Around six decades ago, at an academic conference, John McCarthy coined the term ‘Artificial Intelligence’, and mankind has since been on a quest to discover new possibilities for what computer software can do. Now, around 65 years later, Artificial Intelligence (AI) is in almost everything that surrounds us; it is embedded into the very existence of human life. To speak in a layman’s language, AI can be understood as a computer’s ability to comprehend and execute complex tasks or a set of tasks that are usually carried out by humans; hence, require human-brain-like cognitive functionality. This is made possible by programming software to identify and replicate the thinking patterns of human beings and also with the help of huge data; it can then be processed in numerous combinations to solve new problems or execute complex tasks.
Breaking the myths about AI and at its major purposes
Even though, over the years, inventions and breakthroughs in the field of science and technology have weaved AI across a host of different sectors that concern human life such as medicine, education, banking, finance, the automotive sector, and even mankind’s ventures to space exploration; there have been various myths pertaining to AI, its use or rather its probability of being misused. In the long list of myths surrounding AI, one is that AI is all about big machines, and black boxes; people often tend to surmise that Artificial Intelligence and Machine Learning, aka ML, are the same, but they are not. ML can be understood as a sub-field of AI that focuses more on techniques or patterns to perform certain and limited tasks in a controlled setting such as a factory. Another common misconception about AI among people is that it is exorbitantly expensive, but the fact is with new optimisations the use of AI in both commercial and personalised projects are becoming cheaper and much more affordable.
One of the major purposes of AI is efficacy in work and efficiency in monetary terms. At a micro-level, the initial cost involved in the setup and development of AI may seem higher, but from a macro-level standpoint, AI saves significant costs and efforts involved in the hiring, training, managing, and retaining of human resources. The deployment of AI has been underway for quite some time. In factories and manufacturing plants, AI-driven mechanical ecosystems are responsible for many thriving businesses across sectors. Since AI programming can be done to deliver desired tasks and its operational costs are quite low, it is considered ideal for solving an extensive list of problems.
In the manufacturing world, the use cases and practicality of AI are significant and now rather integral. According to a McKinsey report, with AI and ML working in tandem by the year 2025, approximately $3.7 trillion worth of value can be created, of which AI alone can generate approximately anywhere between $1.2 and $2 trillion in manufacturing and supply chain management. The vast data storage capacity and quick interpretational powers that AI can provide to stakeholders are unfathomable. Thriving manufacturing and logistics companies have utilised this to maximum potential resulting in 10-19% decrease in operational costs and 6-10% uptick in overall revenue. Some major problems that AI can solve are efficiency and waste reduction; lowering of labour costs regardless of geopolitical disruptions; operational challenges; and end-to-end visibility of manufacturing operations.
AI permeates Industry 4.0 ecosystem
In this era of AI, it is not just the tech and data giants of the global business ecosystem that are harnessing the power of AI or developing it, small and medium-sized organisations are now actively involved in the AI-driven revolution across the globe. Lately, post-COVID, the manufacturing sector has observed an influx of new players; many of them are utilising AI in innovative ways and solving recurrent problems. An outcome of this widespread use of new technology to bring about change within an industry is establishing the next frontier of Industry 4.0.
By definition, Industry 4.0 refers to a new phase in the industrial revolution that focuses heavily on interconnectivity, automation, machine learning, and real-time data. This new phase has brought with it certain promises within the manufacturing industry specifically - one that increases competitiveness and boosts profits as well as productivity in tandem with revolutionary technology. The complete change of Industry 4.0 can only be realised when manufacturers embrace Artificial Intelligence, Machine Vision, Machine Learning, cobots (collaborative robots), cloud architecture, data, and more. These aren’t just nice-to-have perks; they’re must-have capabilities that are defining the future of the industry.
AI and tech-driven advancements
With the wave of new AI and other tech-driven advancements, it is evident that software-defined manufacturing based on logic and intelligence is the future rather than hardware. This paradigm shift will apparently be able to tackle persistent issues that have been posing recurrent roadblocks across industrial operations for years. Some of the most common issues that need urgent rectification are – expensive, inflexible equipment that impacts the bottom line, economically and environmentally damaging operational processes, and manual, mundane assembly line tasks that deter fresh talent from pursuing the manufacturing industry.
Diving down to the basics of AI-based software-driven manufacturing is the Microsoft Power Platform, which is an integration platform with a line of business intelligence, app development, and app connectivity software applications. It was developed for expressing logic across platforms such as GitHub and Teams. Manufacturing requires fast analysis to manage complex inventory, quality, suppliers, and production processes; this is where AI plays an integral role and helps with power efficiencies. In the manufacturing industry, technicians are leveraging Microsoft Power Platform to speed up the repair process by looking up the manual for a piece of machinery by taking a picture, even if the UPC/serial number is not visible. To understand its case study, one must first have a basic understanding of the four major components of the Power Platform, namely:
Power BI: It is a business analytics tool.
Power apps: It is a dedicated application for the development of low- to no-code apps.
Power automate: This component enables process automation.
Power virtual agents: Simply put, these are intelligent virtual bots.
Some other ops-based use cases of AI in manufacturing are: invoice scanning; QR code generation and cloud printing; end-to-end tracking of the product processing lifecycle; integration with the ERP system.
Positive case stories keep AI ahead of the curve
A recent use case comes from a leading provider of manufacturing food products for pets. They offer an array of products, including health aids, grooming tools, collars, leashes, halters, leads, food, functional treats, supplements, safety and agility flooring, and other innovative products. As a manufacturer and distributor, their direct customers include pet professionals, retail stores and chains, e-commerce websites, and distributors. With the use of Power Apps Lot Tracking, the production team on the shop floor and the purchasing department used the application to scan and track items across the stages of food preparation. Employees used Power Apps on their mobile devices to observe strict food processes and product dates.
Given the pace of AI in manufacturing and related trends, the market is expected to grow from $1.1 billion in 2018 to $18.5 billion by 2025. (source: Zion Market Research). That is an annual growth rate of 49.7%. over the next 5 years to reach this market size. To be ahead of the curve, players in the manufacturing industry must have a plan, set up a budget; integrate their current technologies; communicate with employees; and improve data collection efforts. The next decade will see an interesting explosion in Artificial Intelligence. Enterprises in the manufacturing industry need to take advantage of it before their competitors do.