Efficiency, productivity, and profitability have been the quintessential mantras for manufacturing industries. We have collectively worked for centuries, beginning with the first industrial revolution, to optimise these three critical factors. With the onset of industrial revolution 4.0, we can fairly say that we are optimising efficiency, performance, productivity, and, ultimately, profitability like never before. We have incorporated some of the most in-demand technologies, like Artificial Intelligence (AI), Machine Learning (ML), data science, Industrial Internet of Things (IIoT), cloud computing, and more, into our manufacturing processes and workflows for precision outcomes and reduced overhead expenses.
We even have real-world examples. A France-based food producing company, Danone Group, shared that with the implementation of Machine Learning, they were able to reduce forecasting errors by 40%, reduce lost sales by 30%, and decrease the workload of demand planners by around 50%. Not just this, even our very own 184-year-old FMCG giant, Procter & Gamble Co, is taking the smart manufacturing route by implementing advanced algorithms, predictive analytics, and IoT in its paper towel manufacturing division. While this is happening across manufacturing companies at scale, there is still a haze around the benefits of AI in digital manufacturing and how it has been key in driving the transformation from conventional manufacturing to digital.
Ways AI is transforming manufacturing industry
AI in logistics
One of the most common ways for manufacturing companies to lose revenue is when they either overmanufacture or under-manufacture. Logistics, an integral part of the manufacturing process, takes care of stocks and inventories, letting retailers and wholesalers get a fair estimate of demand for a specific product.
AI’s role in this reflects on accurately pointing out demand, forecasting, and cutting any inventory-related loss for an organisation. AI and ML-powered algorithms can also optimise supply chains by accurately monitoring and managing fleets of vehicles, saving fuel by suggesting optimal routes, creating club orders from multiple orders bound by a common geographic location, and more. Rolls-Royce deploys concepts like Advanced Image Recognition and Machine Learning to enable its fleet of self-driving ships to improve supply chain efficiency.
Robots in manufacturing
General Electric (GE) was one of the first companies to deploy a concept similar to a robotic arm on its conveyor belts in 1961. It was called Unimate. It sowed the seeds for what was possible in the manufacturing space for technology, and today, AI robots powered by autonomously Machine Learning algorithms are deployed in industries to take care of all the heavy-lifting tasks. Such robots are being controlled by humans to optimise not just the manufacturing process but to pave the way for increased workplace safety as well.
Furthermore, 3D printers are reducing the costs associated with importing intricate parts. An engineer has to just visualise the part on a computer and get it 3D printed in real-time in the actual material. Thanks to the possibilities that 3D Printing opens up, its market is also expected to grow at a rate of 37% on a yearly basis. 3D printers are most predominantly deployed in industries such as healthcare, aerospace, and automotive.
Large-scale factory automation
Automation is a key to improving productivity and efficiency in manufacturing. While many redundant tasks are controlled and executed by humans today, the onset of AI and automation can revolutionise industrial productivity. So, apart from reducing labour expenses, AI can:
Autonomously detect anomalies in equipment, thanks to consistent real-world data processing
Accurately predict when a device is most likely to malfunction through device health supervision
Schedule equipment service even before it stalls
Bring additional clarity to operations by making manufacturing data accessible and interoperable
Drastically reduce the required volume of human power to execute tasks
Scale production up and down as needed, and more
Industrial Internet of Things (IIoT)
A manufacturing company can become smart only when powerful devices can function autonomously on the premises. Thanks to the rise of embedded computers and the IIoT, we can now push the limitations and restrictions we have been facing in conventional manufacturing.
Firstly, workplace hazards can be reduced significantly thanks to detection devices that can sense heat, sparks, toxic gases and substances, and more. Because the goal is not just reducing overhead expenses and increasing profits but also minimising carbon footprints and taking initiatives to become a more sustainable entity as well. With IIoT, the deployment of installations such as smart illumination, HVAC systems, and more is made possible.
Product development
Manufacturing is not just about the production of goods but involves the designing and development of them as well. There are dynamic expenses involved in trial-error methodologies in product development, which can be reduced or even completely eliminated with AI and emerging technologies.
Manufacturing is not just about the production of goods; it also involves the designing and development of them. There are dynamic expenses involved in trial-and-error methodologies in product development, which can be reduced or even completely eliminated with AI and emerging technologies.
Simulation of products in a virtual environment, concepts such as digital twins, AI-powered collaborative product development platforms, and more are revolutionising this phase of the process. With the incorporation of AI, a module for consistent feedback is created, probable bugs and errors in products are predicted, and expenses in go-to market are minimised as well.
Airtight cybersecurity
Some of the most brutal attacks are targeted at manufacturing companies after NBFCs. From ransomware to shutting down assembly lines, there are diverse forms of attacks targeted at them. This not only stalls productivity and their targets for a specific period but also scars their reputation as well.
Also, with the incorporation of IoT systems, we are only opening up more avenues for attackers to exploit. So, when AI steps in for cybersecurity, it makes detecting anomalies in networks more intricate, secures cloud and server architecture, paves the way for a more secure deployment of applications, and more.
Ultimate lead towards growth and profitability
The impact of AI in manufacturing is not just felt in organisations where smart systems and advanced algorithms are deployed. It is, in fact, felt by customers, vendors, and every other stakeholder in the manufacturing ecosystem. From providing the most relevant product for a customer’s concerns to getting them delivered on time, the market benefits of AI implementation in a manufacturing unit are aplenty. This ultimately leads to the growth and profitability of a venture.
We are just knocking on the doors of opportunity with respect to AI in manufacturing. As we make progress and discover new avenues, it will be exciting to see what other possibilities we unlock.
