The manufacturing industry has been under significant pressure over the last few years. Labour costs are rising, and customer demand for faster, more customised products is growing. While the industry is also being disrupted by the digital revolution. AI and IoT have already begun transforming the manufacturing industry and this is set to continue in the years ahead.
Digital manufacturing refers to the application of computer systems to manufacturing services, supply chains, products and processes. Technological advancements, such as AI and IoT play an important role in the digital transformation of manufacturing services, products, and processes by providing manufacturers with valuable insights, automation capabilities, and real-time data to implement change.
Unleashing AI’s potential
AI has the capability to usher in a new era of digital transformation, presenting immense potential to revolutionise industries, enhance efficiency, promote sustainability, and engage the workforce. While the impact of AI applications in manufacturing and value chains is recognised, there remains untapped potential in fully harnessing their deployment and there are various ways in which manufacturers can harness the full potential of AI.
Powered by machine learning algorithms, AI has revolutionised several aspects of manufacturing. One of its key contributions is predictive maintenance. By analysing data from IoT sensors embedded in machinery, advanced algorithms can predict and detect equipment failures before they occur, allowing manufacturers to schedule maintenance proactively. This not only reduces downtime and production losses but also extends the lifespan of machines, optimising resources and reducing costs.
Another area where AI is making a significant impact is in quality control. Traditional quality control processes involve manual inspection, which is time-consuming, subjective, and prone to errors. AI-powered computer vision systems can automate the inspection process, analysing images or videos of products in real-time to detect defects, inconsistencies, or anomalies. This ensures higher accuracy, faster inspection times, and improved product quality, resulting in higher customer satisfaction and reduced waste.
AI is helping even in Product Design phase for improving the Design for Manufacturing and Design for Serviceability by studying Prototypes and 3D models, thereby determining the optimised way of manufacturing a product. Decisions made at the design stage helps in faster workflows by avoiding repetitive manual task. AI helps designers and engineers solve complex problems by creating intelligent algorithms.
Additionally, AI enhancing the efficiency of production planning and optimisation. By analysing historical data and real-time information, AI algorithms can optimise production schedules, minimise bottlenecks, and allocate resources more effectively. This enables manufacturers to streamline their operations, reduce lead times, and respond quickly to changes in demand. AI-powered optimisation can also help in minimising energy consumption and reducing the carbon footprint of manufacturing processes, contributing to sustainability goals.
AI is also assisting manufacturers to capture their workers’ tribal knowledge and aiding in democratising them for the benefit of the greater good – by recommending the optimal settings for the various scenarios and the products to be manufactured, suggesting the Next-Best-Action for process failures and equipment failures, and by implementing closed loop systems in getting feedback from the operators & implementing the same.
Utilising IoT to help transform manufacturing process
The manufacturing sector is under constant pressure to reduce costs, and at the same time to boost efficiency and productivity. IoT has emerged as a potential solution for manufacturers. IoT devices are revolutionising the way machines and systems communicate and interact with each other. By connecting physical devices to the Internet and enabling them to collect and exchange data, IoT is creating a network of interconnected devices and systems, forming the backbone of smart factories.
Through IoT, manufacturers can monitor and control their production processes in real-time. IoT sensors embedded in machines, equipment, and products can collect data on various parameters such as temperature, pressure, vibration, and performance metrics. This data can be transmitted and analysed instantly, providing manufacturers with valuable insights into their operations.
IoT can also help manufacturers in achieving greater visibility and transparency across the entire supply chain. Real-time data on inventory levels, production rates, and demand fluctuations allow manufacturers to optimise their inventory management, reduce stockouts, and improve order fulfilment. IoT-enabled tracking and tracing also enhance supply chain traceability, ensuring compliance with regulations and standards. For example, a global hi-tech manufacturer implemented a component content management system which optimised productivity and improved the overall efficiency of content management & translation processes and also helped the company in saving $6Million in 5 years.
IoT is also enabling the concept of smart products. Through embedded sensors and connectivity, manufacturers can collect usage data and feedback from customers, gaining valuable insights into product performance, customer preferences, and usage patterns. This information can be used to drive product development, improve features, and offer personalised experiences, thereby enhancing customer satisfaction and loyalty.
Best practices and potential pitfalls for an AI and IoT enhanced future
However, along with these opportunities, there are also challenges that need to be addressed.
The first and foremost challenge in exploiting the potential of AI is in establishing the Trustworthy AI platform for critical systems and improving the trust quotient over the period. Notably, there are international entities & governments who are helping in setting up Ethical AI systems, and popular frameworks includes “Draft Ethics Guidelines for Trustworthy AI by European High Level Expert Group on AI” and “AI for Europe by European Commission”. Adopting the framework guidelines and technologies (FEAS – Fair, Explainable, Auditable and Safe) will help in setting up and improving not only the trust quotient, also improves Compliance and legal factors as well.
One major concern is data security and privacy. According to a research, manufacturing is the most targeted sector by cyberattacks. With the massive amounts of data being generated and transmitted in digital manufacturing, ensuring the protection of sensitive information, and guarding against cyber threats becomes critical. Robust cybersecurity measures and protocols need to be implemented to safeguard manufacturing operations and intellectual property. In 2022, the number of ransomware attacks targeting industrial infrastructure experienced a significant increase, effectively doubling from previous years. These attacks pose a grave threat to the smooth operation of supply deliveries, potentially leading to a systemic impact. The disruptive nature of cyberattacks has the power to cripple businesses and disrupt supply chains, thus negating the benefits derived from digitalisation efforts. Consequently, financial and productivity losses are incurred, accompanied by reputational damages.
The success of digital manufacturing transformation endeavour depends on effective change management practises. It can be a difficult and time-consuming procedure to make significant modifications to current manufacturing processes. This is especially true when it comes to making critical changes for complex discrete and process manufacturers. Identification of the digital champion, establishing the clear change management story, developing a cross functional team and deploying incremental digital updates are the key to establish successful change management. Moving to Rollout from the pilot stage continues to be a significant barrier for most manufacturing organisations. In fact, there is a wider gap between piloting and rollout than between perceived relevance and piloting, indicating that scaling is more difficult than getting things started. Prioritising the use cases based on Return-on-value (ROV), ease of implementation along with success measurement framework will help to organise the investment during scale & rollout phases.
Another challenge is the need for a skilled workforce capable of leveraging the potential of AI and IoT. Manufacturers need to invest in training and upskilling their employees to equip them with the necessary digital skills. Collaboration with educational institutions, industry associations, and technology providers can play a crucial role in addressing this skills gap and fostering a culture of innovation in the manufacturing sector. The ability to collect and analyse real-time data, automate processes, optimise operations, and create smart products is reshaping the way manufacturers operate.
Technological advancements, such as AI and IoT, will continue to benefit manufacturers by opening new revenue streams and improving industrial safety, while reducing operational costs. However, to fully reap the benefits of these technologies in digital manufacturing, manufacturers need to understand and address data and cyber security, and workforce skills. By addressing these challenges, manufacturers will be able to harness the power of AI and IoT to help remain competitive and evolve in the digital revolution.
Dr Narayanan Ramanathan is the Global Head of Digital Manufacturing Services at L&T Technology Services, and is responsible for incubating and building the Digital Business focusing in the areas of IoT, UMACS, AR/VR, AI & Digital Transformation to drive the digital strategy with existing & prospective customers.