Ravi KiranProfessor School of Behavioral Sciences & Business Studies,Thapar University, Patiala

Demand Forecasting

Accurately forecasting future demand is very difficult, but is necessary if firms are to succeed in :current capital investmentproduct development introductionsadvertising pricing and other decisions makingThus demand forecasting is central to the planning and control functions of the firm

Why Forecast ?To plan for the future by reducing uncertainty. To anticipate and manage change. To increase communication and integration of planning teams.To anticipate inventory and capacity demands and manage lead times. To project costs of operations into budgeting processes. To improve competitiveness and productivity through decreased costs and improved delivery and responsiveness to customer needs.

Criteria for Selectinga Forecasting Method Cost Accuracy Data available Time span Nature of products and services

Criteria for Selectinga Forecasting MethodCost and AccuracyThere is a trade-off between cost and accuracy; generally, more forecast accuracy can be obtained at a cost.High-accuracy approaches have disadvantages: Use more data Data are ordinarily more difficult to obtain The models are more costly to design, implement, and operate Take longer to use

Criteria for Selectinga Forecasting MethodCost and AccuracyLow/Moderate-Cost Approaches statistical models, historical analogies, executive-committee consensusHigh-Cost Approaches complex econometric models, Delphi, and market research

Criteria for Selectinga Forecasting MethodData Available

Is the necessary data available or can it be economically obtained? If the need is to forecast sales of a new product, then a customer survey may not be practical; instead, historical analogy or market research may have to be used.

Criteria for Selectinga Forecasting MethodNature of Products and Services Is the product/service high cost or high volume? Where is the product/service in its life cycle? Does the product/service have seasonal demand fluctuations?

Forecasting Methods Qualitative Approaches Survey Methods Complete Enumeration Sample Survey End User Method Expert Opinion Method Simple Expert opinion Delphi Technique Market Experimentation Test marketing Laboratory tests

Quantitative Approaches

Trend analysis and projection Moving Average Barometric Forecasting Econometric methods Input/output analysis

Quantitative Forecasting MethodsQuantitativeForecastingRegressionModels2. MovingAverage1. NaiveTime SeriesModels3. ExponentialSmoothinga) simpleb) weighteda) levelb) trendc) seasonality

Survey methodsSurvey methods are qualitative analytical technique in which consumers, managers of various sort, and government agencies are asked for information on their status and future plans.

SURVEYA firm can determine the demand for its products through a market survey. It may launch a new products, if the survey indicates that there is a demand for that particular product in the market.

For example, Coke in India expanded its product range beyond carbonated drinks, after the company conducted a nationwide survey. The survey revealed that about 80% of the youth preferred to drink tea or coffee rather than carbonated drinks at regular intervals. The remaining 20% preferred to have milk products while only 2% preferred to drink carbonated drinks like coffee. The company is now trying to bring tea and coffee brands to India by installing vending machines. It is also planning to introduce a coconut flavored drink in Kerala and a black currant in Tamilnadu named portello.

Coca colaCoco-Cola ushers 2013 on a note of happiness and optimism, as the brand has been voted the country's most exciting brand as per the First Ever Economic Times Brand equity most exciting brand survey. The findings of the survey reaffirm that in spite of supposedly trendier categories like mobile phones and tablets, excitement about colas continues unabated. Three cola brands featured in the Top 5 rankings: Coca-Cola emerges as the most exciting brand followed by Pepsi at 3 and Thums Up at 5.

Coca colaHow the survey was conducted? Conducted by: NielsenRespondents were asked to evaluate each brand they were 'familiar' with on a set of attributes on a 10 point scale.The Most Exciting Brands survey was carried out among 15-24 year old youth, belonging to SEC A households. The study was conducted in four cities - Bengaluru, Delhi, Kolkata and Mumbai.

Complete Enumeration methodEntire population is considered.When all the consumers are interviewed and they are interviewed about their future purchasing plan. Thus the entire demand is estimated. Dp = q1+q2 +q3+-----------+q n n =qi t-=1

Sampling MethodThe sample is the actual group you will have to contact in some way The sample should be a representative sample

Mail SurveysFace to Face InterviewsTelephone Interviews

Sample survey methodA few potential customers and users selected through a sampling method are surveyed Dp =Hr /Hs ( H . Ad )Where Dp=Probable demand forecast,H= census number of households Hs =sample households, Hr= number of households reporting demand for the product and Ad=Average expected consumption by reporting households

Some IssuesBias Issues Administrative IssuesCostsTime Personnel

End User Method End user method of demand forecasting is used for estimating demand for inputs.

Executive Opinion Executive Opinion: It is forecasting method in which the opinions and experiences of one or more managers are summarized to arrive at a single forecast.

Delphi TechniqueIt is a process of gaining consensus from a group of experts while maintaining their anonymity. The Delphi Technique was originally conceived as a way to obtain the opinion of experts without necessarily bringing them together face to face.

Identify experts

Define the problem

Round one questions General questions to gain a broad understanding of the views of the experts relating to the problem. Responses should be collated and summarised.Round two questions Based on the responses to the first questions, these questions should dig more deeply into the topic to clarify specific issues. Again, collate and summarise the results.Round three questions The final questionnaire which aims to focus on supporting decision making.

Market ResearchTest marketingControlled experimentationNot just tryingsomethingout

But scientifictesting

It is a systematic approach to determine consumer interest in a product or service by creating and testing hypotheses through data-gathering surveys.

Market Research :A Lengthy and Costly Procedure

Test MarketingAn experimental procedure that provides an opportunity to test a new product or a new marketing plan under realistic market conditions to measure sales or profit potential.

Atest market, in the field of business and marketing, is a geographic region ordemographic group used to gauge the viability of a product or service in themass marketprior to a wide scale roll-out.

Functions of Test MarketingESTIMATEOUTCOMESIDENTIFY ANDCORRECTWEAKNESSESIN PLANS

Do you think we should give up our day jobs to make these smoothies?

In the summer of 1998 when we had developed our first smoothie recipes but were still nervous about giving up our proper jobs, we bought 500 worth of fruit, turned it into smoothies and sold them from a stall at a little music festival in London.

We put up a big sign saying 'Do you think we should give up our jobs to make these smoothies?' and put out a bin saying 'YES' and a bin saying 'NO' and asked people to put the empty bottle in the right bin. At the end of the weekend the 'YES' bin was full so we went in the next day and resigned.

Selecting a Test MarketPopulation sizeDemographic compositionLifestyle considerationsCompetitive situationMediaSelf-contained trading areaOverused markets - secrecy

Control Laboratory Test Marketing Small centre is chosen for conducting lab test. Changes made in that test centre e.g. price, packaging, display, advertisement. Results of these changes noted down.

The Advantages of Using the Control Method of Test Marketing Reduced costs Shorter time period needed for reading market results Increased secrecy from competitors No distraction of company salespeople from regular product lines

Some Problems Estimating Sales Over-attention Unrealistic store conditions Reading competitive environment incorrectly Incorrect volume forecasts Time lapse

Qualitative Methods

Sheet1

TypeCharacteristicsStrengthsWeaknesses

Executive opinionA group of managers meet & come up with a forecastGood for strategic or new-product forecastingOne person's opinion can dominate the forecast

Market researchUses surveys & interviews to identify customer preferencesGood determinant of customer preferencesIt can be difficult to develop a good questionnaire

Delphi methodSeeks to develop a consensus among a group of expertsExcellent for forecasting long-term product demand, technological changes, and scientific advancesTime consuming to develop

Sheet2

Sheet3

Statistical ForecastingTime Series Models:Assumes the future will follow same patterns as the past

Causal Models:Explores cause-and-effect relationshipsUses leading indicators to predict the futureE.g. housing starts and appliance sales

Composition of Time Series DataData = historic pattern + random variationHistoric pattern may include: Level (long-term average) Trend Seasonality Cycle

Time Series Patterns

Methods of Forecasting the LevelNave ForecastingSimple MeanMoving AverageWeighted Moving AverageExponential Smoothing

Time Series Determine forecast for periods 11Nave forecastSimple average3- and 5-period moving average3-period weighted moving average with weights 0.5, 0.3, and 0.2Exponential smoothing with alpha=0.2 and 0.5

Level

SimpleSimpleWeightedExponentialExponential

NaveSimpleMovingMovingMovingSmoothingSmoothing

MonthPeriodOrdersForecastAverageAverage (N=3)Average(N=5)Average (N=3)(a = 0.2)(a = 0.5)

January1122122122

February291122122122122

March310091107116107

April477100104104102113104

May51157798898710691

June65811510197101101108103

July77558948388799881

August812875918385789378

September911112896879198100103

October10881119710597109102107

11

November118897109921039998

WaightsAlphaAlpha

0.20.20.5

0.3

0.5

Errors

SimpleSimpleWeightedExponentialExponential

NaveSimpleMovingMovingMovingSmoothingSmoothing

MonthPeriodOrders (A)ForecastAverageAverage (N=3)Average(N=5)Average (N=3)(a = 0.2)(a = 0.5)

June658-57-43-39-43-43-50-45

July77517-19-8-13-4-23-6

August812853374543503550

September9111-1715242013118

October1088-23-9-17-9-21-14-19

Absolute Errors

SimpleSimpleWeightedExponentialExponential

NaveSimpleMovingMovingMovingSmoothingSmoothing

MonthPeriodOrders (A)ForecastAverageAverage (N=3)Average(N=5)Average (N=3)(a = 0.2)(a = 0.5)

June65857433943435045

July77517198134236

August812853374543503550

September91111715242013118

October1088239179211419

Total167123133128131133128

MAD33.424.626.625.626.226.625.6

Squared Errors

SimpleSimpleWeightedExponentialExponential

NaveSimpleMovingMovingMovingSmoothingSmoothing

MonthPeriodOrders (A)ForecastAverageAverage (N=3)Average(N=5)Average (N=3)(a = 0.2)(a = 0.5)

June6583249184915211849184925002025

July775289361641691652936

August81282809136920251849250012252500

September911128922557640016912164

October10885298128981441196361

Total7165388544754348497545714986

MSE1791.25971.251118.7510871243.751142.751246.5

Trend

SimpleTrend Adjusted Exp Smoothing

ExponentialSmoothedSmoothedForecastForecastAbsolute

WeekSalesSmoothingLevel (St)Trend (Tt)(FITt)ErrorError

17007007000

2724700719.27.687002424

3720714721.385.48727-77

4728718727.775.8472711

5740724738.727.8873466

6742734742.926.41747-55

7758739756.279.1974999

8750750753.094.24765-1515

9770750767.478.37571313

10775762775.158.05776-11

11770783Total ->81

MAD9

AlphaAlphaBeta

0.60.80.4

Trend

00

00

00

00

00

00

00

00

00

00

00

Sales

Smoothing

Seasonality

00

00

00

00

00

00

00

00

00

00

00

Causal

Enrollment (in thousands)

Year 1Fall124

Winter223

Spring319

Summer414

Year 2Fall526

Winter622

Spring719

Summer817

Year 3Fall927.45

Winter1024.75

Spring1120.925

Summer1217.1

QuarterYear 1Year 2Year 3

Fall242627.45

Winter232224.75

Spring191920.925

Summer141717.1

Total808490

Average202122.5

Calculate Seasonal Indices

QuarterYear 1Year 2Average

Fall1.21.2381.22

Winter1.151.0481.1

Spring0.950.9050.93

Summer0.70.810.76

Total ->4.01

Causal

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

YearAdvertisingSales

148130

252151

350150

455158

570214.4

0

0

0

0

Time Chart of Orders Data

Chart1

122

91

100

77

115

58

75

128

111

88

Orders (A)

Level

SimpleSimpleWeightedExponentialExponential

NaveSimpleMovingMovingMovingSmoothingSmoothing

MonthPeriodOrders (A)ForecastAverageAverage (N=3)Average(N=5)Average (N=3)(a = 0.2)(a = 0.5)

January1122122122

February291122122122122

March310091107116107

April477100104104102113104

May51157798898710691

June65811510197101101108103

July77558948388799881

August812875918385789378

September911112896879198100103

October10881119710597109102107

November118897109921039998

WeightsAlphaAlpha

0.20.20.5

0.3

0.5

Errors

SimpleSimpleWeightedExponentialExponential

NaveSimpleMovingMovingMovingSmoothingSmoothing

MonthPeriodOrders (A)ForecastAverageAverage (N=3)Average(N=5)Average (N=3)(a = 0.2)(a = 0.5)

June658-57-43-39-43-43-50-45

July77517-19-8-13-4-23-6

August812853374543503550

September9111-1715242013118

October1088-23-9-17-9-21-14-19

Absolute Errors

SimpleSimpleWeightedExponentialExponential

NaveSimpleMovingMovingMovingSmoothingSmoothing

MonthPeriodOrders (A)ForecastAverageAverage (N=3)Average(N=5)Average (N=3)(a = 0.2)(a = 0.5)

June65857433943435045

July77517198134236

August812853374543503550

September91111715242013118

October1088239179211419

Total167123133128131133128

MAD33.424.626.625.626.226.625.6

Squared Errors

SimpleSimpleWeightedExponentialExponential

NaveSimpleMovingMovingMovingSmoothingSmoothing

MonthPeriodOrders (A)ForecastAverageAverage (N=3)Average(N=5)Average (N=3)(a = 0.2)(a = 0.5)

June6583249184915211849184925002025

July775289361641691652936

August81282809136920251849250012252500

September911128922557640016912164

October10885298128981441196361

Total7165388544754348497545714986

MSE1791.25971.251118.7510871243.751142.751246.5

Level

0

0

0

0

0

0

0

0

0

0

Orders (A)

Trend

SimpleTrend Adjusted Exp Smoothing

ExponentialSmoothedSmoothedForecastForecastAbsolute

WeekSalesSmoothingLevel (St)Trend (Tt)(FITt)ErrorError

17007007000

2724700719.27.687002424

3720714721.385.48727-77

4728718727.775.8472711

5740724738.727.8873466

6742734742.926.41747-55

7758739756.279.1974999

8750750753.094.24765-1515

9770750767.478.37571313

10775762775.158.05776-11

11770783Total ->81

MAD9

AlphaAlphaBeta

0.60.80.4

Trend

00

00

00

00

00

00

00

00

00

00

00

Sales

Smoothing

Seasonality

00

00

00

00

00

00

00

00

00

00

00

Causal

Enrollment (in thousands)

Year 1Fall124

Winter223

Spring319

Summer414

Year 2Fall526

Winter622

Spring719

Summer817

Year 3Fall927.45

Winter1024.75

Spring1120.925

Summer1217.1

QuarterYear 1Year 2Year 3

Fall242627.45

Winter232224.75

Spring191920.925

Summer141717.1

Total808490

Average202122.5

Calculate Seasonal Indices

QuarterYear 1Year 2Average

Fall1.21.2381.22

Winter1.151.0481.1

Spring0.950.9050.93

Summer0.70.810.76

Total ->4.01

Causal

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

YearAdvertisingSales

148130

252151

350150

455158

570214.4

0

0

0

0

Nave ForecastNext period forecast = Last Periods actual:

Simple Average (Mean)Next periods forecast = average of all historical data

Quantitative Methods---- ContdSimple Moving Average An averaging period (AP) is given or selected The forecast for the next period is the arithmetic average of the AP most recent actual demands It is called a simple average because each period used to compute the average is equally weighted

Simple Moving Average It is called moving because as new demand data becomes available, the oldest data is not used By increasing the AP, the forecast is less responsive to fluctuations in demand (low impulse response and high noise dampening) By decreasing the AP, the forecast is more responsive to fluctuations in demand (high impulse response and low noise dampening)

Example: Central Call Center Moving Average Representative Historical Data

DayCallsDayCalls115972032217819531869188416110168517311198615712159

Example: Central Call CenterMoving AverageUse the moving average method with an AP = 3 days to develop a forecast of the call volume in Day 13.

F13 = (168 + 198 + 159)/3 = 175.0 calls Forecast error: Y- Y estimated

Weighted Moving Average This is a variation on the simple moving average where the weights used to compute the average are not equal. This allows more recent demand data to have a greater effect on the moving average, therefore the forecast The weights must add to 1.0 and generally decrease in value with the age of the data. The distribution of the weights determine the impulse response of the forecast.

Weighted Moving Average

Use the weighted moving average method with an AP = 3 days and weights of .1 (for oldest datum), 0.3, and 0.6 to develop a forecast of the call volume in Day 13.

F13 = .1(168) + .3(198) + .6(159) = 171.6 calls

The WMA forecast is lower than the MA forecast because Day 12s relatively low call volume carries almost twice as much weight in the WMA (0.60) as it does in the MA (0.33).

Exponential SmoothingThis is a very popular scheme to produce a smoothed Time Series. Whereas in Single Moving Averages the past observations are weighted equally, Exponential Smoothing assigns exponentially decreasing weights as the observation get older. In other words, recent observations are given relatively more weight in forecasting than the older observations. If y stands for the original observation. The subscripts refer to the time periods, 1, 2, ..., n. For the third period, S3 = y2 + (1-)S2 = smoothing constant.yt = observed value of series in period t.

Exponential Smoothing

If a smoothing constant value of .25 is used and the exponential smoothing forecast for Day 11 was 180.76 calls, what is the exponential smoothing forecast for Day 13?

F12 = 180.76 + .25(198 180.76) = 185.07F13 = 185.07 + .25(159 185.07) = 178.55

Mkt Share12022232342451862371981792210231118122321

Exponential Smoothing

Mkt ShareFA-F (A-F)*212021-1.001.0022220.701.301.6932321.091.913.6542421.662.345.4651822.36-4.3619.0562321.051.953.7871921.64-2.646.9681720.85-3.8514.8092219.692.315.32102320.382.626.84111821.17-3.1710.05122320.222.787.742186.33

Barometric ForecastingThis approach rests on the logic that key current developments can serve as a barometer of the future, if the key developments can be identified and put into the form of a statistical time seriesA leading indicators series is usually constructed using several carefully chosen statistical time series. When a leading indicators series is working correctly, the significant upward and downward movements in the indicator lead the ups and downs of the real-world phenomenon it is supposed to measure by some period of time.

Barometric forecastingA leading indicator predicts three to six months in the future another event. Examples of indicators include: payroll employment, personal income less transfer payments, an index of industrial production, stock prices, changes in business inventories, consumer expectations, building permits, new orders for goods and materials and retail sales.

Leading IndicatorThese types of indicators signal future events. Think of how the amber traffic light indicates the coming of the red light. In the world of finance, leading indicators work the same way but are less accurate than the street light. Bond yields are thought to be a good leading indicator of the stock market because bond traders anticipate and speculate trends in the economy (even though they aren't always right).

Lagging indicatorA lagging indicator is one that follows an event. Back to our traffic light example: the amber light is a lagging indicator for the green light because amber trails green. The importance of a lagging indicator is its ability to confirm that a pattern is occurring or about to occur. Unemployment is one of the most popular lagging indicators. If the unemployment rate is rising, it indicates that the economy has beendoing poorly.

Coincident IndicatorThese indicators occur at approximately the same time as the conditions they signify. In our traffic light example, the green light would be a coincidental indicator of the associated pedestrian walk signal. Rather than predicting future events, these types of indicators change at the same time as the economy or stock market. Personal income is a coincidental indicator for the economy: high personal income rates will coincide with a strong economy.

Some examplesSatisfied/Motivated Employees is a (well-proven) Leading Indicator of Customer Satisfaction. Similarly, high-performing processes (e.g. to 6 Sigma levels) would be expected to be a Leading Indicator of Cost Efficiency.Arguably, the BSC perspectives focussed on Organisational Capability (or Learning & Growth) and Processes contain Leading Indicators of external performance that are contained within the Finance and Customer perspectives.

Limitations of Leading Indicator Do not provide with very reliable information about the magnitude of that change. Moreover, the magnitude of change of the indicator in any one direction is not necessarily a measure of how good or bad the economy is likely to get. It is only when the indicator clearly reverses direction that its value as a forecasting tool is relevant. The component indicators of the overall leading indicator often are not consistent with one another in their predictions.

Simple Linear RegressionSimple linear regression can also be used when the independent variable X represents a variable other than time.In this case, linear regression is representative of a class of forecasting models called causal forecasting models.

Example: Railroad Products Co.Simple Linear Regression Causal ModelThe manager of RPC wants to project the firms sales for the next 3 years. He knows that RPCs long-range sales are tied very closely to national freight car loadings. On the next slide are 7 years of relevant historical data.Develop a simple linear regression model between RPC sales and national freight car loadings. Forecast RPC sales for the next 3 years, given that the rail industry estimates car loadings of 250, 270, and 300 million.

Simple Linear RegressionRPC Sales (Y)Car LoadingsYearRs millionsmillions (x)19.5 120211.0 135312.0 130412.5 150514.0 170616.0 190718.0 220

xy x2 xy

1209.514,4001,14013511.018,2251,48513012.016,9001,56015012.522,5001,87517014.028,9002,38019016.036,1003,04022018.048,4003,960

1,11593.0185,42515,440

r is computed by:

Coefficient of Determination (r2) The coefficient of determination, r2, is the square of the coefficient of correlation. The modification of r to r2 allows us to shift from subjective measures of relationship to a more specific measure. r2 is determined by the ratio of explained variation to total variation:Where Y is estimated value, y is mean and y is observed value.

Y = 0.528 + 0.0801X

Y8 = 0.528 + 0.0801(250) = Rs. 20.55 millionY9 = 0.528 + 0.0801(270) = Rs 22.16 millionY10 = 0.528 + 0.0801(300) = Rs 24.56 million

RPC sales are expected to increase by Rs 80,100 for each additional million national freight car loadings.

Multiple Regression AnalysisMultiple regression analysis is used when there are two or more independent variables.An example of a multiple regression equation is:

Y = 50.0 + 0.05X1 + 0.10X2 0.03X3

where: Y = firms annual sales Rs millions) X1 = industry sales (Rs millions) X2 = regional per capita income (Rs thousands) X3 = regional per capita debt (Rs thousands)

Coefficient of Correlation (r)

The coefficient of correlation, r, explains the relative importance of the relationship between x and y. The sign of r shows the direction of the relationship. The absolute value of r shows the strength of the relationship. The sign of r is always the same as the sign of b. r can take on any value between 1 and +1.

Econometric Methods

A central advantage of econometric techniques is that it explicitly takes into account causal relationships in economic variables. Simple multiple regression involves a set of independent variables that are assumed to determine or explain the value of the dependent variable in question. The world is often times more complex than this, however, and there may be feedback effects that mean that some of the explanatory variables may depend on or be explained by other variables in the regression equation.

Econometric Methods

In this latter case, there is more than one dependent variable (a variable that depends on the value of the other variables), and so there must be more than one estimating equationThere are special regression techniques that allow for these multiple equations to be estimated simultaneously, where each stage estimates are made, substitutions occur, and new estimates are made, and new substitutions occur. A rule is then used as to when the new estimates are sufficiently close to the old estimates to stop the process.

Input-output AnalysisInput-output (I/O) models are based on a set of tables which describe the relationships between the various sectors of the economy. The govt provides these tables. The main objective of I/O analysis is to determine the overall effect of a change in economic activity in one particular sector.I/O models are particularly useful to local and regional economic development agencies that are attempting to forecast future economic activities, and the impact of particular types of business

WendyThe most notable example of this occurred in the case of Wendy's. During a phone conversation, a nutrition specialist informed us that an individual recently called to inquire about the gelatin in the Reduced Fat/Reduced Calorie Garden Ranch Sauce. The nutrition specialist told us that because of this inquiry, she contacted the supplier and requested that the gelatin be taken out of the sauce. The supplier agreed to the request. The new sauce should now be available in Wendy's restaurants.

SubwaySubway, with over 13,000 locations worldwide, has earned the right to use the "Five a Day for Better Health" logo of the Produce for Better Health Foundation. Subway menu items, several of which are low in fat and 100% of which contain vegetables, meet the rather strict standards of that organization. We encourage other chains to follow the lead and hope that vegetarian consumers will support those chains which offer healthy, vegetarian meals.

*A point you may wish to make here is that only in the case of linear regression are we assuming that we know why something happened. General time-series models are based exclusively on what happened in the past; not at all on why. Does operating in a time of drastic change imply limitations on our ability to use time series models?