Procurement teams have a tough time tracking spends on Maintenance, Repair, and Operations (MRO) category. The problem is persistent not only in tracking but also in ascertaining savings initiatives arising out of it. A study conducted by Grainger reported that businesses spent about $110 billion on MRO materials every year. They further stated that out of the total expenditure around $12 billion of items such as light bulbs, nuts and bolts, and cleaning supplies sits on the shelves never to be used. That is a lot of money sleeping and doing nothing for you. The problems with MRO just doesn’t end there, excess inventory and special projects are causing a surge in the MRO materials cost. There surely must be a fix for all such problems today or someday in the future. So what does the future hold for MRO category management?
IBM Watson has been a prominent name in the market which has made strong associations with AI and machine learning. IBM Watson is looking to solve all existing problem with MRO category management with the help of data. IBM Watson can take inputs from a wide variety of sensors, machine, and maintenance data to accurately predict a breakdown so that company can provide a preventive solution beforehand. Such predictive maintenance eliminates the need to spend money on stocking spare parts and tools. Apart from generating savings from inventory cost, it also saves the company the opportunity costs arising from downtime caused by machine breakdown. Additionally, the ability to predict breakdown in advance will eliminate spot buying activities which in turn will allow procurement professionals to negotiate competitive deals. IBM Watson looks to simplify the work of the procurement professionals in the future, with the smooth flow of operations and optimizing spends in MRO procurement. Alongside, reducing the downtime will automatically lead to an increase in overall output and efficiency.
The future of MRO procurement lies not only in predictive maintenance but also in the ability to automate purchases. Procurement teams will always have data on stocks and inventory handy, combining this data with predictive maintenance data, cognitive computers can ascertain if a specific part’s stock needs to be replenished or not. If a need for purchase is identified, then the AI system can automatically screen preferred lists of suppliers automate the actual procurement component. The process is starting from need identification to supplier screening, fetching quotations, analyzing best deals and drafting contract can be fully automated in the future.
The future for MRO category management looks bright, which multiple complexities solved by IBM Watson and procurement automation. It is clear that the future lies within data, with predictive analytics pointing out adverse situations before they arise and providing optimal solutions.