1. FORCASTING MODELS By
Group-2

2. FORECASTING
A forecast is an estimate of a future event achieved by systematically combining and casting forward in a predetermined way data about the past.

3. Nominal group technique
Forecasting Models
Forecasting
Techniques
Qualitative
Models
Time Series
Methods
Causal
Delphi
Method
Historical Data
Nominal group technique
Naive
Moving
Average
Weighted
Moving Average
Exponential
Smoothing
Trend Analysis
Seasonality
Analysis
Simple
Regression
Multiple
Multiplicative
Decomposition

4. MODAL DIFFERENCES
1. Qualitative – incorporates judgmental & subjective factors into forecast.
2. Time-Series – attempts to predict the future by using historical data.
3. Causal – incorporates factors that may influence the quantity being forecasted into the model

5. QUALITATIVE FORECASTING MODALS
Delphi method
Iterative group process allows experts to make forecasts
Participants:
decision makers: experts who make the forecast
staff personnel: assist by preparing, distributing, collecting, and summarizing a series of questionnaires and survey results
respondents: group with valued judgments who provide input to decision makers

6. Nominal Group Technique (NGT)
Nominal group technique (NGT) is a structured method for group brainstorming that encourages contributions from everyone.

7. When to Use Nominal Group Technique
When some group members are much more vocal than others.
When some group members think better in silence.
When there is concern about some members not participating.
When the group does not easily generate quantities of ideas.
When all or some group members are new to the team.
When the issue is controversial or there is heated conflict.

8. Nominal Group Technique Procedure
Materials needed: paper and pen or pencil for each individual, flipchart, marking pens, tape.
1. State the subject of the brainstorming. Clarify the statement as needed until everyone understands it.
2. Each team member silently thinks of and writes down as many ideas as possible in a set period of time (5 to 10 minutes).
3. Each member in turn states aloud one idea. Facilitator records it on the flipchart.

9. Nominal Group Technique Procedure
4. Discuss each idea in turn. Wording may be changed only when the idea’s originator agrees. Ideas may be stricken from the list only by unanimous agreement. Discussion may clarify meaning, explain logic or analysis, raise and answer questions, or state agreement or disagreement.
5. Prioritize the ideas using multivoting or list reduction.

10. Quantitative Forecasting Models
Time Series Method
Naïve
Whatever happened recently will happen again this time (same time period)
The model is simple and flexible
Provides a baseline to measure other models
Attempts to capture seasonal factors at the expense of ignoring trend

11. Naïve Forecast

12. Naïve Forecast Graph

13. Quantitative Forecasting Models
Time Series Method
Moving Averages
Assumes item forecasted will stay steady over time.
Technique will smooth out short-term irregularities in the time series.

14. Moving Averages

15. Moving Averages Forecast

16. Moving Averages Graph

17. Quantitative Forecasting Models
Time Series Method
Weighted Moving Averages
Assumes data from some periods are more important than data from other periods (e.g. earlier periods).
Use weights to place more emphasis on some periods and less on others.

18. Simple Moving Average
It is used to forecast demand of next period using data from several of most recent periods.

19. We may take three or more periods.
Continued with same number of periods.
All periods are equally weighted.
Demand of oldest period is discarded and newest is added.

20. Simple Moving Average = Sum of demands of periods Chosen number of periods

21. If we have to forecast the demand of car in a city ‘X’ for the month of April, with Simple Moving Average using last three month’s data:
Month
No. of cars
January 96
February 94
March 98
April ?

22. Simple Moving Average =96 + 94 + 98 = 96 3 So the forecast for the month of April is 96 cars.

23. Weighted Moving Average

24. Weighted Moving Average

25. Quantitative Forecasting Models
Time Series Method
Exponential Smoothing
Moving average technique that requires little record keeping of past data.
Uses a smoothing constant α with a value between 0 and 1. (Usual range 0.1 to 0.3)
Both moving averages and weighted moving averages are effective in smoothing out sudden fluctuations in the demand pattern in order to provide stable estimates. Increasing the size of k (number of periods averaged) smoothes out fluctuations even better. This requires keeping extensive historical records.

26. What is Exponential Smoothing?
Exponential Smoothing is a technique that can be applied to time series data, either to produce smoothed data for presentation, or to make forecasts.
The time series data themselves are a sequence of observations.
The observed phenomenon may be an essentially random process, or it may be an orderly, but noisy, process.
Whereas in Single Moving Averages the past observations are weighted equally, Exponential Smoothing assigns exponentially decreasing weights as the observation get older.
Exponential smoothing is commonly applied to financial market and economic data, but it can be used with any discrete set of repeated measurements. The raw data sequence is often represented by {xt}, and the output of the exponential smoothing algorithm is commonly written as {st} which may be regarded as our best estimate of what the next value of x will be. When the sequence of observations begins at time t = 0, the simplest form of exponential smoothing is given by the formulas.

27. If Alpha is set to 1, the forecast for the next period is based entirely on the actual value from the last period. If Alpha is set to 0, the actual value from the last period is completely ignored. Since neither of these cases will provide much insight into future data, we'll constrain Alpha to be between .01 and .99.
In order to minimise costly overstocking and inventory holding, your retail outlet needs useful forecasts of future sales.
For this simple exponential smoothing problem, you have sales data (in $1,000's) for eight months. You need to find Alpha, the smoothing constant, that minimises the sum of the error - which in this case is the difference between the actual and forecast sales for each period.
The objective for this sales forecasting technique is to determine projected sales and the Alpha smoothing constant while minimising the squared error.

28. Example of Exponential Smoothing:
The demand for a product in each of the last five months is shown below.
Month
Demand ('00s)
# Use a two month moving average to generate a forecast for demand in month 6.
# Apply exponential smoothing with a smoothing constant of 0.9 to generate a forecast for demand for demand in month 6.
# Which of these two forecasts do you prefer and why?
Solution
The two month moving average for months two to five is given by:
m2 = ( )/2 = 15.0
m3 = ( )/2 = 18.0
m4 = ( )/2 = 21.0
m5 = ( )/2 = 23.5

29. As before the forecast for month six is just the average for month 5= M5 = 2386
To compare the two forecasts we calculate the mean squared deviation (MSD). If we do this we find that for the moving average
* MSD = [( )² + ( )²+ ( )²]/3 = 16.67
and for the exponentially smoothed average with a smoothing constant of 0.9
* MSD = [( )² + ( )² +( )²+ ( )²]/4 = 10.44
Overall then we see that exponential smoothing appears to give the best one month ahead forecasts as it has a lower MSD. Hence we prefer the forecast of 2386 that has been produced by exponential smoothing.

30. Exponential Smoothing Data

31. Exponential Smoothing

32. Exponential Smoothing

33. Trend & Seasonality Trend analysis Seasonality analysis
technique that fits a trend equation (or curve) to a series of historical data points.
projects the curve into the future for medium and long term forecasts.
Seasonality analysis
adjustment to time series data due to variations at certain periods.
adjust with seasonal index – ratio of average value of the item in a season to the overall annual average value.
example: demand for coal & fuel oil in winter months.

34. Linear Trend Analysis Midwestern Manufacturing Sales

35. Least Squares for Linear Regression Midwestern Manufacturing

36. Least Squares Method X = value of the independent variable (time) b =
Where
= predicted value of the dependent variable (demand)
X = value of the independent variable (time)
a = Y-axis intercept
b = slope of the regression line
b =

37. Linear Trend Data & Error Analysis

38. Least Squares Graph

39. Seasonality Analysis
Ratio = demand / average demand
Seasonal Index – ratio of the average value of the item in a season to the overall average annual value.
Example: average of year 1 January ratio to year 2 January ratio.
( )/2 = 0.957
A seasonal index with value below 1 indicates demand below average that month, and an index above 1 indicates demand above average that month. Using these seasonal indices, the future demand for any future month can be adjusted. For example, if the average demand for answering machines in year three is expected to be 100 units, then the forecast for January’s demand is 100 X = 96 units, which is below average. May’s forecast is 100 X = 131 units, which is above average.
If Year 3 average monthly demand is expected to be 100 units.
Forecast demand Year 3 January:
100 X = 96 units
Forecast demand Year 3 May:
100 X = 131 units

40. Deseasonalized Data
Going back to the conceptual model, solve for trend:
Trend = Y / Season (96 units/ = )
This eliminates seasonal variation and isolates the trend
Now use the Least Squares method to compute the Trend

41. Forecast
Now that we have the Seasonal Indices and Trend, we can reseasonalize the data and generate the forecast
Y = Trend x Seasonal Index

42. THANK YOU