Transcript
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 FORECASTSPASSIVE 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 demandEffective 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
orno data whereas methods such asTime series analysis or Regressionmodels 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 STUDIEDThe 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.
ADVANTAGESHELPS 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 purchase
They may report what they want to buy, but not what they are capable of buying
Customers may not want to disclose real information
Effects of derived demand may make forecasting difficult
EXPERTS OPINION METHODFEATURES
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
ADVANTAGESCAN 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 BIASESOver 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 METHODPANEL 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.
ADVANTAGESUSES 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 METHODOPINIONS FROM MARKETING &
SALES SPECIALISTS ARE COMPILED.
2 TYPES OF TARGETS ESTIMATED-AMBITIOUS TARGETS.CONSERVATIVE TARGETS.
COMBINES EXPERTISE OF HIGHERLEVEL MANAGEMENT & SALESEXECUTIVES.
LIMITATIONSPOWER 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 SMOOTHINGUsed in cases where the variable
under forecast doesn’t follow a trend.
2 Types- Simple and WeightedSimple 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 function4.Estimation of the function.
REGRESSION EQUATION
Y = xαWhereY= value being forecasted = constant value = coefficients of regression = independent variable
x
x
x
HIGHER REVENUESSALES MAXIMIZATIONREDUCED INVESTMENTS FOR SAFETY
STOCKSIMPROVED PRODUCTION PLANNINGEARLY RECOGNITION OF MARKET TRENDSBETTER MARKET POSITIONING IMPROVED CUSTOMER SERVICE LEVELS
BENEFITS OF EFFECTIVE DEMAND FORECASTING
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