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Forecasting

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BUS255

By the end of this chapter, you should know:

- Importance of Forecasting
- Various Forecasting Techniques
- Choosing a Forecasting Method

- Forecasts are done to predict future events for planning
- Finance, human resources, marketing, operations, and supply chain managers need forecasts to plan
- Forecasts are made on many different variables
- Forecasts are important to managing both processes and managing supply chains

- Deciding what to forecast
- Level of aggregation
- Units of measurement

- Choosing a forecasting system
- Choosing a forecasting technique

- Salesforce estimates
- Executive opinion
- Market Research
- The Delphi Method

- What information system is used by UNILEVER to manage forecasts?
- What does UNILEVER do when statistical information is not useful for forecasting?
- What types of qualitative methods are used by UNILEVER?
- What were some suggestions provided to improve forecasting?

- A dependent variable is related to one or more independent variables by a linear equation
- The independent variables are assumed to “cause” the results observed in the past
- Simple linear regression model assumes a straight line relationship

Y = a + bX

where

Y= dependent variable

X= independent variable

a= Y-intercept of the line

b= slope of the line

- Fit of the regression model
- Coefficient of determination
- Standard error of the estimate

- Please go to in-class exercise sheet

- A time series is the repeated observations of demand for a service or product in their order of occurrence
- There are five basic time series patterns
- Horizontal
- Trend
- Seasonal
- Cyclical
- Random

Quantity

Time

(a) Horizontal: Data cluster about a horizontal line

Quantity

Time

(b) Trend: Data consistently increase or decrease

Year 1

Quantity

Year 2

||||||||||||

JFMAMJJASOND

Months

(c) Seasonal: Data consistently show peaks and valleys

Quantity

||||||

123456

Years

(d) Cyclical: Data reveal gradual increases and decreases over extended periods

- Four of the patterns – horizontal, trend, seasonal, and cyclical – combine in varying degrees to define the underlying time pattern
- Fifth pattern
- Random variation: Results from chance causes and cannot be predicted
- Random variation is what makes every forecast ultimately wrong

- Use only historical information rather than independent variables (as used by Regression)
- Assumption is that past pattern continues in future
- In a naive forecast the forecast for the next period equals the demand for the current period (Forecast = Dt)

- This section considers time-series methods with demand that has no trend, seasonal, or cyclical patterns
- All variation in time series is due to random variation, so the following techniques are appropriate:
- Simple moving average
- Weighted moving average
- Exponential smoothing

Sum of last n demands

n

Dt+ Dt-1 + Dt-2 + … + Dt-n+1

n

Ft+1 = =

- The forecast for period t + 1 can be calculated at the end of period t (after the actual demand for period t is known) as

where

Dt= actual demand in periodt

n= total number of periods in the average

Ft+1= forecast for period t + 1

- For any forecasting method, it is important to measure the accuracy of its forecasts. Forecast error is simply the difference found by subtracting the forecast from actual demand for a given period, or

Et = Dt– Ft

where

Et= forecast error for period t

Dt= actual demand in periodt

Ft= forecast for period t

Please refer to problem in the in-class exercise

Using this method, each historical demand in the average can have its own weight, provided that the sum of the weights equals 1.0.

Ft+1 = W1D1 + W2D2 + … + WnDt-n+1

A three-period weighted moving average model with the most recent period weight of 0.50, the second most recent weight of 0.30, and the third most recent might be weight of 0.20

Ft+1 = 0.50Dt+ 0.30Dt–1 + 0.20Dt–2

Please refer to problem in the in-class exercise

- A sophisticated weighted moving average that calculates the average of a time series by giving recent demands more weight than earlier demands
- Requires only three items of data
- The last period’s forecast
- The demand for this period
- A smoothing parameter, alpha (α), where 0 ≤ α ≤ 1.0

- The equation for the forecast is

Ft+1= α(Demand this period) + (1 – α)(Forecast calculated last period)

= αDt+ (1 – α)Ft

or the equivalent

Ft+1 = Ft+ α(Dt– Ft)

Please refer to problem in the in-class exercise

- The emphasis given to the most recent demand levels can be adjusted by changing the smoothing parameter
- Larger α values emphasize recent levels of demand and result in forecasts more responsive to changes in the underlying average
- Smaller α values treat past demand more uniformly and result in more stable forecasts
- Exponential smoothing is simple and requires minimal data
- When the underlying average is showing some trend, different model is needed

- Forecast performance is determined by forecast errors
- Forecast errors detect when something is going wrong with the forecasting system
- Forecast errors can be classified as either bias errors or random errors
- Bias errors (or systematic errors) are the result of consistent mistakes
- Random error results from unpredictable factors that cause the forecast to deviate from the actual demand

- Forecast Error = Demand value – Forecast Value
- Mean absolute deviation (MAD)
- Mean signed deviation (MSD)
- Tracking signal (TS)
- Mean squared error (MSE)
- Mean absolute percentage error (MAPE)

Et = Dt– Ft

|Et|

n

MAD =

- MAD is the average of the absolute values of the errors.
- Stated in the same units as the forecast
- Captures the magnitude of the forecasting error
- Compute MAD for the example problem in Excel sheet (tab 2) and interpret the results

Et

n

MSD =

- MSD is the average of the errors
- Stated in the same units as the forecast
- Signs (+/-) of the error terms tend to cancel each other out
- A large value (+/-) indicates systematic forecast error
- Compute MSD for the example problem in Excel sheet (tab 2) and interpret the results

MSD

MAD

TS =

- Tracking Signal (TS) measures systematic error
- TS is unitless and is between -1 and 1
- Think of it as percentage of forecast error that is systematic

- Calculate TS for the example problem and interpret it

Et2

n

MSE =

- MSE is the average of the square errors
- It is a measure of dispersion of forecast error
- Smaller values indicate that forecast is typically close to actual demand
- Compute MSE for the example problem in Excel sheet (tab 2) and interpret the results

(|Et|/Dt)(100)

n

MAPE =

- MAPE takes the absolute error of each forecast, and divides it by the value of the demand, multiply that quantity with 100 to get the error as a percentage of the demand, and then averages these percentage errors.
- Very useful for comparisons between time series for different SKUs
- Compute MAPE for the example problem in Excel sheet (tab 2) and interpret the results

- Criteria to use in making forecast method and parameter choices include
- Minimizing bias
- Minimizing MAPE, MAD, or MSE
- Meeting managerial expectations of changes in the components of demand
- Minimizing the forecast error last period

- Statistical performance measures can be used
- For projections of more stable demand patterns, use lower αvalues or larger n values
- For projections of more dynamic demand patterns try higher αor smaller n values

Adjust history file

1

Consensus meetings and collaboration

3

Prepare initial forecasts

2

Review by Operating Committee

5

Finalize

and communicate

6

Revise forecasts

4

- Forecasting is not a stand-alone activity, but part of a larger process

You’re right only by accident!

- Krajewski, Ritzman, Malhotra. (2010). Operations Management: Processes and Supply Chains, Ninth Edition. Pearson Prentice Hall.
- Dr. Gary Mitchell, Class Notes
- Dr. Min Yu, Class Notes