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In making conscious decisions under uncertainty,
we all make forecasts.
In business, Sales Forecasting is a critical
component of Supply Chain Management. Forecast helps to determine the
amount of inventory to be kept on hand, raw material to be purchased,
and quantity of products to be manufactured.
Inaccurate forecasts can lead to costly inventory
buildup, stock outs and higher cost of supply.
An interesting survey on sales forecasting from
a fairly diverse population of companies revealed
- 49% companies used simple tools like Excel, Access for demand forecasting
- 9% did not use any software
- 42% used mathematical models
- 93.5% of companies that used some forecasting model achieved significant
- The percentage of companies having forecasting error less than 10%
increased from 25 to 67 on use of forecasting models.
How to do Sales forecasting?
Sales forecasting is a formalized way to understand
the predictable behavior of demand. The following seven steps
can help in building a good sales forecasting model.
I: Forecasting Audit:
Detailed study of forecasting process - roles of people involved, information
available to them, type of analysis done, issues to the process - is
a positive step towards unlocking many hidden
efficiencies in the first place.
II: Identification of objective and decision set:
Forecasting becomes economically justified
if specific decisions are addressed by it to meet the pre-defined objectives.
E.g., if a production batch size meets six-month demand for a product,
then there is no point in spending significant effort on it's forecast.
It is crucial to define forecasting granularity
i.e. town level, state level, national level etc., and the forecasting
horizon - weekly, monthly, rolling basis, etc.
Segmentation of products:
A single forecasting model cannot predict sales of all products
so segmenting them into categories and identifying forecasting model
for each category would be reasonable.
Selection of appropriate methodologies:
Modeling experts can suggest and execute appropriate
methodologies to forecast sales by using techniques such as Artificial
Neural Networks (ANN), Fuzzy Logic and Chaotic Systems Theory. ANN,
for example, is a mathematical technique for forecasting that "learns"
about systems directly from the data.
Data collection and cleaning:
Although reasonably good sales forecasts can be made by the sales
data present in most organizations - identifying,
collecting and cleaning additional relevant data could improve the quality
of forecasts manifolds. The key here is to adjust for disruptive
events like introduction of VAT in some states, natural calamities,
labor strikes, etc.
Repeated testing with users, and implementing the model in a phased
manner, makes the model robust, and helps in developing user's confidence
VII: Monitoring performance of the Model:
Presenting forecasts as scenarios, obtaining feedback about its accuracy,
gaining decision makers' confidence, and
facilitating constant review aids continuous model improvement and gradually
leads to model maturity.
Key considerations before starting
a forecasting exercise are:
- Beginning Early - building forecasting
model takes time
- Taking expert inputs - consulting a
modeling company can add advantages of cross industry learning
- Avoiding over- dependence on tools
- statistical packages can do data analysis, but they cannot replace
- Thinking of multiple solutions - there
is no single solution for sales forecasting of all the products
- Playing one's role - modelers build
the model and domain people provide business insights
2000-10 DecisionCraft Inc.