Artificial intelligence could improve the parts purchasing process, making it quicker and more efficient for suppliers for many industries—from automakers to chemical companies, who are still grappling with supply chain shortages.
AI can improve parts purchasing in several ways. First, AI can simulate future trends in the attributes of a part or component, including the components of its costs.
While “should-costs” have long been associated with a given part type, AI can extrapolate these over time and in response to specific events and scenarios.
Second, AI can add a huge number of weighted variables to matrixes used to allocate a given amount of demand across a group of similar suppliers.
“Demand allocation is traditionally a manual task performed using pivot tables in Excel and thus the number of scenarios that can be compared are limited,” explains Edmund Zagorin, founder and chief strategy officer for Arkestro.
The company’s Predictive Procurement Orchestration (PPO) platform leverages behavioral science, game theory and machine learning to help companies make better buying decisions faster across all addressable spend.
Zagorin explains AI can perform this comparison across a tremendous number of scenarios and then stack-rank those scenarios based on business metrics.
Gartner analyst Pedro Pacheco explains there are a few ways in which AI could improve parts purchasing, depending on the purchasing model.
“If we’re talking about supply agreements, AI could allow a more active competition between bidders, achieving a better outcome for the company,” he says. “If we’re talking about regular order placement, AI could automate the whole process, deciding which parts and how many are needed and which supplier would be the best suited, in line with a set of pre-defined criteria.”
He adds supply chain shortages means a company may have to promptly purchase from another supplier to cover for the shortcomings of another supplier.
“Therefore, AI could support companies to more quickly transition to alternative suppliers,” Pacheco notes. “The use of AI in parts purchasing simply allows for greater automation and reducing the chances of error in decision-making.”
Additionally, AI can help predictive procurement teams flip the procurement process on its head by proposing pricing and terms to suppliers rather than collecting quotes, thus enhancing buyers’ level of negotiating leverage using the anchoring effect.
“Decades of scientific studies show that in the course of any negotiation, the final price agreed upon will anchor to the first offer proposed, provided that this offer is based on credible and trustworthy data,” Zagorin says.
This is a phenomenon known as “anchor bias” – it therefore behooves procurement teams to use AI to make the first offer, because this increases their negotiating power.
“For parts purchasing, which often involves a tremendous number of SKUs or rows of data, the opportunity for incremental cost improvement is significant,” he adds.
Zagorin points out that it’s common knowledge that various components of the supply chain are stretched too thin, especially procurement, because there are just not enough qualified procurement professionals in the workforce today.
“That’s why AI can help smaller, resource-constrained procurement teams make smarter decisions, faster,” he says. “It also can shorten the negotiation process while increasing procurement teams’ confidence that they are not wildly overpaying for a product or service.”
From Zagorin’s perspective, by reducing data entry tasks, AI can eliminate steps such as quote creation and quote analysis from the parts purchasing process, thus speeding up the negotiating process.
“By reducing a dependency on manual tasks, AI can free up employees for more strategic activities like building relationships and achieving more advantageous contracts with preferred suppliers,” he explains.