Systems got added region by region, asset by asset, usually to solve whatever problem was in front of someone at the time.

The future of manufacturing may be defined less by where production happens and more by an organization's ability to adapt, scale and respond wherever it does.

How is artificial intelligence making its mark on supply chain planning?

For many supply chain organizations, putting artificial intelligence to work is proving to be a much bigger undertaking than simply adding a new piece of software.

PING, a leading manufacturer of innovative golf equipment and accessories for everyone from tour pros to weekend players, partnered with John Galt Solutions to strengthen its supply chain planning capabilities. Through implementation of Galt’s Atlas Planning platform, PING was able to create a fully integrated demand and supply planning environment. 

Supply chains by their nature are complex, interconnected systems where the source of a disruption is rarely obvious at first glance. What might take a team of analysts days to unravel can be surfaced and contextualized far more efficiently through GenAI, freeing up planners to focus on response efforts rather than diagnosing the core issue.

Transportation costs are climbing, putting pressure on companies to find savings wherever they can. But the most obvious place to look is the transportation budget itself, where an organization can negotiate carrier rates, rebid contracts or shift freight to a cheaper service.

For today’s Chief Supply Chain Officer, the challenge is no longer simply creating a better plan. It is building an operating model that can continuously sense, reason, adapt, and act as conditions change

Using a practical, nine-step framework and real examples from demand and supply planning, including AI-generated scenarios that surface capacity risks before a Supply Review even begins, JT will show what's possible today, what's still emerging, and how to avoid the governance pitfalls that derail AI adoption.