Data-driven supply chains continues to be a hot topic, given what’s happened over the last couple of years with the pandemic, lockdowns, transportation woes, container ships held outside ports, war in Ukraine and other issues wreaking havoc. Problems caused by these events are ongoing, but if addressed from a proactive rather than reactive standpoint, there are ways to mitigate their detrimental impact, especially when the analytics and processes become clear.
“What we’re seeing with clients, as we focus on a data-driven supply chain, is enabling data-backed decisions at all levels of the organization,” says Singleton. “Historically, supply chains have been slow to adopt technologies and analytics, but great strides have been made to upgrade systems to capture critical data in the supply chain. Now the question is how to return all of the data we have into transforming and enabling our people to make decisions—backed by that data—to create a proactive supply chain versus a reactive one to market conditions.”
Anticipating supply chain issues rather than responding to them is also a principal means to give companies an advantage over their competitors in terms of not only being able to access an increased amount of data, but having the means to effectively utilize that data in a customized and targeted way.
“Data in general has been exploding for years in all facets and all verticals,” says Abel. “And in the area of supply chain in particular, given the challenges of the pandemic, wars, chipageddon and everything else, the ability to leverage that data and create transparency up and down your entire supply chain, and run analytics on it, is the game changer now occurring.”
But when such compound disruption occurs, creating a battle on many fronts, that’s when the analytics and data become even more important because managing multiple crises at different points of the supply chain requires a more refined, targeted and accurate approach than wielding a blunt object. The ultimate goal is to eliminate the climate for crises before they happen in the first place, but the common denominator is talent and getting the right people in place who are equipped to find answers.
“We tend to focus on the technology, which generally relates to databases, BI and analytic solutions,” says Patel. “All of those things are fairly mature and available, and many companies have implemented them over the years. So we have good technology available and we want to use it effectively. But when we look at supply chain, a lot of data tends to be disparate, and getting that collected in one location or connected so you can do these deeper analytics and visualization across all of those data sets is a hard problem to solve. The people side of things is the hardest element. Far too many people are used to reports, dashboards and doing the basics and I think we need to raise the level of understanding of data and then help them with experts who can answer the hard questions.”
Abel, Patel and Singleton recently spoke with Ken Mingis, executive editor of Computerworld and host of the IDG Tech(talk) podcast, about organizational advantages realized through the data-driven supply chain, and enabling the right people to interpret that data to make more informed decisions.
Here are some edited excerpts of that conversation. Watch the full video below for more insights.
John Abel on data management: Supply chain planning has been around forever. I know my role. I’m used to the rearview-looking aspect. Some don’t know the art of the possible or the potential there is, so it’s not that they don’t know what to do with it, but there’s no one on their team with the skill set to create the art of the possible.
So it’s bringing the skill sets into the organization in order to create. That’s where most companies are currently lagging. It’s going beyond the traditional view that supply chain professionals had of just delivering outcomes based on traditional KPIs. So going beyond that and combining traditional supply chain for information with customer data or with usage, or with customer experience, that’s when you start understanding what plays into your ecosystem of delivering better outcomes that bring top-line revenue or bottom-line cost reduction.
Those are the outcomes, ultimately, that drive most organizations. The one key thing is, if you haven’t already begun on this journey, starting sooner rather than later is key. Just look at the available data and understand that. Then arm yourself with the right talent to understand your ecosystem and how you get the right outcomes.
Manesh Patel on handling expectations: One thing many companies did was manage their supply chains in a standard capability. If we think about MRP, communicating downstream to suppliers and vendors and so on, that’s a complex problem statement in the first place. And I think just doing the day-to-day, week-to-week sort of processes was onerous in the first place and a lot of companies were focused on that.
Then with the pandemic, we all started to react and handle these exceptions, which are much harder to do because they’re all different. And I think we’ve become more adept at addressing those exceptions in the last three years. We still have a long way to go though. Grasping those exceptions has become very critical and one thing we’ve realized is this is not a one-off thing. Whether for a war, climate or something else, it is a reality of our future.
Erik Singleton on data literacy: A warehouse supervisor before might have looked at a dock or floor and said, “Okay, I’m doing good for the day.” But now they can see key metrics and concrete UPHs or KPIs. But how do they action on that? Just having the data is not enough. It’s teaching your people to think with a data mindset and really get them articulate, interpret and analyze data that has a meaningful impact. So there are so many components of just integrating, but then it’s also empowering people to use the information they have.
John Abel on data volume: Data volumes are growing everywhere. The good news is the technology side can handle that. We’re able to process and select large amounts of data but the reality is that people are getting overwhelmed. So how do you turn massive recent explosions in data into value, and what are the analytics you use?
One use case is we’re helping a customer in the sporting world by outfitting stadiums with networking devices to get huge amounts of data and give analytics back, which then they can turn into more value for their customers. The people who can look at the volumes coming in, parse it down and turn it into value are a unique skill set and hard to come by. It’s really about taking large amounts of data in your ecosystem and beyond your ecosystem, and finding what value you can drive by using analytics.