3-1 Forecasting

CHAPTER3

Forecasting

Operations Management

3-2 Forecasting

A statement about the future value of a variable of interest such as demand.

Forecasts affect decisions and activities throughout an organization Accounting, finance Human resources Marketing MIS Operations Product / service design

FORECAST:

3-3 Forecasting

Accounting Cost/profit estimates

Finance Cash flow and funding

Human Resources Hiring/recruiting/training

Marketing Pricing, promotion, strategy

MIS IT/IS systems, services

Operations Schedules, MRP, workloads

Product/service design New products and services

Uses of ForecastsUses of Forecasts

3-4 Forecasting

Assumes causal systempast ==> future

Forecasts rarely perfect because of randomness

Forecasts more accurate forgroups vs. individuals

Forecast accuracy decreases as time horizon increases I see that you will

get an A in this semester.

CharacteristicsCharacteristics

3-5 Forecasting

Elements of a Good ForecastElements of a Good Forecast

Timely

AccurateReliable

Mea

ningfu

l

Written

Easy

to u

se

3-6 Forecasting

Steps in the Forecasting ProcessSteps in the Forecasting Process

Step 1 Determine purpose of forecast

Step 2 Establish a time horizon

Step 3 Select a forecasting technique

Step 4 Gather and analyze data

Step 5 Prepare the forecast

Step 6 Monitor the forecast

“The forecast”

3-7 Forecasting

Types of ForecastsTypes of Forecasts

Judgmental - uses subjective inputs

Time series - uses historical data assuming the future will be like the past

Associative models - uses explanatory variables to predict the future

3-8 Forecasting

Judgmental ForecastsJudgmental Forecasts

Executive opinions Sales force opinions Consumer surveys Outside opinion Delphi method

Opinions of managers and staff Achieves a consensus forecast

3-9 Forecasting

Time Series ForecastsTime Series Forecasts

Time Series is a time ordered sequence of observations taken at regular intervals over time.

Trend - long-term movement in data Seasonality - short-term regular variations in data Cycle – wavelike variations of more than one year’s

duration Irregular variations - caused by unusual

circumstances Random variations - caused by chance

3-10 Forecasting

Forecast VariationsForecast Variations

Trend

Irregularvariation

Seasonal variations

908988

Cycles

3-11 Forecasting

Naive ForecastsNaive 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.

3-12 Forecasting

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 ForecastsNaïve Forecasts

3-13 Forecasting

Stable time series data F(t) = A(t-1)

Seasonal variations F(t) = A(t-n)

Data with trends F(t) = A(t-1) + (A(t-1) – A(t-2))

Uses for Naïve ForecastsUses for Naïve Forecasts

3-14 Forecasting

Techniques for AveragingTechniques for Averaging

Moving average

Weighted moving average

Exponential smoothing

3-15 Forecasting

Moving AveragesMoving Averages

Moving average – A technique that averages a number of recent actual values, updated as new values become available.

MAn = n

Aii = 1n

3-16 Forecasting

Period Actual1 422 403 434 405 416 397 468 449 45

10 3811 4012

Example Example

3-17 Forecasting

Simple Moving AverageSimple Moving Average

MAn = n

Aii = 1n

35

37

39

41

43

45

47

1 2 3 4 5 6 7 8 9 10 11 12

Actual

MA3

MA5

3-18 Forecasting

Moving AveragesMoving Averages

Weighted moving average – More recent values in a series are given more weight in computing the forecast.

• Premise--The most recent observations might have the highest predictive value. Therefore, we should give more weight to the more recent time periods when forecasting.

3-19 Forecasting

Exponential SmoothingExponential Smoothing

Ft = Ft-1 + (At-1 - Ft-1)

3-20 Forecasting

Period Actual Alpha = 0.1 Error Alpha = 0.4 Error1 422 40 42 -2.00 42 -23 43 41.8 1.20 41.2 1.84 40 41.92 -1.92 41.92 -1.925 41 41.73 -0.73 41.15 -0.156 39 41.66 -2.66 41.09 -2.097 46 41.39 4.61 40.25 5.758 44 41.85 2.15 42.55 1.459 45 42.07 2.93 43.13 1.87

10 38 42.36 -4.36 43.88 -5.8811 40 41.92 -1.92 41.53 -1.5312 41.73 40.92

Example - Exponential SmoothingExample - Exponential Smoothing

3-21 Forecasting

Picking a Smoothing ConstantPicking a Smoothing Constant

35

40

45

50

1 2 3 4 5 6 7 8 9 10 11 12

Period

Dem

and .1

.4

Actual

3-22 Forecasting

Controlling the ForecastControlling the Forecast

Control chart A visual tool for monitoring forecast errors Used to detect non-randomness in errors Forecast is under control if all errors are within

the control limits

3-23 Forecasting

Sources of Forecast errorsSources of Forecast errors

Model may be inadequate Irregular variations Incorrect use of forecasting technique

3-24 Forecasting

Choosing a Forecasting TechniqueChoosing a Forecasting Technique

No single technique works in every situation Two most important factors

Cost Accuracy

Other factors include the availability of: Historical data Computers Time needed to gather and analyze the data Forecast horizon