IBM’s Kendra DeKeyrel looks at how Asset Lifecycle Management, AI, and automation provide a powerful solution to the problem of defects and downtime dramatically impacting sustainability.
As we approach 2025, “unlock major sustainability gains” should be on every manufacturing executive’s to-do list for the new year. There are compelling business, compliance, and reputational reasons for checking this box: IBM research shows organizations that embed sustainability practices are 52% more likely to outperform peers on profitability. Meanwhile, corporate sustainability regulations are proliferating, and consumers favor manufacturers that prioritize environmental measures.
Yet sustainability goals can still make manufacturers bristle. Executives may associate them with acquiring brand-new assets or overhauling their supply chain. In reality, there are sustainability gains to be had without drastic overhauls or painful transformations. Manufacturing executives across a range of sectors – from discrete to process manufacturing – can confront the defects and downtime affecting their assets and operations. These shortcomings provide an opportunity for operational excellence. When addressed, they can significantly reduce maintenance costs and energy usage.
The key to this sustainability strategy is coupling Asset Lifecycle Management (ALM) with artificial intelligence (AI). By layering ALM and AI into plant floors and production lines, manufacturers can reduce downtime, automate inspections, and instantly spot defects, adding up to major sustainability gains.
Asset Lifecycle Management (ALM) as a strategy for manufacturers
ALM is about using data to care for assets from their very first day on the job to their very last. Too many manufacturers eschew this approach, instead focusing on their assets only when a machine breaks or a process ties itself in a knot. This means assets can be wildly inefficient or on the brink of a pricey (and avoidable) breakdown – but the manufacturer never knows. The approach is a lot like visiting your local mechanic only when your car’s engine fails, rather than booking an oil change as soon as (or before) your engine’s performance begins to slip.
ALM consists of five phases: plan, deploy, operate, optimize, and dispose. During the plan phase, manufacturers weigh the value and costs of a potential asset. This is not math that should be done on the back of a napkin; tools like digital twins allow manufacturers to simulate exactly how a new asset – say, a new wind turbine – will affect the business. The deploy phase is an opportunity for reliability centered maintenance: introducing job plans that set assets up for long-term success.
Asset Lifecycle Management can help manufacturers address defects and downtime. Credit: IBM
During the operate and optimize phases, manufacturers harness sprawling data sets for predictive and preventative maintenance. This grants enhanced visibility into equipment status and workflows, enabling strategic decision making. The final ALM phase – dispose – is when manufacturers determine when an asset has turned into a liability. At this point, it is prudent to retire the asset rather than maintain it.
ALM is not a new idea, but emerging technology has made it more capable. The Internet of Things, like sensors and RFID tags, allow manufacturers to gather coveted real-time data about their assets and weave it together with historical work order data. Frameworks like edge computing allow smartphones, drones, and other lightweight devices to process data from just about anywhere with little or no latency. And AR and VR have improved training and safety. But the advent of AI and advanced automation technology has perhaps been the biggest boon for ALM.
AI and automation as ALM force multipliers
AI systems provide deeper insights and better automation, serving as an ALM force multiplier. Traditional AI like machine learning and newer generative AI technologies can be integrated across all five ALM phases. One of the most impactful integrations is coupling computer vision within the ALM utilization phase.
Computer vision automates and enhances inspection, ensuring no asset issues go unnoticed. Computer vision also allows manufacturers to automate inspection of the products that those assets create, like machine components and car parts. If a product is defective, computer vision can instantly spot the problem, report it, and identify the root cause, avoiding costly downtime. When my company deployed our computer vision-powered tool IBM Maximo Visual Inspection for Ford Motor Company, the manufacturer was able to better detect and correct automobile body defects.
A screenshot from IBM Maximo Visual Inspection. Credit: IBM
AI systems and automation solutions like this are becoming easier to manage. More AI tools are now no-code or low-code, meaning manufacturers do not need computer scientists to deploy them; techniques like visual prompting allow users to easily train bespoke computer vision models in mere minutes. And today’s automation solutions can work with existing applications to provide organizations with full resource management and observability across the infrastructure at a pace humans cannot match.
Computer vision is just one example of AI and automation as ALM force multipliers. Manufacturers are also using AI to accelerate work order processes; automate scheduling and dispatching for field service management; and monitor emissions. Meanwhile, evolving agentic workflows mean AI isn’t just providing manufacturers with insights, but also taking actions for them.
Coupling AI and ALM can unlock unexpected benefits, too, like improved cybersecurity: Deep, real-time insight into assets can reveal when unsanctioned accounts and actors have access they should not have. This can help thwart data breaches, which tend to cost manufacturers more than other sectors – often several million dollars.
ALM and AI make a formidable pair, a combination that manufacturers cannot afford to overlook. In 2025, manufacturing executives can marry this technique and technology to achieve big sustainability gains without painful transformations.
About the author
Kendra DeKeyrel is a Vice President in IBM’s Sustainability Software division, focused on ESG and Asset Management.
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