Business processes are myriad in number. They are also complex, requiring smooth trajectories, from procurement to payment, from order to storage, and tracking payments to and from accounts.
Humans are fallible. Machines, on their own, are limited. Tie the two, though, and it is possible to boost efficiency, save money and even profit where once there was wasted effort and material.
The rise of Big Data means that firms can use technology to pinpoint weaknesses in workflow that stretch across back-office functions, in invoicing to receivables management, and up and down supply chains as firms interact with vendors, timed deliveries and shipments. One firm, Celonis, uses Big Data and machine learning to help firms find heretofore hidden pockets of process weakness.
In an interview with PYMNTS, Alexander Rinke, Celonis cofounder and CEO, told PYMNTS that data mining, which works on a firm-by-firm basis, is especially useful in monitoring high-volume processes and, with a supply chain focus, “can see how a firm aligns with its vendors” and specifically can address the multiple touchpoints that exist, from procurement to payment and any number of other business practice cycles.
The use of data mining, sourced from existing tech and ERP systems, said Rinke, can replace the need for additional sets of eyes, or consultants, to watch, monitor and posit fixes for process bottlenecks. In one example, he noted that repeated interactions with a particular vendor, who may have been late in the past with shipments, can be flagged as a potential source of slowdown, and backup plans can be fashioned on the fly — especially useful in the case of a restaurant, where timely delivery of foodstuffs is crucial. The systems can also track if processes have been duplicated, say, with multiple invoices or payments. As businesses become more global in nature, said Rinke, just-in-time processes become crucial to keep business flow optimal.