Is AI the Secret to Optimizing Your Supply Chain?

(This content originally appeared in the webinar The Rise of AI and ML in Supply Chain Optimization. Click here to watch).

AI – the Hottest Trend in Supply Chain

As the buzz around AI continues to make headlines, many companies are making investments in adopting AI into their tech stack. It’s no surprise as we look back at the history of technological adoption. We’re always enamored with the latest thing, the latest shiny object, as we chase after it with a severe case of FOMO. Right now, that shiny object is AI.

If we look back a few years, the hot topic was cloud. Vendors would lead every sales pitch with, “we’re cloud based,” as if that was all that mattered. But the real question to ask was why there were cloud based. Why was it important? That question was usually greeted with a blank stare.

Sometimes it feels the same with AI.

Go back to the Dot Com days. Everyone had to be a dot com something. A couple years ago, chatbots were the hottest thing. Everybody had to have a chat bot to do something, whether it was adding value or not.

AI is the latest thing, and from a technology perspective, it is something that we should pay attention to. It’s not a fad and it’s not going away any time soon, but let’s put it in perspective.

Artificial Intelligence is Nothing New

• In 1950, Alan Turing published “Computer Machinery and Intelligence,” which proposed a test of machine intelligence called The Imitation Game. (Over 70 years ago, for those keeping count at home).

• In 1952, a computer scientist named Arthur Samuel developed a program to play checkers, which was the first to ever learn the game independently.

• Then in 1955,John McCarthy held a workshop at Dartmouth on “artificial intelligence,” which was the first time the phrase was used, and how it came into popular usage.

Over the decades since then, AI research and development has undergone a steady, but radical transformation, and as Chat GPT, DALL-e, and other popular technologies have shown, AI is now nearly as capable as human intelligence in many ways, and in a fraction of the time.

So why wouldn’t you adopt AI into your supply chain?

There are many ways in which AI can help you optimize your supply chain, saving you time and money, filling gaps in labor shortages, speeding up decision-making, and even bringing to light issues with your systems and processes before you might have spotted them otherwise.

But there are a few questions you should ask yourself before wholeheartedly applying AI to every area of your supply chain. Let’s cut through the current marketing around artificial intelligence and ask some simple but important questions.

Is Your Supply Chain Data Optimized?

Supply chain data is messy. It’s all over the place. CSV files of raw data are not sufficient to teach AI what it needs to know. As the old saying goes, garbage in, garbage out.

Before plugging AI in, you’re going to have to clean up your data. And in our modern, hyper-connected world, your business is connected to many other players in your supply chain, all with different data policies and data hygiene.

At Longbow Advantage, we have helped many customers with WMS implementations. When you’re working on a project like that, even looking only at the data contained within the four walls of a warehouse, it can be eye-opening when you start to work on getting master data cleaned and harmonized so that you can run things properly.

When you explode that to something like AI, you have to be thoughtful and smart about how you leverage it. You need good models, good processes, and good data.

What Use Case Am I Trying to Solve with AI?

A lot of companies get in trouble when they rush to implement a shiny new object like AI without thinking about the use cases they’re trying to solve. They need to consider if AI is the appropriate tool for the job. If AI is a hammer, it’s easy for every problem to become a nail.

There are still many companies in supply chain that use spreadsheets for planning. It seems surprisingly low-tech, but to a certain extent, it works. Of course, when you start sharing a single spreadsheet across different sites, time zones, and even multiple continents, there’s an increasingly large chance of error. As anyone who has ever worked with a spreadsheet knows, it only takes one broken cell to break the entire spreadsheet.

Similarly, if something is broken in your chain, AI won’t fix that. You need to make sure you do the appropriate housekeeping first. AI has a lot of potential, but you have to ask how it can be leveraged in the immediate, not just the short- or long-term.

Are You Implementing AI Because It’s the Hot Tech Right Now?

We’ve had customers ask us if there’s an AI element to Rebus, citing that having AI included accelerates their approval process. And while, AI/ML should be a consideration, we always reiterate that companies should ask the tough question of how and why they should be using AI in a certain space.

AI isn’t fairy dust that will magically solve all your problems.

It’s a tool, like everything else. It’s the right tool for some jobs and for some use cases. But it isn’t the right tool for every job and every use case. In real-world scenarios, sometimes it’s more cost-effective to use the original AI, the one between our ears, before spending the time and resources to connect AI to a system that isn’t already optimized for it.

It’s tempting to want the latest shiny tech. Some CEOs demand automation and robotics because they want people to walk in their warehouse and see a robot zooming around. Forget if it’s productive or cost-effective or actually needed. They want it for the coolness factor. But that’s pretty flimsy ROI at best.

If you haven’t yet, you’ll probably be approached by providers touting AI and how it will make your life and your job easier. Be willing to push back on that. Ask how it will solve your specific use case. Look around to see if there’s a better alternative.

AI is just a tool. From webpages to cloud to AMRs, we’ve learned this lesson before: new tech isn’t always the most cost-effective (or best) way to solve your problem. Sometimes it’s going back to the fundamentals and working on those. You have to crawl and walk before you run.

Have You Considered the Change Management Required?

Do you have a lot of solid data? Is it cleaned up and accessible? Good, that’s the first step. Next step, ask what impact adopting a new technology will have on your change management. Any new technology will always create unexpected issues.

There’s a McKinsey study that says only 13% of the C-suite believes their organization is ready for any type of change management. So what is your organization ready for? A lot of times, we ignore that aspect, because suddenly, we’re rushing to this shiny object.

Maybe you do have a use case that AI can solve for. Maybe you also have the right data and you can start doing it. The other question ask is: is your organization ready for the change management that’s going to necessarily, or invariably, come out of that adoption of that tool?

It’s easy to gravitate towards the shiny, happy answers. It’s harder to step back and ask what the downsides are, what the challenges are. One of those challenges is change management. The other one revolves around sustainability and ESG and AI.

Does Artificial Intelligence Align with Your ESG Program?

ESG is Environmental, Social, and Governance. It’s really all about sustainability. It’s a very important piece to every business plan and should be a consideration when evaluating any new technology, especially something like AI, that will be run at scale.

It’s easy to disregard what it takes to run AI because it’s so easy to run a query in chat GPT. Ask it for best 10 all-time soccer players in the world. It’s really fun, gives us an answer, and we can run it again on something else.

But there’s the reality that you’re asking a bunch of servers, for a bunch of computation to happen. And that takes energy. It takes electricity, water, cooling, etc., it takes all kinds of call outs that are tapping into that ESG world.

Training a large language mode is the equivalent carbon output of driving your car from Earth to the Moon and back. 480,000 miles. A lot of carbon. (Source: https://www.theregister.com/2020/11/04/gpt3_carbon_footprint_estimate/)

Some basic call-outs to AI can use the same amount of electricity needed to run a house full bore for a month.

Supply chain, in many ways, is at the forefront of ESG. Because supply chain is where we can truly make an impact on ESG. Part of that is considering our AI strategy. If we’re going to use this at scale, how much electricity does it burn? How much carbon will be created to run certain queries?

If we could solve a use case with something other than AI, it’s worth considering the impact to ESG that adopting AI would entail. Is it better for the environment, or could you save enough trees to fill a park by sticking with a good, old-fashioned spreadsheet?

We’re going to start seeing more savvy companies thinking about AI from that perspective. And that’s a good thing. It’s just something that all of should take into consideration before we just spout AI as the best solution.

Conclusion

It’s easy to get caught up in the buzz. AI is exciting technology. But the applicability and what it takes to deploy it, you have to get into the weeds on this and make sure that this is the right fit and you have the right data to support it.

If you can control your data, ensure you have good data, good data governance, good data hygiene, then use this AI tool to see what it does. Understand what it’s going to give you as a solution.

When vendors come in and tell you how great AI is and how it’s going to solve everything, don’t be shy. Ask the hard questions. Just because the vendor is telling you from their C-suite down that generative AI is the greatest thing ever doesn’t mean you have to jump on board. Do your due diligence.

Go down to the use case. Ask why this is going to be better than what someone else can give you. Ask why it’s better than the spreadsheet.

Futurologist Roy Amara stated that people tend to overestimate the short-term impact of new technologies while underestimating their long-term effects. It’s known as Amara’s Law. This is what we’re seeing today with AI. There’s a lot of hype about what it can do right now (some of which is wishful thinking) but the reality is, it has the potential to be paradigm-shifting in the long-term.

When it comes to AI (and any new technology), be smart. Ask questions. Use the most powerful AI engine you have: the one between your ears.

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