1. Operations and Supply Chain Management
CHASE | SHANKAR | JACOBS

2. FORECASTING Chapter Eighteen McGraw-Hill/Irwin
Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.

3. Learning Objectives
LO18–1: Understand how forecasting is essential to supply chain planning
LO18–2: Evaluate demand using quantitative forecasting models
LO18–3: Apply qualitative techniques to forecast demand
LO18–4: Apply collaborative techniques to forecast demand

4. The Role of Forecasting
Forecasting is a vital function and affects every significant management decision.
Finance and accounting use forecasts as the basis for budgeting and cost control.
Marketing relies on forecasts to make key decisions such as new product planning and personnel compensation.
Production uses forecasts to select suppliers; determine capacity requirements; and drive decisions about purchasing, staffing, and inventory.
Different roles require different forecasting approaches.
Decisions about overall directions require strategic forecasts.
Tactical forecasts are used to guide day-to-day decisions.

5. Forecasting and Decoupling Point
Decoupling point: Point at which inventory is stored, which allows SC to operate independently.
The choice of the decoupling point in a SC is strategic.
Forecasting helps determine the level of inventory needed at the decoupling points.
The decision will be affected by the error produced in the forecast and the type of product (easily inventoried or easily perishable).

6. Types of Forecasting There are four basic types of forecasts.
Qualitative
Time series analysis (primary focus of this chapter)
Causal relationships
Simulation
Time series analysis is based on the idea that data relating to past demand can be used to predict future demand.

7. Average demand for a period of time
Components of Demand
Average demand for a period of time
Trend
Seasonal element
Cyclical elements
Random variation
Autocorrelation
Excel: Components of Demand
For the Excel template visit

8. Trends
Identification of trend lines is a common starting point when developing a forecast.
Common trend types include linear, S-curve, asymptotic, and exponential.

9. Time Series Analysis Using the past to predict the future
Used mainly for tactical decisions
Short term – forecasting less than 3 months
Used to develop a strategy that will be implemented over the next 6 to 18 months (e.g., meeting demand)
Medium term – forecasting 3 months to 2 years
Useful for detecting general trends and identifying major turning points
Long term – forecasting greater than 2 years

10. Model Selection Choosing an appropriate forecasting model depends upon
Time horizon to be forecast
Data availability
Accuracy required
Size of forecasting budget
Availability of qualified personnel

11. Forecasting Method Selection Guide
Amount of Historical Data
Data Pattern
Forecast Horizon
Simple moving average
6 to 12 months; weekly data are often used
Stationary (i.e., no trend or seasonality)
Short
Weighted moving average and simple exponential smoothing
5 to 10 observations needed to start
Stationary
Exponential smoothing with trend
Stationary and trend
Linear regression
10 to 20 observations
Stationary, trend, and seasonality
Short to medium

12. Simple Moving Average
Forecast is the average of a fixed number of past periods.
Useful when demand is not growing or declining rapidly and no seasonality is present.
Removes some of the random fluctuation from the data.
Selecting the period length is important.
Longer periods provide more smoothing.
Shorter periods react to trends more quickly.

13. Simple Moving Average Formula

14. Simple Moving Average – Example

15. Weighted Moving Average
The simple moving average formula implies equal weighting for all periods.
A weighted moving average allows unequal weighting of prior time periods.
The sum of the weights must be equal to one.
Often, more recent periods are given higher weights than periods farther in the past.
𝐹𝑡=𝑤1𝐴𝑡−1+𝑤2𝐴𝑡−2+ …+𝑤𝑛𝐴𝑡−𝑛

16. Selecting Weights
Experience and/or trial-and-error are the simplest approaches.
The recent past is often the best indicator of the future, so weights are generally higher for more recent data.
If the data are seasonal, weights should reflect this appropriately.

17. Exponential Smoothing
A weighted average method that includes all past data in the forecasting calculation
More recent results weighted more heavily
The most used of all forecasting techniques
An integral part of computerized forecasting

18. Exponential Smoothing
Well accepted for six reasons
Exponential models are surprisingly accurate
Formulating an exponential model is relatively easy
The user can understand how the model works
Little computation is required to use the model
Computer storage requirements are small
Tests for accuracy are easy to compute

19. Exponential Smoothing Model

20. Exponential Smoothing Example
Week
Demand
Forecast 1
820 2
775 3
680
811 4
655
785 5
750
759 6
802
757 7
798
766 8
689
772 9
756 10
760

21. Exponential Smoothing – Effect of Trends
The presence of a trend in the data causes the exponential smoothing forecast to always lag behind the actual data
This can be corrected by adding a trend adjustment
The trend smoothing constant is delta (δ)

22. Example – Exponential Smoothing with Trend Adjustment
Calculate the new forecast, assuming the following:
The previous forecast including trend (FITt-1) is 110 and the previous estimate of the trend (Tt-1) is 10
α = 0.2 and δ = 0.3
Actual demand for period t-1 is 115
Ft = Ft-1 + α(At-1 – FITt-1) = ( ) = 111.0
Tt = Tt-1 + δ(Ft-1 – FITt-1) = ( ) = 10.3
FITt = Ft + Tt = = 121.3

23. Choosing Alpha and Delta
Relatively small values for α and δ are common
Usually in the range 0.1 to 0.3
α depends upon how much random variation is present
δ depends upon how steady the trend is
Measurement of forecast error can be used to select values of α and δ to minimize overall forecast error

24. Linear Regression Analysis
Regression is used to identify the functional relationship between two or more correlated variables, usually from observed data.
One variable (the dependent variable) is predicted for given values of the other variable (the independent variable).
Linear regression is a special case that assumes the relationship between the variables can be explained with a straight line.
Y = a + bt

25. Example 18.2 – Least Squares Method
The least squares method determines the parameters a and b such that the sum of the squared errors is minimized – “least squares”
Quarter
Sales 1
600 7
2,600 2
1,550 8
2,900 3
1,500 9
3,800 4 10
4,500 5
2,400 11
4,000 6
3,100 12
4,900

26. Example 18.2 – Calculations1
600
360,000
801.3 2
1,550
3,100 4
2,402,500
1,160.9 3
1,500
4,500 9
2,250,000
1,520.5
6,000 16
1,880.1 5
2,400
12,000 25
5,760,000
2,239.7 6
18,600 36
9,610,000
2,599.4 7
2,600
18,200 49
6,760,000
2,959.0 8
2,900
23,200 64
8,410,000
3,318.6
3,800
34,200 81
14,440,000
3,678.2 10
45,000
100
20,250,000
4,037.8 11
4,000
44,000
121
16,000,000
4,397.4 12
4,900
58,800
144
24,010,000
4,757.1
Sum 78
33,350
268,200
650
112,502,500
The forecast is extended to periods 13-16

27. Regression with Excel
Microsoft Excel includes data analysis tools, which can perform least squares regression on a data set.

28. Time Series Decomposition
Chronologically ordered data are referred to as a time series.
A time series may contain one or many elements.
Trend, seasonal, cyclical, autocorrelation, and random
Identifying these elements and separating the time series data into these components is known as decomposition.

29. Seasonal Variation
Seasonal variation may be either additive or multiplicative (shown here with a changing trend).

30. Determining Seasonal Factors : Simple Proportions Example 18.3
The seasonal factor (or index) is the ratio of the amount sold during each season divided by the average for all seasons.
Season
Past Sales
Average Sales for Each Season
Seasonal Factor
Spring
200
= 250
= 0.8
Summer
350
= 250
= 1.4
Fall
300
= 1.2
Winter
150
= 0.6
Total
1000

31. Example 18.3 (Continued) Expected Demand for Next Year Average
Sales for
Each Season
(1,100y4)
Seasonal
Factor
Next Year’s
Forecast
Spring
275 X
0.8 =
220
Summer
1.4
385
Fall
1.2
330
Winter
0.6
165
1100

32. Decomposition Using Least Squares Regression
Decompose the time series into its components
Find seasonal component
Deseasonalize the demand
Find trend component
Forecast future values of each component
Project trend component into the future
Multiply trend component by seasonal component

33. Decomposition – Steps 1 and 2
Using the data for periods 1-12, apply time series analysis (decomposition, linear regression, trend estimate & seasonal indices) to forecast for periods 13-16

34. Decomposition – Steps 3 and 4
Develop a least squares regression line for the deseasonalized data.
Project the regression line through the period of the forecast.
Regression Results:
Y = t
Forecast for periods 13-16

35. Decompostion – Step 5
Create the final forecast by adjusting the regression line by the seasonal factor.
Period
Quarter
Y from Regression
Seasonal Factor
Forecast (F x Seasonal Factor 13 I
5,003.5
0.82
4,102.87 14 II
5,345.7
1.10
5,880.27 15
III
5,687.9
0.97
5,517.26 16 IV
6,030.1
1.12
6,753.71

36. Forecast Errors
Forecast error is the difference between the forecast value and what actually occurred.
All forecasts contain some level of error.
Sources of error
Bias – when a consistent mistake is made
Random – errors that are not explained by the model being used
Measures of error
Mean absolute deviation (MAD)
Mean absolute percent error (MAPE)
Tracking signal

37. Forecast Error Measurements
Ideally, MAD will be zero (no forecasting error).
Larger values of MAD indicate a less accurate model.
MAPE scales the forecast error to the magnitude of demand.
Tracking signal indicates whether forecast errors are accumulating over time (either positive or negative errors).

38. Computing Forecast Error

39. Causal Relationship Forecasting
Causal relationship forecasting uses independent variables other than time to predict future demand.
This independent variable must be a leading indicator.
Many apparently causal relationships are actually just correlated events – care must be taken when selecting causal variables.

40. Multiple Regression Techniques
Often, more than one independent variable may be a valid predictor of future demand.
In this case, the forecast analyst may utilize multiple regression.
Analogous to linear regression analysis, but with multiple independent variables.
Multiple regression supported by statistical software packages.

41. Qualitative Forecasting Techniques
Generally used to take advantage of expert knowledge.
Useful when judgment is required, when products are new, or if the firm has little experience in a new market.
Examples
Market research
Panel consensus
Historical analogy
Delphi method

42. Collaborative Planning, Forecasting, and Replenishment (CPFR)
A web-based process used to coordinate the efforts of a supply chain.
Demand forecasting
Production and purchasing
Inventory replenishment
Integrates all members of a supply chain – manufacturers, distributors, and retailers.
Depends upon the exchange of internal information to provide a more reliable view of demand.

43. CPFR Steps Creation of a front-end partnership agreement
Joint business planning
Development of demand forecasts
Sharing forecasts
Inventory replenishment

44. Principles Forecasting is a fundamental step in any planning process.
Forecast effort should be proportional to the magnitude of decisions being made.
Web-based systems (CPFR) are growing in importance and effectiveness.
All forecasts have errors – understanding and minimizing this error is the key to effective forecasting processes.