Demand Forecasting Analysis and Distributor Selection Strategy for Altavox Electronics
Overview
This case study analyzes demand forecasting methods for Altavox Electronics using simple moving average and exponential smoothing models across multiple time horizons. The paper evaluates forecasting accuracy through statistical metrics and addresses a strategic business decision regarding distributor consolidation across five regional markets.
What this case study covers
- Simple Moving Average forecasting comparison (3-week vs 5-week models) with performance metrics
- Exponential Smoothing analysis (α=0.2 and α=0.4) evaluating prediction accuracy and tracking signals
- Distributor consolidation strategy assessment considering sales revenue impact and operational risk
Preview
Altavox Electronics Case Study
Student’s Name
Institutional Affiliation
Altavox Electronics Case Study
Question 1
Simple Moving Average (For 3 Weeks)
Week | Demand | Prediction | Deviation | Absolute Deviation | Cumulative deviation | Absolute Percentage deviation | Error Squared |
di | fi | di-fi | |||||
1 | 162 | ||||||
2 | 199 | ||||||
3 | 189 | ||||||
4 | 213 | 183.3 | 29.7 | 29.7 | 29.7 | 13.8 | 880.1 |
5 | 246 | 200.3 | 45.7 | 45.7 | 75.348.3 | 18.6 | 2085.4 |
6 | 288 | 216.1 | 71.9 | 71.9 | 147.3 | 24.8 | 5184 |
7 | 245 | 249.2 | -4 | 4 | 143.3 | 1.7 | 16 |
8 | 204 | 259.7 | -55.7 | 55.7 | 87.7 | 27.2 | 3098.8 |
9 | 236 | 245.7 | -9.7 | 9.7 | 78 | 4.1 | 93.4 |
10 | 257 | 228.3 | 28.7 | 28.7 | 106.7 | 11.2 | 821.8 |
11 | 174 | 232.3 | -58.3 | 58.3 | 48.3 | 33.5 | 3401.8 |
12 | 248 | 222.3 | 25.7 | 25.7 | 73.9 | 10.4 | 658.8 |
13 | 229 | 226.3 | 2.7 | 2.7 | 76.7 | 1.2 | 7.1 |
Simple Moving Average (For 5 Weeks)
Week | Demand | Prediction | Deviation | Absolute Deviation | Cumulative deviation | Absolute Percentage deviation | Error Squared |
di | fi | di-fi | |||||
1 | 162 | ||||||
2 | 199 | ||||||
3 | 189 | ||||||
4 | 213 | ||||||
5 | 246 | ||||||
6 | 288 | 200.9 | 87.1 | 87.1 | 87.1 | 30.1 | 7586.41 |
7 | 245 | 226.9 | 18.1 | 18.1 | 105.2 | 7.3 | 327.61 |
8 | 204 | 236.1 | -32.1 | 32.1 | 73.1 | 15.8 | 1030.41 |
9 | 236 | 239.2 | -3.2 | 3.2 | 69.9 | 1.4 | 10.24 |
10 | 257 | 243.1 | 13.9 | 13.9 | 83.8 | 5.2 | 193.21 |
11 | 174 | 245.8 | -71.8 | 71.8 | 12 | 41.3 | 5155.24 |
12 | 248 | 222.4 | 25.6 | 25.6 | 37.6 | 10.9 | 655.36 |
13 | 229 | 222.5 | 6.5 | 6.5 | 44.1 | 2.26 | 42.25 |
For 3 Weeks | For 5 Weeks | |
MAD | 33.2 | 31.9 |
MAPE | 15.3 | 15.1 |
MSE | 1623.9 | 1850.2 |
TRACKING SIGNAL | 2.2 | 1.3 |
Question 2
Exponential Smoothing Model (For 3 Weeks, a = 0.2)
Week | Demand | Prediction | Deviation | Absolute Deviation | Cumulative deviation | Absolute Percentage deviation | Error Squared |
di | fi | di-fi | |||||
-3 | 244 | 0 | |||||
-2 | 252 | 244 | 8 | 8 | 8 | 3.18 | 64 |
-1 | 198 | 245 | -47 | 47 | -39 | 23.9 | 2209 |
1 | 162 | 236.6 | -74.6 | 74.6 | -113.6 | 44.8 | 5565.16 |
2 | 199 | 221.3 | -22.3 | 22.3 | -135.9 | 11.1 | 497.29 |
3 | 189 | 216.7 | -27.7 | 27.7 | -163.6 | 15.1 | 767.29 |
4 | 213 | 211.3 | 1.7 | 1.7 | -161.9 | 0.9 | 2.89 |
5 | 246 | 211.6 | 34.4 | 34.4 | -127.5 | 14.1 | 1183.36 |
6 | 288 | 217.9 | 70.1 | 70.1 | -57.4 | 24.2 | 4914.01 |
7 | 245 | 233.1 | 11.9 | 11.9 | -45.5 | 5.1 | 141.61 |
8 | 204 | 235.1 | -31.1 | 31.1 | -76.6 | 15.2 | 967.21 |
9 | 236 | 228.7 | 7.3 | 7.3 | -69.3 | 3.1 | 53.29 |
10 | 257 | 230.2 | 26.8 | 26.8 | -42.5 | 10.4 | 718.24 |
11 | 174 | 236.0 | -62 | 62 | -104.5 | 35.4 | 3844 |
12 | 248 | 224.1 | 23.9 | 23.9 | -80.6 | 9.9 | 571.21 |
13 | 229 | 228.2 | 0.8 | 0.8 | -79.8 | 0.4 | 0.64 |
Exponential Smoothing Model (For 5 Weeks, a = 0.4)
Week | Demand | Prediction | Deviation | Absolute Deviation | Cumulative deviation | Absolute Percentage deviation | Error Squared |
di | fi | di-fi | |||||
-5 | 254 | 0 | |||||
-4 | 153 | 254 | -101 | 101 | -101 | 66.02 | 10201 |
-3 | 244 | 213.5 | 30.5 | 30.5 | -70.5 | 12.45 | 930.25 |
-2 | 252 | 244 | 8 | 8 | -62.5 | 3.18 | 64 |
-1 | 198 | 245 | -47 | 47 | -109.5 | 23.9 | 2209 |
1 | 162 | 236.6 | -74.6 | 74.6 | -184.1 | 44.8 | 5565.16 |
2 | 199 | 221.3 | -22.3 | 22.3 | -206.4 | 11.1 | 497.29 |
3 | 189 | 216.7 | -27.7 | 27.7 | -234.1 | 15.1 | 767.29 |
4 | 213 | 211.3 | 1.7 | 1.7 | -232.4 | 0.9 | 2.89 |
5 | 246 | 211.6 | 34.4 | 34.4 | -198 | 14.1 | 1183.36 |
6 | 288 | 217.9 | 70.1 | 70.1 | -127.9 | 24.2 | 4914.01 |
7 | 245 | 233.1 | 11.9 | 11.9 | -116 | 5.1 | 141.61 |
8 | 204 | 235.1 | -31.1 | 31.1 | -147.1 | 15.2 | 967.21 |
9 | 236 | 228.7 | 7.3 | 7.3 | -139.8 | 3.1 | 53.29 |
10 | 257 | 230.2 | 26.8 | 26.8 | -113 | 10.4 | 718.24 |
11 | 174 | 236.0 | -62 | 62 | -175 | 35.4 | 3844 |
12 | 248 | 224.1 | 23.9 | 23.9 | -50.1 | 9.9 | 571.21 |
13 | 229 | 228.2 | 0.8 | 0.8 | -79.8 | 0.4 | 0.64 |
For 3 Weeks | For 5 Weeks | |
MAD | 30.0 | 26.6 |
MAPE | 14.9 | 12.8 |
MSE | 1425.3 | 1271.5 |
TRACKING SIGNAL | -2.7 | -2.4 |
Question 3
The company is aiming to reduce the number of distributors to one, instead of working with the current number. The company objective is to establish which of the distributors would work best among the five regions. During the implementation of the new system, the firm can reduce it sales revenue. If the demand increases, a single distributor cannot deliver product to all the clients effectively. Moreover, whenever there are challenges with the distributor, there will be no immediate remedies to ensure client receive their orders on time. Therefore, the company can reduce the number of distributors to 2 or 3.