The
primary decisions of inventory control are as follows: What products
or materials should be made or ordered? How Much of those products
and materials should be made or ordered? When should we make or order
those products and materials? Reasons that companies will hold
inventory are to achieve economies of scale, to protect against
variability in demand, lead time, etc., and more. Costs associated
with an inventory control system include holding or carrying costs
that represent the cost of keeping items in inventory over a period
of time, replenishment costs that represent the cost of making or
ordering to increase the inventory level, and penalty or shortage
costs for not having sufficient inventory to meet demand.
A
good tool to use for planning an inventory control system is an
appropriate forecasting model of past demand data. Different
forecasting models exist for different data patterns, such as level,
seasonal, or intermittent demand data. Each model uses different
parameters to analyze past demand patterns and forecast future demand
points. As time goes on and more data becomes available, the
forecasting model can be continuously updated and improved.
Quantitative
inventory control methods can utilize forecasting analysis to
optimize when, how much, and what products are made or ordered by
minimizing the costs associated with these decisions. Just like
different forecasting models exist for different data patterns,
different inventory control techniques exist for different demand
patterns and various levels of uncertainty.
One
of the most commonly known inventory control models is the basic
Economic Order Quantity (EOQ) model. This model is very simple and
easy to use; however, it has several underlying assumptions that do
not typically reflect realistic scenarios such as no replenishment
lead time or allowing a non-integer order quantity. More complex
models that incorporate lead times, both deterministic and
time-varying, quantity discounts, inventory shortages, and variable
demand patterns can be derived from this model.
When
using forecasts and inventory control models, it is important to
remember that they are usually wrong. If this is true, then why would
you ever want to use them to make decisions? The answer is because
they provide insight into making future decisions. “Good”
forecasts are accurate and give you an idea of the range for your
future demand. These methods shouldn’t be used independently of
important market information either, such as the effects of a new
popular movie release on the demand for related movie merchandising.
Better
control over the inventory system provides benefits beyond the costs
of inventory. Holding excess inventory results in greater storage
space requirements, which limits space utilization. Read more about
this in our Space
Utilization post.
