Figure 4
is an IPA shortage chart. A linear trendline with R2
of 0.67
is used. Weeks A & B, with their high service levels,
both had high
coverage (low shortage). Week C had low service level
and low coverage
(high shortage). We were able to hit our 95 percent service level
objective with about 93 percent coverage, or 7
percent shortage.
Figure
5 is an IPA excess chart. A linear trendline is used,
with a
very high R2 of 0.837. Note weeks A and C, with low
excess and low
MOH, and week B with high excess and high MOH.
Remember from
figure 4 that it took 2.5 MOH to achieve a service
level of 95 percent,
but with 2.5 MOH, we had 115 percent excess
inventory. We could have cut our total inventory in half and still
met our service level objective! IPA can be used to identify excess
inventory and to monitor inventory reduction programs.
The purpose of the parallel study was to see if IPA
shortage was a better indicator of service level than MOH. As seen
in
figure 6,
each
of the seven products in our study had a higher
correlation between IPA shortage and service level than for MOH and
service level. The study
confirmed our hypothesis: inventory profile analysis is a better
indicator of service level than months on hand analysis. In
addition,
we discovered a very high correlation of MOH to
excess inventory.
In other words, within the study range, high MOH
inventories had a greater likelihood of more excess inventory than
of superior service
levels.
The correlation between excess inventory and service
level is not shown in figure 6 because product/weeks with high
excess inventory
also tended to have high coverage and therefore high
service levels. The objective of
inventory management is to achieve high service levels
with less, not more, inventory.
You can implement IPA using Microsoft Excel. Initial
decisions
include the product hierarchy, data source and
reporting frequency. For
forecasts, you need to include both the download date
and the forecasted date. Include any other relevant information
such as the name of the person
responsible for the product. Enter the downloaded actual
and target quantities at the
SKU level. For each SKU, calculate the coverage (or shortage) and
the excess. Sum the target, coverage, and excess quantities.
Coverage is the total coverage quantity divided by the total target
quantity. Excess is the total excess quantity divided by
the total target quantity. You
can use the Excel ChartWizard to create a
combination chart showing
coverage, target and excess. Use a scatter
(x, y) chart,
with linear trendline, to compare coverage to service level.
We have seen that inventory turnover is a poor choice
for internal
analysis because it converts SKU data to cost data.
MOH is an improvement because it uses units, but it still
aggregates shortage and excess
into a single value. Inventory profile analysis distinguishes between
shortage and excess at the SKU level. IPA coverage, or shortage,
is a good indicator of service level. IPA excess can be used to
identify waste and
monitor inventory reduction programs. IPA can be implemented with
Microsoft Excel using downloads from your inventory
system and reported as coverage and excess indexes or displayed
graphically.
The question, "How much inventory should we have?"
became "Which ratio should we
use?" Inventory profile analysis is the answer to the second
question, and it will provide the answer to the first.