Artificial Intelligence and Edge Computing leads towards new business growth

Artificial Intelligence and Edge Computing leads towards new business growth

In this first of a two-part series, I want to cover many aspects that need to be considered by switching to a greater AI reliance in a machine environment. Today the benefits of Artificial Intelligence combined with Edge computing are gaining the growing impact, returns and value-adds as a journey for your business in revolutionizing your factory floor.

Today we are witnessing a combination of advanced smart technologies (sensors & devices), AI-empowered machines, Machine learning, digitalization and managing our knowledge gained at the Edge, called Edge Computing.

This combination is changing the working environment, and taking the technology applications and adapted machinery brings us into true industrial automation towards a truly digital enterprise. AI is increasing with the combination of Edge computing within the digital network.

Edge computing provides more value to any AI approach in response; data and physical relationships are closer to the operating environment. Edge computing is a distributed information technology architecture in which client data is processed at the network's periphery, as close to the originating source as possible.

Smart devices have become even more intelligent and self-contained with artificial intelligence (AI).

This new world of game-changing technology concepts allows the AI machine to perform tasks that were only reliant on human intelligence in the past. Artificial Intelligence and Human Intelligence give the combined power to achieve so much more than previously possible.

Taking advantage of change through technology

Large enterprises have a lot to gain from?AI adoption and can fund these innovations. Yet interestingly, it is small- to medium-size enterprises (SMEs)t that seem to become earlier adopters exploring more innovative applications in highly collaborative environments with existing or future customers.

Many SMEs are trying to leapfrog larger competitors by rapidly adopting new machinery or new technology and see the application of AI and Edge computing as a natural competitive edge to explore.

Offering flexible services, SMEs could have more significant AI adoption incentives than large enterprises, often restricted by their own in-house capabilities. Establishing lasting partnerships that can help a small start-up or specialized machine operator in different ways to develop a disruptive foothold in the market is highly valued.

Solution providers can resolve many roadblocks in any collaborative partnership.

Solution providers can offer access to data scientists, training, and data analytics.?They can assist in interpretations and understanding, provide advice in supporting legal aspects and resolving human factors that change, can bring, from their experiences, and – finally - provide use cases that give more substantial resolution. This advice adds to a cooperative environment that drives a digital enterprise learning from business cases and experiences.

Building a strategically designed AI / Human Intelligence environment allows for a different level of manufacturing that can alter the very business model you can provide your customers. For example, work can be completed faster, cheaper, with less effort, and higher efficiencies at a process level.

Today manufacturers are facing a growing complexity and constant changes in product needs and demands. The value of having more insights, flexibility and flexibility in setting up options, reduced downtime, etc., comes from applying a learning environment of AI and HI.

AI offers the potential for reduced downtime.

When manufacturers face more challenging delivery times, minimizing downtime can reduce customer disappointment or delivery penalties.

AI offers the potential to avoid machine failure through AI trained algorithms that can detect a change in machinery running or variations, reduce change over time, or help in machine fault detection, allowing for greater efficiency.

Often, any sudden unplanned downtime, where parts suddenly break, often requiring specific ordering, or simply replacing the piece, becomes expensive downtime. The impact can be overtime, backing up of orders and delays in shipping, all having a significant financial impact.

Operating in a more intelligent way where AI "informs" can direct planning and rostering decisions, which can be anticipated in more efficient ways. A more advanced digital enterprise can predict and detect patterns of change and bring you closer to a faster, adaptive environment where process changes can be accounted for and planned out more effectively.

AI is opening up new options to your business:

*AI makes possible much more precise manufacturing process design and problem diagnosis and resolution when defects so often crop up in the fabrication process to be proactively addressed.

*AI can now use these machines and processes to gather insights from these high volumes of data by themselves and optimize their processes during live operation. If a machine-learning algorithm spots quality defects, it can respond automatically during runtime.

*By combining AI and Edge computing, close to the machine on the factory floor, the local environment changes and becomes more adaptive and responsive.?AI combined with HI learns alongside each other, building out the human experience built up over many years to complement and improve a greater understanding of the operating environment through the combined intelligence.

*Combining AI and HI gives a more significant set of options to be more creative and seek out more efficient and effective process situations that increasingly optimize the processing environment.

*Much of the power of AI comes from the ability of machine learning, neural networks, deep learning, and other self-organizing systems to learn from their own experience without human intervention. These systems can rapidly discover significant patterns in volumes of data that would be beyond the capacity of human analysts.

*In manufacturing today, though, human experts are still primarily directing AI application development, encoding their expertise from previous systems they've engineered has increasing value. In the future, machine learning will provide a new learning environment.

*Another critical area of focus for AI in manufacturing is predictive maintenance; this allows engineers to equip factory machines with pre-trained AI models that incorporate the cumulative knowledge of that tooling. Based on data from the machinery, the models can learn new patterns of cause and effect discovered on-site to prevent problems.

*AI can bring increased reliability and efficiency to the factory floor. For instance, unplanned downtime has always been a significant problem that industrial manufacturers have faced. A digital twin can use AI-based analyses for predictive maintenance, reducing downtime by scheduling or alerting maintenance needs before they become problematic.

*Although AI is becoming more prevalent—and essential—in manufacturing because of its ability to detect patterns in large amounts of data much quicker than humans, the relationship still needs human experts needed to direct AI application development.

*Human experts bring their ideas of what has happened, what has gone wrong, and what has gone well; this adds value when combined with AI to offer a more optimal environment. This experience accelerates AI application development.

Building out the new dynamics in the respective intelligence for new growth opportunities

One thing AI does well is helping creative people do more. AI doesn't necessarily replace people; it shifts their focus and role. The ideal applications help people do what they're uniquely good at, applying their experience and human intelligence to build out the combined intelligence of AI / HI into new concepts, designs and value, in savings and alternative business options.

When adopting new technologies on the plant floor, the human factor is the most important to any change.

These two primary strategies to help make the workforce appreciate the introduction of AI are critically important when building out any AI transformation:

Clear Benefits for Human Workers:?Make the benefits to workers transparent, ease operations, and avoid direct conflict with existing work routines or have resolution processes in place from the start.

Involve Key Stakeholders: Resistance to AI solutions depends on AI team leadership and stakeholder involvement. The need is to involve experts in data evaluations and machine learning in the interested business units where tangible value can be recognized and discussed.

While the machines on the production level become increasingly 'intelligent' and capable of performing dynamic non-repetitive tasks, they still need humans to define the tasks and come up with the algorithms enabling machines to learn through experience with a large set of relevant data. These constantly evolve, building progressive learning environments where skills change and often get upgraded due to the change in nature of the work.

Achieving tangible economic returns from AI / Edge project introductions.

A real key to achieving a tangible economic return on AI projects is having clear strategies for finding value.?Including a strategy of close relationships between the data group and interested business units. By selecting projects with tangible value and a clear path to production improvement, lining up trust from key stakeholders in advance of development is essential. The involvement of all stakeholders provides a new environment of productive return and joint commitment.

The ideal place to begin implementations is to seek out a project that offers a real return with the tangible value shown as the place to start.AI groups should always describe projects in terms of the business case and benefits in outcomes. The demonstration of value can help build credibility and expand to future use cases in future implementations. Joint efforts with the business team also help plan how AI will be used in practice, ensuring the planned performance.

Increasingly, it's about the collaboration of humans and artificial intelligence being effectively combined to advance or provide a greater value.

Artificial intelligence has tremendous potential not only in designing products but also when it comes to making production more efficient, flexible and reliable. As the manufacturing industry continues toward increased digitalization, data in production environments are the basis on which entire plants operate, and systems generate.

There is massive potential for growth, efficiency and innovation when using artificial intelligence. The potential applications for AI are significant across industrial manufacturing.?Today, we are merely at the beginning of the opportunities this technology will provide.

Artificial intelligence will become an integral part of manufacturing and automation across engineering, operations, and maintenance. AI has the potential to change everything on a shop floor.?Technical constraints are less of a problem as we learn and gain implementation and operational experience. It is organizational and cultural conditions and the human factor emerging as the main hurdles to focus upon.

The ability to collectively learn enables the factory floor environment to become increasingly adaptive, fluid and dynamic. The combination of AI and HI with Edge Computing brings this increasingly within our grasp.

Finally here are two links you could refer to:

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 ease of reference to the second article

***For these two articles, I went into significant research, drew down valuable points and validated different aspects of AI from an assortment of different papers, articles and posts that contributed. I took a more specific view of the customers perspective in these to begin his exploring for undertaking this AI journey

?