Powering remanufacturing with AI
Companies are accelerating remanufacturing as a way to mitigate supply chain shortages, reach new customers through affordability, and implement high-margin alternatives for parts. However, those looking to build or optimise their remanufacturing operations face unique challenges, such as pricing a long tail of SKUs and undertaking accurate core forecasting—that is, predicting the volume, timing, and quality of returned products (core) that will be available for remanufacturing. Enter AI. As the costs of cloud storage and processing and prepackaged tools decrease, AI is becoming more accessible for organizations, helping them improve efficiency, yield, and margins. Unlocking AI in remanufacturing AI is an umbrella term for leading-edge techniques and methods of analysing data, predicting outcomes, and generating insights. While AI and gen AI technologies have garnered a lot of attention, they are only part of the AI landscape. Core forecasting The unpredictable nature of core availability is a challenge for remanufacturers, and conventional analytics lack the sophistication needed for predictive analysis. Introducing a specialised system such as a forecasting tool could help remanufacturers evaluate core availability at a per-SKU level. This forecasting tool could be trained on historical part performance data to assess the following: the estimated useful lifetime of a part the historical use rate of a part (such as hours or miles per day) macroeconomic conditions by region and industry that may affect trade-in timelines Adopting AI tools for core forecasting could help remanufacturers reduce core safety stock by 2 to 4 percent and save 3 to 5 percent in freight costs by reducing the cost of expedited shipping. AI tools could also help remanufacturers reduce overtime expenses, lose fewer sales due to stockouts, and ensure they have the parts that end customers need. Case study: Incentives for core availability. A top technology OEM struggled to match the regional availability of core […]