Demand Forecasting

What is Forecasting? Process of predicting a future event based on historical data

Levels of Demand Forecasting Micro Level- Demand forecasting by individuals business firm for forecasting the demand for its product. Industrial Level- Demand estimate for the product of the industry Macro Level- Aggregate demand forecasting for industrial output at the national level- it is based on the national income/ aggregate expenditure of the company.

Types Of Demand Forecasting Short-Term Mid-Term Long-Term

Factors determining demand forecasting Time factor Level of forecasting General or Specific forecasting Problems & methods of forecasting Classification of goods Knowledge of different market conditions Other factors

Forecasting During the Life Cycle Introduction Growth Maturity Decline Qualitative models Quantitative models - Executive judgment - Market research Survey of sales force Delphi method - Time series analysis - Regression analysis Sales Time

Qualitative Forecasting Methods Models Sales Force Composite Delphi Method Executive Judgement Market Research/ Survey 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? Smoothing

Qualitative Methods Briefly, the qualitative methods are: Executive Judgment: Opinion of a group of high level experts or managers is pooled Sales Force Composite: Each regional salesperson provides his/her sales estimates. Those forecasts are then reviewed to make sure they are realistic. All regional forecasts are then pooled at the district and national levels to obtain an overall forecast. Market Research/Survey: Solicits input from customers pertaining to their future purchasing plans. It involves the use of questionnaires, consumer panels and tests of new products and services. .

Qualitative Methods Delphi Method: As opposed to regular panels where the individuals involved are in direct communication, this method eliminates the effects of group potential dominance of the most vocal members. The group involves individuals from inside as well as outside the organization. Typically, the procedure consists of the following steps: Each expert in the group makes his/her own forecasts in form of statements The coordinator collects all group statements and summarizes them The coordinator provides this summary and gives another set of questions to each group member including feedback as to the input of other experts. The above steps are repeated until a consensus is reached. .

Advantages & Disadvantages of Qualitative Forecasting Ability to predict changes Flexibility Ambiguity Disadvantages :- Accurate forecast is not possible Judgmental approach False/ inadequate information

Quantitative Forecasting Methods Time Series Regression Models Models 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? 2. Moving 3. Exponential 1. Naive Average Smoothing a) simple b) weighted a) level b) trend c) seasonality

Try to predict the future based on past data Time Series Models Try to predict the future based on past data Assume that factors influencing the past will continue to influence the future 14

Time Series Models: Components Random Seasonal Trend Composite

Product Demand over Time Demand for product or service This slide illustrates a typical demand curve. You might ask students why it is important to know more than simply the actual demand over time. Why, for example, would one wish to be able to break out a “seasonality” factor? Year 1 Year 2 Year 3 Year 4

Product Demand over Time Trend component Seasonal peaks Demand for product or service This slide illustrates a typical demand curve. You might ask students why it is important to know more than simply the actual demand over time. Why, for example, would one wish to be able to break out a “seasonality” factor? Actual demand line Random variation Year 1 Year 2 Year 3 Year 4 Now let’s look at some time series approaches to forecasting… Borrowed from Heizer/Render - Principles of Operations Management, 5e, and Operations Management, 7e

Quantitative Forecasting Methods Time Series Models Models 2. Moving 3. Exponential 1. Naive Average Smoothing 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? a) simple b) weighted a) level b) trend c) seasonality

2a. Simple Moving Average Assumes an average is a good estimator of future behavior Used if little or no trend Used for smoothing Ft+1 = Forecast for the upcoming period, t+1 n = Number of periods to be averaged A t = Actual occurrence in period t 15

2b. Weighted Moving Average Gives more emphasis to recent data Weights decrease for older data sum to 1.0 Simple moving average models weight all previous periods equally This slide introduces the “weighted moving average” method. It is probably most important to discuss choice of the weights.

3a. Exponential Smoothing Assumes the most recent observations have the highest predictive value gives more weight to recent time periods Ft+1 = Ft + a(At - Ft) et Need initial forecast Ft to start. Ft+1 = Forecast value for time t+1 At = Actual value at time t  = Smoothing constant 24

3a. Exponential Smoothing How to choose α depends on the emphasis you want to place on the most recent data Increasing α makes forecast more sensitive to recent data These points should have been brought out in the example, but can be summarized here.

Correlation & regression Causal method Correlation & regression Econometric model Input /output model

CAUSAL METHODS Casual methods of forecasting are estimating techniques Based on assumptions that demand to be forecasted (dependent variable) has cause & effect relationship with one or more than one variable (independent) Suitable for those industries which are witnessing ups and downs regularly

CORRELATION study of association between 2 variables Explains degree and direction of relationship its coefficient is denoted by “r” . Value lies between -1 and +1

ADVANTAGES LIMITATIONS Helps in analysing relationship between variables It tells us about the degree and direction of relationship Helps in making predictions Easy and simple to understand LIMITATIONS No cause and effect relationship Random relationship

REGRESSION :- its a tool to investigate the relationship between variables Estimates unknown value (dependent variable) on the basis of known value (independent variable) equation Y= a +bx

ADVANTAGES DISADVANTAGES Reliable Calculations through software's Multiple independent factor DISADVANTAGES complex Multiple regression

ECONOMETRIC MODELS Study historical relationships amongst macro variables affecting the economy and try to forecast its impact on business and industry Mai n forecasting models auto regressive integrated moving average (ARIMA) model vector auto regression (VAR) model Bayesian vector auto regression (BVAR) model

ADVANTAGES DISADVANTAGES suitable for macroeconomics indicators identify relationship between independent variables DISADVANTAGES Expensive Specialised knowledge Inaccurate data Wrong interpretations

INPUT OUTPUT METHOOD Very sophisticated and complex method Final output of one industry becomes the basis of forecasting the demand of other industry on which it is dependent for its input Method is used in business to business demand forecasting

ADVANTAGES DISADVANTAGES Long term forecasting Helpful for B2B forecasting DISADVANTAGES Expensive Inaccurate results complex

CRITERIA FOR SELECTING FORECASTING METHOD Accuracy Ease in interpretation Maintenance cost Accessibility Speed Cost saving Ease in implementation Time horizon Availability of data