AI Case Study
Infinera will optimise its supply chain management to make better predictions about delivery dates using machine learning
Infinera will a predictive solution for its supply chain management. Based on machine learning, the technology analyses past variability in production lead times and logistics provider performance to offer real-time order promising and scheduling. The company aims to make better prediction of order delivery dates and optimise its supply chain as part of its recovering process of a net loss of $195 million in 2017. The pilot project will go live in the middle of 2018, starting with one manufacturing plant.
Fixed Line And Integrated Telecommunications Services
"One of the areas Infinera is targeting is supply chain management (SCM), where it will be using machine learning to make better predictions about delivery dates by analyzing past variability in production lead times and logistics provider performance.
Infinera’s first supply-chain AI pilot project will go live in the middle of this year, Tuomala says, starting with one manufacturing plant. 'We also want to provide availability information to our sales team and customers for all products before the end of the year.'
The use of machine learning will speed up the company's ability to make scheduling decisions, says Todd Tuomala, the company's senior vice president for information technology. In addition, it will allow the company to consider many more factors than it is currently able to do.
Infinera is using supply chain management technology from Intrigo Systems, in combination with AI technology from Splice Machine."
"Infinera is deploying a cloud-based order promising and scheduling service that enables automated lookups of order promising and scheduling dates across multi-line order quotes. The solution includes a web-based user interface for performing the order promising and scheduling queries, as well as a scalable backend infrastructure to synchronize inventory reservations with ERP order data." (Splice Machine)
Infinera, a "manufacturer of telecom equipment saw revenue drop from $870 million in 2016 to $740 million in 2017. Gross margin went down from 45 to 33 percent. In the end, the company, which employs about 2,000 people across the U.S., Canada, China, India, and Sweden, reported a net loss of $195 million for the year, compared to a net loss of $24 million in 2016. To turn things around, one of the things the company is focused on is technological improvements, CEO Thomas Fallon told investors earlier this year." (CIO)
Pilot; results not yet available
"The Splice Machine OLPP platform provides customers with predictive applications that go beyond currently available offerings, without being overly complex or costly to develop, deploy, scale and operate. Splice Machine’s scale-out SQL RDBMS can run fast OLTP and in-memory OLAP on the same platform, with machine learning and streaming. This hybrid capability enables real-time order promising and scheduling by keeping every inventory change materialized in tables so that inventory checks are single record lookups of availability, versus doing dynamic calculations netting demand against supply. Materializing every change requires a scale-out architecture with OLTP storage representation and indexes for speed."