1. Chapter 5 DEMAND FORECASTING Prepared by Mark A. Jacobs, PhD
©2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole or in part.

2. Learning Objectives You should be able to:
Explain the role of demand forecasting in a supply chain
Identify the components of a forecast
Compare & contrast qualitative & quantitative forecasting techniques
Assess the accuracy of forecasts
Explain collaborative planning, forecasting, & replenishment
©2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.

3. Chapter Outline Introduction Demand Forecasting Forecasting Techniques
Qualitative Methods
Quantitative Methods
Components of Time Series Data
Time Series Forecasting Methods
Forecast Accuracy
Useful Forecasting Websites
Collaborative Planning, Forecasting, & Replenishment (CPFR)
Software Solutions
©2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.

4. Introduction
Supply chain members find it important to manage demand, especially in pull manufacturing environments.
Suppliers must find ways to better match supply & demand to achieve optimal levels of cost, quality, & customer service to enable them to compete with other supply chains.
Improved forecasts benefit all trading partners in the supply chain & mitigates supply-demand mismatch problems.

5. Demand Forecasting
A forecast is an estimate of future demand & provides the basis for planning decisions
The goal is to minimize forecast error
The factors that influence demand must be considered when forecasting.
Managing demand requires timely & accurate forecasts
Good forecasting provides reduced inventories, costs, & stockouts, & improved production plans & customer service

6. Forecasting Techniques
Qualitative forecasting is based on opinion & intuition.
Quantitative forecasting uses mathematical models & historical data to make forecasts.
Time series models are the most frequently used among all the forecasting models.

7. Forecasting Techniques (Continued)
Qualitative Forecasting Methods
Generally used when data are limited, unavailable, or not currently relevant. Forecast depends on skill & experience of forecaster(s) & available information
Four qualitative models used are –
Jury of executive opinion
Delphi method
Sales force composite
Consumer survey

8. Forecasting Techniques (Continued)
Quantitative Methods
Time series forecasting – based on the assumption that the future is an extension of the past. Historical data is used to predict future demand
Cause & Effect forecasting – assumes that one or more factors (independent variables) predict future demand
It is generally recommended to use a combination of quantitative & qualitative techniques

9. Forecasting Techniques (Continued)
Components of Time Series
Data should be plotted to detect for the following components –
Trend variations: increasing or decreasing
Cyclical variations: wavelike movements that are longer than a year (e.g., business cycle)
Seasonal variations: show peaks & valleys that repeat over a consistent interval such as hours, days, weeks, months, seasons, or years
Random variations: due to unexpected or unpredictable events

10. Forecasting Techniques (Continued)
Time Series Forecasting Models
Naïve Forecast – the estimate of the next period is equal to the demand in the past period.
Ft+1 = At
Where Ft+1 = forecast for period t+1
At = actual demand for period t

11. Forecasting Techniques (Continued)
Time Series Forecasting Models
Simple Moving Average Forecast – uses historical data to generate a forecast. Works well when demand is stable over time.
Where Ft+1 = forecast for period t+1
At = actual demand for period t
n = number of periods to calculate moving average

12. Forecasting Techniques (Continued)
Simple Moving Average
(Fig. 5.1)

13. Forecasting Techniques (Continued)
Time Series Forecasting Models
Weighted Moving Average Forecast – is based on an n-period weighted moving average
Where Ft+1 = forecast for period t+1
Ai = actual demand for period i
n = number of periods to calculate moving average
wi = weight assigned to period i (Σwi = 1)

14. Forecasting Techniques (Continued)
Weighted Moving Average
(Fig. 5.2)

15. Forecasting Techniques (Continued)
Time Series Forecasting Models
Exponential Smoothing Forecast – a type of weighted moving average where only two data points are needed
Ft+1 = Ft+(At - Ft) or Ft+1 = At + (1 – ) Ft
Where Ft+1 = forecast for Period t + 1
Ft = forecast for Period t
At = actual demand for Period t
 = smoothing constant (0 ≤  ≤1)

16. Forecasting Techniques (Continued)
Exponential Smoothing
(Fig. 5.3)

17. Forecast Accuracy
The formula for forecast error, defined as the difference between actual quantity & the forecast –
Forecast error, et = At - Ft
Where et = forecast error for Period t
At = actual demand for Period t
Ft = forecast for Period t

18. Forecast Accuracy (Continued)
Several measures of forecasting accuracy follow –
Mean absolute deviation (MAD)- a MAD of 0 indicates the forecast exactly predicted demand
Mean absolute percentage error (MAPE)- provides a perspective of the true magnitude of the forecast error
Mean squared error (MSE)- analogous to variance, large forecast errors are heavily penalized

19. Forecast Accuracy (Continued)
Mean absolute deviation (MAD)-
MAD of 0 indicates the forecast exactly predicted demand.
Where et = forecast error for period t
At = actual demand for period t
n = number of periods of evaluation

20. Forecast Accuracy (Continued)
Mean absolute percentage error (MAPE) –
provides a perspective of the true magnitude of the forecast error.
Where et = forecast error for period t
At = actual demand for period t
n = number of periods of evaluation

21. Forecast Accuracy (Continued)
Mean squared error (MSE) –
analogous to variance, large forecast errors are heavily penalized
Where et = forecast error for period t
n = number of periods of evaluation

22. Forecast Accuracy (Continued)
Running Sum of Forecast Errors (RSFE) – indicates bias in the forecasts or the tendency of a forecast to be consistently higher or lower than actual demand.
Running Sum of Forecast Errors, RSFE =
Where et = forecast error for period t

23. Forecast Accuracy (Continued)
Tracking signal –
determines if forecast is within acceptable control limits. If the tracking signal falls outside the pre-set control limits, there is a bias problem with the forecasting method and an evaluation of the way forecasts are generated is warranted.
Tracking Signal =

24. Useful Forecasting Websites
Institute for Forecasting Education
International Institute of Forecasters
Forecasting Principles
Stata (Data analysis & statistical software)

25. Collaborative Planning, Forecasting, & Replenishment (CPFR)
A business practice that combines the intelligence of multiple trading partners in the planning & fulfillment of customer demands.
Links sales & marketing best practices, such as category management, to supply chain planning processes to increase availability while reducing inventory, transportation & logistics costs.

26. Collaborative Planning, Forecasting, & Replenishment (Continued)
Real value of CPFR comes from sharing of forecasts among firms rather than sophisticated algorithms from only one firm.
Does away with the shifting of inventories among trading partners that suboptimizes the supply chain.
CPFR provides the supply chain with a plethora of benefits but requires a fundamental change in the way that buyers & sellers work together.

27. Collaborative Planning, Forecasting, & Replenishment (Continued)
VICS’s CPFR Model
(Fig. 5.5)

28. Collaborative Planning, Forecasting, & Replenishment (Continued)
CPFR Model
Step 1: Collaboration Arrangement
Step 2: Joint Business Plan
Step 3: Sales Forecasting
Step 4: Order Planning/Forecasting
Step 5: Order Generation
Step 6: Order Fulfillment
Step 7: Exception Management
Step 8: Performance Assessment

29. Software Solutions Forecasting Software
Business Forecast Systems
John Galt
Just Enough
SAS
JDA Software Group
i2 Technologies
Oracle