Is Your Supply Chain Ready for Machine Learning?

Is Your Supply Chain Ready for Machine Learning?

Businessman holding human brain on his hand with logistics symbols aroundLet’s phrase the title of this post another way: are you ready to leverage your operational data into improved predictions and forecasts, so that you can save costs and respond better to disruptions? We’re guessing that most of you would answer “yes” to that question with little hesitation—but you might be wondering whether your existing supply chain infrastructure is prepared to handle it.

It’s a fair thing to worry about; not every supply chain is in a position to leverage the power of machine learning at this exact moment. But the capabilities that machine learning offers (predictive machine maintenance, improved demand forecasts, improved transportation forecasts, etc.) are rapidly becoming non-negotiable. Within a global supply chain that’s been rocked by the coronavirus crisis, you simply have to be responsive and adaptable in order to survive. Machine learning—and related technologies like time series algorithms—can make that possible.   


Data Quality and Supply Chain Integration

The above leaves us with somewhat of an open question: how do you tell if your supply chain is ready for ML integration, and how do you get it ready if it isn’t already? The first consideration in that regard is: data. Machine learning algorithms “learn” by taking in gargantuan quantities of data (usually in amounts that a human being couldn’t realistically grapple with) and identifying and refining potential patterns that wouldn’t be visible to the naked eye. In this way, an ML algorithm can create a more accurate demand forecast than a human planner—which can, in turn, help to power improved S&OP and demand-capacity planning, potentially reducing your costs as you decrease safety stock.

In order to feed the algorithms enough of the right data, you need to gather high-quality, relevant data from all touchpoints on the value chain and store it in a centralized way with decent data hygiene. The first step on the road to making this happen is to prioritize supply chain integration. Simply put, if your TMS features modules that integrate with the IT your shipping partners are using, you can easily gather data not just from your own logistics network, but from those of your logistics providers. Likewise, if you’re able to use some shared technology solution with your suppliers of raw goods, you can collect large caches of data that will be relevant to your inventory management workflows. Even within the boundaries of your own corporate IT ecosystem, prioritizing integration can go a long way. If you’re able to adopt a suite of solutions designed to be interoperable—thereby connecting, say, sales planning to inventory planning to transportation management to production scheduling—you can reduce and remove data silos in order to create a comprehensive picture of your value chain. If your solutions all remain disconnected, silos are a virtual certainty and any attempts to integrate ML algorithms will likely fail.


How the Cloud Powers AI Deployments

Okay, you’ve figured out how you’re collecting your data—but how are you storing and processing it? Given the current trends in the world of supply chain technology, we’re going to go out on a limb and say that, if you want to get your supply chain ready for machine learning and its attendant benefits, the answer is the cloud. Cloud technology has the benefit of offering connected, decentralized architectures right off the bat, which means that you can more easily store your data in a way that’s accessible across touchpoints. If, for instance, you have multiple production plants in different regions, a cloud deployment would give you the ability to make data easily accessible between plants. In this way, supply chain planners could begin to get more strategic about network utilization (e.g. adjusting production ratios at the various plants based on the geography of incoming orders in order to simplify transportation management). As a bonus, this can also help you to reduce your internal IT costs by a considerable fraction.

Once you’re up and running in the cloud—with end-to-end supply chain integration providing you with high quality data—it’s much easier to find and implement a cloud-based, ML-powered solution that can extract value from that data. Again, because of the decentralized nature of the cloud, your machine learning algorithms can pull relevant data from multiple touchpoints with comparative ease—and they can scale up their processing capacity as needed in order to perform calculation more rapidly. In this way, you’re able to create an environment where planners can open up their various planning modules, generate forecasts based on high-quality, high-velocity information, and share those forecasts across the planning chain to get everyone on the same page.


Do You Really Need Machine Learning?

The combination of factors that signal ML-readiness that we discussed above (i.e. supply chain integration and cloud computing) might seem like daunting propositions to some. But at this point, they’re both becoming increasingly necessary even outside the context of ML, AI, and other value-additive analytics technologies. Lack of integration leads to lack of visibility, which in turn leads to the kinds of single-sourcing debacles that debilitated so many supply chains earlier in 2020. Old school, on-prem technology deployments create inflexible workflows with poor data access, resulting in slow responses to change and disruption. As the global supply chain grows more volatile, these kinds of disadvantages will become untenable.

Luckily, preparing yourself for machine learning doesn’t have to be an uphill battle—you just need to start with an IT audit that identifies areas where legacy IT can be upgraded to the cloud. Your whole ecosystem doesn’t have to cut over all at once, but when you’re selecting new vendors, make sure they can deliver on your integration needs within a cloud environment. In this way—over time—you’ll prepare your supply chain to take advantage of machine learning technology. From there, it’s just a matter of letting the algorithms do their jobs: i.e. create better demand forecasts, predict machine downtime and other disruptions, create smarter transportation pricing estimates, and generally give you the forward-looking information you need to optimize costs and increase resiliency via proactive planning.