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Obsolescence management: Forecasting the handling of obsolete parts

April 4, 2024

Updated on: April 4, 2024

2 min read

Are you using artificial intelligence in your obsolescence management strategy?

In the ever-evolving landscape of industrial automation, one of the most pressing challenges is obsolescence management for machinery parts. As technology rapidly advances, keeping pace with the latest advancements while efficiently handling outdated parts becomes crucial. Here, we’ll explore how Artificial Intelligence (AI) is transforming the approach to forecasting obsolescence. Also covered will be the ways AI can plan for component replacement and repair.

The Role of AI in Obsolescence Management

Data processing and predictive analytics are two areas in which AI excels. This plays a pivotal role in forecasting the lifecycle of automation components. Through leveraging predictive analysis, businesses can anticipate the component lifecycle management effectively. By doing so, this reduces the number of disruptions to operations and maximises the utilization of resources.

Businesses can plan ahead for replacements or upgrades by using AI to predict when parts will become obsolete. Predictive capacity is crucial in industries where technology evolves rapidly, and the availability of parts is a constant concern. By extension, AI tools can make sourcing replacement parts easier than ever before.

Implementing AI for Component Lifecycle Management

The automotive industry displays a notable example of applying AI to manage obsolete components. Here, AI systems analyze usage patterns, performance data and manufacturer updates to predict the obsolescence of components. With this proactive approach, it is possible to place orders for replacement parts in a timely manner. As a result, companies are able to avoid the costly downtime that is associated with sudden equipment failure.

Automated Refurbishment and Replacement Strategies

Beyond forecasting, AI also aids in strategizing the refurbishment and replacement of parts. AI-driven systems can suggest the most cost-effective and efficient solutions. Whether it's sourcing refurbished parts, finding compatible alternatives, or investing in new technology. This intelligent decision-making process ensures that we utilize resources optimally, balancing cost with performance.

Enhancing Supply Chain Efficiency

The predictive powers of AI extend to improving the overall efficiency of supply chain operations. By forecasting the need for replacement parts well in advance, businesses can better manage their inventory. As a result of this, they are able to cut down on surplus stock and negotiate better terms with their suppliers. This streamlined approach to inventory management is particularly beneficial in industries where component lifecycles are short and replacement needs are frequent.

Should you be using AI for obsolescence management?

The integration of AI in managing supply chains and obsolete automation components represents a significant advancement in industrial operations. By accurately forecasting obsolescence, and aiding in effective replacement strategies, AI minimizes downtime and optimizes resource utilization. As AI technology continues to evolve, its application in the lifecycle management of components will become increasingly sophisticated. As a result, they offer more robust solutions to the challenges of obsolescence management in industrial automation.

Marvo is the smart new way to reliably source automation components while avoiding lengthy global lead times. Continue your exploration into an AI-optimized supply chain and find out more about Marvo today.