If you’ve seen any of the recent IBM commercials using IBM Watson to illustrate the power of computer processing you may have noticed they all have a consistent theme, predictive analytics. Several of them even focus on what I’ve previously talked about relative to how data analytics is going to drive the future state of maintenance, repair and operations (MRO) procurement. Namely predicting a breakdown and providing a preventative solution prior to the breakdown. Let’s dissect the commercials below and relate it back to MRO purchasing.

In the first commercial a technician enters a hotel and indicates he is there to service the elevator. The man at the front desk tells him nothing is wrong with the elevator and asks who sent for him. He replies the “new guy”, “Watson”. Of course Watson isn’t a person it’s the IBM Watson computer. Watson’s “analysis of sensor and maintenance data indicates elevator3 will malfunction in 2 days.” This hits directly on something that I’ve spoken about quite a few times concerning advancements in technology and procurement. Which is the analytics behind breakdowns and being able to procure parts prior to the machine actually breaking down, thus eliminating the downtime caused by an actual breakdown.

The next commercial is set within an airport hangar. A group of technicians are chatting about “the new guy...who talks to planes”. The technician then proceeds to ask “the new guy,” Watson a question; “What’s avionics telling you?” Watson replies, “Maintenance records and performance data suggest replacing capacitor C4.” Again, the commercial is focusing on the real life application and power of data analytics within the maintenance, repair and operations space. Accurately predicting when a repair needs to be performed in order to keep things running.

Predictive analytics such as this are going to play a very important role in the future of MRO procurement. Currently, buyers in the MRO space site “spot buys” as one of their biggest challenges. In the industry a spot buy refers to an as needed, unplanned purchase of an item. These purchases occur frequently within the MRO space due to breakdowns. More often than not, the breakdown has a direct effect on operations. Meaning that it needs to be fixed as fast as possible. Therefore, repair parts and services need to be procured as soon as possible regardless of cost. Obviously, within a cost driven procurement organization this is less than ideal. Being able to predict a breakdown prior to it occurring will eliminate much of this spot buying activity and allow procurement professionals to secure items at a competitive price and avoid paying inflated prices for rushed delivery or service.

The next component of the future state is not only being able to predict the parts or repair service needed but also automating the procurement of the items. For example, if you already have preferred suppliers in place for repair service and for the category the replacement part falls within you can easily automate the actual procurement component. Predictive analytics are already telling you what part needs to be replaced, the system can then sync with purchase order automation to populate the actual request with all applicable information. Bingo! Future part repair identified, purchase order issued, purchased order approved and sent, part received and machine repaired prior to breakdown.
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Mike Croasdale

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  1. I looked at the elevator commercial - (1) We put sensors to monitor temperature, pressure and vibration at multiple points. (2) Convey the readings to a remote location (3) Compare the readings against pre-defined criteria to predict failure. Most of this technology is at least 20 years old. What is new here?

    And, why do you want to automate a process that is not routine?

  2. I do not believe as a business manager I would rely on Watson to tell me when something was going to break down. Why? Because these variables have to have been input into its logic. The warranty time, a lengthy period of performances for each and every part in the BOM of that piece of equipment, and finally, the software is not predicting when the purchase of the component needs to occur thus forestalling the downtime. That is where the largest portion of downtime comes from. You do not hold inventory on every single breakable part in a piece of equipment.