplier is only able to deliver 90 units when he/she has poor quality issues. He/she only delivers 85 materials when he/she has a challenge of material defects. The supplier only delivers 80 units when he/she is working with untrained laborers. He/she is also only able to supply 70 units when he/she has a challenge of poor transport. The supplier only delivers 65 front and back windshields when he/she has a downtime of factory systems. Finally, he/she delivers 96 units when he/she does not experience any eventuality “good delivery.” The probability of the supplier having poor quality, material defect, untrained labor, poor transport, downtime of factory system, and experiencing a good delivery are 2 percent, 1 percent, 2 percent, 1 percent, 1, percent, and 93 percent respectively. These values are shown in the frequency table as 2, 1, 2, 1, 1, and 93 respectively. The cumulative frequency was derived by adding the events frequency to the cumulative frequency of the previous eventualities in the table. The random numbers were generated using the Excel Workbook (random number) RAND function with the intervals (1,100). The formula used was RAND()*(100-1)+1.
Batteries.
The number of units delivered by the battery supplier vary depending on whether he/she experiencing material defects, poor quality, poor transportation, shortage of manpower, downtime of factory systems, or he/she has no challenge “good delivery.” In he/she has a material defect, he/she only supplies 42 batteries. He/she supplies only 40 units if he/she has a challenge of poor quality. The supplier is only able to deliver 39 units if he/she has a challenge of poor transportation. He/she is supplies only 35 batteries when he/she has a shortage of manpower. The supplier delivers 37 units when he/she has downtime of factory systems challenge. Finally, he/she is able to supply 48 batteries when he/she has no challenge “good delivery.” The likelihood of the batteries having material defect, poor quality, poor transportation, shortage of manpower, and downtime of factory systems challenge are 2, 1, 1, 1, and 1 percent respectively. There is a 94 percent chance that the supplier will not experience any obstacle in the delivery “good delivery.” The likelihoods are shown in the percentage column. The cumulative frequency was derived by adding the events frequency to the cumulative frequency of the previous eventualities in the table. The random numbers were generated using the Excel Workbook (random number) RAND function with the intervals (1,100). The formula used was RAND()*(100-1)+1.
 
 
 
 
 
 
 
 
 
 
 
References
Bateman, T. & Snell, S. (2016). Management: Leading & collaborating in a competitive world (12th ed.). New York, NY: McGraw-Hill Education.
Jacobs, R. & Chase, R. (2017). Operations and supply chain management (15th ed.). New York, NY: McGraw-Hill Education.
Myerson, P. (2012). Lean supply chain and logistics management (1st ed.). New York, NY: McGraw-Hill Education.
Sarkar, S. (2017). The chain revolution: Innovative sourcing and logistics for a fiercely competitive world. New York, NY: AMACOM.
Suthikarnnarunai, N. (2008). Automotive supply chain and logistics management. International MultiConference of Engineers and Computer Scientists, 2(IMECS 2008), 1-7.