Chapter 4ForecastingMike DohanBUSI 2016

Forecasting

• What is forecasting?• Why is it important?• In what areas can forecasting be applied?

Forecasting

• What is forecasting?• “art and science of predicting future events”

• Why is it important?• allows for planning of future events, within a defined time horizon

• What should a good forecast be?• ACCURATE• Timely, interpretable, ability to detect trends

• In what areas can forecasting be applied?• SCM – demand quantity from suppliers• HR – labour demand for scheduling and planning• Capacity – how many units to meet customer demand

Seven Steps

1. Determine the use of the forecast2. Select items to be forecasted3. Determine time horizon4. Select forecasting model5. Gather the data needed to make the forecast6. Make the forecast7. Validate and implement the results

Qualitative Approaches

• Jury of experts• Delphi method• Sales force composite• Market survey

Quantitative Approach

• Time Series Models – “the future is a function of the past”• Naïve Approach• Moving Averages• Exponential Smoothing• Trend projection

• Associative Models – “what other numbers are related to demand”• Linear Regression

Time Series Forecasting

• Future values predicted based on past values• Four Components: Trend, Seasonality, Cycles, Random Variations

Naïve Approach

• Demand of next period is equal to last period• Example

• Demand in January was 87• Therefore demand in February will be 87

Moving Averages

• Demand of next period is equal to the average of the last X periods

• Demand in January is 10• Demand in February is 12• Demand in March is 13• Therefore, demand in April (based on a three-month moving average) is

predicted to be:

• (10 + 12 + 13)/3 = 11.67 units

Weighted Moving Averages

• Demand of next period is equal to the weighted averages of the last X periods

• Weights assigned by forecaster• Demand in January is 10• Demand in February is 12• Demand in March is 13• Weights for last month, two months ago and three months ago are 3, 2, and 1• Therefore, demand in April is predicted to be:

• [(3*13) + (2*12) + (1*10)] / (3+2+1) = 12.17

Exponential Smoothing

• A weighted moving average technique that uses an exponential function for weighting

• Smoothing Constant• Goal is to be accurate• Low values will be better for stable demand over time

Trends and “Lag”

Measuring Forecast Error of Time Series Models• Why

• To determine overall accuracy (methods, weights, alphas)

• Mean Absolute Deviation

• Mean Squared Error

• Mean Absolute Percent Error (smaller number)• ]/n

Exponential Smoothing w Trend Adjustment• Forecasting model may need to change as trends arise• Trend accounted for with the use of β (beta)

• where

Trend Projection

• Find equation for trend line, use that for prediction

• Figure 4.4• Assumes linear

relationship

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Variations in Data

• Seasonal• Ie Swimming trunks sell more in the summer than in the winter• Forecasting can incorporate a “Seasonal Index”• SI = 3 year historical ave. demand for the month (ie all Januaries) / average

monthly demand• Forecast = (yearly demand / number of months) * SI

• More accuracy can be achieved by incorporating Seasonality and Trend to the forecast..

Associative Forecasting Methods

• Predicts trends based on the impact of some variables on other variables

• Y = dependent variable. Variable you want to predict• a = y-axis intercept (y when x = 0)• b = amount of change in x for each increment of y• x = independent variable. Factor that does the predicting

• Need a dataset to produce this.

Correlation Coefficient

r = -1

r = -0.5

r = 1

r = 0.5

r = 1

Multiple Regression

• When several predictors are tested against a dependent variable

• What variables are relevant to predict y?• Again, need a fairly large dataset and a computer

Monitoring and Controlling Forecasts

• Tracking Signal: used to make sure forecasts are acceptably accurate

Business Intelligence- the use of existing data in new ways is a popular area in industry and research

Also: http://www.cio.com/slideshow/detail/119877