Using AI to manage obsolescence

08 November, 2023

Inventory management

Beyond prediction, generative AI also optimises resource allocation and reduces surplus inventory in demand forecasting. By integrating predictive capabilities into the supply chain, manufacturers can maintain optimal inventory levels, thus removing the reactive nature of last minute inventory adjustments.

Predictive analytics, powered by generative AI, leverage historical sales data, market trends and external factors to generate highly precise demand forecasts. These forecasts are the foundation for proactive inventory management as automated systems continuously monitor inventory levels, and when predefined thresholds are reached, they trigger replenishment orders.

What’s more, these technologies facilitate real time communication with suppliers, enabling them to align production schedules and deliveries with actual demand. This minimises lead times and reduces reliance on last minute orders.

Like ABB and Microsoft, who have integrated Genix Copilot, companies can turn to AI technology to vastly improve their operations. As BW Businessworld reports, the technology has the potential to minimise unplanned downtime by up to 60% and even extend asset lifespan by up to 20%.

This should not come as a surprise, as real time actionable insights allow for better decision making and increased productivity. Therefore, manufacturers who are aided with predictive maintenance capabilities and streamlined inventory management can reap efficiency gains, while leaving behind the era of last order letters and reactive supply chain disruptions.

For companies that aren’t yet ready to advance to fully automated inventory management, AI can still be helpful in predicting when parts are likely to break, allowing plant managers to replace them before downtime occurs

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