A knowledge-based approach drives Ping’s product development and custom fitting operations

A knowledge-based approach drives Ping’s product development and custom fitting operations

Whether it’s the banking industry, healthcare or even sporting goods, big data has a profound effect on how, and why, companies do what they do. Ping Golf, for instance, is a company that relies heavily on data analysis. It wasn’t always that way. Back in the early 2000s, Ping thought of itself as a product-focused company.

“Our effort was on how to make the best products,” says Dr. Paul Wood, Ping’s VP of Engineering. “Now, we’re a knowledge-focused company and the product is a natural consequence of it. We do as much as we can [as an engineering department] to grow our knowledge base by focusing on foundational stuff that’ll apply to any driver or iron and as little as possible specific to a given project.”

Ping’s assembled a small army of 50 to 60 engineers. It’s critical to the common cause that every member of the team be able to access each person’s knowledge. One way the company achieves its goal is through “P3s.” These one-page reports provide the “CliffsNotes” version on a particular subject, summarizing the most important information in a concise and useful way. (There may still be a 50-page deep-dive analysis, albeit with a much smaller readership.) Of course, the documents have to be factually correct, findable and applicable to today’s products. “We have a knowledge library with three-thousand P3s that’s constantly growing,” adds Wood. “Plus, a separate test library with several thousand P3s.” As you’d expect, these summaries cover wide-ranging topics such as the effect of aerodynamics in a driver head, how to adjust bag straps on the company’s old and new designs, material property differences from supplier to supplier, and much more.

The company’s distinctive turbulators, small ridges on the crown of G30 and G400 woods, is an idea that blossomed from Ping’s commitment to data science. A few years ago, the engineering team investigated whether aerodynamics mattered in a driver head. One designer wrote a P3 summarizing work that found the difference to be “a few miles per hour in swing speed” between a highly aerodynamic driver and one that isn’t aerodynamic at all. About that same time, another P3 analyzed multiple software packages and recommended a new tool for use in simulation testing. What follows is the company’s development process and, specifically, how turbulators came to be.

Phase 0 – Idea Review

“Coming up with the idea is one of the easier parts of the process. It’s proving the idea that’s really hard (laughs),” says Wood. A cross functional team analyzes ideas and decides whether to pursue or not.

Phase 1 – Concept Validation

Here’s where the basic research takes place. Ping ran fluid dynamic simulations on drivers and learned ridges “at a certain height, distance from one another, and location on the crown produced beneficial aerodynamic effects.” Improved aerodynamics with a head shape that wouldn’t have to sacrifice moment of inertia was nirvana.

Phase 2 – Concept Integration Readiness

Focus on ways to manufacture large quantities rather than a one-off prototype. The company tested different techniques with suppliers for making the turbulators and conducted durability tests as well.

Phase 3 – Primary Design  

Marty Jertson, product designer in charge of the G30 driver, pulled various ideas from the company’s knowledge library. He had the foresight to incorporate turbulators onto the chassis.

Phase 4 – Manufacturing Readiness

“You’d be amazed at how much testing goes on for just one driver as it goes through our system,” says Wood. He estimates a couple hundred tests, which include performance and durability testing using robots, extensive player testing and extreme temperature durability tests. Ping also runs critical dimension tests to evaluate how well the physical parts adhere to the design specifications. For instance, they analyze the thickness of a driver crown at multiple locations, the shape of a wedge groove, wall thickness the entire length of a shaft, head weight, loft, lie, etc. “The most extreme example is comparing a three-dimensional scan of a clubhead to the CAD model,” adds Wood.

Phase 5 – Production

The company places orders for production quantities with suppliers. The finished product will now be available for purchase.

Performance data influences Ping’s approach to testing, too. Companies will often make a prototype design, then test it, fix what went wrong, and make new iterations until they have a production-ready model. Ping’s modus operandi is to test first, then design. They run experiments with three to four prototypes, or a single, complex prototype with adjustable features, to evaluate an idea as thoroughly as possible. By testing the “whole limit curve,” Ping engineers can learn everything they need, pick the winner and that one becomes the design. “But it requires more testing upfront before you get to a design,” admits Wood. “When we make the final design, we already know it works.”

Ping Chart

The heat maps show the relative performance for two drivers. All results are overlaid on a golf hole. One driver shows a tight dispersion pattern (see area in orange) with the majority of shots in the fairway. The other driver displays a much wider pattern, with several misses in the right rough and woods.

Ping’s formula is working. Its market shares are consistently among the best in the business. The aforementioned G30 driver, in fact, was the top-selling model shortly after it was introduced.

The comprehensiveness of Ping’s fitting process is also the byproduct of data analytics. Ten years ago, dynamic fitting was primarily based on a player’s feel and the club fitter observing ball flight. “When we built our nFlight fitting software [in 2008], we were the first ones to turn a fitting into something visual by overlaying shots onto a golf hole,” says Wood. (Converting data to plots and graphs is more important than ever to Ping.) Currently, all the good fitting companies combine the essence of how a player feels about a club with the data component. Grouping data so the fitter and consumer see dispersion plots, optimal launch and spin windows, and more, helps players understand their potential. Find a PING fitter near you

And, Ping’s knowledge-centric procedures feed into static fitting. Take lie angle, for instance. The venerable color-code chart gets you “in the ball park” for proper lie. Last year, the company revised the chart based on thousands of dynamic fittings recorded with nFlight software during the past decade. After crunching numbers from all those fittings, Ping adjusted the chart to get it “centered where the average person is today.”

There are so many other examples that show Ping’s reliance on data. Four cases that come to mind are tuning the shape of a driver crown to affect how the face flexes, understanding how texture on both the sole and face of wedges affects spin and turf interaction, creating the volcano-shaped ring called COR-Eye behind an iron clubface to control face bending, and the iPing app for putter fitting and practice.

The next time you presume a particular club design or fitting process lacks rhyme or reason, think again.

Learn more at PING.com

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