AI is not the only weapon to fight manufacturing headwinds in 2024
Market resiliency will be a defining factor for manufacturing success in a year of increased competition, new industry regulations such as SEC disclosure and ever-growing customer demands.
One thing remains clear amid this ongoing market volatility — resilience, agility and adaptability will be crucial to competitive differentiation. New priorities need to be established for manufacturers to fight back — from circular business models, artificial intelligence (AI) data pattern recognition tools, smart integrations and the connected worker.
1: Building circularity begins in product design
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. To reduce the dependence on the availability and cost volatility of raw materials and make their business more resilient in the future, manufacturers must rethink their existing business models to embrace circularity.
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.
For manufacturers, there is increasing urgency surrounding the circularity as the regulatory landscape and circular economy policies evolve rapidly. These already are, or shortly will, have a profound impact on the ways manufacturers can operate, both in the near and long term. As various regulations come to fruition, such as the SEC disclosure requirement, the transition to the circular economy has been accelerated.
To ensure manufacturers are prepared for their transition to a circular business model, they need to be enabled by the right technology.
Manufacturers must design for circularity. 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 products throughout their lifecycle. That way, manufacturers never lose sight of a product’s journey.
2: AI pattern recognition identifies value out of values
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.
As manufacturing systems becoming more complex, AI-driven data pattern recognition is crucial for sharpening quality control, predicting equipment issues and optimizing production for fewer defects, higher OEE and significant cost savings. With Industry 4.0 and the emerging Industry 5.0, there will be too much data being generated every second for the human mind to cope with — AI will become an indispensable tool for manufacturers.
3: Smarter enterprise software integrations allow manufacturers to plan and adapt in real-time
The traditional static planning approach is no longer sufficient for modern manufacturing. 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%.
Recent IFS research has shown that manufactures 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.
4: Power to the people to address productivity issues
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.
However, as highlighted in a recent IFS Customer Advisory Board meeting, “capturing the right skills is only half the battle, training and retaining talent is the bigger one.” Moreover, new hires may not be as efficient or experienced as departing employees, resulting in lower productivity and potential quality issues.
To address this, 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.
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.
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.
As people remain a company’s most critical asset, connected worker tools and platforms can enhance engagement, boost productivity and improve job satisfaction — driving competitive differentiation. In the future, AI will also play an important role in further empowering the connected worker, by providing insight and accuracy to improve efficiency.
The path to manufacturing resilience involves more than just AI tech
There needs to be a strategic focus across multiple technology fronts for manufacturers looking to ensure resilience against market volatility, influence change and drive competitive differentiation in 2024. AI is not necessarily the industry panacea. While it can certainly help focus on analyzing key data patterns inside the factory, a more holistic view of manufacturing operations will include everything from factoring circularity into every product design, data integration into other business-critical systems and, not forgetting the most important asset of all, the people on the factory floor. A truly resilient manufacturing organization will prioritize all these areas for the next year and beyond.
