The company had been scrambling for additional revenue to circumvent a volatile economy and demand. Let's say, I had a bit of an understanding of the global steel market from my first startup when we helped the world's largest steel company with revenues of ~$120 billion so we knew where the markets were heading. It was no secret!
After peaking at $65.9 billion in 2012, sales plunged nearly 16% a year later as capital investment by the global mining industry tanked as expected. More sales warnings were on the horizon.
I though it was time to map their defined global driver, which I'm sure a strategy consultant might have created for them, to data-driven goals and actions. I won't share that here but you can see the rationale in the questions I tied up to these business drivers..
Include the overall ecosystem and partner network in your data-driven, AI transformation strategy. In fact, include them all and try to capture both machine-chatter and consumer-chatter in a unified console.
If you don’t have an AI transformation program underway, involve all those 178 business from the start. Ask your CIO to get you a current-state analysis of where you are!
Educate your partner ecosystem so they can effectively use the digital & AI technologies and platforms to make more calls and secure targets within their territories. Just knowing about those IoT-ready billion machine parts is not enough, dealers and consumers want to know only what applies to their domains and territories. In other words, contextualize that chatter!
I think since then a lot has happened within Caterpillar and I'm hoping that they have made some progress by now. My intention was to scratch the surface and see if there was a way to start mapping and identifying potential data-driven MVP (minimum viable AI projects) where machine, and deep learning, could be potentially applied to create services, solutions that could help both bridge the gap as well as build new bridges to the additional revenue stream that the CEO was hoping for.
So there is more than a dime to make out of data, that's for sure!