Download - Demand forecasting
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.