The Key Requirements Of Industry 4.0 Systems & MRP Software

The key requirements of industry 4.0 systems and mrp software explained

The Key Requirements Of Industry 4.0 Systems & MRP Software

Manufacturing companies are facing a number of challenges in how they conduct business today. Challenges range from a shortage of skilled labour to facing online competition.

Here’s a look at what manufacturing companies need to do today with advice from the head of a research group for advanced manufacturing automation.

Blueprint For Industry 4.0: What Manufacturers Must Do Today


The manufacturing industry is no stranger to misconceptions and buzzwords. Collaborative automation, Industry 4.0, artificial intelligence, blockchain – the real reason we allow ourselves to spin in circles on these topics is because we’re inherently hopeful and practical people: we want to build better and believe there is a path to doing so, if only we could find the way.

As part of my series to uncover what leaders in the manufacturing space are actually doing to build better, I sat down with someone at the bleeding edge of evaluating new technologies: Juan L. Aparicio Ojea, head of the research group for advanced manufacturing automation at Siemens. Aparicio Ojea’s role grants him unique insight into the latest research across universities, startups, and government agencies. Access to so many different types of technologies that are all working to provide value in one way or another has enabled him to create a simple blueprint for the key requirements of an Industry 4.0 system. He acknowledges that we’re far from seeing completed Industry 4.0 systems in practice, but that there are two steps every manufacturing leader can and should be taking today.

Interoperability: One solution from many parts

Much of the challenge of bringing new technologies to the factory floor is in the interfaces between them. Aparicio Ojea asserted, “Being able to interoperate machines from different vendors is key.” These connections will allow for the flow of previously underutilized data, enabling faster integration and time to value. There are two schools of thought on this issue. Some, like Aparicio Ojea believe that industry standardizations laid out by industrial consortiums, which includes frameworks for OPC UA and DDS, will be key. Others, like Andrew Scheuermann, CEO of Arch Systems, believe that the industry cannot wait for the long cycle of old equipment to be replaced. Legacy equipment and new-fangled collaborative robots already have to work together, so Arch Systems, who counts top tier electronics manufacturer’s among their customers, has built out an extensive library of software and hardware retrofit integrations where manufacturers can expedite a path towards interoperability with what is on their floors today, while leveraging modern standards for their new equipment.

Modularity: One piece that can fit in many places

The second requirement is modularity, or as Aparicio Ojea clarifies, “not having a monolithic approach to manufacturing.” An easy-to-see example of modularity on an electronics factory floor is the SMA (surface mount assembly) line. Instead of one huge machine that can make only one kind of PCB, there are modular machines for each step in the process: solder paste deposition, pick and place machines, reflow ovens, and inspection. But the SMA process has been around for decades, so what does it mean in the modern context? I believe it means that the time for custom-built, single-purpose machines is coming to an end, to be replaced by generalized technologies that can be applied to a much wider variety of products and problems. Universal Robots is tackling this by creating easy-to-program robot arms that can be reprogrammed to different functionality when the program is over, enabling the technology to be viable for products with short life cycles (like consumer electronics).

Aparicio Ojea believes the third element of the blueprint is the creation and use of a digital twin, or simulation, of factory processes. I’ll be honest, “digital twin” is a buzzword that has always made my head spin and sent up my skepticism antennae. Here’s the point: if you want better outputs from your process (such as higher yields or higher throughput), as engineers we would measure the inputs (such as individual machine parameters) and to try to use statistics to figure out which variables matter. If you can find a mathematical correlation between the inputs and the outputs, you may be able to “turn the knobs” on the input parameters to get the outputs you need. A digital twin is the concept of doing that at a much larger scale, where the goal is to replicate every single process for a holistic model of the factory. 

While it’s possible there are successful implementations of true digital twins out in the wilds of the manufacturing world, in general, this is viewed as an aspirational concept. As Aparicio Ojea rationalized, “It is not a greenfield, it is a brownfield” – meaning that most factories already exist and are filled with both legacy equipment and manual processes that are difficult to digitalize. While digital twins might be obtainable for highly automated bottling plants, it feels like fantasy for electronics assembly, which still has hundreds of human hands on the line. In those cases, leaders should focus on wrapping their arms around the data they can get at the highest possible resolution, if not from the process, then from the products themselves. Engineers can use this data to create these correlations the old fashioned way: with experiments, spreadsheets, and statistics.

Flexibility: Pieces that can adapt

Arguably the most exciting element of the blueprint is flexibility. This element is all about reducing waste – not scrap waste, but equipment waste. Single-purpose machines are not easily repurposed and yet are how short life cycle production lines have been able to automate to date. How do we create more flexibility in the production process and the machines we use? AI, computer vision, and robotics can be combined to enable machines that are both more adaptable to variation, and more adaptable from product to product. A quick example is in the quality control process. The industry is moving away from the expensive “one-issue, one-camera” model towards cameras systems that can program themselves to find anomalies more broadly during an inspection – allowing greater inspection coverage than humans or traditional and more reliable quality control.  The combination of adaptability, automation, and access to data is a triple threat that will unlock the lion’s share of the potential of Industry 4.0.

Where to start?

How does one adopt Industry 4.0 technologies and embody these smart manufacturing principles? Aparicio Ojea recommends investing in two key areas: digitalization and strategic partnerships. Without digitalization there will be no data foundation, a requirement for a wide array of initiatives. Simply decreasing paper processes represents a first step that many can take. When it comes time to adopt innovative technologies, Aparicio Ojea recommends, “viewing vendors as strategic partners and having a co-creation mentality. Partnering with a startup, automation vendor, or university and working together to solve a problem that has a real KPI and a clear goal merits investment now.”

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Digitization & Strategic Partnerships Come Together With Nubik and MRP Software

At Nubik, we aim to be the strategic partnership your manufacturing company can rely on so you can step into Industry 4.0 with confidence. Our MRP software solutions can be customized to your unique business, offering an innovative way to digitize your company.Learn more about our manufacturing solutions today.