The goal in manufacturing has long been optimal operational agility. That seemingly unreachable objective means peak levels of both efficiency and effectiveness, where you only produce what the customers want, when they want it – zero waste, 100% service level, at the lowest cost possible.
While manufacturers have been striving for that goal for years, the dream has been re-fueled with the hype around Industry 4.0.
Industry 4.0 is manufacturing driven by data, where processes are so automated and autonomous that the cost of producing a single unit or a run of a hundred is the same. When most people think of Industry 4.0, they think of using technologies such as the Internet of Things, Artificial Intelligence and Big Data.
Unfortunately, a lot of mid-sized manufacturers feel discouraged from pursuing this because the whole idea feels unapproachable – and expensive.
However, Industry 4.0 is a philosophy, not a well-defined “all-or-nothing” methodology - just like Lean Manufacturing.
Think about when Lean and the Toyota Production System (TPS) became prevalent and how they have been used and implemented the past 60 years. Even today, Lean is still a philosophy – you will never find a completely Lean company, with all its tools, concepts and philosophies. But just as manufacturers for decades have adopted the Lean tools and processes that made the most sense for them (5s, Kanban, JIT etc.), manufacturers today don’t need to implement the full Industry 4.0 package to have a significant impact on their operations.
One of the keys in both Industry 4.0 and Lean is continuous improvements – so it’s not an “either-or,” “you-do-it-or-you-don’t” scenario. The greatest enabler of Industry 4.0 is the Cloud and the rapidly falling cost of the data and automation-related technologies offered in the Cloud. This means companies of all sizes can now embrace the next generation of manufacturing without going all-in. The tools currently being made available via simple Cloud subscriptions eliminate the need to invest in expensive and inflexible hardware (and software for that matter) and allow even small companies to initiate inexpensive proof-of-concept projects.
What we see from the companies that seem to be leading the mid-market is that they are starting to take advantage of new technologies to support strategic processes or tackle specific problems – and yielding results that will give them an edge in the long run.
From my experience, the most popular starting point of the journey seems to be predictive maintenance. The operational risk is low, and the initial investment may be lower, but the impact is high. Internet of Things (IoT) devices in form of simple sensors can help you collect critical data, and easy to deploy Machine Learning (ML) tools are able to pick up trends and trigger alerts or even automated actions in your maintenance system. Imagine your system telling you that a part is about to fail and stall the production line. Instead, you can replace the part before that happens. These types of applications can be a steppingstone to building trust within your organization that technology can drive better decisions. In fact, you could take the data that you already have in your system and use today’s cloud-based analytics platforms to drive even more improvements in operations.
From an operational perspective, another area that we see gaining traction is quality event predictions and trend analysis. We especially see this trend when we talk to companies in the food and pharma industry, where quality is essential, and a safety event has huge implications from both a cost and reputational perspective. Live data is being compared with normalized historical data from the ERP system or old legacy databases and via machine learning models to help the quality assurance (QA) team proactively address high-risk production runs.
We also recently spoke with a mid-sized manufacturer that is taking a more radical approach to implementing aspects of the Industry 4.0 philosophy. They are evaluating the use of data and AI to drive more precise predictions of how many parts – and at what specifications – they need to meet frequently unpredictable demand in their markets. That will allow them to ramp up and down production as needed, rather than stock a bunch of parts just in case.
They are also looking to test new technology like additive manufacturing/3D printing in work cells, and combined with the robust predictive analytics initiatives, they estimate they can reduce the cost per part by 30% to 40%. The process will also significantly reduce production time and inventory carrying costs because they’re not producing parts that their customers don’t need.
The vision behind the investment is that it’ll make them faster than the competition, save them significant money and increase efficiencies, as they are no longer constrained by the number of people in their factory.
I foresee that the focus I outline above will fast become a need-to-have, rather than a nice-to-have or something that will happen in the future. If you ignore this trend, the gap between your operations and those of your competitor will quickly widen. But those that invest in technology that drives these continuous improvements will gain valuable knowledge more quickly and be able to apply the new know-how to more and more processes, making it tougher for the competition to catch up over time. Just as in Lean, incremental improvements are often less risky and less expensive.
From a high-level industry perspective, there is a great chance that we will see the mid-size companies that embrace these opportunities become more agile than larger corporations, by adopting the right technology more effectively and way faster than larger enterprises. These companies will disrupt industries and potentially even defeat industry giants.
