In Supply Chain Management reducing inventory is a critical component of cost control. While there are many methods for reducing inventory, one of the primary ones is demand forecasting – trying to obtain an accurate picture of what demand will look like in the future.
Approaches to demand forecasting vary, but generally fall into one of two categories:
Quantitative Forecasting
Qualitative Forecasting
Quantitative forecasting utilizes statistical models to predict future values. These models may take into account current and historical trends.
Qualitative forecasting is
less mathematical and more intuition-based. In many instances,
especially those that feature roll outs of “unique” products (like the
original iPhone), statistical models can provide inadequate results
because they don’t have enough past data to make an accurate prediction
of future demand. This is where human expertise enters the process:
smart demand forecasters can often take into account factors that
statistical models cannot turn into mathematical equations.
The time series method is
a quantitative forecasting technique that bases calculations of future
demand on historical patterns. It assumes that the past is a good
indicator of the present and future – the assumption of continuity. The time series method statistically analyzes three main components: basic value, trend, and seasonality. Basic value represents the “baseline” – the historical rate of sales. The trend analyzes which way (if any) demand is trending – is demand for this item increasing or decreasing? Seasonality takes
seasonal variations into account: obviously, some goods (like ice
cream) are in higher demand at certain times of the year (in this case,
summer).
Under
a time series analysis, a projection of future demand is constructed
from historical data of these three components. While the results can be
skewed if other factors need to be taken into account (say, price
changes), it is usually reasonably accurate
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