PROCUREMENT TECHNOLOGY
Historically, procurement has applied predictive tools to 10-20 % of the most critical assets because that has typically been the easiest business case to prove. If the most critical equipment failed, production could stop, or safety could be at risk.
As sensors become cheaper and easier to deploy, companies are now starting to look at the other 80-90 % of assets. Organisations are realising that deploying more predictive technology will lead to more learning and more data.
Kevin Clark, Vice President and Head of Industrial Strategy, Asset Management at Brightly( a Siemens company), says:“ Predictive analytics has moved away from solely trying to prevent machines from failing, and instead, it’ s becoming a way to help organisations understand everything from energy consumption to inventory planning. That’ s making a huge impact on organisations.”
While predictive analytics has crossed over the experimental phase, Mita Gupta, EVP and Business Unit Head at WNS, points out that it hasn’ t become mainstream either. She argues that it is important to separate the technology from organisational readiness.
Mita says:“ The technology has matured significantly, and the capability exists today to combine internal and external data, monitor supply markets continuously and generate predictive insights in real time. In that sense, we’ ve moved well beyond experimentation.
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