Manufacturers Turn to Cloud Services to Improve Production

Manufacturers are adopting the cloud to advance productivity, improve efficiency, and manage complexity and uncertainty.

December 1, 2022

4 Min Read
Image courtesy of Alamy

Kaushik De, VP, Cloud Center of Excellence, Capgemini Americas

The pandemic exposed manufacturers’ need to centralize complex operations management and to pivot and innovate quickly to adapt to change. Now, the manufacturing industry is moving toward the cloud to protect business continuity and to improve productivity, efficiency, and autonomy. Close to 70% of all organizations now host at least half their workloads in the cloud. And while  “future fit” manufacturers are already using the cloud to drive innovation, others are behind the cloud adoption curve.

The reason for this lag is the same reason cloud adoption holds such potential for manufacturers. Physical plants within the same organization may be widely distributed and contain different types of hardware and software, all of which need periodic updates and replacements that are usually done manually on-site. Leveraging the cloud requires standardizing hardware across plants and pushing software updates through the cloud. It is possible to set the stage for cloud expansion, but it takes a clear understanding of how this process can help attain goals, a map for implementation or expansion, and a continuous transformation mindset.

Key Considerations to Prepare for Coud Expansion

With centralized hardware management and cloud-based data gathered from the internet of things (IoT), operational technology (OT), and industrial control system (ICS) devices, manufacturers can make several improvements. These include optimizing operations and total cost of ownership, shortening the time to market for new products, improving quality management, adding product-data-driven services for customers, and moving to a smart factory model. As cloud, data, and analytics help manufacturers operate more efficiently, they can also reduce the carbon footprint associated with their equipment and distribution to contribute to the organization’s sustainability efforts. Already, half of the process manufacturers and 53% of industrial and discrete manufacturers are embedding emissions data in their decision-making processes with the goal of reducing greenhouse gas emissions.

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Organizations can also integrate software and the cloud to make the most efficient use of data, providing better insights on manufacturing stock flows, manufacturing execution systems (MES), and product lifecycle management systems (PLM). However, because several manufacturing components rely on physical hardware operating in the plant, not all of them can move to the cloud. As a result, in addition to the security and data sovereignty challenges that all industries must solve when they move to the cloud, there is the additional roadblock of managing the interface between physical and virtual environments.

For example, an organization with global manufacturing flows might want to send operational data to the cloud from their physical equipment at each plant. However, if a handful of their plants are in remote areas without strong, reliable connectivity, they risk unplanned downtime if their cloud connection goes down. Rather than take that risk, or leave remote plants unconnected to the cloud, the manufacturer can develop a solution – such as a 5G network with an in-plant edge solution– within the plant to collect the data locally and then push it to the cloud when connectivity allows. Finding creative solutions is the key to successfully implementing the cloud in plants.

The Manufacturing Cloud Maturity Curve

Even without cloud connectivity challenges to overcome, manufacturing environments are usually complex. They typically include standalone applications, data silos, upstream-downstream communication, and often archaic on-prem solutions. There are also equipment sensors, and PLM, ERP, and MES systems at the management level that all need to connect to each other and the cloud.

Once connectivity and automation are completed, organizations should shift their focus to efficiency improvements. This stage is where most of the transformation and innovation can take place. Here, a manufacturer might create digital twins for remote equipment training and troubleshooting, or to collaborate on product design in real time with remote expert teams using augmented reality tools. The final stretch of the maturity curve is autonomy, where the AI and ML algorithms help to plan a systematic deployment of operations and cyber-physical systems – the basis of a smart factory.

Navigating the cloud maturity curve requires distributed architecture to connect disparate equipment and systems, a collaboration platform to bring that data together, and a system-to-system digital twin that models the real-world connections between the OT and IT.

Achieving the Continuous Transformation Mindset

Reaching cloud maturity is the goal, but it’s not the end. Achieving the best ROI on the cloud initiative requires identifying and monitoring the right KPIs, such as those related to operating costs, efficiency and asset utilization, quality, lead time, and schedule adherence. There may also be compliance KPIs to monitor and optimize for, which can help to enhance regulated practices like safety, security, and environmental protection. In other words, the need for transformation and adaptation never ends.

Continuous transformation can also help manufacturers deal with complexity and uncertainty over the long term, because there will always be new technology to leverage, new challenges to address, and new customer needs and expectations to meet. Creating a culture of constant evaluation and improvement is the key to making a cloud transformation that increases productivity. It also supports centralized management and maintenance of complex systems and drives innovation that can help manufacturers pivot and adapt in the face of uncertainties.

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