Comment: How cognitive manufacturing is bringing Industry 4.0 closer

The manufacturing industry on a global scale has experienced a period of significant instability in recent years. Even though ONS data revisions have shown that the sector recovered more quickly than originally thought after the pandemic, the UK's industrial production has steadily declined since early 2021. The combined effects of inflation, energy costs, and tax policy affected the industry. This is an urgent and tangible problem that almost all manufacturers are facing. With the right technology partners, digital transformation can help businesses reduce operational costs while driving forward sophistication and innovation in manufacturing supply chains.

Manufacturers are also expected to meet customers’ needs at every interaction to gain customer loyalty. Many seek high levels of personalisation and customisation, continuing to blur the line between consumer and creator. A new global consuming class continues to emerge in developing nations, gifting global manufacturers substantial new opportunities, albeit in an increasingly uncertain economic environment.

Sustainability continues to remain a strong focus due to increased attention from stakeholders, regulatory change, and technological innovation. With industry research highlighting that manufacturers account for 30 per cent of global greenhouse gas emissions, many will make decarbonisation their biggest environmental priority alongside waste reduction. Using renewable electricity resources or green hydrogen will become next to normal to run factories, while energy management will become integral to the net-zero journey.

Preparing for Industry 4.0

Digital transformation is key to addressing these challenges and ensuring the long-term viability of firms through improved efficiency and innovation. In order to excel in the Industry 4.0 era, manufacturers must effectively leverage legacy, and unstructured data to make informed decisions that optimise both quality and throughput.

Manufacturers are increasingly utilising in-house and third-party data to enhance their capabilities in big data, artificial intelligence, machine learning, deep learning and robotic process automation (RPA). This cognitive manufacturing approach involves uniting hundreds of thousands of data points across systems, equipment, and processes, enabling insights across the entire value chain. This data can be harnessed to assess performance and generate predictive insights, enabling informed decisions and responsiveness. Additionally, manufacturers can leverage the Industrial Internet of Things (IIoT) to enhance their operations.

The IIoT continues to revolutionise many aspects of manufacturing operations. As the number of networked sensors increases across production, supply chains and products, manufacturers are starting to use new systems that enable real-time, automatic interactions among machines, systems and assets, and things. A strong expansion of IoT applications is expected over coming years, with the global industrial IoT market expected to reach $949.4bn by 2025 - representing a compound annual growth rate of 29.4 per cent.

The importance of IoT & advanced analytics

IoT is essential for manufacturers to improve their sustainability credentials. By collecting data on factory floor materials using sensors, businesses can track their flow throughout the supply chain. Data-analysing capabilities can then determine how much material was used. In doing so, organisations can analyse just how much material is needed and therefore reduce the generation of waste. Real-time access to these valuable data enables organizations to analyse material usage and ensure their supply chain processes comply with ESG standards.

Role of cognitive manufacturing in enhancing efficiency

To generate actionable insight, cognitive manufacturing harnesses all available data from equipment, systems, and processes. The IoT, analytics, and cognitive technology are used in Industry 4.0, which enables manufacturing environments to be more reliable and efficient. In addition to improving fundamental business metrics, it allows organisations to reduce downtime and lower costs, while improving productivity, product quality, safety, and yield.

According to Forrester Consulting, over 30 per cent of manufacturers are unaware of the inventory, raw materials, work-in-progress, and finished goods levels in their distributor networks. There is a lack of appropriate data to drive this transparency, which is the most frequent cause. Enhanced predictive analytics driven by cognitive manufacturing can allow smart manufacturers to capture, cleanse and analyse machine data to reveal insights. In manufacturing, this brings predictive and proactive maintenance within touching distance.

Data captured by IoT devices connecting different assets and systems enable businesses to predict, plan, and take proactive steps for any events such as parts repair or equipment failure before it occurs. Predictive analytics can also extend the lifespan of equipment, helping factories to boost their sustainability credentials. By monitoring, maintaining and optimising assets, manufacturers can predict machine failure and identify parts that need replacement. This quick and proactive action is essential for extending the lifespan of machinery.

Emerging technologies such as the IoT and cognitive computing are beginning to deliver greater margins, driving faster and more meaningful innovation across the factory floor. A recent Gartner survey found out 74 per cent of manufacturers believe smart manufacturing will increase their competitiveness. A growing number of operations are becoming automated and streamlined, with greater visibility across the supply chain. With the pace of change only expected to increase in the coming years, manufacturers must look to strengthen cognitive computing capabilities today - an essential step for unlocking the next phase of growth and innovation.

Jinender Jain, head of sales UK & Ireland, Tech Mahindra