Complex market challenges are part and parcel of the manufacturing landscape, and businesses are no stranger to embracing technology in order to keep abreast with new industry regulations and ever-changing customer expectations. In 2024, manufacturers must look toward AI as well as beyond it to improve efficiency and strengthen supply chains, helping them stand out from the competition.
1) Manufacturers must redesign for circularity to eliminate excess waste
It’s becoming increasingly evident that manufacturers’ “take-make-waste” linear business models have become unsustainable, unpopular, and exposed to more business risk. Nealy half of global manufacturers are worried about the lack of key supplies, with the same percentage expressing concerns about the increasing costs for raw materials. There is also increasing urgency surrounding circularity as the regulatory landscape and circular economic policies evolve rapidly. As various regulations come to fruition, such as the SEC disclosure requirement, the transition to the circular economy has been accelerated.
Meanwhile, Bain & Consulting found that 33% of executives expect their industry to be disrupted by circularity start-ups that put products or materials back into the supply chain. By re-using materials over and over again, companies not only create resilience through a decreased dependence on virgin materials but also yield more profitability out of the same product.
To ensure manufacturers are prepared for their transition to a circular business model, they need to be enabled by the right technology. Indeed, approximately 80% of all product-related environmental impacts are determined during the design phase of a product. At this stage, manufacturers need to think about the choice of their suppliers, but also how redesigning their products them can make them easier to disassemble, repair, and recycle in the future to enable circularity.
Going further, the industry at large needs the ability to handle returns and incorporate reverse logistics, a strategy that Gartner recognizes as a key engine to drive circularity strategies. Using a circular business model, reverse logistics allows manufacturers to return goods at their end-of-life, creating an efficient flow of goods and reducing waste.
Finally, traceability is another key capability needed for circularity, as it enables manufacturers to track and trace materials, parts, and products throughout their lifecycle. That way, manufacturers never lose sight of a product’s journey and environmental impact.
2) People are a business’ greatest asset – invest in employee productivity to unlock $100 billion in value
The manufacturing industry is facing a talent crisis so deep it could threaten its growth and recovery. In the U.S. alone, the manufacturing industry is expected to have 2.1 million unfilled jobs by 2030. Aging workforces and ‘disintegrating behaviors’ inclusive of a change in work ethics and demands are key causes of this talent crisis.
Workers increasingly expect more flexibility and other non-monetary rewards, a phenomenon likely accelerated by the COVID-19 pandemic. Meanwhile, increasing employee turnover has significantly disrupted shop floor productivity, schedules, and workflows.
To address these dynamics, manufacturers have called for the integration of technology to improve productivity. Indeed, a recent study has shown that almost two-thirds (62%) of employees could get more work done if they had better tech tools, with more than half (58%) claiming that their technology needs have increased in the last five years.
Utilizing connected worker technology and digital collaboration has the potential to unlock more than $100 billion in value for the manufacturing industry. In addition, it could lead to productivity boosts of 20%-30% within intensive work processes.
An IDC study commissioned by IFS revealed that 45% of manufacturers have made it a priority to augment the worker experience with the help of technology. Embedding technology by involving workers in the process—a concept also known as the “connected worker”—will enable manufacturers to drive productivity, efficiency, and improve the shop floor worker experience.
3) Manufacturers connect the dots with AI-driven data recognition
In just a few years, AI spending in IT is expected to rise by 40%. This rise in investment will help manufacturers improve efficiency through AI data pattern recognition.
By using historical data, AI swiftly analyses real-time production data, identifying patterns and anomalies. The long-term value of AI and data pattern recognition will provide manufacturers with ongoing root cause analysis, streamlining work, and predicting potential product quality issues by comparing various data points. According to a McKinsey report, using AI pattern recognition tools can lead to a 4% increase in revenue, up to 20% reduction in inventory, and a decrease in supply chain costs up to 10%.
As manufacturing systems become more complex, AI-driven data pattern recognition also is crucial for sharpening quality control, predicting equipment issues, and optimizing production for fewer defects, higher OEE, and significant cost savings.
4) From reactive to proactive – AI integrations bring a new level of control to inventory management
Recent IFS research has shown that manufacturers continue to face ongoing supply chain challenges. However, by leveraging AI, ERP, and EAM technologies, manufacturers can optimize their inventory with real-time machine data. With the addition of AI-powered tools, manufacturers will have the ability to respond swiftly to demand shifts, supply chain disruptions, and market changes.
For example, using AI embedded within ERP systems, manufacturers will be able to swiftly adapt to unexpected raw material changes, predicting potential supplier delays. By doing so, manufacturers can enhance their adaptability, reduce lead time, and minimize the impact of supply chain disruption for efficient production.