This paper discusses the impact of item level RFID in inventory accuracy. The paper uses a simple mathematical approach for simulating the effects. The results are shown visually as well as in numeric data.
INVENTORY ACCURACY DETERIORATION
Understanding inventory accuracy
deterioration is a starting point for evaluating impacts of
RFID counts. The best way to find out how accuracy deteriorates,
would be to use real measurements and tests. Also, in most real
life cases responsible persons in stores can give good
estimates and explanations from their experience. For generic
analysis a mathematical model is useful, with mathematical model it
is easy to analyse effects of various parameters into end
INVENTORY ACCURACY DETERIORATION
- MODEL AND EXAMPLE
As an example we use a store that has 1000 items in 1000
locations. An item file describes where each item is. In this item
file there are 1000 data points, each of them is either correct or
In the store may occur 5 different types of incidents that
deteriorate the item file accuracy. These incidents have different
probabilities of occurrence, i.e. they occur at various intervals.
The incidents occur for 1000 item population as follows:
A: 3 times per 1 day
- B: 2 times per 1 day
- C: 1 time per 1 day
- D: 1 time per 2 days
- E: 1 time per 3 days
In the beginning (day 0) the item file has 1000 correct lines.
Based on the occurrence rates above there are 3+2+1+0.5+0.3
incidents = 6.8 incidents during the first day. After the first day
the item file has 993.2 correct points and 6.8 incorrect ones.
In the example only incidents deteriorating the inventory
accuracy are being used. This model includes also a possibility to
model incidents improving accuracy - such as store personnel
placing misplaced items correcly, and incidents effecting both ways
- such as a customer placing items randomly. For understanding and
analyzing the item file accuracy development in a generic level it
is better to focus on some clear incident types.
The model used in this paper is a simple excel chart that is
used for calculating item file accuracy based on the parameters
described above after each day. For simplicity only the accuracy
end of each day is calculated, as if all incidents would occur at a
single point in time at the end of each day. In the model also
the item quantity is static - all items sold are replaced with same
items during the day. In the model incidents and developments
happening during the day are not factored in.
BASE LINE - 100% ACCURATE INVENTORY
With the model and parameters described above the item file
deteriorates as described in the graph below.
Picture 1: Item file accuracy over 360 day period without
For over 1 year period the average accuracy with these
parameters is 37.1 %.
As we can see the item file accuracy deteriorates faster, when
the file is correct. Over a long time period the asymptothically
QUARTERLY 100% ACCURATE INVENTORY
When inventory counts are made quarterly with 100% accuracy, the
Item file starts deteriorating after each count and in the count
returns to 100% accuracy. The result is shown in a picture
Picture 2: Inventory accuracy with quarterly
In this case average annual accuracy is 73.5%.
FULL WEEKLY RFID COUNT WITH 97%
In the first RFID based scenario counts are done weekly and the
accuracy of the count is 97%. Results are described in a graph
Picture 3: Inventory accuracy with weekly full counts using
In this case annual average accuracy is 94.5%.
From the graph the deterioration and correction of the accuracy
level up to 97% can be seen. In this case the first count
actually decreases item file accuracy since in a week it has not
deteriorated to 97%.
The Nordic ID Merlin UHF RFID Cross Dipole is the best suited
RFID reader for this type of use scenario. It has a long battery
life making full counts efficient. Nordic ID Merlin UHF RFID Cross
Dipole has a RFID performance of top level making the
inventory accuracy high.
10% OF THE ITEMS ARE COUNTED DAILY
WITH 93% ACCURACY
The second RFID based scenario is to use more frequent partial
counts. In this case item file accuracy reaches a minimum level.
For example if 10% of the items are counted daily with 93% accuracy
the results are as shown in the graph below.
Picture 4: Inventory accuracy with daily partial RFID
The average annual accuracy is 87.9% and over a long period of
time the accuracy settles to 87.6%.
The Nordic ID Morphic UHF RFID Cross Dipole is an ideal reader
for this kind of scenario. It is small sized, light weight and thus
easy to carry around. Store personnel can carry the device and
perform inventory counts for individual shelves, rounders, display
tables etc. when the work load allows.
COMBINATION OF PARTIAL DAILY RFID
COUNTS AND MONTHLY FULL RFID COUNTS
The third RFID based scenario analysis combines partial and
full counts. In the example 10% of the items are counted daily
with 93% accuracy and a full count with 97% accuracy is performed
monthly. The results are shown in the graph below.
Picture 5: Inventory accuracy when partial daily counts and
monthly full counts are combined.
In this scenario average annual inventory accuracy is
Mathematical model is a usefull tool for evaluating and
visualizing item file accuracy with various RFID based count
scenarios - it can be used as a simple way to evaluate the effect
of RFID count accuracy and RFID count frequency.
Item level RFID enables frequent counts and thus the inventory
accuracy improves. In the example case average annual
inventory accuracy is
- 37.1% with annual count
- 73.5% with quarterly counts
- 94.5% with weekly full counts with 97% accuracy (Nordic ID
- 87.9% with daily 10% partial counts with 93% accuracy (Nordic
- 90.2% with combining 10% partial daily counts with 93% accuracy
with 97% accurate monthly full count
Using the item level RFID has clear benefits in the
example case. Both full periodical counts and frequent partial
counts are usable. Also a mixed model is feasible. Selection
between these models can be based on the other
Per request Nordic ID can produce inventory accuracy
simulations with different parameters such as various RFID
count intervals and accuracies, various deterioration parameters
and various starting positions.