1. Applying Quantitative Methods for Managerial Decision Making
Toyota’s New Plant :
Applying Quantitative Methods for Managerial Decision Making
Business Decision Methods | RA70700 | Dr. Jeh-Nan Pan | 2017/01/06
By: Arthur Kurek Harman Warsono
Sergei Katsuba
Susan C. Bahari

2. Introduction
In recent years, Toyota was in alert of loss because of the hit in the industry caused by the financial crisis in However, starting from 2015, Toyota is planning to make a ‘purpose driven investment’ to develop the future of the automotive industry and to maintain its competitiveness.
In order to accomplish this, Toyota-Motor Corporation is considering the construction of a plant in either China or Mexico. Each plant has a different production capacity. It is unclear, however, which plant is the most recommendable alternative. Therefore, it was our goal to analyze this.

3. Objectives
Implement Business Decision Method theories to the real business case of Toyota
Compare and learn from Toyota forecasting method
Provide in-depth cost benefit analysis on each of the expansion plans
Provide recommendations on which plan will be more beneficial (according to EMV)

4. Company Background
Established in 1937 by Kiiciro Toyoda. The headquarter is located in Aichi, Japan
In March 2016, Toyota had 348,877 employees with 53 overseas production plants in 28 countries
Toyota currently is the 13th largest global brand, measured by revenues
By 2016, Toyota has produced and sold more than 8 millions vehicles in more than 170 countries

5. The new factory ¥ 52,5 billion ¥ 117 billion
Maximum production is 100,000 units/ year
The location of the plant is Guangzhou. It will be a joint venture between Toyota and Guangzhou Automobile Group.
The new plant is restructuring the existing lines. Therefore, Toyota will use their current work force.
¥ 117 billion
Maximum production is 200,000 units/ year
The location of the Mexican plant is Guanajuato.
This new plant features Toyota New Global Architecture. This is because the firm will change the layout and workflow of the plant in order to have shorter production lines and flow of part supplies.

6. Research Assumption
Due to the data collection limitation, we can only assume the analysis by using global average revenue and operating expenses of all Toyota cars.
For the decision tree analysis, we assumed the following three scenarios: construct the North American facility in Mexico, build the Asian plant in China or neither build the plant in Mexico nor the plant in China.

7. Systematic Problem Solving Flowchart

8. Methodology & Data Collection
Forecasting Sales
Break-Even Analysis
Decision Tree Analysis
1. Toyota Financial Report ( )
2. Toyota expansion plan from
3. Global Automotive Industry Reports & Online newspapers

9. Technical Difficulties
Forecasting analysis
BEP analysis
Decision Tree Analysis
Toyota’s forecasting data is limited
It is only available from
Due to the Earthquake in 2011, there is no forecasted data for 2012
BEP calculation depends on the global average numbers to be performed
Finding the right probabilities for decision tree analysis
(Favorable market vs. Unfavorable market)

10. Analysis 1 – Forecasting
Collect 15 years sales data
Forecast using 2 different methods
Compare with Toyota’s forecast
Analyze which forecast method is more accurate by using MAD

11. Analysis 1 – Forecasting
Description of the data
The quantities that are to be forecasted:
Income revenue of Toyota company in considered markets.
Number of sales of Toyota cars in North American and Asian markets.
Methods of forecasting:
Weighted Moving Average
Least square regression
The duration of forecasting:
20 years for ( )
Estimation of accuracy :
Mean average deviation (MAD)
Investigation of Toyota’s corporate forecasting

12. Analysis 1 – Forecasting
Comparing results of revenue forecasting for North America
Weighted Moving Average
Simple Linear Regression
MAD = 1,604.1
MAD = 1,259.5

13. Analysis 1 – Forecasting
Comparing results of revenue forecasting for North America
Weighted Moving Average
Simple Linear Regression

14. Analysis 1 – Forecasting
Comparing results of revenue forecasting for Asian region
Weighted Moving Average
Simple Linear Regression
MAD =
MAD = 270.6

15. Analysis 1 – Forecasting
Comparing results of revenue forecasting for Asian region
Weighted Moving Average
Simple Linear Regression

16. Analysis 1 – Forecasting
Comparing results of sales unit forecasting for North America
Weighted Moving Average
Simple Linear Regression
MAD = 361,043.2
MAD = 363,794.8

17. Analysis 1 – Forecasting
Comparing results of sales unit forecasting for North America
Weighted Moving Average
Simple Linear Regression

18. Analysis 1 – Forecasting
Comparing results of sales unit forecasting for Asian region
Weighted Moving Average
Simple Linear Regression
MAD = 179,591.4
MAD = 119,273.5

19. Analysis 1 – Forecasting
Comparing results of sales unit forecasting for Asian region
Weighted Moving Average
Simple Linear Regression

20. Analysis 1 – Forecasting
Analyse which forecast method is better
Forecasting method
Asian market
North American market
Estimated income in (billion of ¥)
Estimated regional sales in 2017 (in car units)
Estimated regional sales in (in car units)
WMA
4975,23
143,6784.5
10,104.62
274,6238.5
MAD
495,53
179,591.43
1,604.08
361,043.17
Regression
5435,08
167,
8,646.42
268,
270,60
143,
1.259,55
264,232.85
Due to its lower MAD, Least square regression method is preferable.

21. Analysis 1 – Forecasting
A comparison with Toyota’s corporate forecast
Year
Actual income
Forecasting data
Toyota’s corporate forecasting
Error
Regression
2007
23,948.00
22,300
1,648.00
19,655.65
4,292.35
2008
26,289.24
25,000
1,289.24
20,391.75
5,897.49
2009
20,529.57
-4,470.43
21,127.85
2010
18,950.97
16,500
2,450.97
21,863.95
-2,912.98
2011
18,993.69
19,200
22,600.05
-3,606.36
2013
22,064.19
22,000
64.19
24,072.24
-2,008.05
2014
25,691.91
23,500
2,191.91
24,808.34
883.57
2015
27,234.52
25,700
1,534.52
25,544.44
1,690.08
2016
28,403.12
27,500
903.12
26,280.54
2,122.58
MAD
600.58
640.04

22. Analysis 2 – Break even analysis
Analyze cost of the new plants
Perform BEP analysis
Calculate BEP in units and JPY

23. Analysis 2 – Break even analysis
Toyota said the cost of manufacturing a vehicle at the new factories will be approximately 40% less than what it spent to produce a car in 2008.
Cost of a Toyota car in 2008
Net revenue
¥26,289,200,000,000
Operating income
¥2,270,300,000,000
Operating expenses
¥24,018,900,000,000
Vehicle sales
8,913,000.00
Average revenue per car
¥2,949,534
Average cost per car
¥2,694,817
Average profit per car
¥254,718
Perform BEP analysis

24. Analysis 2 – Break even analysis
Mexico new plant
China new plant
Max. annual production capacity
200,000 units
100,000 units
Investment in USD
$1 billion
$0.453 billion
Investment in Japanese Yen
¥ billion
¥52.5 billion
Estimated cost per car
¥1,616,890
Analyze cost of the new plants
Average cost = (100%-40%) x ¥2,694,817
Average revenue = ¥2,949,534

25. Analysis 2 – Break even analysis
BEP = 𝑇𝑜𝑡𝑎𝑙 𝑖𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 𝑅𝑒𝑣𝑒𝑛𝑢𝑒 −𝐶𝑜𝑠𝑡
Total Investment
¥117,158,000,000
¥52,500,000,000
Average revenue per car
¥2,949,534
Average cost per car
¥1,616,890
BEP in unit
87,913.92
39,395.35
Estimated total variable cost
¥142,147,135,235
¥63,697,951,483
BEP in revenue
¥259,305,135,235
¥116,197,951,483
Calculate BEP in units and JPY

26. Analysis 3 – Decision tree analysis
Analyze overall global industry growth
Decide global automotive favourable market probability
Calculate estimated profit for each plan
Conduct decision tree analysis

27. Analysis 3 – Decision tree analysis
North American plant (Mexico)
● Annual Capacity: 200,000 vehicles ● Probability of a favorable market: 70%
○ Unfavorable market: 30%
● Monetary value fav. market: 80% of capacity
○ Unfav. market 40% of capacity
● Median time to finance investments: 40yrs
Asian plant (China)
● Annual Capacity: 100,000 vehicles ● Probability of a favorable market: 60%
○ Unfavorable market: 40%
● Monetary value fav. market: 80% of capacity
○ Unfav. market 40% of capacity
● Median time to finance investments: 40yrs
Analyze overall global industry growth and
Decide global automotive favourable market probability

28. Calculate estimated profit for each plant
Analysis 3 – Decision tree analysis
EP = ( 𝐴 𝑋 𝐵 𝑋 𝐶) ( 𝐼 40 )
Calculate estimated profit for each plant
A = Average profit per vehicle
B = Maximum capacity per plant
C = Favorable / Unfavorable market value
I = Investments per plant

29. Analysis 3 – Decision tree analysis
FM = 0.7 6
¥ 38.41
UM = 0.3 4
Mexico plant 7
¥ 19.21
EMV = ¥32.65
Build new plant 2
FM = 0.6 8
¥ 19.33
EMV = ¥32.65
Calculate expected monetary value and perform decision tree analysis 1
UM = 0.4 5 9
¥ 9.66
EMV = ¥32.65
China plant
EMV = ¥15.46 3
Do not build
EMV = 0
Decision = Build Mexican plant

30. Conclusion
Forecasting Sales Different regions have different demand and therefore may require specific forecasting methods. Compared to our academic forecasting method, Toyota has a more accurate method. We believe, however, that it can be improved since the MAD is still at ¥640 billion.
Break Even Analysis According to our calculation and assumption, the plant only needs to sell around 40% of full capacity in order to break even. However, the number can be increased if more accurate data (e.g. additional fixed cost, labor cost, material cost, etc.) can be obtained.
Decision Tree Analysis If Toyota can only build one plant at a time, the company should build the Mexican plant rather then the Chinese plant or no plant as it represents the highest expected monetary value (EMV).

31. References
Joseph, N. (2015). Toyota investing $1 billion in Mexico plant. Retrieved from ____ mexico-plant-official/
Guilford, D. (2016). Mexico plant to show off Toyota's revamped system. Retrieved from ____ ____ ____ ____http://
The Guardian. (2015). Toyota plans new factories in China and Mexico, say reports. Retrieved from ____https://
Toyota Motor Corporation. (n.d.). Financial Results. Retrieved from
KPMG. (2016). Global Automotive Executive Survey Retrieved from
Gao, P., Hensley, R., & Zielke, A. (2014). A road map to the future for the auto industry. Retrieved from ____http://
PwC. (2016) Auto Industry Trends. Retrieved from