2. Forecasting supply chain requirements
Chapter 6
Forecasting supply chain requirements

3. Contents Introduction Forecasting in general Types of forecasting
Long-term and short-term forecasting
The forecasting process
Appropriate forecasting models
Validating forecasting models
Stationary data
Data with trend
Forecasting seasonality
Conclusion

4. Introduction
Demand forecasting refers to the process of determining the amount of product and related information that consumers will require, either in the short or long term.
The responsibility for preparing demand forecasting usually lies in the marketing and sales departments.

5. Forecasting in general: features of forecasting
It is difficult to forecast accurately.
Forecasts for groups of items are often more accurate than forecasting demand for a single item.
Forecasts for a shorter time period are usually more accurate than forecasting for a longer time horizon.
What happened in the past can be used as an important guideline when making forecasts.

6. Types of forecasting
A time series is a time-ordered sequence of observations taken at regular intervals, e.g. daily, weekly, monthly, quarterly or annually.
Explanatory models, also called regression models, rely on the identification of related variables that can be used to predict values of the variable of interest. A mathematical relationship is developed between demand, for example, and some other factors that cause demand behaviour.

7. Types of forecasting: qualitative forecasting techniques
Qualitative forecasting techniques are appropriate when little or no quantitativeinformation is available, but sufficient qualitative knowledge exists. It is usually a product of judgement and accumulated knowledge.
Four techniques:
The Delphi method
Jury of executive option
Sales force composite
Consumer market survey

8. Long-term and short-term forecasting
Short-term forecasting is crucial for day-to-day planning, such as determining safety stock levels and production plans.
Medium-term forecasts are usually needed for budget purposes, including forecasts of sales, prices and costs for the entire company and different divisions.
Long-term forecasting is primarily needed for capital expansion plans, selecting research and development projects, launching new projects, formulating long-term goals and strategies and adapting to environmental changes.

9. The forecasting process
Specify the objectives clearly.
Determine what to forecast.
Determine the time horizon of the forecast.
Gather the data needed to make the forecast.
Do the model selection.
Validate the forecasting models.
Make the forecasts.
Implement the results.
Track the results. Stopped for FT on 10/09/15

10. Appropriate forecasting models: types of data patterns
a. Level or horizontal pattern
b. Trend pattern
c. Seasonal pattern
d. Cycle

11. Appropriate forecasting models: selecting an appropriate forecasting model

12. Measures of accuracy
These measures evaluate how well each forecasting method explains past behaviour of the time series variable. Three of the most common quantitative measures of accuracy are as follows:

13. Evaluate forecasting technique
Step 1: Divide time series into an initialisation set and a test set.
Step 2: Choose an appropriate forecasting method.
Step 3: Use the initialisation set to estimate the trend and seasonal components. Establish fit accuracy.

14. Evaluate forecasting technique (continued)
Step 4: Apply the method to the test set and establish forecast accuracy.
Step 5: Repeat steps 2 to 4 for several forecasting techniques, and select the forecasting method which performs best with regard to forecast accuracy and which is also appropriate depending on the data pattern.
Step 6: Use the complete data set and apply the selected technique to do the forecast.

15. Stationary data: naïve forecasting
The naïve method assumes that the next period’s forecast is equal to the current period’s actual value.
The forecast is as follows:
Ft+1 = At, where
Ft+1 is the forecast for the next period t+1
A t is the actual value for the current period t and
T is the current time period.
Week number
Actual sales
Naïve forecast
  t
  At
  Ft 1
250 2
245 3
247 4
251 5 6 7 8 9
Note that F6 = A5 and F7 = F6, etc.

16. Stationary data: moving averages
The simple moving average uses an average of n of the most recent observations to create a forecast for the next period.
The following formula is used:
where
Ft+1 is the forecast for the next period t+1;
A t is the actual value for the current period t;
t is the current time period, and
n is the number of periods included in the moving average.

17. Stationary data: moving averages (continued)
Week number
Actual sales
MA-3
  t At Ft 1
250 2
245 3
247 4
251
247,33 5
247,67 6
249,33 7
248,67 8
247,89

18. Stationary data: simple exponential smoothing

19. Data with trend: Holt’s method

20. Data with trend: linear regression

21. Forecasting seasonality
Multiplicative seasonality: seasonality is expressed as a percentage of the average.
Step 1: Calculate the forecast demand for the known observations using linear regression or Holt’s method.
Step 2: Calculate the actual sales as a percentage of the trend by dividing the actual sales by the forecast sales for each time period.
Step 3: Calculate the average seasonal index for each season by adding up the values calculated in Step 2 for that season and dividing by the number of time periods.
Step 4: Multiply the forecast by the average seasonal index for the corresponding season. This will produce a forecast adjusted for seasons.

22. Forecasting seasonality: example
Quarter
Time period
Unit sales
Linear
Actual as %
Seasonal X Y
forecast
of trend
Coefficients 1
240
242,37
0,99
239,63
Intercept
241,4158 2
245
243,33
1,01
242,79
0,955639 3
247
244,28
245,14 4
249
245,24
1,02
247,69 5
243
246,19
243,41
Seasonal index 6
247,15
246,61
0,988685 7
248
248,11
1,00
248,98
0,997807 8
251
249,06
251,55
1,003527 9
250,02
0,98
247,19
1,009983 10
252
250,97
250,42 11
253
251,93
252,82 12
255
252,88
255,41 13
253,84 14
254
254,79
254,24 15
257
255,75
256,65 16
259
256,71
259,27 17
257,66
254,75 18
256
258,62
258,05 19
259,57
260,49 20
263
260,53
263,13 21
261,48
258,53 22
262,44
261,86 23
263,40
264,32 24
264,35
266,99
Forecast demand using linear regression.
Divide actual sales by linear forecast to get actual as % of trend.
Calculate average seasonal index for each quarter.
4. Calculate the seasonal forecast by multiplying the linear forecast with the appropriate seasonal index.

23. Conclusion
This chapter presented several methods for forecasting future demand or sales.
Different methods for stationary time series data and non-stationary time series data were presented.
A practical method for addressing seasonality was also suggested.
In each case, the goal was to fit models to the past behaviour of a time series, then to first test the models on test data, and then to fit the appropriate model to project future values.
More advanced techniques were discussed in the appendix of the chapter.
This chapter is only an introduction to forecasting and the techniques should be seen as such.