In this second part of two articles, I cover many of the challenges to be faced switching into a greater AI reliance and what hurdles and issues need to be considered.

Disclaimer: This article is published in partnership with Siemens. Siemens is paying for my engagement, not for promotional purposes. Opinions are my own.

Growing impact, returns, and value from your AI /Edge journey and what it can provide

The aim here is to briefly discuss the approaches that can be taken to determine the benefits to be gained and the growing impact, returns, and value adds this AI /Edge journey can provide. This article can be the starting point of your conversation and build up an understanding of what needs addressing.

Skepticism about AI can be significant, especially on the shop floor. There is a genuine concern that AI will take decisions away from the well-understood controller or machine specialist when the experience and the relationship with handling complex machines are deeply embedded. This questioning the proposal to apply AI techniques, when not 100 per cent transparent initially, often builds the natural reluctance to change, which can be a real drawback in a generally conservative industry such as industrial automation.

There are also fears of several roadblocks; most prominent among these is the lack of data scientists, lack of available data expertise, ability to complete the analytics, and understanding the data results. Then you have to consider legal aspects, human factors, and – finally – unclear use cases that do not give the necessary resolution or impetus to make the change.

Any undertaking is a series of learnings and conversations. In strategic partnerships, you can understand and begin to plan out the steps to any AI / Edge journey.

Bringing AI into the thinking equation

Analytics Artificial Intelligence Services Keyvisual

AI solutions in this context form a natural extension of motion control and automation systems. While current motion control systems can quickly and accurately control repetitive tasks, the AI system can handle more complex scenarios with low predictability.

Current automation systems are designed for highly repetitive tasks. They can accurately cut, shape, form and assemble a system from components. Yet, they become restricted in today’s growing environment demanded by customers to be far more responsive in fluid, adaptive and recognizing increasing complexity needed in a finished product.

Increasingly non-repetitive tasks or adjustments need a growing intelligence. Patterns of change to the machine or the work in progress can be more effectively managed in an AI /HI environment where precision, downtime and machine knowledge become increasingly important.

Today the top three benefits gained from AI combined with edge computing can help significantly is in the following:

Maintenance (predictive and prescriptive): Maintenance is one of the main applications for AI in machinery. Growth is driven by end-user needs for machine efficiency and to minimize or eliminate unplanned downtime.

Quality control: Intelligent quality inspection is a key AI application in the machinery space. The main driver is the stringent quality requirements for consumer products.

Operational simulation and optimization: This is another prominent application segment for AI in machinery. Dynamic simulation and optimization of processes allows end-users to plan their machine use efficiently, plan material flow and supply dynamically, and anticipate possible shock scenarios.

Applying Edge-cloud solutions and AI-implementation- critical points of benefit

Firstly the differences between Edge computing versus cloud computing for IoT devices

Edge computing can process data where it’s generated, whether that’s at the plant or the machine.

Traditionally, cloud computing is the model for IoT device analytics and prediction. In the central cloud computing model, the data is sent from the end-user device (the “edge”) to the cloud for analysis; then, the decision is transmitted back to the device for implementation. While the data centres in the central computing model have an immense capacity to process and store data, they are expensive and power-intensive to maintain.

Data transfer between the edge and the cloud is expensive and time-consuming and causes potential latency (lag time). In addition, the energy required for data transfer where low-energy wireless IoT devices are present and often transfer cannot be supported, causing even more lag. Nor does it make logistical, operational, or financial sense to transfer and plan to keep all data in the cloud when only a fraction of what is collected may prove helpful in central evaluations. Finally, extending data transfer may have an adverse effect on data integrity and security.

In contrast, data is collected and analyzed at the IoT or Edge devices for quick inference (or decision-making) with edge computing. Later, the valuable data required centrally can be moved to the cloud. Edge computing offers several advantages. Because there is no need to transfer data from the IoT devices to the central cloud, the resulting lag time, consumption of bandwidth, and cost will be low, and decisions can be reached quickly based on data analytics.

Of course, the cloud connection is still highly relevant for several reasons. These include the ability to provide the delivery of centralized software updates more efficiently in larger environments. Also, you can often undertake broader data analysis as more optimal in the cloud at the cloud level. Many processes involving AI in industrial applications do need the computing power of the cloud to learn, compute and evaluate.

Edge computing can continue to run even when the system is offline.

Another significant advantage of AI implementation working with local edge computing devices is the independence from cloud connectivity. On those occasions when your internet connection breaks down, the machine can continue performing its tasks, as this is held locally.

In a system with AI running in a cloud, your machine would instantly lose the ability to have an intelligent device that can react to complex, be “intelligently” adaptive and respond to unpredictable situations. It goes back to being fairly “dumb” and reliant on the human-only elements that may not be good enough for managing the complexity in the process, and the machine environment called for today might be forced to shut down if connectivity is lost.

Working with a supplier like Siemens significantly reduces the issues and worries

At Siemens, they have been dealing with machine learning for years, providing different AI use cases on industrial applications. The Motion Control team of Siemens are continually dealing with machine learning for Machine Tools or Additive Manufacturing, especially in fields like

quality control
process optimization
predictive maintenance
and self-adjusting machines that can handle and identify algorithms

Edge-cloud solutions like Siemens Industrial Edge offer enhanced shop floor functionality with complete data control. Edge devices allow local data processing close to the machinery where this data originates while maintaining cloud connectivity for centralized software updates and data analysis across multiple locations.

Again, in the motion control and automation area, there are several reasons why AI-solutions need to be implemented on a local level, e.g. into edge devices, rather than into cloud-operated systems:

sensors produce enormous amounts of data, which mostly is of short-lived relevance and cannot be stored due to data security regulations
low latency of information transmitted to the AI system and decisions made based on this information
implementation of AI systems into local contexts for specialized tasks and environmental situations

Then there is the matter of distributed computing. The network has to function as a big brain for the entire operation to be reliable. Through Siemens offering operational support with their Mindsphere Cloud environment, they provide a fully integrated service to support your operations.

The need to collaboratively resolve your unique pain points

Working with Siemens and their consulting services, you gain a partner that builds your business case.

It is essential to search for that initial low-hanging fruit to create tangible value and strengthen the strategic ambitions towards a new automated environment.

A place to start is designing a new initiative that improves value by investing in a more flexible manufacturing environment. These might involve acquiring new customers, preventing customer attrition or churn, or deliberately building a new business model that promotes the new environment of how you manufacture and provide additional economic value.

Why? AI is time-consuming and expensive to set up and deliver because it involves extensive testing, integration with existing systems architectures, redesign of business processes, and upskilling or reskilling employees. As a result, it is important to have strong partners, clear sponsorship from the leaders of the functions or units in which the systems will be implemented, including collaboration on cost estimates and expectations for necessary changes in the business to build a robust change environment.

By demonstrating growing value, seeing benefits emerge and validating capital outlay progressively, enables the sponsorship and increasing identification of others to study, evaluate and take these learning into building their digital transformation of the automating needed. Drawing on experienced gained can significantly advance any digital transformation and save significant time.

Finally, there is the need to retrain, orientate and provide new skills that come through working with an experienced partner.

There is a need to recognize the shifts that are occurring.

As with any fundamental or seismic shift, there has been resistance to AI adoption. The knowledge and skills required for AI can be expensive and scarce; many manufacturers don’t have those in-house capabilities. They see themselves as effective in specialized competencies, so working with experienced partners to assist and support the digital transformation becomes a sensible investment to shift towards a digital enterprise.

AI developments will undoubtedly change the nature of the work humans carry out. Operators will be freed from many menial, repetitive tasks and gain spare time for creativity, strategic planning, and innovation.

Technology will always have some impact on market growth. AI will fundamentally disrupt the way we manufacture products, to build out the use of AI on a broad scale in progressively learning paths that give new scope to grow a business, perhaps even new business models.

Finally here are two links you could refer too

https://new.siemens.com/global/en/products/automation/topic-areas/artificial-intelligence-in-industry.html

https://new.siemens.com/global/en/products/automation/topic-areas/industrial-edge/machine-tools.html

**For these two articles, I went into significant research, drew down valuable points and validated different aspects of AI. I took more of the customers perspective in these to begin his exploring for undertaking this AI journey