2025 will bring new changes and challenges to the manufacturing industry, with a continued push toward digital transformation centered around data-driven decision-making and predictive maintenance. Businesses have been collecting data for years, but the real challenge is how to use that data to fuel future business opportunities, streamline operations and enhance decision-making, which really drives competitive advantage.
There is no doubt that industry has faced many challenges this year, primarily driven by geopolitical and economic issues outside of our control. Yet, what we can control is where we focus our investment — and smart technology investments are critical to future-proofing organizations against uncertainty.
This year, the challenge will be not only keeping pace and embracing technological change but mitigating the risks of increasing labor shortages and supply chain disruptions.
Manufacturers continue moving towards digital transformation
In recent years, we have observed a significant acceleration in the adoption of digital transformation within the manufacturing sector. This move has been characterized by a drive for competitiveness, efficiency and advantage, which we anticipate will continue into 2025. This momentum shows no signs of slowing as the clear benefits of automation, artificial intelligence (AI) and the Industrial Internet of Things (IIoT) are putting pressure on businesses to adopt new tools to stay ahead.
While organizations commonly embrace new technologies to increase reliability and break down information silos in manufacturing plants, we are seeing this transformation go far beyond the plant floor. Technicians are using AI-based analytics to troubleshoot a broken asset. C-suite executives are using advanced modeling and forecasting tools to assist them in deciding to repair or replace big-ticket equipment. We have witnessed a profound shift in the way companies are enabling using data-driven decision-making – spanning every role and becoming an integral part of every level of the enterprise. AI and advanced technology empower people across organizations to make smarter, faster and more impactful decisions.
We are already seeing how AI can transform predictive maintenance and process optimization processes, enabling manufacturers to improve uptime and efficiency while reducing operational costs. Yet, beyond these gains, the ability to plan things such as inventory, resources and production is proving to be a game-changer for efficiency and the bottom line. Similarly, we are also seeing how customers use digital twin technology as a strategic asset on their plant or factory floor. By creating real-time simulations and virtual replicas of plants or production lines, businesses can simulate, test and refine new strategies before rolling them out in full.
The value lies in selecting the right use cases for each business. There is tremendous pressure on decision makers to adopt every new technological solution that comes onto the market to avoid falling behind their competitors. However, in order to achieve ROI, businesses must first be clear on their priorities and desired outcomes and align the adoption of new solutions to support these. That being said, there is a risk in being too cautious to adopt. Those who invested in pilot projects this year will likely be in the next phase of rollout in 2025 — it is a fine balance between making sure you have the right use case and keeping up to stay competitive.
Building supply chain resilience through improved management practices augmented by AI
The past few years have not seen much progress in mitigating global supply chain disruptions. Disruptions due to mismatches in supply and demand have eased. However, these have replaced with increased risk to macroeconomic and geopolitical trends. We are facing increased turmoil with uncertainty around trade headwinds, raising concerns about overseas suppliers' reliability. As a result, businesses are actively seeking ways to enhance adaptability and transparency within their supply chains. This includes reshoring operations, exploring alternative suppliers outside regions more susceptible to disruption and adopting dual-source strategies for critical equipment.
Organizations are continuing to search for other solutions to tackle persistent supply chain issues, aiming to find the sweet spot between adaptability, transparency and accessibility. The integration of advanced technologies such as AI and predictive maintenance into supply chain planning enables businesses to forecast demand for spare parts in advance and with greater precision. This facilitates a transition from “just in case” to “just in time” MRO inventory management. The result is not only a reduction in spare part inventory levels with the associated capital costs, it also helps minimize unplanned downtime.
Effective inventory management is a big focus here. More organizations are moving from spreadsheet- or pen-and-paper-based inventories to virtual storerooms that give an accurate, up-to-the-minute picture of supply levels. RFID technology and IIoT devices can provide real-time parts tracking. These tools provide workers with real-time visibility into supply levels, enabling them to search for parts quickly, see what is on hand and where it is located, and reserve it for their tasks, all from a mobile device. This also benefits multi-site facilities, which can share spare parts and reduce the uncertainty of relying on supply chain vendors. For example, our customer, Hexpol, has seen success by streamlining their supply chain by facilitating inter-plant sharing, circumventing long lead times between plants in neighboring states, and driving efficiency.
AI is also going to be critical for strengthening these ecosystems by making recommendations for restocking schedules so that companies have a strategic edge to maintain optimal inventory levels. Utilizing predictive insights, companies gain the foresight needed to restock replacement parts and avoid unexpected downtime due to shipment delays. It is all about predictability, and every day gained from increased visibility into their operations is another day of avoiding downtime.
The future of skilled labor fueled by technology
Skilled labor shortages continue to be an industry-wide challenge. Fortunately, technology’s value is coming to the fore and helping to bridge this gap. Businesses are increasingly turning to advanced technology to enhance productivity and reduce their reliance on a shrinking supply of skilled labor. Many of our customers are leveraging automation and advanced functionality within tools to help streamline tasks and increase output with fewer hands-on roles, particularly for repetitive tasks.
Fluke Reliability’s recent survey underscores this shift — 98% of respondents consider AI a viable solution to the skills shortage, with 36% stating their primary motivation for implementing AI is to address this problem. While numerous industry use cases support this, we have seen some of the strongest emerge within manufacturing, where pilot projects are driving newfound efficiency and value. By increasing automation, organizations are reducing the demand on employees, streamlining tasks and reducing the reliance on hands-on roles for repetitive or dangerous jobs.
For example, a common challenge facing our customers is how to efficiently assets across the plant floor in single facilities or cross-site operations with a smaller, potentially lower-skilled workforce. Remote condition monitoring solutions powered by AI analytics coupled with wireless sensor technology enables a smaller group of technicians to maintain visibility into asset health that would have previously required a large number of distributed boots on the ground. This approach, enabled by AI-powered analytics and condition monitoring, makes it possible for expertise-constrained operations to adopt a data-based maintenance strategy and deliver benefits in uptime and efficiency. It is a win-win.
Additionally, emerging technologies such as virtual reality (VR) and augmented reality (AR) capabilities are reshaping how we train and upskill the workforce. While there are still questions about the safety of AR on the factory floor, it presents an opportunity for off-floor simulation, helping people become familiar with operations in a risk-free environment before progressing to the real task at hand. Less intensive, we see an opportunity for GenAI to bridge the knowledge gap as a co-pilot to walk less experienced maintenance technicians through more complex fixes.
As an industry, we need to focus on what we can control to fix the skills shortage: automating processes through AI to relieve teams, adopting new technology or training to upskill existing employees and ensuring knowledge transfer from those set to retire. These steps, coupled with investing in the next generation, are steps most businesses have within their control — it is our collective job to protect the future of the industry.
Predictive maintenance practices to continue moving sustainability initiatives forward
Predictive maintenance is increasingly being discussed in conversations around sustainable operations. It optimizes equipment for reliability and environmental impact. By enhancing renewable energy asset availability, extending equipment lifespans and lowering emissions through improved energy efficiency, predictive maintenance supports companies’ Environmental, Social and Governance (ESG) goals. With customers placing more emphasis on sustainable operations, this strategy enlists operational reliability in support of global environmental priorities.
The current economic climate has amplified the focus on efficiency and maintaining equipment rather than replacing it. More businesses want to automate their operations with tactics such as condition monitoring and connected reliability. Similarly, as organizational PdM initiatives build momentum, companies collect more data and effectively gain more knowledge about their equipment and its health. The next step, and the real opportunity, lies in analyzing the data, which AI can do with ease. Using an AI-powered diagnostic engine can compare new data to historical data to detect issues within an asset before it fails — and even make recommendations for how to fix the problem, all of which contribute positively to sustainable operations.
Accessibility is improving, too. Advances in technology, such as wireless sensors, have made continuous monitoring more affordable, allowing for broader asset coverage and improved efficiency. This approach cuts energy use, reduces waste and lowers costs, demonstrating that sustainability-focused maintenance can drive both environmental and economic benefits.
Conclusion
This new era of connectivity will enable data to flow seamlessly between departments, orchestrated by intelligent algorithms that analyze information in real time. Imagine a company where predictive analytics, powered by AI, continuously forecasts demand, identifies problems before they escalate and swiftly adapts to shifts in market conditions or supply chain volatility.
In 2025, economic uncertainty will continue as companies look into an unclear future, including inflation, tariffs and continued talent shortages. Many organizations are slowing their investments in new physical assets and instead shifting their focus to maintaining current equipment with AI-enabled tools and predictive maintenance. By using these methods, companies will reduce their carbon footprint by making technology investments that are both financially economical and environmentally impactful.
