Download presentation

Presentation is loading. Please wait.

1
Forecasting OPS 370

2
**Forecasting What is Forecasting? Some Thoughts on Forecasts**

Determining Future Events Based on Historical Facts and Data Some Thoughts on Forecasts Forecasts Tend to Be Wrong! Forecasts Can Be Biased! (Marketing, Sales, etc.) Forecasts Tend to Be Better for Near Future So, Why Forecast? Better to Have “Educated Guess” About Future Than to Not Forecast At All! Forecasting - Chapter 4

3
**Total Sales, New Offerings**

What to Forecast? Demand for Individual Products & Services Short Term (0-3 Months) Demand for Product & Service Families Medium Term (3 Months – 2 Years) Total Sales, New Offerings Long Term (>2 Years) Forecasting - Chapter 4

4
**How to Forecast? Qualitative Methods Quantitative Methods**

Based On Educated Opinion & Judgment (Subjective) Particularly Useful When Lacking Numerical Data (Example: Design and Introduction Phases of a Product’s Life Cycle) Quantitative Methods Based On Data (Objective) Forecasting - Chapter 4

5
**Qualitative Methods Executive Judgment Sales Force Composite**

Market Research/Survey Delphi Method Forecasting - Chapter 4

6
**Quantitative Methods Time Series & Regression**

Time Series Popular Forecasting Approach in Operations Management Assumption: “Patterns” That Occurred in the Past Will Continue to Occur In the Future Patterns Random Variation Trend Seasonality Composite Forecasting - Chapter 4

8
**UK Airline Miles Observe: Increasing trend, Seasonal component.**

Random variation. Thousands of Miles

9
**Forecasting Steps Collect Relevant/Reliable Data**

Data Collection Collect Relevant/Reliable Data Be Aware of “Garbage-In, Garbage Out” Data Analysis Model Selection Monitoring Forecasting - Chapter 4

10
**Forecasting Steps Plot the Data Identify Patterns Data Collection**

Data Analysis Model Selection Monitoring Forecasting - Chapter 4

11
**Forecasting Steps Choose Model Appropriate for Data**

Data Collection Choose Model Appropriate for Data Consider Complexity Trade-Offs Perform Forecast(s) Select Model Based on Performance Measure(s) Data Analysis Model Selection Monitoring Forecasting - Chapter 4

12
**Track Forecast Performance (Conditions May and Often Do Change)**

Forecasting Steps Data Collection Track Forecast Performance (Conditions May and Often Do Change) Data Analysis Model Selection Monitoring Forecasting - Chapter 4

13
**Time Series Models Short Term Naïve Simple Moving Average**

Weighted Moving Average Exponential Smoothing Forecasting - Chapter 4

14
Forecasting Example L&F Bakery has been forecasting by “gut feel.” They would like to use a formal (i.e., quantitative) forecasting technique. Forecasting - Chapter 4

15
**Forecasting Methods Naïve Forecast for July = Actual for June**

Ft+1 = At FJul = AJun = 600 Forecast Very Sensitive to Demand Changes; Good for stable demand Forecasting - Chapter 4

16
Forecasting Methods Naïve (Excel) =C4 =C5 Forecasting - Chapter 4

17
**Forecasting Methods Moving Average**

Forecast for July = Average of June, May, and April Ft+1 = (At+At-1+…)/n FJul = ( )/3 = 500 Values Equally Weighted; Good for stable demand; Sensitive to fluctuation; Lags Common application: Stock price forecasting

18
Forecasting Methods 30 Day Moving Average of AAPL Price

19
**Forecasting Methods Moving Average (Excel) =AVERAGE(C4:C6)**

20
**Forecasting Methods Moving Average Example Assume n = 2**

( )/2 = 150 ( )/2 = 162.5 ( )/2 = 150 ( )/2 = 155

21
**Forecasting Methods Weighted Moving Average Ft+1 = (W1At+W2At-1+…)**

Assume that W1 = 0.5, W2 =0.3 and W3 = 0.2 FJul = (0.5)(600) + (0.3)(500) + (0.2)(400) = = 530 Typically Gives More Weight to Newer Data Lags; Sensitive

22
**Forecasting Methods Weighted Moving Average =$G$6*C6+$G$5*C5+$G$4*C4**

23
**Forecasting Methods Weighted Moving Average Example**

Assume n = 2, W1 = 0.7, W2 = 0.3 (0.7)(175) + (0.3)(125) = 160 (0.7)(150) + (0.3)(175) = 157.5 (0.7)(150) + (0.3)(150) = 150 (0.7)(160) + (0.3)(150) = 157

24
**Forecasting Methods Exponential Smoothing**

Forecast for June = Forecast for May + a(Forecast Error in May) a is a constant between 0 and 1 Forecast Error = Difference Actual Demand and Forecasted Demand General Formula: Ft+1 = Ft + aet

25
**Forecasting Methods Exponential Smoothing Assume that a = 0.3**

What is the forecast for July? = June Forecast + a(Forecast Error in June) = (0.3)(257) = 420 Requires less data; Good for stable data

26
**Forecasting Methods Exponential Smoothing (Excel) Initial forecast**

=D4+$G$4*(C4-D4) =D5+$G$4*(C5-D5)

27
**Forecasting Methods Exponential Smoothing Example Assume a = 0.4**

Need initial forecast; Assume 125 (125) + (0.4)( ) = 125 (125) + (0.4)( ) = 145 (145) + (0.4)( ) = 147 (147) + (0.4)( ) = 148.2 (148.2) + (0.4)( ) = 152.9

28
**Forecasting Methods How to Select Value of a?**

Alpha determine importance of recent forecast results in new forecasts Small alpha Less importance on recent results (Good for products with stable demand) Large alpha Recent forecast results more important (Good for product with varying demands)

29
**Determining Forecast Quality**

How Well Did a Forecast Perform? Determine Forecast Error Error = Actual Demand – Forecasted Demand Average Error 121.8

30
**Determining Forecast Quality**

Why is Average Error a Deceiving Measure of Quality? Better Measures: Mean Absolute Deviation Mean Squared Error Root Mean Squared Error

31
**Determining Forecast Quality**

Measure of Bias: Tracking Signal = Sum of Errors/MAD =731/131.8 = 5.55 *OK if between -4 and +4 MAD MSE

32
**Determining Forecast Quality**

For this MA(2) forecast. What is MAD, MSE, and TS?

33
Linear Regression <SKIP Section in Textbook on Exponential Smoothing with Linear Trend> Linear Regression Statistical technique that expresses the forecast variable as a linear function of one or more independent variables Commonly Used for Causal Data Example: Relationship Between Temperature and Ice Cream Sales Also Used for Time Series Data (x Variable is Time, y is Demand, Sales, etc.)

34
**Linear Trend Line Given Data Parameters to estimate**

Y = Values of Response Variable X = Values of Independent Variable Parameters to estimate a = Y-intercept b = slope Use “least squares” regression equations to estimate a and b. Or …

35
**Excel for Linear Regression**

Use SLOPE Function Use INTERCEPT Function

Similar presentations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google