Artificial intelligence (AI) as a technology had lay people associating it with the chillingly villainous HAL 9000 or the indestructible and highly destructive Terminator! Fortunately, the negative press hasn’t stopped the continued interest in developing the necessary algorithms to create machine learning technology that drives cars on busy streets, or plays winning games of chess against redoubtable grandmasters like Garry Kasparov and Go against Lee Sedol. Today AI is all about capabilities that enable collaborative scenarios and improvements to human efforts that were the stuff of dreams until now.
As tracking data becomes a way of life, algorithms that can make smart decisions are advancing by leaps and bounds to take over tasks that were always the prerogative of the human mind until recently. Machine learning has enables machines now to diagnose cancer, drive and do many of these tasks much better than humans could! Aye, there’s the rub; but we are not complaining because we do want to improve all we can in our quest for the bigger, the better and the most perfect results, at all times provided the human mind controlled the inputs and enhance its abilities to arrive at the right conclusions.
How does it work? Data is accumulated over time and analyzed for opportunities and insights. The speed of the process improves as well as its scalability, as the process improves with each strategic decision made. When the capability of the machine to execute a well-defined task or solve a well-defined problem are combined with the AI’s capability to mimic the human mind, the results can go beyond the immediate scope of a task and can deal with ambiguity, connect disparate pieces of information to suit broader contexts and face totally new challenges.
AI as a technology has finally arrived at a stage where the machines need not be programmed or configured through human intervention. Instead, they learn by themselves and automatically program themselves to improve, change and learn to exceed their own capabilities. A machine with AI goes through problem definition, signal processing, pattern recognition, abstraction and conceptualization, analysis and prediction, where it keeps redefining the problem in iterative steps to achieve greater insights which enable it to analyze, forecast, optimize and recommend better options. When the inventory forecast points to an increased demand, corresponding changes are automatically made to inventory levels and pricing is optimized – all in one fell swoop.
Look at the benefits that could accrue from employing the predictive powers of AI to the process of cognitive procurement, which is defined by Spend Matters as:
AI can help the procurement process to turn into a prescriptive process that uses actionable business intelligence and data to take strategic initiatives and business decisions to influence events to be favorable to your interests. For example:
This has brought procurement a long way away from the days when it used to process orders using paper and kept accounts using ledgers. But how efficient and effective the program will turn out to be depends on the initial procurement function and the maturity levels of its process.
DCR has been investing in machine learning for years and has rolled out several first-mover advantages within Smart Track that no other Vendor Management System (VMS) can boast. If your VMS provider isn’t able to offer you prescriptive analytics, perhaps you should switch to one that can.
Email us at firstname.lastname@example.org to see our full capabilities, including our prescriptive machine learning capabilities, and how we can help your organization transform your procurement processes.
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