…And Why Good Data Paves the Path to AI for Supply Chain
The importance of good data for business operations of all kinds has become a kind of truism in our technology-enabled world. Even those of us who don’t have daily, direct tasks involving data, nonetheless feel its reach and pervasiveness. And we feel it even when we’re not at work: whether we’re driving to a new location with turn-by-turn advice from our phones, ordering something online, or visiting the doctor’s office, the ubiquity and utility of data is simply part of the fabric of modern life.
And that kind of omnipresence has a paradoxical effect on the way we think: at some unconscious level we understand just how pervasive data is; but as human beings do, we also tend to take common things…omnipresent things…for granted.
Sure, we know that data must be clean, must be up to date, must be normalized… but our grasp of why this is all necessary may not be as specific as it could be. That’s why we wanted to get down to the details for at least five serious knock-on effects of data quality in this short blog piece — and keep our focus on supply chain operations.
Our short and sweet thesis is that data quality for supply chain is “even more important than you think it is.” And our goal is to show the reach and variety of effects data-quality exerts on supply chain, so we all might never take data for granted again.
How AI Fits In To This Conversation
One more key goal, a little bit more subtle, is to make the connection to AI that this series of blogs has been focused on — and also to make that connection in context of all the other key issues that good data drives. One key takeaway is seeing how serious AI plans should also help you shake out related data-reliant projects and issues as you progress toward completion. Because there’s a good chance you’ll have to conquer all of the other four data-driven issues & milestones on your way to any major AI implementation.
Without further ado then, let’s take a look at the big five on this list:
When we think about accountability, obviously, data reliability plays a crucial role. Accountability is tricky enough as it is: without good data, it’s basically undoable.
For supply chain, accountability has two main venues:
Internal Accountability: Supply chain is almost always (much) more decentralized than other functions, like planning, manufacturing, logistics, etc. It’s not just that the supply chain teams aren’t in the same building — often as not, they’re not even on the same continent. Staying up to date on priorities, next steps, and deadlines requires efficient, effective collaboration, and a bedrock of transparent, shareable, and exactly comparable data.
Partner Accountability: Here’s the truly daunting part: internal decentralization is less than half the story. 90% of supply chain operations are being executed and managed outside your company’s four walls. The same critical data needs apply here as well: perhaps doubly so, because while you’re protecting your best interests, all your partners are undoubtedly protecting theirs.
Supply chains are built through partnership; partnerships that work smoothly are built on trust; and trust just can’t be built without good data.
2. Being Proactive
Supply chain is increasingly a game of calculation, anticipation and decisiveness. We’ll go all the way out on this limb and say it loud enough for the neighbors to hear: there really is no other business function where being proactive is more important. And this is particularly true in the climate of continuous disruption and urgency that we’re operating in right now. Today, availability and regular use of real-time data is helping some avoid potential catastrophes and — often in the same stroke — creating new market leaders.
3. Continuous Improvement
One of the potential lessons of 2022 is that sometimes, even outstanding performance in the face of strong headwinds…isn’t enough. For every business, there’s now an existential connection to continuous improvement. (Some would say this has always been the key to growing a great business, and we wouldn’t argue.)
What we also saw this year is conclusive proof that supply chain must be a part of any continuous improvement blueprint. And we also saw just how difficult sustained improvement can be for a supply chain continuously riled by powerful, external, global factors. With supply chains as decentralized as they are; with so many systems and teams, it’s just impossible to identify trends and common root causes without good data. Luckily, modern cloud technology provides the means to bring these teams together under a common data asset, concepts, and process protocols. This is how leaders are leaving behind “continuous repair” and moving into bluer seas and greener pastures — like Automation and AI.
According to a recent survey by IBM, 72% of CSCOs expect their processes and workflows to be automated over the next three to five years. Automation requires good data to (i) trigger actions at the right time, (ii) follow the appropriate conditions, (iii) execute the appropriate outcomes.
Automation also should “inherit” the other keys we’ve identified thus far: It has to be designed to support accountability; it must contemplate and enable the need for proactive interventions; and it also has to be flexible enough to be a contributor (not a hurdle) to continuous improvement.
This is a kind of “Jenga tower” of interdependence — not a bad metaphor for the supply chain itself. And as we’ve seen here all the blocks are essentially made of data.
What sits at the tippy top of this tower… for some, perhaps many, it will be AI.
5. Artificial Intelligence
Are you part of the 80% of supply chain leaders who plan to use AI to improve your operations?
If so, your team will soon be having a very intimate experience with the myriad ways that AI truly requires good data — and as much of it as possible — to properly do its magic.
Most AI’s work on some version of pattern recognition, and data is where those patterns are discerned and acted-on. At its best, AI is a complementary capability: it does a supremely good job of things (most) people don’t do very well. It ignores emotion, hunches, and habit; and sees clearly through all of those distorting lenses to fully comprehend what data is really saying. Added to the other four keys on this list is a last, critical task for people who want AI to help them continuously improve their supply chain operations. That task is preparing the processes, people, and policies for your data in order for AI to make the most of it.
Want to Learn More?
Wondering how AI can improve your supply chain? Look back to the first blog in this series here, and stay tuned for our next installation: A Closer Look at the current and emerging applications of AI in Supply Chain.