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Magic Clusters


Demonstrating the Magic Clusters

By Richard Turner | Apr 30, 2020 11:45:05 AM

How to Save Money by Clustering your Parts

There is a magic combination that exists in your part data.  If you take a category of parts that are all described by the same attribute characteristics and combine them with commercial data - pricing, supplier, plants and volumes - you get magic.

It's amazing some of the things you can do when you cluster your data.  You can see a lot of interesting things especially if you are clustering data from different business groups that have worked independently of each other.

Classify your parts first:

It all starts with classifying your parts into categories - you first need to group similar parts from across your entire organization.  Once grouped – you then enrich them with valuable attribute data.  Now determine what are the most important attributes for the parts you are clustering we call those key attributes.  It’s the critical attributes you would use to find the part you need.  Key attributes must have good fill rates and be normalized before you can cluster.

Clustering Process:

Once your parts are classified - you can now cluster.  Start by weighting your key attributes then set your neighbor distance - this is like a proximity to duplicates factor.  The smaller the neighbor distance the more related the items will be in your cluster.  You must iterate the neighbor distance to find the clusters you desire. Once you are done your clusters they should look like the chart below.

Cluster circuit breaker example-1Example of a Cluster of Circuit Breakers - Similar Key Attributes with Different Pricing

Weighting Clusters:

How we produced this cluster above is to weight the attributes in the white columns - AC Voltage, Current, Frequency, Height, Length and Width.  You can see the weighting factors we used in the chart below.  There are about 540 circuit breakers in this batch so it's good to have at last 400 occurrences for each key attribute to get true clusters.  Numeric key attributes are the best for clustering - they make it easy to group very similar parts that have slightly different values.  If you look at the chart above many of the dimensional numeric values are slightly different - they are not exact but very similar.

Cluster weight factors

Example of Cluster Weight Factors

Benefits of Clustering - Price Alignment:

Now comes the magic.  As you can see most of these 30 capacitors are very similar looking at the data in the white columns.  Now look at the commercial data in yellow to the right.  This data shows you the price of the part, what plant buys the part and the vendor that provides the part.  In many cases different plants can be buying the same part or a similar part from different suppliers at different prices.  It’s recommended taking these clusters to your supply chain organization so they can look at rationalizing the spend globally to achieve better price alignment on the similar parts.

Identify the Differentiating Characteristic:

Another thing clusters can expose is which attributes or part characteristics are making the clusters smaller.  What are the differentiating characteristic - that if you standardize - you would have a lot less parts.  We have clustered fasteners for a customer and noticed that if you removed length from the cluster - the clusters became big.  This customer did a good job standardizing other attributes like material and finish.  When we look at the lengths, we noticed many similar lengths were used - from this data it appears there were no guidelines or governance process approving new screws. The key to successful clustering is identifying the characteristic that is breaking up the large clusters - zero out that characteristic for a test - if the cluster sizes suddenly jumps you found the culprit that is driving proliferation.

Cluster Video Demonstration:

We recommend you check out this video below on how Convergence Data’s clustering process works with our DFR tools.  You can see how to find multiple clusters in a single category, and you can see the effects of changing the weighting factors on the cluster sizes.


Topics: Classification, convergence data, DFR, DFR University, Cost Reduction, Spend Rationalization, Value Engineering, Cost Savings, Direct Materials, Part Standardization

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