1. Fall, 2017 EMBA 512 Demand Forecasting

2. Fall, 2017 EMBA 512 Demand Forecasting
Objectives
Understand the role of forecasting in the organization
Understand forecasting challenges
Understand and apply basic forecasting techniques
Fall, 2017 EMBA 512 Demand Forecasting

3. Fall, 2017 EMBA 512 Demand Forecasting
What is Forecasting?
Estimating the magnitude and timing of future events
Product demand
Customers
Prices
Usage levels
Fall, 2017 EMBA 512 Demand Forecasting

4. Forecasting in the Organization
Necessary Input to all Planning
Operations: inventory, production planning & scheduling
Finance: plant investment and budgeting
Marketing: sales force allocation, pricing promotions
Human Resources: workforce planning
Fall, 2017 EMBA 512 Demand Forecasting

5. Fall, 2017 EMBA 512 Demand Forecasting
How can forecasting be used to support decisions in manufacturing?
How can forecasting be used to support decisions in services?
Fall, 2017 EMBA 512 Demand Forecasting

6. Characteristics of Forecasts
What are some characteristics shared by all forecasts?
Forecasts are always ________________
Forecast accuracy __________________ with an increase in the forecast horizon
Aggregate forecasts are more ___________ than individual forecasts.
Fall, 2017 EMBA 512 Demand Forecasting

7. Fall, 2017 EMBA 512 Demand Forecasting
Forecasting Demand
Different demands
Independent Demand
Dependent Demand
Fall, 2017 EMBA 512 Demand Forecasting

8. Approaches to Forecasting
Qualitative or Judgmental
Ask people who ought to know
Historical Projection or Extrapolation
Time Series Models
Moving Averages
Exponential Smoothing
Regression based methods
Fall, 2017 EMBA 512 Demand Forecasting

9. Basic Approach to Demand Forecasting
Identify the Objective of the Forecast
Integrate Forecasting with Planning
Identify the Factors that Influence the Demand Forecast
Identify the Appropriate Forecasting Model
Monitor the Forecast (Measure Errors)
Fall, 2017 EMBA 512 Demand Forecasting

10. Fall, 2017 EMBA 512 Demand Forecasting
Time Series Models
Appropriate when future demand is expected to follow past demand patterns.
Future demand is assumed to be influenced by the current demand, as well as historical growth and seasonal patterns.
Components of a time series: base, seasonal, trend, random.
Fall, 2017 EMBA 512 Demand Forecasting

11. Time Series Models
With time series models observed demand can be broken down into two components: the signal and the noise.
Observed Demand = Signal+ Noise
The signal is the expected demand value. It is comprised of the underlying average demand, the trend in demand, and the seasonal fluctuations (seasonality) in demand.
Fall, 2017 EMBA 512 Demand Forecasting

12. Fall, 2017 EMBA 512 Demand Forecasting
Time Series with
Demand
random and trend components
Time
Fall, 2017 EMBA 512 Demand Forecasting

13. Fall, 2017 EMBA 512 Demand Forecasting
Exponential Trend
Fall, 2017 EMBA 512 Demand Forecasting

14. Fall, 2017 EMBA 512 Demand Forecasting
Trend & Seasonality
Fall, 2017 EMBA 512 Demand Forecasting

15. Fall, 2017 EMBA 512 Demand Forecasting
Forecasting Models
How can the noise be filtered from the data?
How can we estimate trend effects?
How can we estimate seasonal effects?
Fall, 2017 EMBA 512 Demand Forecasting

16. The Perfect (Imaginary) Forecast
Fall, 2017 EMBA 512 Demand Forecasting

17. A More Realistic Forecast
Fall, 2017 EMBA 512 Demand Forecasting

18. Fall, 2017 EMBA 512 Demand Forecasting
Forecast Error
Building a Forecast
Fit to historical data
Project future data
Forecast Error
How well does model fit historical data
Do we need to tune or refine the model
Can we offer confidence intervals about our predictions
Fall, 2017 EMBA 512 Demand Forecasting

19. Fall, 2017 EMBA 512 Demand Forecasting
Forecast Error
The forecast error measures the difference between the actual demand and the forecast of demand. The forecast is based on the systematic component and the random component is estimated based on the forecast error.
Forecast Error = Actual – Forecast
Fall, 2017 EMBA 512 Demand Forecasting

20. Measures of Forecast Accuracy
Forecast Errort (Et)= Demandt-Forecastt
Mean Squared Error (MSE)
Mean Absolute Deviation (MAD)
Bias
Tracking Signal
Relative Forecast Errors
Fall, 2017 EMBA 512 Demand Forecasting

21. Fall, 2017 EMBA 512 Demand Forecasting
Issues
Forecasting is a necessary evil, try to reduce the need for it.
Complexity costs money, does it provide better forecasts?
Aggregation provides accuracy, but precludes local information
Forecast the right thing
Fall, 2017 EMBA 512 Demand Forecasting

22. Forecasting Success Story
Taco Bell
Fall, 2017 EMBA 512 Demand Forecasting

23. Fall, 2017 EMBA 512 Demand Forecasting
Feed the dog
Taco Bell
Labor is 30% of revenue
Make to order environment
Significant “seasonality”
52% of days sales during lunch
25% of days sales during busiest hour
Balance staff with demand
Fall, 2017 EMBA 512 Demand Forecasting

24. Fall, 2017 EMBA 512 Demand Forecasting
Value Meals
Drove demand
Forecasting system in each store
forecasts arrivals within 15 minute intervals
Simulation system
“predicts” congestion and lost sales
Optimization system
Finds the minimum cost allocation of workers
Estimated savings: Over $40 Million in 3 years
Fall, 2017 EMBA 512 Demand Forecasting