Why does AI have a Huge Demand in Manufacturing?

I’m sure you have heard the following buzzwords frequently in recent years: IoT, artificial intelligence, machine learning, virtual reality, 3D printing, and the list goes on. Among these, artificial intelligence (AI) has received its fair share of attention. That is because of AI’s ability to mimic the human brain and perform tasks with utmost accuracy, productivity, and adaptability. There is no doubt, this technology can and will change the lives of many people and businesses around the world.

Today, businesses in the manufacturing sector are traveling along the fourth industrial revolution called “Industry 4.0.” Needs have evolved, as has technology. AI’s algorithms have been continually improving over the years and can help manufacturers gain a competitive advantage over their competition.

Why on demand?

With AI, manufacturing organizations can reduce their operational costs and increase production efficiency. They can streamline the process of quality checking, automation, safety, and security. Problem-solving becomes a cakewalk in production units, and companies can now attain an overall increase in production and profits.

As per the latest Global Market Insights report, by the year 2025, the anticipated growth rate in market size for AI in the field of manufacturing will surpass nearly 40%.

The new normal that manufacturing companies face today post the COVID-19 coronavirus pandemic shows the signs of the new age of intelligent manufacturing. Intelligent manufacturing will become the new buzzword in years to come. It revolves around the cycle of intelligent products (produced by the process of intelligent manufacturing) and intelligent services.

How can AI help in manufacturing?

A chapter in DAAAM International Scientific Book lists out the areas in which AI helps manufacturing companies. Here’s a glimpse of the article:

  • Automation

To transform a legacy manufacturing or production line into an automated unit, it requires deep analysis and learning on different patterns, processes, data, and tools. Thus, AI helps develop and create accurate automation that self-adjusts to the tailored needs of the production lines. By automation, the manufacturing industry can live the dream of maximum accuracy in productivity and efficiency. 

  • Accurate Quoting

The time consuming and increasingly dependent process of quoting becomes easier with the intervention of artificial intelligence. AI helps with quick analysis on various costs, including labor, material, machine time, etc. With this quick information processing, based on the order/production requirements, the AI system can come up with projected quotes that are precise in terms of time and cost.

  • Virtual Reality

Simulation and virtual product creation are key to any product development or manufacturing. Virtual Reality allows manufacturing companies to create products in simulated environments and perform testing in a virtual world. This greatly reduces the expenses involved in conventional methods of product development.

  • Production Maintenance

The primary goal of any manufacturing company is to reduce its maintenance costs. A computing system befitted with AI and ML can help these companies perform predictive maintenance in production lines. Predictive maintenance notifies the operators in the production lines about the approaching malfunctioning, as well as suggests the best possible solutions on behalf of maintenance engineers. The latter part of this article gives more insights into how AI and ML aid predictive maintenance in production floors.

A McKinsey report claims that factories with AI integration have reduced their machine downtime by almost 50%. The report also states that AI integration has increased their machine’s life span by 40%. Collectively, this forecasts a predictive maintenance savings of about $0.5 to $0.7 trillion.

  • Product Design

Artificial intelligence uses the expertise, knowledge, and creativity of designers and helps engineers design products at ease. An AI algorithm can interfere with a product design process even at varying levels. Components such as material, method, budget, design, and time are taken into consideration.

  • Quality

Days are gone when quality issues are identified post the production or manufacturing process. Today, with AI technology, manufacturing companies can predict upcoming quality issues that might emerge out of hassled production lines. This drastically reduces the cost of machining.

  • Digital Twins

At present, manufacturing companies can create a digital twin of their production process or a product or service. With cloud computing, IoT (Internet of Things), and machine learning, anything can be accessed from a remote environment or a location in a manufacturing plant.

AI based maintenance application, eKnows

eKnows acts as a knowledge base that continues to grow everlastingly. Knowledge may fade but not the data or the insights. This AI-based application is capable of analyzing historical maintenance data from maintenance engineers. Whenever the production line is about to face an issue that has already been a part of the historical references, the application keeps the respective stakeholders informed of the issue. With quick intimation or communication, unplanned or unexpected production downtimes can be reduced. Also, this eliminates the need for intensive training, all-in-all manuals, and knowledge transfer from maintenance engineers to the shop floor operators.

Other advantages of eKnows:

  • Reduced machine downtimes that lead to efficient and continuous production

  • Cloud-stored knowledge and data of both essential and non-essential maintenance history

  • Catalyzes the operators on the shop floor with the maintenance engineers’ expertise

  • Reduces labor costs incurred due to populated maintenance teams

  • The knowledge of maintenance engineers is not forgotten over time. They are digitally stored and accessed whenever required

  • Reduces the dependency and waiting time involved

More than streamlining, AI integrates the production process with the maintenance teams.

Recent research carried out by Wo Jae Lee et al. presented in the 26th CIRP Life Cycle Engineering (LCE) Conference stated that the predictive maintenance of machine tool systems reduces the machine downtime and increases the remaining useful lifetime in addition to ensuring machine safety.

AI technology combines raw big data, historical data, conceptual data, data science, geographic data, sensor data, and machine learning. Such combined data translates to useful and intelligent interpretations. These interpretations are guided by actionable insights. When companies integrate such future-proof AI technology, they can continue their journey toward rapid growth.

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