Over the past few years, there’s been a distinct tendency to associate smart manufacturing with mega-enterprises in sectors such as automotive, aerospace, and the process industry. In fact, the roots of this radical, data-driven approach lie firmly in the German Mittelstand of family-owned, industrial small and medium-sized enterprises (SMEs).
Back in 2011, the German government’s ambitious Industry 4.0 initiative encouraged many such companies to start gathering and analyzing the information generated by their plant and equipment. More recently, it’s true that much of the momentum behind smart manufacturing has been provided by larger organizations. However, those pioneering German SMEs still tell an important story. There are compelling reasons why, when carefully planned and implemented, smart manufacturing is every bit as valuable for smaller businesses. In some cases, the benefits of deployment may be even more accessible, and the returns realized faster.
The data is out there
Making the argument in favor of smart manufacturing for SMEs typically involves a fair amount of myth-busting. To start with, deployment of smart manufacturing systems is rarely likely to demand high levels of capital investment or major implementation of new infrastructure. Most modern plants will already be generating the data around which new solutions can be built. Even if that’s not the case, it is generally a straightforward task to retrofit sensors to legacy equipment. In other words, the data is already out there. The real challenges lie in collating and preparing information from disparate sources, then transforming it into actionable insight.
Accessible to all
Another common misconception is that smart manufacturing will inevitably require significant in-house IT and/or data science expertise. Again, that’s often not the case. Smart manufacturing solutions are now being built around principles of democratization and accessibility. Low- or no-code technology is the way forward here. In fact, too much involvement from IT specialists may be more hindrance than help. That’s because, regardless of the size of the enterprise involved, effective smart manufacturing systems are almost invariably shaped by those directly responsible for production.
For an SME, an ideal approach might be a machine-learning solution that can be used intuitively by its own operations team. The multivariate time series data that is generated on the shopfloor can then be fed into the system and, based on the predictive insights and alerts it provides, will enable frontline staff to determine the corrective action required.
Crucially, such an approach recognizes that the operations team is best-placed to reach the right decisions. What’s more, it provides them with all the control and visibility needed to do so. And because the entire process, from identifying the use case to verifying the results, resides with the operations team, much shorter time-to-value is delivered.
Where is the return?
A third potential pitfall lies in a simple failure to identify, in advance, the anticipated ROI for a smart manufacturing initiative. This is reflected in a number of cases where businesses have set out to reap data, and only then tried to determine where and when the payback will materialize.
In many respects, smaller organizations are less likely to fall into this trap. By their very nature, they tend to be focused and agile, and adopt a more cautious approach to investment. In addition, smaller organizations often opt to introduce smart manufacturing in specific areas rather than throughout the entire production process or as part of a broader digital transformation strategy. This makes it easier to monitor the results. As experience grows and lessons are learned, smart manufacturing systems can then be extended. Deployment therefore becomes an organic process, driven by and from the shopfloor.
Getting more from less
Fortunately, problems with smart manufacturing deployment are the exception rather than the rule. Enterprises across numerous industries are now achieving positive, quantifiable results. Potential benefits encompass reduced plant downtime, elimination of bottlenecks, and improvements in product quality that in turn realize significant warranty cost savings. For smaller enterprises, the most worthwhile returns will often be found in enhancements to productivity and efficiency. That’s not just because these businesses tend to work on very tight margins. With fewer resources in terms of both plant and manpower, it is even more important that they are consistently doing the right things, at the right times, and in the right places. For example, in one SME use case we’re familiar with, data analytics is pivotal to ensure that production lines are continuously balanced to optimize inventory utilization.
Delivering for SMEs
Another compelling smart manufacturing application in the SME sector involves the monitoring of process data to help build in quality, rather than effectively add it at the end of the production line. For example, one SME is using smart manufacturing to visualize and monitor in real time the quality of assembly processes such as torque audits. Any deviations and trends are flagged as soon as they become an issue. The aim is zero defects and a completely transparent manufacturing process; expensive rework at the end-of-line testing stage is significantly reduced.
SMEs are also highly active in the fast-growing market for smart products. Here, understanding rapidly changing customer requirements and innovating accordingly are key to remaining competitive. Typically, artificial intelligence (AI), ML, and the Internet of Things are regarded as the enabling components. However, when it comes to creating products that can become smarter and more agile, smart manufacturing’s ability to integrate these elements with the design and simulation processes can help enterprises innovate and change product direction more quickly.
Levelling the playing field
Data is now recognized as a critical commercial resource. On the face of it, mega-enterprises might appear to be at an advantage, simply on the basis that they have more to work with. However, by taking advantage of characteristics such as flatter structures, proximity to the production process, and an ability to focus on more modest and better-defined applications, SMEs can make smart manufacturing work just as hard.
Far from being the preserve of larger enterprises, smart manufacturing is a thoroughly democratic asset that can and should be considered by businesses of any size. Not merely to level the playing field, but to tilt it in favor of manufacturers that are simply better at exploiting the rich insight buried within their production data.
Sam Mahalingam is chief technical officer, Altair.
