The 2020s represent a decade of newly realised potential for procurement to drive new sources of value creation, reduce costs across the supply chain, and be a leader of sustainable reform.
However, equally significant pain points and challenges stand in the way. From inflation, rising costs, political turmoil, and an increasingly strict regulatory landscape, to the looming reality of the climate crisis and a widespread skill shortage, procurement leaders have a lot to contend with.
Much of the digital transformation aimed at creating greater visibility and efficiency in the procurement process is, some argue, targeted more at providing executives with glossy dashboards than meaningful ways to reduce procurement workload. The result is that, while both procurement departments and budgets are increasing in size, it’s nowhere near enough to account for the increase in the amount and complexity of procurement work itself.
A recent report by the Hackett Group found that “the procurement workload is predicted to increase by 10.6%.” This figure reflects the broadening of priorities with only a little increase in headcount and operating budget. As a result, McKinsey analysts expect the industry to suffer from a productivity gap of 7.4% and an efficiency gap of 7.8%.
Some argue that CPOs could face the issue by working smarter not harder, leveraging artificial intelligence (AI) to power new technology applications like predictive procurement orchestration in an effort to increase efficiency and circumvent risk before it appears. The Hackett Group’s report argues that procurement is likely to “rely on technology and digital transformation to close the gaps” and “do more with less through better intelligence and increased speed, customer-centricity, and competitive advantage.”
What is predictive procurement orchestration?
Using AI and machine learning, predictive procurement orchestration analyses large amounts of data to identify the most successful purchases in an organisation’s history from the companies with the highest quality products and services.
A predictive procurement orchestration system then uses that historical data to optimise an organisation’s procurement strategy, described by software vendor Arkestro as “a combination of behavioural science, game theory, and machine learning that helps procurement teams predict and win faster value across every category of addressable spend.”
In short, AI and machine learning combine to predict which outcomes will be better for the business. The technology then uses human behaviour and game theory to create competition among suppliers. The process then encourages these suppliers to engage more closely with the company by means of dynamic feedback. Lastly, an embedded intelligent platform can make resources go farther without increasing the number of employees needed by the business.
In field trials of its own predictive procurement orchestration system, Arkestro reportedly achieved over $8 million in savings for one company, while another achieved 88% savings on individual purchases.
