sep 6ismt 162/stuart zhu1 lecture 2: time series forecasting forecasting and production data and...

Sep 6 ISMT 162/Stuart Zhu 1

Lecture 2: Time Series Forecasting

• Forecasting and production• Data and demand patterns• Stationary demand forecasting model• Naïve method• Moving average method• Exponential smoothing method• Summary• Readings: Page 68-87


Forecasting and Production• Business planning is based

on forecast (and strategy)– Is this new product going to

sale?

– What is the potential market for this new product?

– Will customer accept this new technology?

– How much to produce in each period?

– Availability of raw materials?

– Changes in interest rates, exchange rates, material prices?

• Bad forecasts are costly– Sony’s video technology,

Apple computer (customer/tech)

– IBM’s notebooks (new product sales potential)

– Stock-out and markdown can cost more than manufacturing cost (Fisher et al. 1994)


Making and Using Forecasts • Forecasts are usually made by marketing and sales• Forecasts are decision inputs for marketing and

production/operations• Forecasting horizon in operation planning

– short term product sales forecast in days or weeks for inventory management and production plan (MRP)

– intermediate term forecast of sales patterns in weeks or months of product family for labor and resource requirement

– long term demand trend forecast in months or years for capacity planning


Steps in the Forecasting Process

Step 1 Determine purpose of forecast

Step 2 Establish a time horizon

Step 3 Select a forecasting technique

Step 4 Obtain, clean and analyze data

Step 5 Make the forecast

Step 6 Monitor the forecast

“The forecast”


Subjective Forecasting Methods

• Sales force composites– sales manager aggregates

salesmen’s individual sales estimates

– could be biased

• Consumer survey (market research)– for signals of future trend

and shift of preference patterns,

– survey and sampling design needs specialist

• Executive opinion– no data, expert opinion is

the only source of information

– interview or consensus meeting

• The Delphi method– formal and iterative method

of coming up a forecast from

– experts’ opinion, a group of experts and a facilitator


Sport Obermeyer, an Example6 managers look at the new styles and estimate sales (short life-cycle products)

ForecastsMember Pandora Parka Entice JacketCarolyn 1,200 1,500Laura 1,150 700Tom 1,250 1,200Kenny 1,300 300Wally 1,100 2,075Wendy 1,200 1,425Average

Std. Dev1,20070.7

1,200627.1


Objective Forecasting Methods

• Time Series models– past data contains future demand information and

can be used to project future demands– used for operation planning

• Associative models– uses explanatory variables to predict the future


Data and Demand Patterns

Data Analysis

• We need to know the demand pattern before selecting an appropriate forecasting model

• How?

• Plot the data to examine the pattern– Example data sets:

forecast-s1– Also Figure 3.1 (P. 73)

• What are the common patterns?

• Why is it important to determine the pattern first?


Demand Patterns

t tA a

t tA a bt

t t tA ac

( )t t tA a bt c

Stationary/constant

Linear trend

Cyclic/seasonal

Cyclic/seasonal with trend

forecast-s1

εt : a random fluctuation; a, b: constant;

ct : time-varying coefficient


Forecasting Model for Stationary Demand

• For a stationary demand pattern, there is only one parameter a that we need to estimate from the past demand data

• Time series forecasting model– Use Ft to denote the estimate of a made at time t, i.e., At


Time Series Forecasting

Methods to obtain Ft

• Naïve • N-period moving

average• Exponential

smoothing

• Purpose:– To estimate the

parameters of the demand model, using past data

– To filter out the random element from the past data


Naive Forecasts

Uh, give me a minute.... We sold 250 wheels lastweek.... Now, next week we should sell....

The forecast for any period equals the previous period’s actual value.

Ft = At –1 (1)


• Simple to use

• Virtually no cost

• Quick and easy to prepare

• Data analysis is nonexistent

• Easily understandable

• Cannot provide high accuracy

• Can be a standard for accuracy

Naïve Forecasts


N-Period Moving Average

• Select only the recent data

• Example 1 Passed demand data: 106 110 118 105 115 100 112 106 118 102 112 110

• What is the forecast for period 13? Or for period 15?

• With N =3

• With N =5

• Choice of N: ≥3 • forecast-s2

1

1 n

t t ii

F An

(2)


Exponential Smoothing

• Use Ft to denote the estimate of a at period t,

is the smoothing constant

• Data: 106 110 118 105 115 100 112 106 118 102 112 110

• What is the forecast for period 13?

forecast-s2

1 1 1

1 1

( )

(1 )t t t t

t t

F F A F

A F

(3)


Information Content

• All past data are used • The weight to the data of i periods old is

It decreases exponentially as the data gets older

1 1

21 2 2

10

(1 )

(1 ) (1 )

(1 )

t t t

t t t

it ii

F A F

A A F

A

(1 )i


Choice of Smoothing Constant • Between 0 and 1 (why?)• If the demand is stable, choose a small ; if the

demand is rapidly increasing or decreasing, choose a large . (why?)– determines the weight on the most recent data

• We should test the forecast model to fit a good • Usually is from 0.1 to 0.3


Summary

• Distinguish three things in time series forecast:– the underlying process

– parameter estimation

– and forecasting formula/model

• Equations (1), (2), (3)

• Keys to good forecast– Good data

– Right model

– Proper balance of forecasting stability and sensitivity to the recent change in data, through selection of N and


Forecast Variations

Trend

Irregularvariation

Seasonal variations

908988

Cycles


Review Problems

• Problem 1 at page 112




A Caveat on Forecasts

• “Man won’t fly for 1000 years”Wilbur Wright - 1901

• “No woman in my time will be Prime Minister”Margaret Thatcher – 1969

• “I think there is a world market for about five computers”

Thomas J. Watson - 1958

sep 6ismt 162/stuart zhu1 lecture 2: time series forecasting forecasting and production data and...

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