Economic Order Quantity (EOQ)
By Dave Piasecki
As published in the January, 2001 issue of Solutions
for calculating optimal order quantities and reorder points have been in existence long before the arrival of the computer.
When the first Model T Fords were rolling off the assembly line, manufacturers were already reaping the financial benefits
of inventory management by determining the most cost effective answers to the questions of When? and How much?.
Yes long before JIT, TQM, TOC, and MRP, companies were using these same (then unnamed) concepts in managing their production
and inventory. I recently read Purchasing and Storing, a textbook that was
part of a “Modern Business Course” at the Alexander Hamilton Institute in New York. The textbook published in 1931 (that’s right 1931)
was essentially a how to book on inventory management in a manufacturing environment.
If you’re wondering why I would want to read a 70-year-old business text, my answer would be that the fundamental
concepts of managing a business change very little with time, and reading about these concepts in a vintage text is a great
way to reinforce the value of the fundamentals. The occasional reference to “The
War” (referring to WWI) also keeps it interesting and the complete absence of acronyms is refreshing.
As you may have
guessed, this 70-year-old book contained a section on Minimum Cost Quantity, which
is what we now refer to as Economic Order Quantity (EOQ). I can imagine that in the 1930’s an accountant (or more likely a room full of accountants) would
have calculated EOQ or other inventory related formulas one item at a time in a dimly lit office using the inventory books,
a mechanical adding machine and a slide rule. Time consuming as this was, some
manufacturers of the time recognized the financial benefits of taking a scientific approach to making these inventory decisions.
So why is it that,
in these days of advanced information technology, many companies are still not taking advantage of these fundamental inventory
models? Part of the answer lies in poor results received due to inaccurate data inputs. Accurate product costs,
activity costs, forecasts, history, and lead times are crucial in making inventory models work. Ironically, software advancements
may also in part to blame. Many ERP packages come with built in calculations for EOQ which calculate automatically. Often
the users do not understand how it is calculated and therefore do not understand the data inputs and system setup which controls
the output. When the output appears to be "out of whack" it is simply ignored. This sometimes creates a situation in
which the executives who had purchased the software incorrectly assume the material planners and purchasing clerks are ordering
based upon the systems recommendations. I should also note that many operations will find these built-in EOQ calculations
inadequate and in need of modifications to deal with the diversity of their product groups and processes.
and strategies may sometimes conflict with EOQ. Measuring performance solely by inventory turns is one of the most prolific
mistakes made in the name of inventory management. Many companies have achieved aggressive goals in increasing inventory
turns only to find their bottom line has shrunk due to increased operational costs.
EOQ is essentially
an accounting formula that determines the point at which the combination of order costs and inventory carrying costs
are the least. The result is the most cost effective quantity to order. In
purchasing this is known as the order quantity, in manufacturing it is known as the production lot size.
While EOQ may not apply to every inventory
situation, most organizations will find it beneficial in at least some aspect of their operation. Anytime you have repetitive purchasing or planning of an item, EOQ should be considered. Obvious applications
for EOQ are purchase-to-stock distributors and make-to-stock manufacturers, however, make-to-order manufacturers should also
consider EOQ when they have multiple orders or release dates for the same items and when planning components and sub-assemblies. Repetitive buy maintenance, repair, and operating (MRO) inventory is also a good application
for EOQ. Though EOQ is generally recommended in operations where demand is relatively
steady, items with demand variability such as seasonality can still use the model by going to shorter time periods for the
EOQ calculation. Just make sure your usage and carrying costs are based on the
same time period.
conflict with Just-In-Time? While I don’t want to get into a long discussion
on the misconceptions of what Just-In-Time (JIT) is, I will address the most common misunderstanding in which JIT is assumed
to mean all components should arrive in the exact run quantities “just in time” for the production run. JIT is actually a quality initiative with the goal of eliminating wasted steps, wasted labor, and wasted
cost. EOQ should be one of the tools used to achieve this. EOQ is used to determine which components fit into this JIT model
and what level of JIT is economically advantageous for your operation. As an
example, let us assume you are a lawn equipment manufacturer and you produce 100 units per day of a specific model of lawn
mower. While it may be cost effective to have 100 engines arrive on your dock
each day, it would certainly not be cost effective to have 500 screws (1 days supply) used to mount a plastic housing on the
lawn mower shipped to you daily. To determine the most cost effective quantities
of screws or other components you will need to use the EOQ formula.
The basic Economic Order Quantity (EOQ) formula
is as follows:
While the calculation itself is fairly simple the task of determining the correct data inputs to accurately represent your
inventory and operation is a bit of a project. Exaggerated order costs and carrying costs are common mistakes made in
EOQ calculations. Using all costs associated with your purchasing and receiving departments to calculate
order cost or using all costs associated with storage and material handling to calculate carrying cost will give you highly
inflated costs resulting in inaccurate results from your EOQ calculation. I also caution against using benchmarks or
published industry standards in calculations. I have frequently seen references to average purchase order costs of $100
to $150 in magazine articles and product brochures.
Often these references trace back to studies performed
by advocacy agencies working for business that directly benefit from these exaggerated (my opinion) costs used in ROI calculations
for their products or services. I am not denying that some operations may have purchase costs in this range,
especially if you are frequently re-sourcing, re-quoting, and/or buying from overseas vendors. However if your operation is
primarily involved with repetitive buying from domestic vendors — which is more common — you’ll likely see
your purchase order costs in the substantially lower $10 to $30 range.
As you prepare to undertake this project
keep in mind that even though accuracy is crucial, small variances in the data inputs generally have very little effect on
the outputs. The following breaks down the data inputs in more detail and gives insight into the aspects of each.
EOQ calculations are frequently
used in business, both by production, purchasing and inventory managers. This tool provides everything it takes to make reliable
calculations. In addition, it automatically computes the reorder point (= the amount of inventory at which new orders must
be placed) and order cycle time in days, both if there is a leadtime (= period between the placement of an order and its receipt
in inventory) or none. An automatically generated chart based on input data can be directly exported to any presentation.
It takes on average 8 hours to develop the model and to create the resulting chart. By using
MyWorkTools.com, you will save a minimum of 6 hours developing the initial template. You will, however, need to input the
annual product quantity demand, product price per unit, and selected costs to calculate the EOQ and Reorder Timing.