We hear so much about computing moving to the edge. Predictions like 75% of data will be created and processed outside of traditional centralized data centers leave us to wonder what future is there for the data center. Unlike past shifts, this doesn’t imply all compute will move from the data center to edge, just as it didn’t for edge to cloud. It means that today’s edge compute capabilities are significantly improved, integrate with more things, and act at the point where the process executes. Today’s edge can also pass relevant data up the stack to other stakeholders, including engineers, maintenance, supply chain, quality, and leadership.
Contrast the technology predictions with corporate objectives, and this will make more sense why edge computing technologies are advancing in their adoption. According to a recent survey by Deloitte, 74% percent of CEOs say their organizations are pursuing large-scale digital transformation initiatives ranging from optimizing processes to addressing workforce shortages to creating a more resilient company. More important than any single rationale for change, the research indicated the path forward wasn’t mutually exclusive. Today’s manufacturing leaders cannot select a single smart factory initiative by itself. Instead, leaders must identify multiple initiatives that ride along shared enabling technologies to deliver more significant long-term benefits for the business.
The Move to the Edge
The future of manufacturing isn’t just ensuring your company is digitally connected. It’s augmenting your existing workforce to improve productivity, filling gaps due to labor shortages, creating a more sustainable organization, and developing a more competitive and resilient business. How is your organization tackling these business challenges? Is it looking to an integrated business strategy and leveraging enabling technologies effectively? Will your smart initiatives not only connect and integrate data, but also drive improved action by both employees and machines?
For those companies investing and reaching the “use” phase, 73% are leveraging data and edge automation to fuel industrial change. Beyond using this technology to connect heterogeneous devices across the factory, organizations can capitalize on automation and decision-making at the edge. It’s because of this value add that we see a future in edge compute and smart solutions. It’s also important to understand that not all use cases support “lights out manufacturing.” Improved decision-making at the edge can take two forms: autonomous and augmented workforce.
Augmented Workforce
Not all manufacturers have a workforce issue, and many do not view job elimination in alignment with their core values. This doesn’t mean automation isn’t possible—it’s just not the same use cases. There are many areas where automation can augment employees to reduce fatigue, stress, and mundane or error-prone tasks from a process. The truth is that some tasks aren’t a good fit for people. Eliminating non-value-added tasks leaves retained employees to focus on higher-value activities.
Examples of automation augmentation include paper elimination, data entry, or providing recommendations at the edge to operators from data gathered by equipment, environment, and business systems. These are a mix of monotonous tasks no employee really enjoys, along with data not typically available. Freed of boredom and empowered with meaningful insight, these valued employees can improve how machines operate through adjustments, observation, or experience to ensure their process area delivers better results for the company. And that’s good for business and employees.
People matter. Through augmentation, we can see a future where automation and valued employees co-exist by outfitting low-value positions with automation or augmenting existing roles with job aids. This action allows us to take those workers who choose to stay and invest and reskill them for new roles in the company while still meeting corporate goals and offsetting the broader workforce challenges.
Autonomous Workforce
On the other hand, if businesses continue to experience increasing no-show rates as well as labor churn, at some point, organizations must discern how to remain operational. Some manufacturers are already reaching a point where they must choose—wait to hire or pursue automation.
Automation doesn’t only mean robots. While robotics is always an option for the proper application, most manufacturers are full of waste in manual processes from traceability collection, measurements, data entry, handoffs, and workflow mired down in manual transactions. Such transactions continue to be impacted by absenteeism or labor shortages, drive down production, and affect operational resiliancy.
For those companies that choose automation, there exist many practical options available to start the journey.
- Process and Regulatory Control: Almost all factories require process controls for safety, proper operation, and quality. For some manufacturers, there are regulatory, internal compliance, or corporate policies. Today, many options exist to collect real-time data and compute to ensure process compliance. Combine monitoring with decisions at the edge; factories can quickly identify unsafe work conditions, quality defect precursors, drift in process controls, or even detect and remediate an event that would trigger an audit or regulatory risk.
- Maintenance Management: All organizations have some form of machine or facility maintenance, including building equipment, HVAC, and production equipment that must be monitored and maintained. Maintenance inspections and updating business systems with machine status can be laborious and people-dependent. The typical maintenance monitoring tasks can be fully automated by connecting and integrating with maintenance software, including proactive action, maintenance ticket creation, and even some emergency actions like machine shutdown.
- Quality Inspection: Employees are involved in all sorts of visual inspections. Today’s edge computing and machine visions combined with software are capable of many visual inspection use cases leading to improved defect detection, quality, and speed. Machine vision provides decision-making to fully replace a manual inspection or combine with expert employees to extend their scale and effectiveness across multiple work cells.
- Setup and Configuration: Yes, even robotics can be more effectively integrated into existing processes to augment or address workforce gaps. Take a largely manual factory work cell with a simple machine load/unload scenario. Many operations with CNCs, automated production lines, or other machines require employees to retrieve parts, load a machine, review the work order for job-specific details, load a configuration file, wait for the machine to process, and then unload the machine. Suppose your organization can no longer hire skilled workers to operate this work center. In that case, this is an example where decisions at the edge, combined with integration and automation, can allow a work cell such as this to become fully autonomous.
Agility Is Key
The ability to ingest data and camera feeds from any device in the factory and combine it with robust and scalable compute offerings means companies can solve a wide range of business challenges, with or without employees. And in a world with labor shortages, high turnover, and increasing business costs, making decisions at the edge implies resiliency, digitally lean platforms that systematically improve over time, and solutions that empower companies to advance their corporate goals.
Whatever business headwinds your company faces, automation and decision-making at the edge are increasingly common. Connection’s Manufacturing Practice has a range of practical solutions to help your business augment employees, manage workforce gaps, break down data silos, and improve productivity.
To learn more about Connection’s Manufacturing Practice or to discuss the automation and edge decision capabilities highlighted in this article, contact one of our manufacturing specialists today!
Ryan Spurr
Ryan Spurr is the Director of Manufacturing Strategy at Connection with 20+ years of experience in manufacturing, information technology, and portfolio leadership. He leads the Connection Manufacturing Practice, go-to-market strategy, client engagement, and advisory services focusing on operational technology (OT) and information technology that make manufacturers more digitally excellent.
