How artificial intelligence for manufacturing boosts productivity and revitalizes processes

Artificial intelligence (AI) has revolutionized many sectors including manufacturing. The term artificial intelligence refers to a set of established and emerging technologies that can be integrated into various processes to enable continuous learning, comprehension, and a course of action. The output lies in being able to optimize such processes across various industries. AI for manufacturing is the perfect example of the intersection between technology and process optimization.

AI-enabled smart machines are designed to stretch technology capabilities through a process quite similar to human beings: just as people sense, gain knowledge, make decisions, act upon them, and learn from the experience, technology can now do the same.

Why manufacturing needs artificial intelligence

Artificial intelligence has the potential to turbo-charge manufacturing processes and hike profitability. In the case of manufacturing, artificial intelligence may boost the gross value added (GVA).

This is good news. When corporate profits are sliding into a decline, the pinch is felt in the manufacturing sector as well. A decrease in investment can lead to a drop in innovation downstream, diminishing the company’s market value and ability to grow or meet targets in a constantly shifting, disruptive environment. This impacts the bottom line and affects all stakeholders. Additionally, artificial intelligence could prove to be beneficial for a less profitable industry sector.

Challenges: what does it take for a successful integration of artificial intelligence?

Management and employee acceptance

For artificial intelligence to successfully become a part of manufacturing processes and reach its full potential, the first step is human acceptance. This calls for the forging of a novel relationship between humans and machines: to view automation not as a threat to human jobs, but rather as an ally that boosts human performance by cutting time spent in mundane, repetitive tasks—freeing up mental space for innovation and diminishing human error. Humans can harness technology and mold it per requirements, and, in the case of AI-driven tech, enable it to continually improve upon itself.

By taking on low value-add jobs from workers, artificial intelligence can free them up to increase productivity across key output areas. Artificial intelligence is nothing short of a virtual colleague, adding value to work life. Modeling employee attitudes based on this premise will go a long way in promoting its adoption in manufacturing as well as other production-dependent industries.

Effective practices for manufacturers adopting artificial intelligence can include developing an inclusive communications methodology for stakeholders, employees, partners, investors, and customers.

Other challenges to artificial intelligence deployment

Other challenges to using AI systems and solutions include unclear technology implementation in traditional manufacturing set-ups. This goes hand in hand with a lack of training and skill sets needed to work with the new systems. However, this is not unsurmountable.

Manufacturers might find it difficult to integrate artificial intelligence and achieve digital transformation with minimal disruption to production cycles and timelines. Key safety and quality standards still must be met, and commitments adhered to.

Projections for artificial intelligence- powered tech in manufacturing and industry

The main driver behind artificial intelligence is the rise of the Internet of Things (IoT). IoT enables a connection and dialogue between physical devices and the digital realm.

Companies should be quick to capitalize on this know-how. There are a range of new job descriptions being created and will probably continue over the next five years.

  • Machine learning engineers or specialists
  • Collaborative robotics specialists
  • Data-quality analysts and artificial intelligence solutions programmers or software designers

They also predict fusion skills to be in demand. The term is applied to the unique ability to merge human and machine talent in a business process to deliver a better outcome than what each would as a standalone entity (essentially combining discrete forces to expand capabilities across human resources and machines). Individuals with fusion skills will be deployed to train, teach, and shape human judgment to harness the ability of smart machines. An example of this is in iterative or repetitive processes where each party learns from the other one.

Manufacturers are also developing in-house artificial intelligence skill sets through dedicated learning and development programs.

How artificial intelligence can revitalize the manufacturing industry

Experts believe that artificial intelligence, as a factor in production and manufacturing, can be a power player in at least three critical ways:

  1. Intelligent automation: the new ‘virtual workforce’ complementing human resource
  2. Augmenting skills and boosting abilities of the existing human assets
  3. Helping make the most of the budget and directing a portion to innovation, research, and development.

From an on-the-ground perspective, there are the various ways in which artificial intelligence gives manufacturing support and helps with growth.

Automate processes

While automation in manufacturing is not new, artificial intelligence has added such a valuable new dimension that the future impact of smart automation can be nothing short of phenomenal.

Large manufacturing companies that wish to remain market leaders are invested in harnessing artificial intelligence. They use technology to enable machines to be self-sufficient to the extent of self-diagnosing problems and failures, estimating maintenance needs, and preemptively ordering spare parts.

AI-driven automation confers a massive competitive edge.

Streamline supply chains

Artificial intelligence can be an incredible money-saver, shortening supply chains to better utilize time and resources. Artificial intelligence systems can track events that delay any of the links along the supply chain, from major issues to the smallest one-off incident. It can track transportation and scan a multitude of records to maintain real-time visibility along the supply chain. Systems can analyze millions of incidents a day in real-time. Artificial intelligence capabilities lend major muscle to early warning systems, strategizing, and finding solutions to supply chain issues.

Set benchmarks, conduct, and monitor quality control

Machines can be fitted with cameras several times more sensitive than the human eye, which can detect even the smallest defects. This feature can be of value in almost every manufacturing industry, from metallurgy to retail and everything in between.

Shorten timelines in product design and development

Artificial intelligence drives innovation as a direct side-effect of doing away with redundant costs. Saved money can be diverted to research and product development, which then generates fresh revenue streams for better profitability.

Improve asset utilization and production reuse

Companies often invest huge amounts upfront into equipment, which may or may not be utilized fully. In addition to dormant assets, manufacturing downtime and equipment breakdown all contribute to loss of revenue. Artificial intelligence can help to prevent these issues by flagging problems, predicting failures, and suggesting preventative measures before a piece of equipment fails.

Predict outcomes

Artificial intelligence can discern patterns in manufacturing and other business areas to predict future outcomes and formulate strategies. AI can empower companies to uncover, structure, and analyze unstructured data both in-house, and from within the industry, in addition to incorporating human feedback.

Adopting artificial intelligence into work streams opens up a whole new world of processes—disrupting and eliminating age-old procedures that no longer serve a purpose.

Examples of artificial intelligence in action

Digital twins

A digital twin is a virtual model of an operating process, product, or service; this model shows the asset in action, dissected, tweaked, refined, and tested in the virtual world. Creating a digital twin effectively builds a bridge between the physical and virtual world. This phenomenal pairing of worlds, so to speak, allows the object or process to be monitored on a granular level not possible in real life, across the entire life cycle. This kind of simulation allows stakeholders to predict problems or prevent them altogether. It opens up mind-boggling new avenues for product development, strategizing, and innovation. The potential is staggering.

Pairing technology, the ancestor of today’s digital twin technology, was used by NASA. It was important to the success of its missions, given the imperative to work on its units in space, far beyond normal visibility or physical proximity.

A digital twin works in a two-step process:

  1. Smart device components use built-in sensors to garner data and information about key operational aspects—such as real-time status, position, or working condition of a physical item. The components are linked to a cloud-based system that collects and processes this data input. This is further analyzed in various contexts relevant to the business.
  2. The insights from this virtual world are then applied to the physical world or the entity being studied.

This process can be transformative to a business enterprise. Digital twins not only build bridges between the physical and virtual world but also foster cross-workstream collaboration between aspects such as product design and data science for more intuitive product development.

The concept of digital twinning emerged around the new millennium but reached fruition only through the development of the Internet of Things. Today, companies find it a cost-effective measure to implement and incorporate into their strategic technology arsenal. Manufacturers are increasingly investing in digital twin technology to revitalize their business and give it a new direction.

It is no surprise that the digital twin market is expected to grow.

Computer-aided design systems

A computer-aided design system is capable of harnessing artificial intelligence to draw upon the cloud and create thousands of virtual prototype iterations. It can compare and contrast their functioning, cost of construction, and materials needed. The program starts with a solid mass representing the desired shape. Then it begins to strip away layers or pieces of material to see whether doing so hurts or helps performance, and remembers the needed outcome, building it into the design criteria. This helps the algorithm uncover how the role of each piece in a product affects performance.

Computer aided design in manufacturing across sectors

In the Healthcare industry, computer aided design has been used to design a type of facial implant that accelerates recovery and boosts tissue regrowth. Also, avant-garde 3D printing techniques for organ transplants have been developed using AI-aided design and manufacturing inputs.

In the automotive sector, artificial intelligence can analyze millions of information inputs to streamline production and design new vehicles.

Artificial intelligence has endless possibilities. Manufacturers can use AI to remain competitive, highly functional, and profitable.

diagram AI for manufacturing