How can manufacturers gain confidence in moving to autonomous operations? Combining the agility of SaaS with the reliability of edge at the plantwide level is an emerging approach for MES. This eBook walks through why MES is core to moving from automation to autonomy. It discusses the challenges of gaining the resilience between the plant floor and SaaS applications. It also explains edge-to-cloud architectures and their benefits.
Please enjoy an overview of our findings, below. For the full research, please visit our sponsor, Rockwell Automation.
Table of Contents
- Autonomous Manufacturing Vision
- MES is Core, but Must Evolve
- Seamless Data Access and Other Challenges
- Industrial Edge-to-Cloud for Autonomy
- Cloud-SaaS Solutions
- A Workforce Win
- Benefits of Cloud-SaaS and Edge MES
- Recommendations
- Acknowledgments
Autonomous Manufacturing Vision
Autonomy in Action
What if you could run your manufacturing operation autonomously? All facets of manufacturing are monitored in real time. Simple decisions are executed without human intervention, and AI agents present possible solutions to more complex decisions to the workforce, allowing them to choose the best action. AI is a crucial element for achieving autonomy, enabling more work to occur reliably without human interaction.
The Goal
The result of human and digital intelligence working together can be timely, high-confidence decisions. In turn, these sound decisions can ensure maximum throughput of quality sellable product, with minimum variability and cost to achieve it. The ability to run consistently 24×7 with available staffing and skill sets, even as the workforce, ingredients, recipes, products, and packaging change, is a worthy goal.
Resilience Required
To realize these benefits, IT and OT data must flow in context, rapidly and securely, and systems must be agile and upgradable to keep pace with change. These technology characteristics provide a foundation that operates seamlessly, regardless of connectivity issues, unplanned downtime, or other technology-related issues that may arise. In short, autonomy requires resilience.
What’s Missing
This autonomous manufacturing vision is not new. However, cloud SaaS, artificial intelligence (AI) / machine learning (ML), automation, IIoT, and edge technologies are maturing. For most manufacturers, they provide incremental improvements to manufacturing performance. Are there missing building blocks to support resilience? What technology mix and deployment approach can deliver comprehensive industrial data in context to achieve the goal of autonomous manufacturing operations? Between on-premise automation and cloud-based MES, companies need a deterministic way to execute at the edge.
Seamless Data Access and Other Challenges
People and Process Issues
Beyond MES and other technologies, there are people and process issues to address as well. Workforce skills gaps make the need for always-on MES guidance more critical to prevent profit-draining downtime. Even highly automated operations will need process changes to ensure autonomous operations succeed. SaaS MES with edge execution can support that agility and certainty.
Technology Issues
Many companies have technical debt from on-prem software that’s almost impossible to update. Why? because the older platforms took a toolkit approach, in which people built custom systems or extensions that were very difficult to maintain and integrate. “Cloud-enabled” systems that use the lift-and-shift approach have also proven difficult to implement. In a sense, they carry forward the pitfalls of the past by deploying old software designs in a new way.
Data Issues
Beyond normal data access, autonomous operations need resilience. 24/7 autonomous operations need seamless, low-latency data access and data management across IT and OT. Still, most companies, even Top Performers, rate their capabilities for the seamless movement of operational data from collection to analysis between ‘Not at all’ and ‘OK’. AI intensifies the pressure to improve industrial data movement and governance. Our research shows that the #1 issue for AI success is a lack of data readiness.
Recommendations
Move from Automation to Autonomy
- Set your sights on the future, to move beyond automated to autonomous operations, ready for higher throughput and more AI-backed decisions.
- Plan for resilient execution enabled by a cloud-native MES that works in concert with a deterministic, purpose-built edge layer.
- Understand how your current processes align with manufacturing/business goals and how effectively they support the competency and decision-making skills of your workforce.
- Evaluate your current manufacturing technology environment and your ability to manage production data seamlessly, including the integration required to deliver data to stakeholders and the systems that support them.
- Use technology to standardize processes and data management, enabling progress toward more autonomous operations.
- Adopt a new edge layer to provide resiliency in cloud-edge technology, integrated into your manufacturing operations to minimize or eliminate downtime.
- Use solutions that empower the manufacturing workforce without disrupting their workflow or forcing non-value-added tasks.
- Consider resilience and security as foundational targets for your autonomous manufacturing technology stack.
*This summary is an abbreviated version of the ebook and does not contain the full content. For the full research, please visit our sponsor, Rockwell Automation.
If you have difficulty obtaining a copy of the research, please contact us.
