Toshiba: Using AI to improve the procurement process

How one buyer at Toshiba looked for a better way and improved the procurement process

With so much pressure on procurement teams to find value in a changing global marketplace, staff are always looking for new ways of doing things, and challenging existing work processes.

At Toshiba, one person keenly aware of challenges facing buyers is Seita Shimizu, who works in procurement at Toshiba Infrastructure Systems & Solutions Corporation.

Toshiba Infrastructure Systems & Solutions Corporation wide-ranging businesses include communications solutions that support the mobile networks essential to modern life. Among its products are equipment for local 5G networks and repeaters that realise use of mobile phones in areas with weak signals, and Shimizu’s job is to procure their cable harnesses: assemblies of cables that supply power or send signals, with already soldered connectors for connection.

Seita Shimizu, Procurement Group 1, Fuchu Procurement Dept., Procurement Div., Toshiba Infrastructure Systems & Solutions Corporation

“I’ve been a buyer of parts for manufactured products since I joined Toshiba,” explains Shimizu. “We get internal orders, ask our suppliers for quotes, and scrutinise the specs and price. If they look good, we place the order. We also coordinate delivery, as failure to get the parts in time brings production lines to a halt. So buying is all about confirming the product, setting up the delivery day and making the buy. That’s what I threw myself into in my first year of work.

As he entered his second year, Shimizu took another look at the procurement operation and realised just how individualistic the process was, and that price decisions differed from person to person.

“Experience is a big factor in price decisions,” he says. “A buyer like me, with limited experience, can sometimes struggle to make decisions. I started to wonder if we could create a system that used past data to determine appropriate prices, even with orders for new product specifications, with no precedent.”

From that beginning, Shimizu went on to develop a completely novel pricing tool based on AI machine learning.

The products Shimizu procures are usually custom made, and it is rare to reorder parts with the same specifications. To meet the specification, orders of such parts are placed by fine-tuning the size of previously procured one. Naturally enough, it’s the kind of work where extensive experience is a big help—and where individualistic decisions are a cause for concern. Say someone spends five or 10 years cultivating expertise. If they are replaced, the new person will have to build up a knowledge base from scratch.

“That is a real waste,” says Shimizu. “The way I saw it, it would be a real asset for the entire company if we could use a common standard for deciding prices. I wanted to develop a pricing system where you could enter variables like cable harness length, type of connector and quantity, and other conditions, and get a price estimate,”

Shimizu did just that, applying his training in data analysis and testing the tool’s practical application. He took know-how and experience gleaned from work processes, and successfully advanced his desire to turn tacit knowledge into explicit knowledge, an asset that enhances the quality of procurement operations.


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