How to future-proof your business with connected product data

How can product innovators stay competitive amid global disruption? That's the million-dollar question as the world faces a confluence of supply chain delays, increasing product complexity, and shrinking time-to-market windows.

The transportation sector is facing all these challenges and more – but companies implementing connected product data strategies are gaining a vital advantage, says Joseph Felicelli, director of product strategy at Phillips Industries, a global supplier to the transportation industry

The transportation industry was dealing with disruption long before Covid-19 hit. We’re fast approaching a future where all vehicles will be electrified, intelligent, and increasingly connected – a huge shift for any company, even without the current manufacturing pressures.

What are the biggest challenges for Phillips, and where does product data sit against this backdrop?

The primary challenge we face is that our customers don't fully understand the spectrum of available data and don't really know how they would utilize it.

For the companies buying our products the value proposition is no longer what it would've been 10 years ago—a decision based solely on price, the lowest upfront cost. It's shifted dramatically to asking, "Who can help me preserve my value, gain efficiencies, or improve my cash flow and ROI? That's who I'm going to work with."

That is also getting exaggerated by the shortages and transportation of the chips that go into vehicle controllers. So, a fleet that typically trades their trucks every four years is now stretching to five or six years because they don't have access to new vehicles at the same levels they did before. They must find ways to optimize the performance of that truck over a longer period.

Where do you see the biggest bottlenecks in getting new products to market, and where can efficiencies be made?

It's really in the refinement of design. With the accessibility of 3D printing, we can produce prototypes and put them on trucks in a fraction of the time it took us before. But I think the tweaking and adjustment before you get to a final design has extended significantly.

I also think the overall time-to-market has shrunk. We're probably taking less than half the time to get from concept to actual production than we were even as recently as two or three years ago. But suppose you could get past the refinement stage and get to where the customer truly understands their needs and how to meet them. In that case, you're probably talking about cutting the development time in half again.

So again, back to the data. Understanding how it works, getting ahead of the industry and knowing where it must go next, and pre-emptively developing solutions for problems they don't even know yet.

What about test? As an organization, do you feel you are maximizing the value of your test data, or could you make it go further?

In our case, we're maximizing and trying to find other ways to look at it. Analytics in the transportation industry is still a relatively new concept, [in terms of] really diving deep into the data and understanding what it's telling us. Field testing was always the holy grail for us, while laboratory testing was about proving that it can live in this environment: salt spray testing or reliability and cycling requirements, and basic things.

But now we're simulating conditions. We're manufacturing emergency cases. We're simulating oddball things, asking, "What would happen if something like this were to occur?" We're taking that idea to the next evolution and creating it in a lab environment, rather than potentially destroying a $125,000 commercial tractor just to play around and see what happens. We can futureproof better by getting deeper into the lab testing versus the on-vehicle testing.

I think that will continue to evolve, but it's undoubtedly becoming as prevalent as it was 25 years ago before it was kind of deemphasized. And now, lab testing is really the more important part. Field testing is validating everything you learned in the lab instead of the other way around.

We often see companies hesitate to invest in data, instead choosing to plug skills gaps by bringing new people into the business. What’s the thinking behind this?

I think in young companies you’ll see more investment in people than in data analytics. They view it as easier to bring in an expert who already knows how data works and how to integrate it into your design process, rather than learning it yourself by aggregating stacks of data, coming to conclusions, and testing the theories… But data investment should be a process and a journey.

Read our interactive research report to learn more about implementing an effective product data strategy.