MBA2038 1T10 Wk4 Forecasting

8/3/2019 MBA2038 1T10 Wk4 Forecasting

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MBA 2038

Operations Management

Engr. Jovenal M. Arnaiz, PME,MMBMNov 2011

Forecasting


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Operations Management

Module 4: Forecasting


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Learning Objectives

Identify or Define:

Forecasting & strategic importance

Types of forecasts

Time horizons

Approaches to forecasts

Moving averages

Exponential smoothing Trend projections

Regression and correlation analysis

Measures of forecast accuracy


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What is Forecasting?

Art and science ofpredicting a futureevent.

Underlying basis ofall business decisions

Production

Inventory Personnel

Facilities

??


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ResourcePlanning

Sales and operationsplanning

Demandmanagement

Master productionscheduling

Detailed capacity

planning

Detailed material

planning

Material andcapacity plans

Shop-floorsystems

Suppliersystems

Front End

Engine

Back end

Manufacturing Planning and Control System


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Strategic Importance ofForecasting

Human Resources Hiring, training,laying off workers

Capacity Capacity shortages can resultin undependable delivery, loss ofcustomers, loss of market share

Supply-Chain Management Good

supplier relations and price advance


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Long-range forecast 3+ years

New product planning, facility location,research and development

Medium-range forecast 3 months to 3 years

Sales and production planning, budgeting

Short-range forecast Up to 1 year, generally less than 3 months

Purchasing, job scheduling, workforcelevels, job assignments, production levels

Forecasting Time Horizons


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n uence o ro uct eCycle

Introduction and growth require longerforecasts than maturity and decline

As product passes through life cycle,forecasts are useful in projecting

Staffing levels Inventory levels

Factory capacity

Introduction Growth Maturity Decline


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Product Life Cycle

Product designand developmentcritical

Frequent product

and processdesign changes

Short productionruns

High productioncosts

Limited modelsAttention toquality

Introduction Growth Maturity Decline

OM

Strategy/Iss

ues

Forecasting critical

Product andprocess reliability

Competitiveproductimprovements andoptions

Increase capacity

Shift towardproduct focus

Enhancedistribution

Standardization

Less rapid productchanges moreminor changes

Optimum capacity

Increasing stabilityof process

Long productionruns

Productimprovement andcost cutting

Little productdifferentiation

Costminimization

Overcapacityin the industry

Prune line toeliminate itemsnot returninggood margin

Reducecapacity

Figure 2.5


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Types of Forecasts

Economic forecasts

Address business cycle inflationrate, money supply, housing starts,etc.

Technological forecasts

Predict rate of technological progress

Impacts development of new products

Demand forecasts

Predict sales of existing product


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Seven Steps in Forecasting

o Determine the use of the forecast

o Select the items to be forecasted

o Determine the time horizon of the

forecasto Select the forecasting model(s)

o Gather the data

o Make the forecasto Validate and implement results


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The Realities!

Forecasts are seldom perfect

Most techniques assume anunderlying stability in the system

Product family and aggregatedforecasts are more accurate thanindividual product forecasts


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Forecasting Approaches

Qualitative Methods

Used when situation is vague and little data exist,i.e. new products, new technology

Involves intuition, experience, e.g., forecasting saleson Internet

Used when situation is stable and historical dataexist, i.e. existing products, current technology

Involves mathematical techniques, e.g., forecastingsales of color televisions

Quantitative Methods

O i f Q lit ti


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Overview of QualitativeMethods

Jury of executive opinion

Delphi method

Sales force composite

Consumer Market Survey

Nave


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Overview of QuantitativeApproaches

Moving averages

Exponentialsmoothing

Trend projection

Linear regression

Time-SeriesModels

Associative Model


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Set of evenly spaced numerical data

Obtained by observing response

variable at regular time periods

Forecast based only on past values

Assumes that factors influencing pastand present will continue influence infuture

Time Series Forecasting


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Components of Demand

Dema

ndforproductorservice

| | | |1 2 3 4

Year

Average

demand overfour years

Seasonal peaks

Trendcomponent

Actualdemand

Randomvariation

Figure 4.1


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A series of arithmetic means

Used if little or no trend

Used often for smoothing Provides overall impression of data

over time

Moving Average Method

Moving average = demand in previous n periods

n


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Used when trend is present

Older data usually less important

Weights based on experience andintuition

Weighted Moving Average

Weightedmoving average=

(weight for period n)x (demand in period n)

weights


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Exponential Smoothing

New forecast = last periods forecast+ a(last periods actual demand

last periods forecast)

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

where Ft = new forecastFt 1 = previous forecast

a = smoothing (or weighting)constant (0 a 1)


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Trend Projections

Fitting a trend line to historical data points toproject into the medium-to-long-range

Linear trends can be found using the leastsquares technique

y = a + bX^

where y = computed value of the variable to bepredicted (dependent variable)

a = y-axis interceptb = slope of the regression lineX = the independent variable

^


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Least Squares Method

Time period

Values

ofDependent

Variable

Figure 4.4

Deviation1

Deviation5

Deviation7

Deviation2

Deviation6

Deviation4

Deviation3

Actual observation(y value)

Trend line, y = a + bx^


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Least Squares Method

Time period

Values

ofDependent

Variable

Figure 4.4

Deviation1

Deviation5

Deviation7

Deviation2

Deviation6

Deviation4

Deviation3

Actual observation(y value)

Trend line, y = a + bx^

Least squares methodminimizes the sum of the

squared errors (deviations)


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LeastSquares Method

Equations to calculate the regression variables

b =Sxy - nxySx2 - nx2

y = a + bX^

a = y - bx


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Common Measures of Error

Mean Absolute Deviation (MAD)

MAD = |actual - forecast|

n

Mean Squared Error (MSE)

MSE = (forecast errors)2

nMean Absolute Percent Error (MAPE)

MAPE =

100 |actuali - forecasti|/actuali

nn

i = 1


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Associative Forecasting

Used when changes in one or more independentvariables can be used to predict the changes in

the dependent variable

Most common technique is linearregression analysis

We apply this technique just as we did inthe time series example


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Associative Forecasting

Forecasting an outcome based on predictorvariables using the least squares technique

y = a + bX^

where y = computed value of the variable to bepredicted (dependent variable)

a = y-axis interceptb = slope of the regression linex = the independent variable though to

predict the value of the dependentvariable

^

Standard Error of the


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Standard Error of theEstimate

where y = y-value of each data point

yc

= computed value of the dependentvariable, from the regressionequation

n = number of data points

Sy,x

=(y - yc)

2

n - 2

Standard Error of the


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Standard Error of theEstimate

Computationally, this equation isconsiderably easier to use

We use the standard error to set upprediction intervals around the point

estimate

Sy,x =y2 - ay - bxy

n - 2


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How strong is the linear relationship between thevariables?

Correlation does not necessarily imply causality!

Coefficient of correlation, r, measures degree ofassociation

> Values range from -1 to +1

Correlation


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Correlation Coefficient

r =nSxy - SxSy

[nSx2 - (Sx)2][nSy2 - (Sy)2]


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Measures how well the forecast is

predicting actual values Ratio of running sum of forecast errors

(RSFE) to mean absolute deviation (MAD)

Good tracking signal has low values

If forecasts are continually high or low, theforecast has a bias error

Monitoring and ControllingForecasts

Tracking Signal

M i i d C lli


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Monitoring and ControllingForecasts

TrackingSignal

RSFEMAD

=

Trackingsignal =

(actual demand inperiod i -

forecast demandin period i)

(|actual - forecast|/n)


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Tracking Signal

Tracking signal

+

0 MADs

Upper control limit

Lower control limit

Time

Signal exceeding limit

Acceptablerange


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End of module 4

MBA2038 1T10 Wk4 Forecasting

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