The Company
PING is a leading manufacturer of golf clubs, golf bags and related soft goods based in Phoenix, Arizona. The company was founded in 1959 by Karsten Solheim, an engineer at General Electric. Solheim first invented a revolutionary putter in his garage in Redwood City, California that was similar in design to a tuning fork, making a distinct “ping” sound when striking a ball. It was patented in 1967, at which time he quit his job at GE and never looked back. PING has been making innovative golf equipment and piling up pro tour victories ever since.
The Challenge
PING’s planning challenges weren’t about a lack of data but a lack of alignment. Demand and supply planning lived in separate systems, supported by spreadsheets and disconnected processes that weren’t always reconciled. That fragmentation made it difficult to see how high-level forecasts translated into procurement decisions and component buys.
“We needed to house them in one central location to be able to see data flow from a high-level demand forecast through various scenarios, eventually to component procurement,” says Scott Niemann, senior production & material planner with PING. “We lacked that visibility, and it was tough getting by without it. We had disjointed systems and processes that we hoped spoke to each other but often didn’t. In those times, it was tough to recognize it.”
Beyond consolidation, PING needed transparency. Demand planners required visibility into how forecasts flowed downstream to supply planners and purchasing decisions. Centralizing planning would create accountability, clarity and confidence across teams.
The Solution Journey
PING met with various industry partners. The goal was to take a high-level forecast and break demand down into assembly planning at its manufacturing facility in Phoenix.
“From the model level, we wanted to break it down further into the component SKU level, resulting in a materials plan,” Niemann says. “We wanted a partner that could show us very clearly what it would look like to level-load our production and bring it down from the model level to the SKU level.”
Niemann says it was obvious in meeting with prospective solution providers that many of them couldn't show PING executives what they were looking for.
The Selection
The PING team winnowed the candidate list down to John Galt Solutions, a company that provides forecasting and end-to-end supply chain planning solutions for more than 6,000 global enterprises and mid-market companies worldwide.
PING did a virtual session and live demo on Atlas, John Galt’s AI-driven software-as-a-service platform that connects and orchestrates a company’s entire supply chain. “It became pretty clear right from the start that they were the partner for us,” Niemann says. “It was the best solution that we saw.”
The ability to tailor views and uses for the various teams across demand and supply planning, based on roles and data access, was a huge plus for PING. “Whatever we came up with, Atlas said, no problem,” Niemann says.
John Galt delivered the right fit for PING by combining flexibility with proven configure-to-order expertise. Rather than forcing rigid item-level planning, the team modeled PING’s unique demand and supply dynamics across multiple levels, from forecast to component procurement.
“We were able to model the model, so to speak, that PING was looking for,” says Matt Hoffman, vice president of product and industry solutions at John Galt. “We build in functionality without having to write custom code.” That flexibility, paired with experience supporting complex bespoke environments, gave PING confidence it could plan accurately across its many product variations without the need for customized software.
The Implementation
This required balancing transformation with day-to-day operations. The first step was understanding what data existed and where. Over decades, PING had built homegrown systems to support demand and supply planning, resulting in critical information being housed in multiple locations.
“The first challenge was just figuring out what we could get our hands on to feed the engine,” says Niemann. “Once we identified the right data, it had to be cleansed and structured to support high-level model forecasting that could ultimately translate into detailed component planning.”
Rather than rushing to full-scale rollout, PING adopted a phased approach. The team began with model-level forecasting, layered in elements of material requirements planning (MRP), and only then tackled the complexity of component mix. That scenario-by-scenario progression proved essential to maintaining business continuity while modernizing processes.
Watch: Achieving End-to-End Planning at Ping: A Case Study
Frequent iterative feedback ensured that the system aligned with user roles and planning realities, creating a stable, scalable foundation for long-term planning.
John Galt supported the effort with its services team, working closely with PING to configure Atlas without custom code. “A big element of project success was the back and forth between our teams,” Hoffman says. “This resulted in clarifying language around roles so we could make the process work for each persona, such as a buyer versus a demand planner.”
The Results Achieved
With Atlas in place, PING achieved its goal of bringing demand and supply planning into a single, connected environment. Forecasting, assembly planning, component mix and material requirements now flow seamlessly within one system. The result is a more synchronized process and stronger cross-functional alignment.
Demand planners are no longer isolated from downstream decisions. They actively participate in supply planning meetings, reviewing level-loaded production plans and monthly buy recommendations. That circular feedback loop ensures that market insight and operational realities are reconciled before decisions are finalized.
“We build the demand plan, flow it through assembly and materials, and our demand planners sit in on supply meetings to give input and final feedback,” Niemann says. “That synchronized collaboration alone has made a big difference.”
The system also supported PING through a recent enterprise resource planning go-live, helping the team stabilize planning processes during the transition. With a stronger foundation in place, PING is now positioned to expand into more advanced statistical modeling, scenario planning and AI-driven enhancements to further refine launch forecasting and capacity decisions.
“Our focus is on using AI to help PING improve launch forecasting, optimize capacity shifts and strategically position inventory,” Hoffman says. “This will enable faster, smarter decisions while improving service levels and reducing overall inventory investment.”
Watch the case study video here.
Resource Links:
PING, https://ping.com/en-us/
John Galt Solutions, https://johngalt.com/
