Imagine that you’re tasked with designing a factory, but you don’t know what product or products the factory is going to be tasked with producing. What challenges might you encounter? Well, you might be able to make some basic design choices that adhere to broad industry best practices (adding fire exits, for instance), but when it came time to make any specific decisions you’d probably be at a loss. If you’re used to creating plants for automotive manufacturing, you might be loath to rely on your usual instincts, in case the plant will actually be making much smaller goods; you might not even know how large the facility itself should be.
This might sound like a completely far fetched scenario to most readers—and with good reason! But it’s exactly the sort of situation that many IT leads find themselves in when it comes to Industry 4.0 transformations: they’re being tasked with adopting new technology to drive an ongoing digital transformation, but they have no idea what that transformation is supposed to accomplish. Maybe this is why, according to Deloitte, there’s a significant gap between the 94% of companies who see digital transformation as top strategic objective and the only 68% who view it as a means to improved profitability.
Ideally, those two things would go hand in hand. The fact that this disconnect exists suggests just how challenging IT management in the Industry 4.0 era can be.
1. Develop a Roadmap
It should come as little surprise based on the hypothetical factory we described above that the first step on the road to successful Industry 4.0 transformations is a strong roadmap. To put it bluntly: you can’t use technology to build towards a goal if you don’t know what that goal is. Sure, Industry 4.0 sounds like something that every manufacturer should be striving towards, but there is no one-size-fits-all approach. As such, you need to ask yourself:
- What are the most impactful recurring disruptions that plague my supply chain?
- Where do data silos, planning silos, and instances of shadow IT exist within my operation, and how did they arise?
- What do I envision the future of my supply chain looking like?
- What’s my corporate plan for the next year, five years, ten years, etc.?
Only once you have these questions answered can you really get into the nitty-gritty of which solutions to deploy under what circumstances.
2. Deploy Automation Strategically
Whatever your operational goals turn out to be, it’s a fairly safe bet that any Industry 4.0 roadmap will include some amount of automation—after all, automation is one of the pillars of this new technological paradigm. That said, even with things that are foundational to the concept, you still need to be guided by your own operational goals as much as possible. For instance, if one of your short-term aspirations is to decrease unplanned downtime, you might prioritize the automation of maintenance scheduling for machines based on advanced analytics and machine learning. If, by contrast, you’re targeting faster order fulfillment, you might want to focus your automation efforts on client-facing activities, such that new, custom orders are transmitted straight to the factory floor, for instance. In this way, you build towards added value, rather than innovation for innovation's sake.
3. Utilize a Postmodern ERP Approach
Another area in which all Industry 4.0 deployments will overlap significantly is in the profound importance of visibility and interoperability between different touchpoints on the supply chain and disparate elements of your IT infrastructure. For some IT leads, this results in the temptation to throw money and resources at implementing an overarching, modern ERP system to create intra-operational cohesion. While this seems like a good idea in theory, in practice it rarely pans out. Why? Because inflexible, monolithic solutions often leave niche areas without the functionality they need, leading them to rely on unsanctioned tools to get their jobs done. When this happens, you lose out on the data visibility that you desperately need in order to take advantage of Industry 4.0 technology. The alternative to this is utilizing a Postmodern ERP approach—i.e. a strategy in which disparate IT elements can all be networked together and made interoperable. This gives the entire value chain the functionality it needs without sacrificing data visibility by risking shadow IT.
4. Create Alignment Between Skills and Technology
Of course, one of the reasons that data visibility is mission critical is that advanced analytics, AI, machine learning, and other technologies can now turn information into insights in unprecedented ways. Generally speaking, it’s IT’s job to put the tools in place to make sure that those insights can actually be extracted—to do so, you’ll have to make sure you’re implementing technology that your teams can actually utilize. Adopting a low-level, in-the-weeds analytics solution for a team that doesn’t have existing data science expertise will leave a gap between expectations and reality, for obvious reasons. By contrast, if you utilize something whose user interface only requires some rudimentary coding abilities that your production planners already have, you can easily get to the point where you’re optimizing your production plans on the fly, even in the face of changing parameters and requirements. For most businesses, this is a moving target, meaning that you’ll be constantly looking to shift the balance as people re-skill and develop new areas of expertise. The trick, again, is to be as flexible as possible.
5. Don’t Be Afraid to Adapt
We emphasized the importance of having a roadmap above. You know what the best part of having a roadmap is? Knowing when to ignore it. Industry 4.0 is going to bring untold change to the modern supply chain, giving manufacturers, shippers, freight forwarders, and other supply chain players the power to improve their forecasts, find new efficiencies, automate more than ever before, and mitigate disruptions even before they’ve happened. This general outline is pretty well established, but some of the details are admittedly fuzzy. That means that you won’t always know what your operational needs really are until you see, for instance, how well your technology integrates with that of your partners, or how your much your forecast accuracy KPIs are improving, or what’s happening to your bottom line. You might easily realize that you need to integrate a new tool or module into your ecosystem to mitigate a planning inefficiency or remove a data silo. When things like that come up, don't be afraid to stay agile and think on your feet. Even as you move towards autonomous machine decision-making, it’s solid human decision-making that’s going to form the foundations of a successful IT landscape.
