Student’s Name
Institution Affiliation
Motor Vehicle Logistics
Introduction
Reflection
The motor vehicle industry is not one of the most critical sectors in the world; it is also one of the most complicated due to its high demands on quality expectations, process complexities, and product variety (Advantech, 2016). The industry is further complicated by the increased globalization and customer requirements, which forces car manufacturers to develop a wide range of vehicles that can satisfy each market. Due to these complexities, car manufacturers employ an intricate supply chain model for all their logistical processes.
Strategies
The supply chain process of car manufacturers is complicated, especially due to costs and the variations in each car model. For example, premium German automobile can have as much as 1017 variations, which makes the supply chain process complex (Advantech, 2016). These huge varieties and complexities necessitate the use of industrial computing in almost all of the automotive supply chain. Additionally, companies prefer shipping vehicle components to be assembled locally, which minimizes tax and enables them to fulfill the unique variations demanded by customers.
Monte Carlos Simulation
The Monte Carlos simulation is one of the conventional methods used to predict possible business performance. Normally, a researcher factors in the events that affect his/her organization when performing the simulation. In this paper, the Monte Carlos simulation is used to estimate the number of complete tires, engines, chassis, front and back windshields, and batteries that can be manufactured for delivery. The paper also examines the number of vehicles that can be completely assembled and also the likely number of the completed cars that will be delivered to suppliers and retailers.
Methodology/Approach
Suppliers
The company received tires, engines, front and back windshields, chassis, and batteries from its suppliers. In this paper, the expected supply quantities were tested using a Monte Carlos simulation. The random numbers used in the analysis were generated using the Microsoft Excel random number function for between zero and one hundred. The frequency of each event happening represents the probability of the event’s occurrence.
Tires
| Tires Manufactures in the Year | |
| Total predicted Production | 11588 |
| Average predicted weekly production | 222.846154 |
| Expected ideal production (annual) 52 weeks | 12220 |
| Shortage | 632 |
| Tires | |
| Event | Probability |
| Bad Material | 3% |
| Shortage of Labor | 2% |
| Different Size | 2% |
| Expired Tires | 2% |
| Mistake of Manufacturing | 1% |
| Good Delivery | 90% |
Engines
| Engines Manufactured | |
| Total Predicted Production | 2239 |
| Average predicted weekly production | 43.057692 |
| Expected annual production (ideal case) 52 weeks | 2444 |
| Shortage | 205 |
Engines
| ENGINE | |
| Event | Frequency |
| Utility Failure | 3% |
| Broken Machine | 2% |
| Delay in Delivery | 2% |
| Shortage of Labor | 1% |
| Low Inventory | 2% |
| Good Delivery | 90% |
Chassis/ Frame
| Chassis | |
| Total predicted annual production | 2356 |
| Average predicted weekly production | 45.307692 |
| Expected ideal annual production | 2444 |
| Shortage | 88 |
| CHASSIS (FRAME) | |
| Event | Frequency |
| Shortage of material | 2% |
| Shortage of Labor | 1% |
| Blackout in factory | 1% |
| Broken machine | 2% |
| Failure to meet specifications | 1% |
| Good delivery | 93 |
Front and Back Windshields
| Front and Back Windshield | |
| Total predicted annual production | 4829 |
| Average predicted weekly production | 92.865385 |
| Expected ideal annual production | 4888 |
| Shortage | 59 |
| FRONT & BACK WINSHIELD | |
| Event | Frequency |
| Shortage of material | 3% |
| Shortage of Labor | 2% |
| Blackout in factory | 2% |
| Broken machine | 2% |
| Failure to meet specifications | 1% |
| Good delivery | 90% |
Batteries
| Batteries | |
| Total simulated batteries production | 2328 |
| Average simulated weekly production | 44.7692308 |
| Expected ideal annual production | 2444 |
| Shortage | 116 |
| BATTERIES | |
| Event | Frequency |
| Shortage of material | 1% |
| Shortage of Labor | 2% |
| Blackout in factory | 2% |
| Broken machine | 4% |
| Failure to meet specifications | 5% |
| Good delivery | 86% |
Assembly
| Assembly | |
| Total simulated production | 2343 |
| Average simulated weekly production | 45.0576923 |
| Expected ideal annual production | 2444 |
| Shortage | 101 |
| ASSEMBLY | |
| Event | Frequency |
| Shortage of material | 2% |
| Shortage of Labor | 1% |
| Blackout in factory | 3% |
| Broken machine | 5% |
| Failure to meet specifications | 4% |
| Good delivery | 85% |
Distributors and Retailers
Distributor 1
| Distributor 1 | |
| Total simulated production | 1084 |
| Average simulated weekly production | 20.846154 |
| Expected ideal annual production | 1092 |
| Shortage | 8 |
| Distributor 1 | |
| Event | Frequency |
| Shortage of lifting equipment | 2% |
| Shortage of Labor | 1% |
| Blackout in factory | 2% |
| Broken machine | 1% |
| Lack of transport equipment | 2% |
| Good delivery | 92% |
Distributor 2
| Distributor 2 | |
| Total simulated production | 503 |
| Average simulated weekly production | 9.67307692 |
| Expected ideal annual production | 520 |
| Shortage | 17 |
| Distributor 2 | |
| Event | Frequency |
| Shortage of lifting equipment | 1% |
| Shortage of Labor | 2% |
| Blackout in factory | 2% |
| Broken machine | 2% |
| Lack of transport equipment | 3% |
| Good delivery | 90% |
Distributor 3
| Distributor 3 | |
| Total simulated production | 811 |
| Average simulated weekly production | 15.596154 |
| Expected ideal annual production | 832 |
| Shortage | 21 |
| Distributor 3 | |
| Event | Frequency |
| Shortage of lifting equipment | 2% |
| Shortage of Labor | 2% |
| Blackout in factory | 1% |
| Broken machine | 3% |
| Lack of transport equipment | 1% |
| Good delivery | 91% |
Retailer 1-1
| Retailer 1-1 | |
| Total simulated production | 258 |
| Average simulated weekly production | 4.961538462 |
| Expected ideal annual production | 260 |
| Shortage | 2 |
| RETAIL 1-1 | |
| Event | Frequency |
| Shortage of lifting equipment | 1% |
| Shortage of Labor | 2% |
| Blackout in factory | 1% |
| Broken machine | 1% |
| Lack of transport equipment | 1% |
| Good delivery | 94% |
Retailer 1-2
| Retailer 1-2 | |
| Total simulated production | 465 |
| Average simulated weekly production | 8.942307692 |
| Expected ideal annual production | 468 |
| Shortage | 3 |
| RETAIL 1-2 | |
| Event | Frequency |
| Shortage of lifting equipment | 1% |
| Shortage of Labor | 1% |
| Blackout in factory | 2% |
| Broken machine | 2% |
| Lack of transport equipment | 2% |
| Good delivery | 92% |
Retailer 1-3
| Retailer 1-3 | |
| Total simulated production | 361 |
| Average simulated weekly production | 6.94230769 |
| Expected ideal annual production | 364 |
| Shortage | 3 |
| RETAIL 1-3 | |
| Event | Frequency |
| Shortage of lifting equipment | 1% |
| Shortage of Labor | 2% |
| Blackout in factory | 3% |
| Broken machine | 1% |
| Lack of transport equipment | 2% |
| Good delivery | 91% |
Retailer 2-1
| Retailer 2-1 | |
| Total simulated production | 251 |
| Average simulated weekly production | 4.8269231 |
| Expected ideal annual production | 260 |
| Shortage | 9 |
| Retail 2-1 | |
| Event | Frequency |
| Shortage of lifting equipment | 2% |
| Shortage of Labor | 3% |
| Blackout in factory | 1% |
| Broken machine | 3% |
| Lack of transport equipment | 1% |
| Good delivery | 90% |
Retailer 2-2
| Retailer 2-2 | |
| Total simulated production | 101 |
| Average simulated weekly production | 1.94230769 |
| Expected ideal annual production | 104 |
| Shortage | 3 |
| Retail 2-2 | |
| Event | Frequency |
| Shortage of lifting equipment | 1% |
| Shortage of Labor | 2% |
| Blackout in factory | 2% |
| Broken machine | 3% |
| Lack of transport equipment | 1% |
| Good delivery | 91% |
Retailer 2-3
| Retailer 2-3 | |
| Total simulated production | 151 |
| Average simulated weekly production | 2.9038462 |
| Expected ideal annual production | 156 |
| Shortage | 5 |
| Retail 2-3 | |
| Event | Frequency |
| Shortage of lifting equipment | 1% |
| Shortage of Labor | 2% |
| Blackout in factory | 1% |
| Broken machine | 2% |
| Lack of transport equipment | 3% |
| Good delivery | 91% |
Retailer 3-1
| Retailer 3-1 | |
| Total simulated production | 256 |
| Average simulated weekly production | 4.9230769 |
| Expected ideal annual production | 260 |
| Shortage | 4 |
| Retail 3-1 | |
| Event | Frequency |
| Shortage of lifting equipment | 1% |
| Shortage of Labor | 2% |
| Blackout in factory | 3% |
| Broken machine | 2% |
| Lack of transport equipment | 1% |
| Good delivery | 91% |
Retailer 3-2
| Retailer 3-2 | |
| Total simulated production | 354 |
| Average simulated weekly production | 6.80769231 |
| Expected ideal annual production | 364 |
| Shortage | 10 |
| Retail 3-2 | |
| Event | Frequency |
| Shortage of lifting equipment | 2% |
| Shortage of Labor | 1% |
| Blackout in factory | 3% |
| Broken machine | 2% |
| Lack of transport equipment | 1% |
| Good delivery | 91% |
Retailer 3-3
| Retailer3-3 | |
| Total simulated production | 200 |
| Average simulated weekly production | 3.846153846 |
| Expected ideal annual production | 208 |
| Shortage | 8 |
| Retail 3-3 | |
| Event | Frequency |
| Shortage of lifting equipment | 2% |
| Shortage of Labor | 2% |
| Blackout in factory | 1% |
| Broken machine | 2% |
| Lack of transport equipment | 3% |
| Good delivery | 90% |
Analysis
From the simulation analysis, the company will have shortages in the supply of tires, engines, chassis, front and back windshields, and batteries by 632, 205, 88, 59, and 116 units respectively. On its part, the company will have a shortage in the completion of assembly of 101 units. There will also shortages in the delivery of completed units to distributor 1, 2, and 3 by 8, 17, and 21 units respectively. Similarly, retailer 1-1, 1-2, 1-3, 2-1, 2-2, 2-3, 3-1, 3-2, and 3-3 will have annual shortages in their deliveries by 2, 3, 3, 9, 3, 5, 4, 10, and 8 units respectively.
Discussion and Recommendation
The suppliers should increase their weekly production capacities by employing more workers and having a higher weekly target to avoid shortages in supply. The company on its part should increase the production capacity in its assembly line to prevent delays in the completion of finished vehicles. Additionally, it should improve its logistics systems to avoid shortages in the supply of completed cars to distributors and retailers.
Conclusion
Overall, the company has an efficient logistics systems. The number of the overall shortages are few. Therefore, the firm should increase its order quantities to ensure that it has buffer stocks when there are shortages. On its part, it should increase its production capacity at the assembly lines to ensure timely completion of vehicles. Finally, it should increase the number of its logistics providers so that it can regularly deliver its cars to its clients.
References
Advantech. (2016). Supply chain management in automotive industry. Retrieved from