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Time Series models are simple yet powerful techniques available
to develop supply chain forecasts. No where the cliché "History
repeats itself" is more true than in sales
forecasting.
In Time-series modeling, we just postulate that all we need is
past values of the variable we are trying to forecast. So if
we are trying to predict the demand for a specific product over the
next six months, we use the monthly history of the product over the
past two to three years. We just ignore other factors such as
price elasticity, promotional sensitivity, macro-economic activity,
or Governmental policy changes or our own corporate policy decisions
that we may be aware of.
Time series forecasts can be good starting points before
incorporating other causal effects. Time series methodology
examines the past history for the following elements:
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Historical
Average: This is also called as the level of sales that
you have achieved on average.
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Trend:
This is the growth or decline in Sales over time.
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Seasonality:
The tendency for sales to either peak in specific periods or dip
in specific periods during the week, month or the quarter.
You may have strong sales in the summer but weak sales in the
spring and fall, for example.
-
Cyclicality
(less often): Sales volume may go through and be affected
by economic cycles. Typically, since supply chain
forecasting is more focused on a time window less than one
month, this is often ignored as a relevant factor affecting
time-series forecasts.
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Outliers:
Sales may be subject to a one-time, sporadic event that may not
be expected to repeat.
Popular Time Series Techniques:
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Moving average
and growth models
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Simple
Exponential Smoothing
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Winters Models
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Holt Winters
Methodology
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Simple Trend
Seasonal Models
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Logarithmic
Models
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ARIMA models
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