Demand ForecastingDemand Forecasting

SO

WHAT

“IS”

DEMAND

FORECASTING?

Forecasting customer demand for products and services is a proactive process of determining what products are needed where, when, and in what quantities. Consequently, demand forecasting is a customer–focused activity.

Demand forecasting is also the foundation of a company’s entire logistics process. It supports other planning activities such as capacity planning, inventory planning, and even overall business planning.

Characteristics of demand 5 main characters of demand are- AverageDemand tends to cluster around a specific level. TrendDemand consistently increases or decreases over time. SeasonalityDemand shows peaks and valleys at consistent intervals. Theseintervals can be hours, days, weeks, months, years, or seasons. CyclicityDemand gradually increases and decreases over an extended period of time, such as years. Business cycles (recession/expansion)product life cycles influence this component of demand. ElasticityDegree of responsiveness of demand to a corresponding proportionate change in factors effecting it.

TYPES OF FORECASTS

PASSIVE FORECASTSWhere the factors being forecasted are assumed to be constant over a period of time and changes areignored.

ACTIVE FORECASTSWhere factors being forecasted are taken as flexible and are subject to changes.

Why Study forecasting?Reduces future uncertainties, helps study markets that are dynamic, volatile and competitive Allows operating levels to be set to respond to demand variationsAllows managers to plan personnel, operations of purchasing & finance for better control over wastes inefficiency and conflicts.Inventory Control-reduces reserves of slack resources to meet uncertain demand

Effective forecasting builds stability in operations.

Setting Sales Targets, Pricing policies, establishing controls and incentives

THE FORECASTHow?

Step 6 Monitor the forecast

Step 5 Prepare the forecast

Step 4 Gather and analyze data

Step 3 Select a forecasting technique

Step 2 Establish a time horizon

Step 1 Determine purpose of forecast

LEVELS OF FORECASTING

AT FIRMS LEVEL

AT INDUSTRY LEVEL

AT TOTAL MARKET LEVEL

KEY FACTORS FOR SELECTING A RIGHT METHOD

TIME PERIODSHORT TERM

3-6 Months, Operating Decisions, E.g- Production planning

MEDIUM TERM 6 months-2 years, Tactical Decision E.g.- Employment changes

LONG TERM Above 2 years, Strategic Decision E.g.- Research and Development

DATA REQUIREMENTS

Techniques differ by virtue of how much data is required to successfully

employ the technique. Judgmental techniques require little or

no data whereas methods such asTime series analysis or Regression

models require a large amount of pastor historical data.

PURPOSE OF STUDY

It means the extent of explanation required from the study. Some

techniques are based purely on the pattern of past data and may do quite well at forecasting, whereas many a

times these are not useful by themselves since it is difficult to explain the causal

factors underlying the forecast.

PATTERN OF DATA STUDIED

The pattern of historical data is an important factor to consider. Though most of the times, the major pattern is

that of a trend, there are also cyclic and seasonal patterns in the data. Certain

techniques are best suited for capturing the different patterns in the data.

Regression methods incorporates all these variations in data whereas trend analysis methods study these factors

individually.

SO ,WE KNOW WHAT IT’S ALL ABOUT!!!

NOW LETS ANALYSE THE

METHODS OF

DEMAND

FORECASTING.

2 MAIN CATEGORIESMICROECONOMIC METHODS(QUANTITATIVE)- involves the prediction of activity of particular firms,

branded products, commodities, markets, and industries. - are much more reliable than macroeconomic methods

because the dimensionality of factors is lower and often can easily be incorporated into a model.

MACROECONOMIC METHODS(QUALITATIVE)

- involves the prediction of economic aggregates such as inflation, unemployment, GDP growth, short-term interest rates, and trade flows.

- is very difficult because of the complex interdependencies in the overall economic factors

QUALITATIVE METHODS- SURVEY OF BUYERS INTENSIONS

- EXPERTS OPINION METHOD

- DELPHI METHOD

- MARKET EXPERIMENTATION METHOD- COLLECTIVE OPINIONS METHOD

QUANTITATIVE METHODS- TIME SERIES MODELS - TREND ANALYSIS - MOVING AVERAGES METHODS - EXPONENTIAL SMOOTHING

- CAUSAL MODELS - REGRESSION MODELS

BUYERS INTENSION SURVEYFEATURES

EMPLOYS SAMPLE SURVEY TECHNIQUES FOR GATHERING DATA.

DATA IS COLLECTED FROM END USERS OF GOODS - CONSUMER, PRODUCER,MIXED.

DATA PORTRAYS BIASES AND PREFERENCES OF CUSTOMERS.

IDEAL FOR SHORT AND MEDIUM TERM DEMAND FORECASTING, IS COST EFFECTIVE AND RELIABLE.

ADVANTAGES

HELPS IN APPROXIMATING FUTURE REQUIREMENTS EVEN WITHOUT

PAST DATA.ACCURATE METHOD AS BUYERS NEEDS AND WANTS ARE CLEARLY IDENTIFIED & CATERED TO.MOST EFFECTIVE WAY OF ASSESSING DEMAND FOR NEW

FIRMS

LIMITATIONSPeople may not know what they are going to purchaseThey may report what they want to buy, but not what they are capable of buyingCustomers may not want to disclose real informationEffects of derived demand may make forecasting difficult

EXPERTS OPINION METHOD

FEATURES

PANEL OF EXPERTS IN SAME FIELD WITH EXPERIENCE & WORKING KNOWLEDGE.COMBINES INPUT FROM KEY INFORMATION SOURCES.EXCHANGE OF IDEAS AND CLAIMS.FINAL DECISION IS BASED ON MAJORITY OR CONSENSUS, REACHED FROM EXPERT’S FORECASTS

ADVANTAGES

CAN BE UNDERTAKEN EASILY WITHOUT THE USE OF ELABORATE STATISTICAL TOOLS.

INCORPORATES A VARIETY OF EXTENSIVE OPINIONS FROM EXPERT IN THE FIELD.

LIMITATIONS

JUDGEMENTAL BIASES FOR EXAMPLE

Availability heuristicInvolves using vivid or accessible eventsas a basis for the judgment.

Law of small numbersPeople expect information obtainedfrom a small sample to be typical ofthe larger population

COMPETATIVE BIASES

Over reliance on personal opinions.Possibility of undue influence in

certain cases.

STATISTICAL INADEQUACY

Lack of statistical and quantifiable data or figures to substantiate the

forecasts made.

DELPHI METHOD

PANEL OF EXPERTS IS SELECTED.ONE CO-ORDINATOR IS CHOSEN BY

MEMBERS OF THE JURYANONYMOUS FORECASTS ARE MADE BY EXPERTS BASED ON A COMMON QUESTIONNAIRE.CO-ORDINATOR RENDERS AN AVERAGE OF ALL FORECASTS MADE TO EACH OF THE MEMBERS.

3 CONSEQUENCES- DIVERSION, CONSENSUS OR NO AGREEMENT.

2 TO 3 CYCLES ARE UNDERTAKEN.

CONVERGENCE AND DIVERSION IS ACCEPTABLE.

FORECASTS ARE REVISED UNTIL A CONSENSUS IS REACHED BY

ALL.

ADVANTAGES

ELIMINATES NEED FOR GROUP MEETINGS.

ELIMINATES BIASES IN GROUP MEETINGS

PARTICIPANTS CAN CHANGE THEIR OPINIONS ANONYMOUSLY.

LIMITATIONS

TIME CONSUMING -REACHING A CONSENSUS TAKES A LOT OF TIME.

PARTICIPANTS MAY DROP OUT.

MARKET EXPERIMENTATIONINVOLVES ACTUAL EXPERIMENTS & SIMULATIONS.COUPONS ARE ISSUED TO FEW SELECT CUSTOMERS.SELECTED CUSTOMERS PURCHASE THE PRODUCTS.PROXIMITY WITH CONSUMERS MAKES

INFORMATION COLLECTED RELIABLE.INFORMATION FROM INTERACTIONS BETWEEN SALES PERSONNEL & CUSTOMERS IS USED FOR FORECASTING.BEST USED IF SALES PERSONNEL ARE HIGHLY

SPECIALISED AND WELL TRAINED.

ADVANTAGES

USES KNOWLEDGE OF THOSE CLOSEST TO THE MARKET.

HELPS ESTIMATING ACTUAL POTENTIAL FOR FUTURE SALES.

PROVIDES FEEDBACK FOR IMPROVING CUSTOMIZING & OFFERING MADE TO

CUSTOMERS.

COLLECTIVE OPINIONS METHOD

OPINIONS FROM MARKETING & SALES SPECIALISTS ARE COMPILED.

2 TYPES OF TARGETS ESTIMATED-AMBITIOUS TARGETS.CONSERVATIVE TARGETS.

COMBINES EXPERTISE OF HIGHERLEVEL MANAGEMENT & SALESEXECUTIVES.

LIMITATIONS

POWER STRUGGLES MAY OCCUR BETWEEN SPECIALISTS.

CONSENSUS MAY NOT BE REACHED IN GOOD TIME.

DIFFERENCES AND PREJUDICES IN OPINIONS MAY ALSO EXIST.

“ARE YOU STILL THERE??”

THAT FINISHES THE QUALITATIVE METHODS.

NOW LETS LOOK AT THE

“QUANTITATIVE METHODS”

TIME SERIES MODELS

Past data is used to make future predictions .

Known or Independent variables are used for predicting Unknown or dependent variables, using the trend equation- “ Predictive analysis”

Based on trend equation, we find ‘Line of Best Fit’ and then it is projected in a scatter diagram, dividing points equally on both sides

TREND ANALYSIS

TREND EQUATION

Y^ = a + bX + E

Y^ = Estimated value of Y a = Constant or Intercept b = slope of trend line X = independent variable E = Error term

= EXPLAINED VARIATION

1 - = UNEXPLAINED VARIATION

Explained variation - means the extent to which the independent variable explains the relative change in the dependent variable.

Higher the explained variation, lower the error value leading to accurate forecast

MOVING AVERAGE METHODData from a number of consecutive

past periods is combined to provideforecast for coming periods.Higherthe amount of previous data, betteris the forecast.

Since the averages are calculatedon a moving basis, the seasonal and cyclical variations are smoothened out.

EXPONENTIAL SMOOTHING

Used in cases where the variable under forecast doesn’t follow a trend.

2 Types- Simple and Weighted

Simple smoothing- simple average of specific observation called order.

Weighted smoothing- weights assigned in decreasing order as one moves from current period observations to previous observations.

The equation for exponential smoothing follows a Geometric Progression.Values may be written as-

a, a (1-a), a(1-a)^2….. a(1-n) where, a = value of weight assigned

to the observation a(1-a) = weight assigned to 1 period previous observationa(1-a)^2 = weight assigned to 2 periods previous observationSum of all weights always equals Unity.

CASUAL MODELS

It is a statistical technique for quantifying the relationship between

variables. In simple regression analysis, there is one dependent variable (e.g.

sales) to be forecast and one independent variable. The values of the independent variable are typically those assumed to "cause" or determine the values of the

dependent variable.

REGRESSION MODEL

For exampleAssuming that the amount of advertising dollars spent on a

product determines the amount of its sales, we could use

regression analysis to quantify the precise nature of the

relationship between advertising and sales. For forecasting

purposes, knowing the quantified relationship between the

variables allows us to provide forecasting estimates

STEPS IN REGRESSION ANALYSIS1.Identification of variables influencing demand for product under estimation.

2.Collection of historical data on variables.

3.Choosing an appropriate form of function

4.Estimation of the function.

REGRESSION EQUATION

Y = xα

Where

Y= value being forecasted

= constant value

= coefficients of regression

= independent variable

x

x

x

HIGHER REVENUES

SALES MAXIMIZATION

REDUCED INVESTMENTS FOR SAFETY

STOCKS

IMPROVED PRODUCTION PLANNING

EARLY RECOGNITION OF MARKET TRENDS

BETTER MARKET POSITIONING

IMPROVED CUSTOMER SERVICE LEVELS

BENEFITS OF EFFECTIVE DEMAND FORECASTING