Strengthening Supply Chains with Data-Driven Solutions
20th September 2024
Supply chains can be strengthened by using data-driven solutions, says Brian Fitzgerald (pictured), Growth Strategist at Augury.
It is no secret that today’s purpose-built AI tools can not only analyse vastly more data than they could a few years ago but synthesise it into more easily accessible forms, from graphic and narrative to speech. In recent years, AI has revolutionised manufacturing by enhancing predictive maintenance, improving quality control, and optimising supply chains. Additionally, AI facilitates data-driven decision-making, optimises energy consumption for sustainability and enables personalised and customised products.
These advancements have significantly increased efficiency, reduced costs and have improved product quality in the manufacturing industry. Augury’s recent research shows that 39% of European manufacturers already use AI to improve production health. To stay competitive in an evolving landscape, it is crucial for manufacturers to extend AI adoption to logistics and supply chain management.
Cutting costs and significant boosting sustainability
By implementing AI-driven solutions in logistics and supply chains, manufacturers can optimise machine health and maintenance practices by detecting problems long before they happen, which reduces energy consumption and minimises waste. AI can be used to identify process inefficiencies, helping to streamline operations and reduce waste, boosting sustainability and ensuring a more efficient and reliable supply chain. The primary reason? AI systems can make sense of vast and varied streams of input and draw from millions of hours of training, finding correlations and patterns between the thousands of potential data inputs in a manufacturing process that no human could ever spot.
AI can also significantly reduce manufacturing costs by optimising production processes. AI-driven solutions offer predictive maintenance and prescriptive analytics, preventing costly equipment failures and unplanned downtime by monitoring machine health and finding issues before they occur. This allows for efficient scheduling of maintenance activities and avoids mishaps such as loss of product and unexpected breakdown expenses.
Integrating AI with IoT
AI can be strategically combined with various technologies to revolutionise supply chain and logistics operations, driving substantial gains in efficiency, productivity, and quality. When integrated with IoT devices and sensors, AI can turn every manufacturing asset into a digital asset, continuously collecting and analysing massive volumes of data from across the supply chain, including from transport vehicles, warehouses, and inventory systems. This real-time data analysis enables predictive maintenance for equipment, which reduces unexpected downtime and optimises maintenance schedules, thereby ensuring smoother and more reliable logistics operations.
The synergy between AI and IoT extends to optimising supply chain management by enhancing demand forecasting, improving route planning, and streamlining inventory management. With accurate, data-driven insights, companies can better anticipate and respond to disruptions, minimise costs, and enhance overall operational efficiency. This integration not only supports cost savings and operational improvements but also provides a significant competitive advantage by enabling more agile and responsive supply chain strategies.
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