Commonality is often present in most businesses. 3C identifies,
classifies and uses commonality under two different categories:
Commonality at the subassembly level, and commonality at the
purchasable-item level. We will address, in this paper, only the
later category and leave the discussion of the important issue of
commonality at the subassembly level for a later publication. Let us
just emphasize that the same GAGE tool is used to measure,
understand and make decisions related to the MM issues of
Capitalizing on the presence of commonality gives the business a
competitive advantage. A general measure of commonality, however,
had not been devised until 3C literature became available during
3C contains a general measure of commonality called CIND
(Commonality INDex). The CIND is derived from the mathematical
formula of a straight line.
A separate document (pending publication) deals with the details of
the "commonality index" CIND. This future paper is focused on the
commonality aspects from the 3C point of view.
GAGE reports data that is used to build several types of graphs that
characterize the business and its performance when the MRP or 3C MM
methods are used to service the business.
In this section we present a summary of the comparisons obtained
through intensive simulation studies of businesses containing
various degrees of "forecast inaccuracy" or as we call it "System
Customer satisfaction is without a doubt the most important
ingredient for success and its importance is expected to increase as
global competition intensifies and local markets become more
aggressive. Henceforth, GAGE keeps track of all the customer orders
received and prepares the data used for the profile shown in figure
MRP-NSS represents the results of an MRP model running with No
Safety Stock(or protection for MPS variations). Observe that even at
variations as low as 10% (from the quantities and products planned),
this type of system already reports stock-outs of up to 30%.
MRP-YSS represents the results of an MRP running with different
percentage levels of over-planning in all items of the MPS. It can
be observed that the delivery performance improves to the same
levels as 3C. The cost of this protection for MRP is increasingly
higher as more flexibility is demanded. For 3C, the situation is
entirely different. The cost of this protection and increased
flexibility in customer service could be expensive, relatively cheap
and in some cases even free, depending on the CIND value of the
Rotations of Inventories (TOR)
Figure 1 presents the conclusions that we have reached from results
obtained through the simulation models. The inventory turns (also
know as TOR for Turn Over Ratio) are calculated as the annual sales
(shipments) divided by the average inventory carried throughout the
year. Both nominator and denominator are evaluated at material cost
When a simulation request to simulate less than a year, GAGE
performs the simulation as requested and estimates the annual
results with the values obtained during the time requested. We
emphasize that these TOR values represent rotations of purchased
items (raw materials) only. The real TOR of the business, we all
know, should include absolutely all inventories held by the
business, regardless of its place of storage or the age of it.
The horizontal coordinate on this graph represents what in some
businesses is known as "Forecast inaccuracy." While some of these
businesses keep pouring money into fruitless projects aimed at
reducing these "inaccuracies," 3C recognizes that these
"inaccuracies" actually represent the "flexibility" that is being
demanded and expected from our business in these competitive times.
If only this simple fact were accepted, we would really be on our
way cementing the foundations of the systems of the time to come.
The simulation studies performed clearly indicate that 3C can double
the rotations of inventories for businesses facing markets requiring
flexibility of more than 30% (30% forecast inaccuracy).
From an operational point of view, we have recorded a great number
of differences when using 3C comparing it to MRP II (the system used
before by professionals who carried out the introduction of the new
Figure 2 shows two outlines with the functionality and the
environment of the computer system developed to operate applying the
new theory. This system is called HERMES (5) and is capable of
managing a logistic chain in the 3C environment. This system
includes all functionality necessary to obtain the required
flexibility from 3C.
Once the 3C parameters have been calculated and the correct function
of the hole system has been secured, there is a drastic contrast
between the 3C environment and the MRP II environment. In future
publications we will explain in more detail each of these
differences. Basically, the CYCLIC and COMPLEX activities in MRP II
are substituted by others SIMPLE and CONTINUOUS which cause a
double effect: less resources (since it is simpler) and better used
(since it is continuous).
From a flexibility point of view, excellent results are obtained
based on the elimination of the "time fence concept" which is so
decisive in MRP II.
Inventory and Service levels reached at our test-factory are far
better than those of the factories to which they are compared.
This paper has discussed briefly the new MM method called 3C and
have provided comparisons of the performance of MRP and 3C, the
first serious MRP challenger. An important attribute of 3C, in
contrast with other methods, is that it provides advantages in terms
of Customer Service, while producing economic improvements in
inventory investments and revenues with a direct positive impact on
the Company's Botton-Line.
Hopefully our article will also trigger scepticism and interest
among researchers and especially MM professionals. More research
will be needed to test, try and evaluate the new alternatives that
undoubtedly will be lining up as proposed substitutes for MRP.
For balance of this article, click on the below link:
Lean Manufacturing Articles and go to Series 01
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