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QBUS3330 Business Analytics Methods Of Decision Analysis

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  • Course Code: QBUS3330
  • University: The University Of Sydney
  • Country: Australia

Question:

Write a report along the following guidelines. Please make sure your policy suggestions are based on simulations with at least 1000 iterations for each case. You are required to use @Risk.

1. Provide a description of your spreadsheet model. Explain how each problem input was computed and modelled using the appropriate distribution. Also provide a description of how you compute the various characteristics of the system (total demand, the number of cars sold, the number of unsatisfied customers, etc).

2. If you are restricted to importing the same number of cars every month, what would be your optimal strategy? Describe the profit and other relevant characteristics of this policy.

3. If you are allowed to import a different number of cars every month, what would be your optimal ordering policy? Describe the profit and other relevant characteristics of this policy. Compare this policy to that in (2).

4. Assume you decided to take orders 6 months ahead and that the number in the first row of the above table were the actual demands. What would be your optimal ordering policy? Would your mean profit increase?
 
 

Answer:

Introduction

Demand forecasting is treated as one of the important aspects of revenue management. In case of luxury car segment, it is further essential as any deviation leads several consequences, like if the number of cars imported is less than the demand of a specific month, the company will lose a huge amount of revenue and again if the number of cars imported are greater than demand for a specific month, there will be additional holding cost. Under such circumstances, it is imperative to conduct extensive amount of data analysis basis the historical data so that a specific number of cars are imported, which will help the organisation to reduce the risk of loss.

In this report, a study has been performed on behalf of a dealer of a luxury car in Australia market. The study has been performed using @Risk software and excel platform to forecast monthly demand over the next six months from January to June. The study has considered few information provided as a starting point and performed a three step analyses as described below:

 

Overview of the spreadsheet model

The excel model is built assuming below aspects:

  1. At any given month an order has to be placed and the shipment arrives before the actual demand is known.  
  2. You can ignore the duration stock is held during a given month. Holding costs are only incurred on the end of the month stock.  
  3. Assume that cars not sold in June can be returned to the importer for $15,000
  4. Minimum number of car will be imported in any month is 8 and maximum car will be imported in that month is 15.

The excel model consist of three segments, the first segment is the model building part, the second one is information provided as a starting point and the third part is all about calculations:

In the model building stage, demand, selling price and import cost and chances of unsatisfied customers are estimated with the help of @Risk software. The demand estimation is followed Poisson distribution, import cost, selling price are followed normal distributions and chances of unsatisfied customers are followed discrete distribution.

In the opening stock row, excel formula is used to calculate the opening stock for a particular month. As given, the opening stock for January is 5 cars. From February to June, the opening stock is calculated using below formula:

Here, D6 represents the demand of cars in previous month, D13 customer waited in the previous month, D4 opening stock of previous month, D5 is number of cars imported in the previous month.

Similarly, the excel function is used to find the unsatisfied customers, customer will wait for next month etc. In the calculation segment, cost and revenue for a particular month is calculated and thus the total profit as shown below:

The last row is used to estimate the number of car will be imported with the help of risk optimizer model. Three different risk optimizer models are built to perform specific task.

Same number of cars imported over the next six month

Here, the objective was to find out the number of cars will be imported to meet demand for luxury car and thus to maximize profit. As per given information, the number of car will be imported are same for each of the six month. Considering this constraint, the risk optimizer model was defined as follow:

In @Risk software, 1000 iteration were performed to find out the optimum profit level and below are the results:

 

 

From the above risk optimization log, it can be said that if the company wants to import same number of cars over the next six month, then they have to import 12 cars each and every month to maximize the profit level and the total estimated profit will be $11,06,000.00

Different number of cars imported over the next six month

Here, the objective was to find out the number of cars will be imported to meet demand for luxury car and thus to maximize profit. As per given information, the number of car will be imported are different for each of the six month. Considering this constraint, the risk optimizer model was defined as follow:

In @Risk software, 1000 iteration were performed to find out the optimum profit level and below are the results:

 

 

From the above risk optimization log, it can be said that if the company wants to import different number of cars over the next six months, then they have to import 15 cars over the first five months and in last month they have to import only 4 cars to maximize the profit level and the total estimated profit will be $13,78,788.45

Specific Demand:

Here, the objective was to find out the number of cars will be imported to meet demand for luxury car and thus to maximize profit. As per given information, the number of car will be imported are different for each of the six month. However, the demand for each month will be fixed instead of normally distributed. Considering these constraint, the risk optimizer model was defined as follow:

In @Risk software, 1000 iteration were performed to find out the optimum profit level and below are the results:

 

From the above risk optimization log, it can be said that if the company wants to import different number of cars over the next six months, then they have to import 5 cars in the month of January, 12 cars in the month of February, 15 cars in the month of March, 15 cars in the month of April, 13 cars in the month of May, and in last month they have to import only 9 cars to maximize the profit level and the total estimated profit will be $11,29,000.00

Conclusion

Thus to conclude, it can be said that the dealer has to consider several aspects to gain maximum profit and each of the cases, the number of cars will be different basis the condition given.

 

References

Arias, M. B., & Bae, S. (2016). Electric vehicle charging demand forecasting model based on big data technologies. Applied energy, 183, 327-339.

do Rego, J. R., & de Mesquita, M. A. (2015). Demand forecasting and inventory control: A simulation study on automotive spare parts. International Journal of Production Economics, 161, 1-16.

Qian, L., & Soopramanien, D. (2015). Incorporating heterogeneity to forecast the demand of new products in emerging markets: Green cars in China. Technological Forecasting and Social Change, 91, 33-46.

Sak, H., Yang, G., Li, B., & Li, W. (2016). Modeling Dependence Dynamics of Air Pollution: Pollution Risk Simulation and Prediction of PM $ _ {2.5} $ Levels. arXiv preprint arXiv:1602.05349.

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