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