DEMAND FORECASTING-PRESENTED BY-

2. Gautam Agarwal3. Hitesh Agarwal

11. Kandarp Desai15. Vaibhav Gumaste

26. Omkar Kelkar29. Aditya Krishnan

OBJECTIVES FOR DEMAND FORECASTING• Understand the role of demand

forecasting• Identify reasons for demand forecasting• Study of Forecasting methodologies• Selecting the right forecasting method.• Measurement of forecasting errors.

INTRODUCTION Predicting future demand of

products/services of an organisation Forecast = To estimate/calculate in

advance. Guiding factor- for deciding the capacity

and location of new facility. The staffing decisions should be in line

with the demand forecasts. It affects administrative plans and policies.

Material requireme

nt planning

Maximize gains for external environm

ent

To develop policies

To develop administrative plans

Maximize gains for actions of organisati

on

In decision making

for budgetin

g

To offset the

actions of

competitor

To provide adequate staff to support

requirements

To minimize losses of

uncontrollable events

REASONS FOR DEMAND

FORECASTING

VARIOUS METHODS

Qualitative Analysis1) Consumers Survey: Complete Enumeration Method The forecaster undertakes a complete survey of all consumers whose demand he intends to forecast. Once this information is collected, the sales forecasts are obtained by simply adding the probable demands of all consumers. The principle merit of this method is that the forecaster does not introduce any bias or value judgment of his own. But it is a very tedious and cumbersome process; it is not feasible where a large number of consumers are involved

2) Consumer Survey-Sample Survey Method

Under this method, the forecaster selects a few consuming units out of the relevant population and then collects data on their probable demands for the product during the forecast period. The total demand of sample units is finally blown up to generate the total demand forecast. Compared to the former survey, this method is less tedious and less costly, and subject to less data error; but the choice of sample is very critical. If the sample is properly chosen, then it will yield dependable results; otherwise there may be sampling error.

3) Sales Force Composite

The sales force composite method of forecasting starts with the forecaster asking for opinions about future sales from every member of the sales staff currently working in the field. Each sales force member states how many sales she thinks she'll make during the given forecasting period. Department managers look over and adjust salespeople's predictions before turning the numbers over for forecasting. Predictions are usually checked against historical sales numbers.

4) Executive Opinion Poll

Forecasters using the executive opinion or expert opinion method poll executives or experts from within the company and ask their opinion on the optional sales for the given forecasting time period. The forecaster will then average the individual judgments or try for a group consensus. Executive opinion polls are often used to verify (or invalidate) other qualitative methods, especially sales force composites.

Dis-advantages: Biased , non-response situation , time consuming.Advantages: No pressure.

5) Delphi Method

6) Past Analogies

Sometimes data on the exact time of a particular event (or events) are available.Experts use cases where similar events have occurred at different times or in different geographic areas and compare them to the issue at hand. If occurrence or no-occurrence of an event is on a regular basis, then the data can be thought of as having a repeated measurement structure. It helps to select a large number of similar situations, rather than basing a decision on comparison with only one case.

Quantitative analysis Forecast future demand by using quantitative data

from the past and extrapolating it to make forecasts of future levels.

Demand for existing products can be forecasted by using this method.

They are used when historical data is available.

There are of two types of techniques 1. Time series analysis 2. Causal analysis

Time series analysis Time series of historical demand data with respect to time

intervals (periods) in the past is used to make predictions for the future demand.

Following are the five popular methods

Simple moving average Simple exponential smoothing

Holt’s double- exponential smoothing

Winters’ triple- exponential smoothing

Forecasting by Linear regression analysis

Simple moving average It is suitable under situations where there is

neither a growth nor a decline trend shown by the actual past data for forecasting.

For eg : If we have past data of the actual sales of a product for the months of Jan, Feb and March, we take the simple average of these sales figures for the three months. This simple average becomes the forecast for the next month i.e April.

Simple Moving Average MethodExample : four week moving average

WEEK ACTUAL SALES(IN UNITS)

FORECAST(IN UNITS)

CALCULATION

1 1634

2 1821

3 2069

4 1952

5 2178 1869 (1634+1821+2069+ 1952)/4

6 2005 (1821+2069+1952+2178)/4

Example: Three Period Moving average. Given below are the actual sale of a toy for the past 5 weeks. We need to predict the sales for the 6th week.

Weighted Moving Average Method

WEEK ACTUAL SALE(IN UNITS)

FORECAST(IN UNITS)

CALCULATION

1 1634(0.1)2 1821(0.2)3 2069(0.3)4 1952(0.4)5 1929 (1634*0.1+1821*0.2

+2069*0.3+1952*0.4)/ 1

The data in the recent past periods should be given more weight or importance compared to the data in the periods far off from the current time.

Linear Regression Analysis It is applied in situations where two variables

are linearly correlated to each other.

In time series analysis, the independent variable is time while the dependent variable is the actual demand in the past.

A graph showing the points for the corresponding values of two variables is called scatter diagram. These points should display an approximately linear trend.

Example of linear regression

Y= 1060X + 440 is the regression equationInterpretation: As the age of the car increase by 1 year the maintenance cost is expected to increase by Rs1060.

How to choose a demand forecasting technique

Objectives of a forecast

Cost involved

Time perspective (short run or long run)

Complexity of the technique

Nature and quality of available data

QUANTITATIVE ANALYSIS

EXPONENTIAL SMOOTHING METHODS

The problem with Moving Averages Methods

Forecast lags with increasing demandForecast leads with decreasing demand

Exponential Smoothing Methods Single Exponential Smoothing–– Similar to single Moving Average Double (Holt’s) Exponential Smoothing–– Similar to double Moving Average–– Estimates trend Triple (Winter’s) Exponential Smoothing–– Estimates trend and seasonality

Single Exponential Smoothing

Holt’s Exponential smoothing(Double Exponential Smoothing) Sometimes called exponential smoothing

with trend. If trend exists, single exponential

smoothing may need adjustment. There is a need to add a second

smoothing constant to account for trend. It adds a growth factor (or trend factor) to the

smoothing equation as a way of adjusting for the trend

Winter’s Exponential Smoothing(Triple Exponential Smoothing) Winter’s exponential smoothing model is the

second extension of the basic Exponential smoothing model.

It is used for data that exhibit both trend and seasonality.

It is a three parameter model that is an extension of Holt’s method.

An additional equation adjusts the model for the seasonal component.

TREND ANALYSIS

Forecasting method used in causal quantitative analysis based upon linear regression analysis.

The dependent variable should have a causal relationship with the independent variable.

For eg. Dependent variable : No. of units produced Independent variable : No. of labors present

Trend Analysis Chart

MEASUREMENT OF FORECASTING ERRORS

Running sum of forecast errors Mean forecast error Mean absolute deviation Mean squared error Mean absolute percentage error Tracking signal

Tracking signal Dynamic measure of forecasting errors as

can be updated after every time new actual demand data is added.

TS=RSFE/MAD In ideal forecast system ,TS should hover

closely around zero. Region above centre zero line shows Actual demand > forecast Region below centre zero line shows Actual demand < forecast

Tracking signal plotted against number of days

Forecast Control Limits

Used in controlling the forecasting errors. Here assumed that forecasting errors follow a

normal distribution curve and are randomly distributed around the mean(assumed,=0).

Forecasting system is said to be performing well if all the forecast error points fall within the control limit.

Upper control limit= 0+3s (s=(MSE)½) Lower control limit= 0-3s (s=(MSE)½) Any point not lying in the limit is a signal to

forecaster to look for cause of variation.