Sponsored Feature It’s no surprise that people have grown more familiar with the intricacies of epidemiology over the last two years due to the pandemic, but the fact that supply chains and manufacturing have made it onto the mainstream news is perhaps more unexpected.
Shortages of raw materials and rising energy prices have had clear knock-on effects, forcing manufacturers to re-double their efforts at becoming more efficient. But there are other, perhaps less obvious pressures. The manufacturing sector is struggling with more than 800,000 unfilled roles worldwide, in part because the workforce is getting older and closer to retirement.
At the same time, manufacturers face the challenge of accommodating the needs of new workers, who have grown up with touchscreens rather than dials and levers. These people expect to be able tap into vast pools of up-to-the-minute data to aid their decision making.
And of course, with machine learning and AI taking over decision making in other areas, why wouldn’t we expect the same to happen in our factories and in the wider supply chain? That frees up workers to focus on adding value, fueling innovation, and getting products to market quicker.
Increased adoption of smart manufacturing
All this feeds into the increasing adoption of Smart Manufacturing. Non-profit industry community group the Manufacturing Enterprise Solutions Association (MESA) defines “smart manufacturing” as “intelligent real-time orchestration and optimization of business, physical and digital processes within factories and across the entire value chain”.
Plex Systems, the leading cloud-native smart manufacturing platform recently acquired by Rockwell Automation, produces an annual . The most recent of these surveyed 321 industry insiders, and revealed that smart manufacturing adoption grew 50 percent in 2021, while 83 percent of respondents considered smart manufacturing as key to their future success.
75 percent were banking on smart manufacturing to help solve their workforce issues. 80 percent indicated they want to use software to connect people, systems, machines and supply chains, with 78 percent expecting it to help them automate processes and 77 percent hoping to gain better analytics and insight into the business.
The survey also highlights the challenges companies face in adopting smart manufacturing, including wariness around “overhyped technology” based on previous experience of early adopters. Despite this, 24 percent of respondents cited technology paralysis as a “growth obstacle” to their overall business plans.
The workers, united by technology
The biggest barrier to the adoption of smart manufacturing remains cost, cited by 36 percent of respondents. A lack of skills hampers 32 percent, with a similar number highlighting difficulty replacing or upgrading legacy systems and employee resistance to smart manufacturing adoption and implementation.
These obstacles are not a million miles away from the sort of concerns companies typically face in other forms of technology adoption, particularly when it comes to breaking up data silos to fuel analytics and increase visibility.
As Sachin Mathur, EMEA director for software and control at Rockwell Automation explains: “Smart manufacturing is not just what happens on the factory floor. It’s a broader issue that incorporates making sure assets – raw materials, plant, people and data – are utilized to the maximum in order to extract every ounce of productivity.
“This means the manufacturing team needs to work much more closely with the IT department, using enterprise resource planning (ERP) and manufacturing execution system (MES) software. And they both need to adopt a holistic approach to information security.”
Or at least they should be.
Information harvested from multiple layers
Rockwell Automation’s approach to helping them do this lies in its Connected Enterprise Production System strategy, a holistic systems-engineering approach that converges all levels of the customers’ enterprise.
The technological innovations, capabilities and domain expertise span throughout the design, operations and maintenance elements of the production system. Information is harvested from all layers, supervisory control and data action devices (SCADA), programmable logic controllers (PLCs) and robots.
“The next level,” Mathur explains, “is ensuring the data is securely communicated through network communications infrastructure and human machine interfaces connected via Wi Fi or 5G.”
Platforms such as the Plex MES must then be integrated – alongside supply chain planning applications – by connecting directly to the organization’s ERP system, with MES automation and orchestration taking over manual tasks and even analyzing machine data to automate decisions and workflow.
“Our core strategy is to enable an upstream and downstream flow of information, data and process parameters via Factory Talk Hub, Rockwell Automation’s cloud-based platform. This brings all of the critical elements together in a manufacturing value chain,” says Mathur, “and then creates a highly interconnected, hyper-intelligent system. Then you can build newer, better, deeply insightful digital-age applications.”
But it’s one thing to have a comprehensive mix of tools underpinned and connected by the cloud, it’s another to be able to apply them to the wide range of companies that are looking to bring their manufacturing operations up to speed.
That’s why Rockwell approaches customer problems with a five-stage Connected Enterprise Execution Model, which aims to converge information technology and operations technology (OT) into a single architecture that ultimately improves enterprise, operations, and supply chain performance.
Data is the new capital
The model starts with an assessment stage, covering IT/OT infrastructure and legacy processes and workflows. This lays the groundwork for securing and upgrading the IT/OT networks and control architectures to modern systems and deploying a backbone to ensure connectivity between operations and enterprise business systems. Organizations can then address working data capital, to improve both decision making and business processes.
Predictive analytics can be applied to further improve operational benefits in areas like planning and asset management. And adopting a continuous improvement model to support collaboration helps to increase visibility and access to assets, processes, and subject-matter experts.
The aim is to help customers become “more insightful and data driven” in managing assets and production lines. “Rather than just a simple offline spreadsheet-based decision, they want to be more usage-based, they want to be more sustainability-based, supported by data from sensors and actuators,” advises Mathur.
On a practical note, he says: “I have seen customers reduce a workflow from 150 lines of a spreadsheet to a 35-step approach. Because, as they’ve digitalized it, they’ve been able to learn quickly and adapt those processes and take out the wastage.”
Of course, every customer organization – their starting points and specific objectives – is going to be different; and adapting smart manufacturing is rarely a big-bang issue. Mathur says that around three quarters of Rockwell’s engagements involve existing brownfield sites: “It’s never going to be a double-click install, we know that.”
Benefits for “Mom and Pop” shops
And while Rockwell counts many global brands among its customers, it also includes what Mathur describes as “Mom and Pop operations”.
A smaller company, with only a few production lines might not have AI-powered manufacturing on its radar. “But,” he explains, “if they switch off the light with a production cycle running, they want notifications on their mobile phone if something goes wrong. Lights-out manufacturing is now an achievable dream, that is becoming a reality.”
“In medium- to larger-tier organizations,” he says, “rather than just getting smart devices and notifications, they want to have a factory-wide dashboard for monitoring, assessment, and data-driven reporting. On top of notifications and alerts, they also want to have hourly or daily reporting and analysis, so they can define how to make the next shift, or the next month better.
“From there, they might start thinking about autonomous feedback loops. This is where machine learning models come into the picture with self-learning based on inputs and outputs.”
And this provides a base for fully exploiting other coming technologies. 5G, for example, has the potential to transform connectivity throughout the organization and enable edge analytics, as well as the use of augmented and virtual reality (AR/VR).
“5G is a strategic area for us, where we are now partnering with some industry powerhouses,” says Mathur.
5G and augmented experiences are also essential when it comes to those desperately needed new entrants to manufacturing, who often have different expectations as to how they should be able to access information and manage systems.
“They’re not going to be satisfied walking from one part of a facility to another to work things out,” Mathur says. “Rather they’ll expect to be able to access data, solve problems, and be able to update and adapt workflows on the fly. Their expectation is going to be ‘okay, how do we bring this together?’, because they know that technology can make that happen.”
Sponsored by Rockwell Automation.
