Business analytics and Intelligence
Assignment Task
Task One-Business analytics concepts
a. Describe what you have learned about the Clustering. Give proper examples
b. Critically discuss and analyzefive published papers to explore how Clustering Analysis is used in businesses. They need to be interpreted properly
Task Two- Prescriptive Analytics
By analysing historical data for a headphone sale company, it can be seen that the sale volume has been changed according to its price in different periods. The values have been reported in the following table. This company is going to launch a new headphone and the price needs to be decided based on the market analysis. Determine the appropriate price for the newly developed electric bicycle with the aim of maximizing the company's profit. The demand forthe newly developed product has a direct relationship to its price.
Table 1- Demands of an electric bicycle based on a price level according to previous experiences
Price (£)
|
Demand
|
55
|
418
|
57
|
395
|
61
|
373
|
62
|
360
|
63
|
345
|
64
|
302
|
66
|
270
|
67
|
255
|
68
|
247
|
69
|
240
|
71
|
233
|
72
|
222
|
73
|
216
|
74
|
214
|
76
|
210
|
77
|
205
|
The main objective of the company is to maximize profit regarding the new product. It is assumed that the unit production and supply cost of each electric bicycle is £45, so the profit of each unit will be (the sale price - unit production cost).
a) Use Excel to determine the estimated demand quadratic equation function and describe why quadratic form is preferred.
b) Use Excel solver to find the optimal price which maximizes the company's profit.
c) Determine the optimal demand.
d) Compute the optimal profit.
e) Interpret the results.
f) If the supply cost of each electric Bicycle is £30 and then £61 determine the optimal price and interpret the results appropriately.
g) Suppose that the supply cost is £61 and the company has decided to set the price less than £71, then what will be the optimal price? Interpret the results
Task Three- Forecasting
Suppose that you have gathered sales data of a retail company from 2016-2022 in different quarters. You are going to consider the company sales value and to forecast the company amount of sales in 2023 in different quarters. Look at the gathered data of previous years related to the amount of sales in each quarter which have been reported in Table 2.
Table 2- Data of amount of sales among previous years
Year
|
Q
|
Revenue
|
Four Moving average
|
Baseline
|
Seasonality
|
Deseasonality
|
Trend
|
Forecast
|
Error
|
MAD Error
|
MSE
|
MAPE
|
2016
|
1
|
123
|
|
|
|
|
|
|
|
|
|
|
2016
|
2
|
192
|
|
|
|
|
|
|
|
|
|
|
2016
|
3
|
307
|
|
|
|
|
|
|
|
|
|
|
2016
|
4
|
163
|
|
|
|
|
|
|
|
|
|
|
2017
|
1
|
147
|
|
|
|
|
|
|
|
|
|
|
2017
|
2
|
208
|
|
|
|
|
|
|
|
|
|
|
2017
|
3
|
337
|
|
|
|
|
|
|
|
|
|
|
2017
|
4
|
170
|
|
|
|
|
|
|
|
|
|
|
2018
|
1
|
150
|
|
|
|
|
|
|
|
|
|
|
2018
|
2
|
216
|
|
|
|
|
|
|
|
|
|
|
2018
|
3
|
348
|
|
|
|
|
|
|
|
|
|
|
2018
|
4
|
176
|
|
|
|
|
|
|
|
|
|
|
2019
|
1
|
157
|
|
|
|
|
|
|
|
|
|
|
2019
|
2
|
229
|
|
|
|
|
|
|
|
|
|
|
2019
|
3
|
374
|
|
|
|
|
|
|
|
|
|
|
2019
|
4
|
192
|
|
|
|
|
|
|
|
|
|
|
2020
|
1
|
172
|
|
|
|
|
|
|
|
|
|
|
2020
|
2
|
252
|
|
|
|
|
|
|
|
|
|
|
2020
|
3
|
415
|
|
|
|
|
|
|
|
|
|
|
2020
|
4
|
210
|
|
|
|
|
|
|
|
|
|
|
2021
|
1
|
186
|
|
|
|
|
|
|
|
|
|
|
2021
|
2
|
273
|
|
|
|
|
|
|
|
|
|
|
2021
|
3
|
440
|
|
|
|
|
|
|
|
|
|
|
2021
|
4
|
221
|
|
|
|
|
|
|
|
|
|
|
2022
|
1
|
196
|
|
|
|
|
|
|
|
|
|
|
2022
|
2
|
285
|
|
|
|
|
|
|
|
|
|
|
2022
|
3
|
462
|
|
|
|
|
|
|
|
|
|
|
2022
|
4
|
229
|
|
|
|
|
|
|
|
|
|
|
a. Use the data of mentioned table to compute the four-period moving average and enter your values in the appropriate columns.
b. Plot the data and describe the main features of the series.
c. Calculate the Centred Moving Average (CMA)/Baseline. Interpret it.
d. Calculate the Trend and interpret the trend.
e. Determine the Seasonality (St) and interpret it properly .
f. Forecast the revenue for 8th year.
g. Calculate the Error, mean absolute percentage error (MAPE), Mean Square Error (MSE) and Mean Absolute Deviation (MAD).
h. Write a brief report to explain and evaluate and make comments on the error variables, the forecasted and actual revenues
Learning Outcome 1: Define and evaluate key concepts of business analytics.
Learning Outcome 2: Critically apply business analytics skills for decision making.
Learning Outcome 3: Critically analyse and interpret the outputs of data mining models and forecasting results for end-users.
Learning Outcome 4: Solve managerial problems and make systematic decisions by applying business data analysis techniques.
Learning Outcome 5: Have ability to apply business analytics to various international business contexts by selecting appropriate techniques.