Assimilating Innovation (Part 3 of 3): Tracking “The Game Changers” (3D by Jon Hansen

Posted on April 16, 2013


Sometimes – and it doesn’t happen often – one’s own rich history and depth of experience in a particular area intersects with emerging market shifts to provide a moment of epiphanic realization.  What makes this the more exciting, has little to do with being right or calling the shot.  It is instead the actualization of the potential to provide a higher degree of service to the industry sectors one seeks to serve.

This is perhaps the biggest reason why, upon reading the Forbes article on London-Based startup 3D, I was instantly captivated by the simple concept of advanced 3D search technology.  Specifically, the ability to visually capture, graphically analyze and reliable identify the right component for the right requirement at the right time from a seemingly infinite number of parts and components.


This is big news folks, especially for those who procure Indirect Material MRO components.

Back in the late 90s when I was called in to rescue a floundering service maintenance initiative with the Department of Defence, the challenge set before me was monumental.  Without going into too much detail, the service provider had contractually committed to a minimum Service Level Agreement or SLA of 90% next day delivery/resolution of service calls.  Up until that point in time they had only been able to achieve a 57% SLA, and were therefore on the brink of losing a multimillion dollar contract.

Right out of the gate I utilized a number of the intelligence tools that I had developed including the impact that Time of Day order placement had on both cost and delivery performance.  As anyone in the service industry will tell you, one of the biggest problems at that time was the tendency for service technicians to sandbag their part orders until the end of the day as opposed to ordering the required part from the customer site immediately following each call.  The negative implications of this practice were enormous in that the later in the day a part was ordered the greater the cost (refer to Dynamic Flux versus Historic Flat Line commodity characteristics).  Obviously meeting the next day delivery requirement was virtually impossible under this scenario – especially when dealing with international suppliers.

Based on the knowledge gained through analyses such as the one above, and the subsequent introduction of new processes, I was able to improve SLA performance within the first 3 months from 57% next day to 93%.  There was of course room for improvement in many other areas.  And this brings us back to today’s post.

The Emergence of The Virtual Trunk

One of the ways that service organizations had attempted to accelerate call resolution was through the stocking of commonly used parts.  This approach was often times extended to mobile units or service trunks managed by individual technicians.

There were a myriad of problems with this practice including inventory management and the costs of maintaining large quantities of products.  Unfortunately, maintaining a unnecessarily high level of diverse service parts became the proverbial crutch for organizations whose real-time ordering capability was inefficient.

Utilizing my parts compression tool I was able to identify the fact that the DND, as well as other clients such as the New York City Transit Authority, were routinely carrying 90% more service parts than what was necessary to meet their contractual commitments.

Given the above, finding ways to reduce the reliance on physical inventory became a primary focus.

One of the reasons for this overstocking of products is that it was not uncommon for a part to have 3, even 4 different SKU’s.  I am talking about a service part number, a warehouse part number and a sales part number.  Reconciling and then managing these variables became paramount to achieving maximum efficiency and savings.

One of the areas in which I saw a tremendous potential for reducing inventory relating to order process improvement was through visual part identification.  If we could somehow remove disruptive variables such as the ordering of incorrect parts, we could in effect build greater confidence in the sourcing process.  While part visualization represented only one aspect of the savings equation, it was nonetheless an important element in the big picture.

Unfortunately back then, PC cameras were less than ideal in terms of resolution let alone being able to efficiently leverage advanced algorithms to the point of accurately identifying the right part.  Remember we are not just talking about taking a snapshot of a part for an electronic catalog.  We are talking about visual 3D identification that would enable a technician to take a picture of the required service part and through intelligent imaging would be accurately identified with corresponding part numbers and other relevant data.

In essence, the technician’s physical trunk would become a virtual trunk with access to an unlimited inventory of parts.

Again, this was in late 1998, and sadly such technological prowess was confined to the realms of science fiction.

Here we are in 2013 and what was once only imaginable is now reality thanks to 3DI founder and CEO Dr. Seena Rejal.

Next week Dr. Rejal will be joining me on the PI Window on Blog Talk Radio to talk about his breakthrough technology and how as reported in Forbes his 3DI solution will do for shapes and 3D models “What Google did for words and text on the web.”


Posted in: Commentary