Focus on a practical approach to implementing artificial intelligence (AI) and machine learning (ML) in your supply chain planning.
That’s the advice from industry-leading experts, as heard in our recent webinar, A pragmatic approach to getting started with artificial intelligence in supply chain planning, now available on-demand. Hosted by Robert Bowman from SupplyChainBrain, guest speakers Brian Tessier from Schneider Electric, Paul Cocuzzo from Merck and Trevor Miles from Kinaxis, discussed what you could do right now to take advantage of this trend.
From finding a practical application that provides tangible value for your business, to ensuring you have the right organizational structure in place, Tessier and Cocuzzo provided real-world advice driven by their organization’s own quests to implement AI in supply chain planning.
“Having AI take a world view based on best practices, and then applying it to your legacy data structures and business rules can give you insights into things you don’t even know are problems for you,” notes Tessier. “We found very quickly we had some very poor assumptions about lead times, both from suppliers and interplant shipments from within our supply chain. Given the number of transaction we do, the complexity of our product portfolio and the number of entities involved, there’s no way we would have found this any other way.”
Twenty-five years ago, I bought my first personal computer. One of the first applications I installed was a cookbook — the killer home PC application of the time. The second application was Quicken to manage my finances. Funds were tight then and I really needed to keep tabs on my spending.
Every transaction was meticulously entered, every statement validated against my records. Then I discovered the calendar function. With the calendar, I was able to schedule my known income (paycheck) and my known expenses (car loan, mortgage, utilities, taxes, groceries, etc.) and Quicken would project my bank balance into the future.
For me, this was game changing!
When making discretionary purchases, I could look at my projection to make sure that if I made that purchase, I would have enough money in the bank, not only now, but at the end of the month when my mortgage and car loan came out. It was my crystal ball, and I regularly asked it questions like:
What if I buy that awesome new 27″ Sony Trinitron television this week, could I still make my mortgage payment? What if I save my money this month? Or don’t go out for supper on the weekend? Then could I buy it?
Today’s supply chain professionals need a crystal ball, too. The only difference is that the decisions are much more complex and far reaching than balancing my finances.
Every business plans, but not every business runs as planned. Delays, shortages, quality issues, catastrophic weather events and fluctuating commodity prices are just a few examples of the exhaustive list of worries that will throw plan into disarray. Achieving a realistic forecast and aligning supply plans is an extreme long shot at best. The best supply chains need to manage business when it’s not business as usual. That’s what sets them apart.
However, even the best supply chains struggle with a recurring issue – data integrity. The alignment of demand and supply is more difficult because most, if not all, supply chains have data integrity issues. That means even if you take away all the supply chain disruptions, your plans are off before you even get started.
Successful supply chain planning starts with data
Setting yourself up for successful planning starts with your data. What could arguably be the single biggest deterrent to undertaking a supply chain planning improvement project is, “my data is crap.” Even though it’s likely true, you’re using the current state of your data to plan, and there’s still value in that. Data integrity shouldn’t be the reason not to take on a process improvement initiative, it should be a part of any supply chain planning improvement project.
Why the data issues?
Like the supply chain disruptions listed earlier, there are just as many reasons why data accuracy is as difficult as maintaining forecast accuracy. Here are the big reasons:
Real, practical steps you can take today to start implementing artificial intelligence (AI) and machine learning (ML) in your supply chain planning
Stop struggling and start implementing! Join us on Tuesday, November 28 at 2pm ET to learn directly from companies like Merck and Schneider Electric in our latest webinar, A pragmatic approach to getting started with artificial intelligence in supply chain planning.
Find out how these companies overcame barriers and put their latest AI and ML innovations into action. Hosted by SupplyChainBrain’s Senior Editor Robert Bowman, our expert panelists include:
- Brian Tessier, VP Global Supply Chain, Schneider Electric
- Paul Cocuzzo, Senior Director ERP Program Integration and Operation, Merck
- Trevor Miles, Thought Leader, Kinaxis
Marketing spin or actually making a difference?
When it comes to supply chains, certain words seem to be bandied about like the ball at a championship tennis match. Back and forth, over and over, these supply chain buzzwords seem to have an endless lifespan. But are they just creative marketing spin (after all, developing them is kind of part of the job), or is what they stand for actually making a difference in your supply chain planning? I set out to find the answer and share my findings on whether they’re all hype, or actually helpful.
Internet of Things – HYPE
Ok, ok, I know a lot of folks may disagree with me on this one. But I stand by my claim that IoT in supply chain planning is more hype than helpful. At least for now. Let me explain.
The Internet of Things, often referred to as simply IoT, is hard to ignore. With advancements in technology and new IoT-enabled devices launched daily, there’s little question as to why supply chain leaders are taking note. IHS Markit Ltd. estimates the number of IoT-enabled devices will surge to more than 30 billion by 2020 and 75 billion by 2025.
According to Gartner Research Director Andrew Downard, IoT enabled devices power supply chain planning by letting you continuously sense, communicate, analyze and act. In one of my earlier blogs, I recapped his presentation at this year’s Gartner Supply Chain Executive Conference, where he noted several real-world IoT examples, including Coca Cola’s Freestyle machines, HP’s Instant Ink subscription model and Tesco’s virtual grocery stores. He also provided commentary on the rise of IoT order buttons, like Amazon Dash, and the impact they’re having on customer orders.
I am reading this absolutely fascinating book “Deep Thinking: Where machine intelligence ends and human creativity begins” by Garry Kasparov, former world chess champion. As the title suggests, in this book Kasparov shares a highly provocative point of view on artificial intelligence and its implications for the human race, with the backdrop of his 1997 loss in a highly publicized chess match up against IBM’s chess computer Deep Blue. The book did make me reflect on my own experiences and views on the division of labor between the machines and human supply chain planners.
Much has been written and said about how machine intelligence is impacting supply chain planning in the form of automating a human planner’s function, with implications on the future of the profession itself. I would be remiss in stating that automation will have no impact on planning profession. Yes! The focus on automation in planning is increasing and will continue to increase. However, this has to take place in the context of empowering planners and significantly augmenting their productivity to handle activities with larger scope and with higher levels of cognition that can drive strategic value for business. When done in a thoughtful and deliberate manner, automation initiatives can significantly benefit planners who are willing to adapt and change, and organizations as a whole.
There’s no getting around it. Artificial intelligence (AI) is here. From self-driving cars to intelligent digital assistants (one of whom I share a name with) to advanced robots working on the shop floor. But conspicuously absent in all this fervor around what’s new and next in AI are details and examples of how to implement these emerging technologies in supply chain planning. Everyone it seems is focusing on supply chain execution.
Don’t get caught in the spin cycle
While examples of AI and machine learning in supply chain planning are few and far between, that doesn’t mean folks aren’t making progress in this area. The trick it seems is not getting caught up in all the hype – and there’s certainly a lot of it. Nearly every company under the sun is touting their software as having advanced AI capabilities. Well it’s time for a little truth – most of those claims are just creative marketing spin (it’s ok, I work in marketing so I can say that!). There isn’t a supply chain management tool on the market today that can catapult your supply chain planning into a realm where humans are strictly hands-off.
That’s in part because AI in supply chain planning is still in its relevant infancy, but mostly because that isn’t the direction we’re heading. The robot apocalypse isn’t here. And there’s a good chance it’s never going to come. The likelihood of robots and smart machines putting everyone in the unemployment line is miniscule at best.
There’s no denying the Internet of Things (IoT) has taken hold of nearly every aspect of our lives. With the number of connected devices estimated to surpass six billion next year and more than 20 billion by 2020, the steady stream of data these devices are providing can easily crowd and clog your supply chain planning processes if you’re not prepared.
Gartner Research Director Andrew Downard addressed the issue during his presentation at the Gartner Supply Chain Executive Conference by outlining three macro trends affecting supply chain planning, chief among them IoT.
He defined IoT as a system of inanimate internet-connected devices linking the physical and digital worlds, and predicted that retailers engaged in IoT partnerships with major manufacturers will take significant market share from their competitors as early as 2018.
When it comes to your supply chain planning, the data sourced from IoT-enabled devices lets you continuously sense, communicate, analyze and act.