How Artificial Intelligence is streamlining supply chain processes

March 4, 2024

Updated on: May 23, 2024

5 min read

The AI revolution in supply chain management

AI is more than just a buzzword in the context of supply chains. Its applications range from Machine Learning (ML) algorithms for precise demand forecasting to Natural Language Processing (NLP) for enriched customer interactions alongside advanced analytics for streamlined inventory management. The integration of these technologies has been shown to yield substantial improvements in the supply chain processes performance.

Demand forecasting with machine learning (ML)

One of the most impactful applications of AI in supply chains is in demand forecasting. Being able to predict and respond to changes in demand is critical for many businesses. This includes changes in volume, timing, location, or demographic.

Traditional methods, often reliant on historical data, struggle to accommodate external factors that are not present in the source data. Machine Learning excels in this domain, particularly through unsupervised learning techniques. It analyzes various parameters. These parameters include seasonal trends, weather conditions, and competitor pricing. The purpose of analyzing these parameters is to predict customer demand more accurately. Businesses can then use this data to plan inventory capacity with greater accuracy and inform purchasing strategies.

IKEA’s ‘Demand Sensing’ project exemplifies this approach. IKEA integrated over 200 data points, including local store data and external influences like the season, weather and holidays. As a result, IKEA boosted its demand prediction accuracy from 92% to an impressive 98%.

Optimizing routes and logistics in the supply chain

AI also plays a critical role in optimizing logistics and transportation in the supply chain. Algorithms are equipped to process and respond to real-time data to help in planning efficient routes. Taking into account factors like weather, traffic conditions and delivery timelines. This dynamic routing ensures that logistics operations are more responsive to real-time changes in customer demand. It also maximizes the efficiency of supply chain networks.

Many global giants, such as Google and Amazon, have begun combining data from artificial intelligence algorithms with their existing software platforms. They have done this to further improve the efficiency of last-mile deliveries. Google’s Cloud Fleet Routing, for instance, uses a combination of AI and data from Google Maps to provide accurate delivery estimates. Similarly, Amazon use the ‘Amazon Forecast’ to predict when they will need stock replenishment at relevant locations.

There will be more ready-made solutions as AI develops. This is even if not all manufacturers have the capital of companies like Amazon and Google.

Revolutionizing inventory management

Inventory management, a crucial aspect of supply chain operations, is also being revolutionized by AI. Spreadsheets and other traditional manual methods are making way for neural networks and reinforcement learning algorithms. These algorithms can more efficiently navigate complex inventory challenges. These can learn from each iteration. They continually refine strategies for stock levels and warehouse management. This reduces costs and enhances operational efficiency.

Inventory management applications need access to a wealth of data to ensure an effective solution. Changes to lead times, the status of the supply chain, product obsolescence and even reductions in warehouse capacity are all necessary to ensure the best results. However, once the data is put to use, the improvements in efficiency and ability to navigate real-world variables will be unmistakable.

Enhanced customer and supplier interactions

AI’s capability extends to improving interactions across the supply chain. NLP, for instance, automates customer support and supplier communication, making these exchanges more consistent and efficient. For customers, AI can summarize correspondence, track order statuses and provide real-time updates. On the supplier front, AI can assist in routine communications. It can also help with orders, price negotiations, and even in determining supplier suitability for future procurement cycles.

Microsoft's Dynamics 365 Copilot showcases an example of this technology. The AI-powered application promises to "lighten the workload with AI." It assists users with writing emails to customers using context from CRM data and extracting information from recorded Microsoft Teams calls. So, whether it’s revolutionizing entire processes or simply lending a hand with your to-do list, there’s an AI application out there for the job.

Is there room for AI in your supply chain?

The integration of AI in supply chain management signifies a monumental shift in how businesses operate and compete. From demand forecasting to inventory management and from optimizing logistics to enhancing customer and supplier interactions, AI is not just an incremental improvement but a fundamental change agent. As technologies evolve and more data becomes available, the potential for AI to revolutionize supply chain processes is boundless. Businesses that embrace this change will reap significant benefits in efficiency. They can also enjoy cost savings and customer satisfaction. Ultimately, they gain a competitive edge in the ever-evolving global market.

Whether you’ve got the resources to go big or your preference is to start small, there is a solution out there that will make artificial intelligence sound less like a buzzword and more like a no-brainer. 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.