Demand Forecasting-22 Slides

7/28/2019 Demand Forecasting-22 Slides

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Demand Forecasting


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Demand Forecasting

Forecast:

It is a statement / an estimate about the future.

It helps managers by reducing some of the uncertainties &

enables them to develop meaningful plans.It helps managers to determine in the present what course of

action they will take in future

Forecasting :It is an art & science of predicting /estimating future events.

It is not a mere guess or prediction about the future withoutany rational basis.

It may involve taking historical data & projecting them in tothe future.

It may include a managers good judgment or a intuitive

prediction in the absence of historical data


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Demand Forecasting: contd

Any business organization can not afford to avoid forecasting &just wait to see what happens & then take its chances.

Effective planning in both short & long run depends on forecast

of demand for the companys products.Uses of forecasts:

Helps in planning the productive system involving i) Long range

& ii) Short & intermediate plans.i) Long range plans relate to a) what products & services to

offer, b) what facilities & equipments to have, c) where tolocate them,

ii) Short & intermediate plans involves a) Planning inventories& workforce levels, b) Planning purchasing, c) Production,scheduling & budgeting


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Forecasts are also used to predict Profits, revenue, costs. pricesproductivity changes, availability of energy, raw materialsinterest rates, movements of key economic indicators e.g.GNP, inflation etc, prices of stocks & bonds.

Forecasting is necessary in Production & operationsmanagement for the followings:

i) New facilities planning:

ii) Production Planning:

iii) Workforce Scheduling:

iv) Financial Planning:


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Demand Forecasting: contdTypes of forecasts:

1 Technological forecasts

2 Economic forecasts

3 Demand forecasts:

Features common to all forecasts:- These generally assume that same underlying reasons that

existed in the past will continue to exist in the future

-These are rarely perfect. Actual demand, differs from

forecasted demand. Allowances should be made forinaccuracies.

- Forecasts for group of items tend to be more accurate thanforecasts for individual items.

- Forecast accuracies decreases as the time period for theforecast (forecasting time horizon) increases.


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Elements / requirements of a good forecast:

A forecast should be

- Timely i.e. the forecasting horizon must have the time

necessary to implement possible changes in productioncapacity, financial needs etc.

- Accurate & the degree of accuracy should known

- Reliable i.e. consistent in accuracy- Expressed in meaningful units e.g. Rs, units of productsmachines & skills needed.

- Written form to permit an objective basis for evaluating the

forecast once the actual results are known.

- The technique should be simple to understand & use(comfortable for users)


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Objectives of demand forecasting i) Short range objectives-

ii) medium or long range

i) Short range objectives-

a) Formulation of production strategy & policy: to bridge thegap between demand & supply & to ensure

b) Formulation of pricing policy- Demand forecasting enablesmanagement to formulate suitable mechanism for fixing theprices for products to be sold.

c) Planning & control of sales- Demand forecasting facilitateterritory design & determination of quotas to be assigned tosales people.


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ii) Medium or Long-range objectives:a) Long-range planning for production capacity:

The installed capacity of the plant is usually based onlong-term demand forecasts.

b) Labour requirements (Employment levels)-

These are based on reliable medium/long term demandforecasts so as to optimise the cost of production over the

long-term planning horizon.c) Restructuring the capital structure-

Long-term forecasts facilitate planning for long-termfinance requirements at reasonable financial costa & other

terms & conditions for obtaining finance from financialinstitutions as well as planning for internal financialresources to meet long-term financial needs.


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Classification of demand forecasting methods:i) Qualitative - These consist mainly of subjective inputs, often of

non-numerical description &

ii) Quantitative These involve either projection of historical data

or the development of association models which attempt touse casual variables to arrive at the forecast.

Overview of qualitative methods:

1) Jury of executive opinion- In this the opinions of a small groupof high-level executives (managers) are taken, based onwhich a group estimate of demand is obtained as theforecast.

Advantage- i) uses experience & knowledge of two or moremanagers to arrive at a single forecast .ii) Can be used fortechnological forecasting iii) Can be used for forecasting fornew products. iv) Can be used to modify an existing forecastto account for unusual circumstances.


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Jury of executive opinion- contd

Disadvantages- i) It can be costly because it takes valuableexecutive time. ii) some times gets out of control or gets

delayed. iii) difficult to get consensus opinion of severalexperts.

2) Salesforce composite method - Also known as Pooledsalesforce estimate method.

In this each sales person estimates what sales will be inhis / her territory.

These estimates are then reviewed to ensure that they

are realistic.Then they are combined at the district or national level toarrive at the overall forecast.


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Demand Forecasting: contd2) Salesforce composite method-contd:

Advantages- i) The sales force is the group closest to thecustomers. They are most likely to know which product orservices, customers will be buying in the near future & in what

quantities. ii) Sales territories are divided in to districts orregions & forecasts for district or regions will be useful ininventory management, distribution & sales staffing.

Disadvantages- i) Individual bias of sales people may affect the

sales forecast (Some are optimistic & some pessimistic)ii) Sales people may be unable to distinguish between whatcustomers would like to do & what they actually will do.iii) some sales people may be overly influenced by their recent

experience. iv) If the firm uses sales persons estimate as aperformance measure , sales people may deliberately underestimate their forecast so that their performance will look good

when they exceed their quotas which are fixed based on theirestimates


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3) Market Research method:A sample of consumers survey is the systematic approach todetermine their interest in a product or service. This methodmay be used to forecast demand for short, medium or long

term.Advantage: i) Consumers opinion regarding their futurepurchasing plans are better than executive opinion because it isthe consumers who determine demand. Also this is first hand &

direct information obtained by consumer survey.Disadvantages: i) As opinions are obtained from a sample ofcustomers, it may lead to forecast error if the sample size isinadequate. ii) Survey require considerable knowledge & skills

to handle correctly. iii) Survey can be expensive & timeconsuming. iv) Response rate of mailed questionnaire may bepoor. v) The survey results may not reflect the opinion of the

market.


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4) Delphi Method: It is also a judgemental method.- In this method opinions are solicited from a number of other

managers & staff personnel.-The decision makers consist of 5-10 experts who will be

making actual forecast.-The staff personnel assist decision makers by preparing,

distributing, collecting & summarising a series ofquestionnaires & survey results.

-The managers whose judgements are valid are therespondents. This group provides inputs to the decisionmakers before forecast is made.Responses of each respondent are kept anonymous

which tends to encourage honest responses.- Each new questionnaire is developed using the informationextracted from the previous one, thus enlarging the scopeof information on which participants base theirjudgements. The goal is to achieve consensus forecast.


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Delphi Method: contdAdvantages:

i) This method can be used to develop long-range forecasts

of product demand & sales projections for new productsii) A panel of experts may be used as participants

(respondents)

Disadvantages:

i) The process can take a long time

ii) Responses may be less meaningful because respondentsare not accountable due to anonymity.

iii) High accuracy may not be possible.

iv) Poorly designed questionnaire will result in ambiguous orfalse conclusions


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Overview of quantitative methods: 5-methods, under 2-catagorieThey all use historical data.

1- a) Nave approach (lacking experience or judgement)b) Moving averages methodc) Exponential smoothing method

These are time series models

2- a) Trend projection ] These areb) Linear regression analysis ] Causal models

Time series models:

-Predict on assumption that the future is a projection of past.

-They look at what has happened over a period of time & use a seriesof past data to make a forecast for the future.

Causal (or association) models: eg linear regression incorporate the

variables or factors that might influence the quantity being forecast.The demand or sales forecast is a dependent variable & other factorsthat affect demand are independent variables ( causal variables)

In linear regression the dependent variable (Demand) is related to one

or more independent variables by a linear equation


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Time series forecasting methods:-A Time series is a time-ordered sequence of observations

taken at regular intervals over a period of time (hourly,daily, weekly, monthly, quarterly or annually)

-Data may be measurement of demand, earnings, profits,outputs, productivity, consumer price index etc.

Decomposition of time-series:

-Analysis of time-series data requires the analyst to identifythe underlying behaviour of the series.

This can be done by plotting the data with time on X-axis,

& data on Y-axis & visually examining the plot.One or more patterns might appear. They are- trends,seasonal variations, cycles & random or irregularvariations ( errors)


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Demand Forecasting: contdTrend- refers to gradual, long term, upward or downward

movement in data over time. Changes in income, population,age, distribution or cultural views may account for suchmovements.

Seasonality- refers to short term, fairly regular variationsrelated to factors such as weather, holidays, vacation etc.Seasonal variations can be daily, weekly or monthly.

Cycles- are wave like variations of more than one year duration

or which occur every several years. They are usually tied withbusiness cycle related to a variety of economic , political, oragricultural conditions.

Random Variations are residual variations which are blips inthe data caused by chance & unusual situations, whichcannot be predicted (eg war, earthquake, flood etc)


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Demand Forecasting: contdTechniques of averaging-

Historical data contain a certain amount of random variation, ornoise which tends to observe systematic movement in data. Itis desirable to completely remove any randomness from the

data& leave only real variation such as change in demand.Averaging technique smooth fluctuations in a time series so that

the forecast can be based on average, to exhibit lessvariability than the usual data. Averaging techniques generate

forecasts to reflect recent values of a time series.

3-Techniques of averaging are- a) Nave approach b) movingaverages (simple & weighted) & c) Exponential smoothing.

i) Nave approach: It is the simplest way to forecast & itassumes that demand in the next period is equal to actualdemand in the most recent period i.e the current period.example- if actual sales of a product in March 2011 is 300

units, the forecast demand in April 2011 will also be 300 units.


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ii) Moving averages method- It uses a no. of most recenthistorical actual data values to generate a forecast

The moving average for n number of periods in the

moving average is calculated as:Moving average= [ demand in previous periods] n

n may be 3,4,5,or 6 for 3,4,5,or 6 period moving average

Simple moving average method:- Used to estimate the average of a demand time series &

remove the effects of random fluctuations.

-Most useful when demand has no pronounced trend orseasonal fluctuations.

-** In this we use n period moving average. The averagedemand of the n most recent time periods is calculated

& used as forecast for the next time period


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Demand Forecasting: contd (Simple Moving Average)

[d1+d2+d3] 3 Forecast for 4th month



Arithmetic mean values

d1

d2d3

d4

d5

d6

1

23

4

5

6

Forecast based on

simple movingaverage

Actual

Demand

Month

(period)

Weighted moving average Decreasing values of weightsForecast for 4th month : such that w = 1

[w1d1+w2d2+w3d3] {w1+w2+w3} w3 w2 w1Forecast for 5th month :[w2d2+w3d3+w4d4] {w2+w3+w4} w4 w3 w2

Forecast for 6th month :[w3d3+w4d4+w5d6] {w3+w4+w5} w5 w4 w3


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iii) Exponential smoothing method:

-It is a sophisticated weighted moving average method

-Relatively easy to understand & use.

-Requires only three items of data:

a) this periods forecast,b) actual demand of this period &

c) , a greek alphabet called alpha, which is referred to assmoothing constant & having a value between 0 &1.

The formula used is-

Next Periods forecast = This Periods forecast + { This periods actualdemand - This Periods forecast }

Or Ft = F t -1 + [ A t -1 F t -1 ] Ft = Forecast for period (t),= smoothing constant ] Ft -1 = Forecast for period (t -1)

value between 0 &1] A t -1= Actual demand for period (t -1)

Commonly used value of ranges between 0.05 to 0.5


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Factors to be considered in selection of a forecasting methodare: i) Cost & accuracy, ii) Data available, iii) Time span,iv) Nature of products & services v) Impulse response &noise dampening.

i) Cost & accuracy- A trade-off may result between cost &accuracy. High forecast accuracy can be obtained- at ahigh cost- will require more data which are difficult to obtain& models are difficult to design, implement & operate.

Delphi, market research, jury of executive opinion,statistical models based on historical data are of low ormoderate cost.

ii) Data available: Relevant data availability is important in thechoice of forecasting method. Example

Demand Forecasting-22 Slides

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