The document discusses forecasting methods used to predict future demand based on past sales data, emphasizing the importance of the chosen method and its impact on the results. It outlines various forecasting techniques such as time series, causal relationships, and simulation, along with their specific formulas and applications. Additionally, it details the significance of weighting recent data more heavily in forecasts to capture trends accurately.

1. NAMA KELOMPOK :
1. Josua Putra Pratama Manurung
2. Lanang Bagas Adi Candra
3. Dhafin Izzudin
4. Venna Vincencia
5. Thirza Frigia Grace Manurung

2. ❑ Forecasting is a tool used for predicting future
demand based on past demand information.
❑ A forecast is only as good as the information
included in the forecast (past data).
❑Forecasting is a based on the assumption that the
past predicts the future.

3. ▪That is forecasting based on sales data in the past.
Forecasting results are made very dependet on the
method used in the forecasting.The use of different
method will get different results.

4. ▪ TIME SERIES : Models that predict future demand on past history
trend.
▪ CAUSAL RELATIONSHIP : Models that use statistical tecniques to
estabilish relationships beetwen various items and demand.
▪ SIMULATION : Model that can incorporate some randomness and
non-linear effects

5. 𝑡 is the current period
𝐹𝑡+1 is the forecast for next period
𝑛 is the forecasting horizon (how far back we look)
𝐴 is the actual sales figure from each period

6. Month Bottles
Jan 1,325
Feb 1,353
Mar 1,305
Apr 1,275
May 1,210
Jun 1,195
Jul ?

8. 1000
1050
1100
1150
1200
1250
1300
1350
1400
0 1 2 3 4 5 6 7 8
5-month
MA forecast
3-month
MA forecast
5-month average smoothes data more;
3-month average more responsive

9. 𝑡 is the current period
𝐹𝑡+1 is the forecast for next period
𝑛 is the forecasting horizon (how far back we look)
𝐴 is the actual sales figure from each period
𝑤 is the importance (weight) was give to each period
𝐹𝑡+1 = 𝑤𝑡 𝐴 𝑡 + 𝑤𝑡−1 𝐴 𝑡−1 + … + 𝑤𝑡−𝑛 𝐴 𝑡−𝑛 𝑤𝑡 + 𝑤𝑡−1 + … + 𝑤𝑡−𝑛 = 1

10. Make the weight for the last three months more
than the first three months…
Month Bottles
Jan 1,325
Feb 1,353
Mar 1,305
Apr 1,275
May 1,210
Jun 1,195
Jul ?
The higher the importance we give to recent
data, the more we pick up the declining trend in
our forecast.
6-month SMA
WMA
40%/60%
WMA
30%/70%
WMA
20%/80%
July Forecast 1,277 1,267 1,257 1,247

11. Assume that we are currently in period 𝑡.We calculated the forecast
for the last period (𝐹𝑡+1) and we know the actual demand last period
(𝐴 𝑡−1) …
𝐹1 = 𝐹𝑡−1 + 𝑎(𝐴𝑡−1−𝐹𝑡−1)
The smoothing constant 𝑎 expresses how much our forecast will react
to observed differences…
If 𝑎 is low : there is little reaction to differences.
If 𝑎 is high : there is a lot of reaction to differences.

12. Month Bottles Forecasted
Jan 1,325 1,370
Feb 1,353 1,361
Mar 1,305 1,359
Apr 1,275 1,349
May 1,210 1,334
Jun ? 1,309
𝑎 = 0.2

13. 𝑎 = 0.8Month Bottles Forecasted
Jan 1,325 1,370
Feb 1,353 1,334
Mar 1,305 1,349
Apr 1,275 1,314
May 1,210 1,283
Jun ? 1,225

14. 1,200
1,220
1,240
1,260
1,280
1,300
1,320
1,340
1,360
1,380
1 2 3 4 5 6 7
Actual
a=0,2
a=0,8

15. Linear regression is based on
1. Fitting a straight line to data
2. Explaining the change in one variable through changes in other variables.
Depend variable = a + b × (independent variable)

16. 1. Data availability
2. Time horizon for the forecast
3. Required accuracy
4. Required resources

17. ➢ www.csb.uncw.edu
➢ www.prism.gatech.edu
➢ web.calstatela.edu
➢ www.forecasters.org
➢ www.csun.edu