Gartner SCC - Day 1

Supply chain conferences are energizing for me, because I get to have lots of conversations about what folks are working on all around the world. So with the first day of the Gartner Supply Chain Conference behind us, here are a few of my key insights.

1) Blockchain. We know that blockchain is coming at us, but we're still struggling to clearly explain why it matters, and which supply chain management problems it solves. In terms of Gartner's Hype Cycle, it feels like blockchain is dropping down the Curve of Disillusionment. And that is actually good news, because it means we're getting closer to the Plateau of Productivity. Given the speed at which blockchain is moving through the Hype Cycle, we're faced with an interesting challenge: it's probably too soon for most of us to run projects that will deliver clear value from blockchain, but if we wait until the technology is mature then we're likely to have missed the early opportunities. So, small scale experiments and "Skunk Works" projects are probably the way to go, right now.

2) Artificial Intelligence. There are lots of names for the digital tools that can learn and make decisions, and these are now converging on the supply chain. We're not really talking about Big Data or the Cloud anymore - they're too ubiquitous. Now we're having conversations about what to do with all of that data in the cloud, and who owns it. The IBM Watson Supply Chain team shared the use case of using AI to create a supply chain control tower. (See Chapter 18 of Supply Chain Management for Dummies). The data is out there, but considering the effort involved in connecting multiple ERPs, TMSs, SRMs, etc., I can see a "plug-and-play" approach being a compelling use case for AI. The challenge - getting access to the data, and getting permission for everyone to use it.

3) Analytics. I've been thinking that analytics is a three-step journey. From Descriptive Analytics, to Predictive Analytics, and finally to Prescriptive Analytics. Thanks to a hallway conversation yesterday, I am now convinced that there's a fourth step - Automation. Once the computers are good enough at making recommendations about what we should do, we'll reach a point where we'll just let them do it. They may not be 100% right all of the time, but they'll be good enough to provide a marginal improvement. They only need to be 1% better, anyway, and besides - machine learning is basically the computer equivalent of continuous improvement. (By the way, this conversation also reinforced my belief that we need to start looking at forensic technologies like the stuff David Kovar is working on at Ursa.)

4) Face-to-Face Matters. There really is no substitute for the experience of standing face-to-face with other people with whom we have shared interests and challenges. There's a higher level of trust and candor, and that means we share more information and learn better and faster. As much as I love the idea of working from my home office and having all of my meetings online, the reality is that we need to gather up as a community once in a while in order to share experiences and build relationships. So I'm not likely to lose my airline status anytime soon.

About the Author: Daniel Stanton, PMP is President of SecureMarking, which uses advanced technology to protect supply chains from counterfeiting. He's the author of "Leading Projects" and "Business Acumen for Project Managers" on LinkedIn Learning, and of Supply Chain Management for Dummies from Wiley.

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