Solaris Pte Ltd is a manufacturer of solar panels used by many organisations in solar farms to produce electricity in Singapore. The last few years had been tough. The COVID19 pandemic had shutdown many economic activities leading to poor sales in solar panels. In more recent times, with many countries in the world embracing endemic COVID and opening up their borders, economic activities are restarting. The CEO of Solaris is optimistic even though there are other challenges like sharp spikes in oil and gas prices, war in Ukraine and frequent supply chain disruptions Table 4. Solar Panel Sales Period Units Sold Actual 2019 Q1 25000 2019 Q2 22500 2019 Q3 17500 2019 Q4 12500 2020 Q1 10500 2020 Q2 10750 2020 Q3 12500 2020 Q4 17500 2021 Q1 21250 2021 Q2 23750 2021 Q3 25000 2021 Q4 27500 2022 Q1 60825 2022 Q2 57500 2022 Q3 ? 2022 Q4 ? 2023 Q1 ? 2023 Q2 ? Table 4 shows the past quarterly sales data of solar panels sold by Solaris in terms of units per quarter for the last three years. Sales data are only available right up to the second quarter of 2022. The CEO would like its sales manager to forecast sales for the next 4 quarters ahead from 2022 Q3 to 2023 Q2. Suppose you are the Sales Manager at Solaris. Using the Weighted Moving Average (WMA) method, develop a quarterly sales forecast for the solar panels. (i) What is your sales forecast for 2022Q3 through 2023Q2? You may assume that the weights are 2:3:1 where weight 2 is for the oldest data point and weight 1 is for the most recent data. What is the Mean Absolute Deviation (MAD) of your forecast? (Note: you need to show how the first two (2) values of your WMA, Absolute Error and final MAD are computed.) (ii) Comment on your new forecasts in terms of its reliability for business planning. (Hint: consider plotting a graph of your new forecast for analysis.) Do provide all equations, tables, graphs and working Do show all equations, workings, tables and graph.
Solaris Pte Ltd is a manufacturer of solar panels used by many organisations in solar farms to
produce electricity in Singapore. The last few years had been tough. The COVID19 pandemic
had shutdown many economic activities leading to poor sales in solar panels. In more recent
times, with many countries in the world embracing endemic COVID and opening up their
borders, economic activities are restarting. The CEO of Solaris is optimistic even though there
are other challenges like sharp spikes in oil and gas prices, war in Ukraine and frequent supply
chain disruptions
| Table 4. Solar Panel Sales | |
| Period | Units Sold |
| Actual | |
| 2019 Q1 | 25000 |
| 2019 Q2 | 22500 |
| 2019 Q3 | 17500 |
| 2019 Q4 | 12500 |
| 2020 Q1 | 10500 |
| 2020 Q2 | 10750 |
| 2020 Q3 | 12500 |
| 2020 Q4 | 17500 |
| 2021 Q1 | 21250 |
| 2021 Q2 | 23750 |
| 2021 Q3 | 25000 |
| 2021 Q4 | 27500 |
| 2022 Q1 | 60825 |
| 2022 Q2 | 57500 |
| 2022 Q3 | ? |
| 2022 Q4 | ? |
| 2023 Q1 | ? |
| 2023 Q2 | ? |
Table 4 shows the past quarterly sales data of solar panels sold by Solaris in terms of units per
quarter for the last three years. Sales data are only available right up to the second quarter of
2022. The CEO would like its sales manager to
from 2022 Q3 to 2023 Q2.
Suppose you are the Sales Manager at Solaris. Using the Weighted Moving Average (WMA)
method, develop a quarterly sales forecast for the solar panels.
(i) What is your sales forecast for 2022Q3 through 2023Q2? You may assume that the weights
are 2:3:1 where weight 2 is for the oldest data point and weight 1 is for the most recent
data. What is the Mean Absolute Deviation (MAD) of your forecast? (Note: you need to
show how the first two (2) values of your WMA, Absolute Error and final MAD are
computed.)
(ii) Comment on your new forecasts in terms of its reliability for business planning.
(Hint: consider plotting a graph of your new forecast for analysis.)
Do provide all equations, tables, graphs and working
Do show all equations, workings, tables and graph.
The forecast values from 2022 Q3 to 2023Q2 can be calculated by using the concept of linear regression. Under this concept, there are certain variables that are dependent and independent in nature. One variable may be dependent on one or multiple independent variables. This concept helps in better prediction for business decisions and strategies. The regression is represented as . Here "x" may represent the period number and "y" be the number of units to be sold. The letter "m" represents the slope and "b" is the intercept.
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