How is Machine Learning Influencing Supply Chain Management? ML also helps to keep the supply chain updated about weather forecasts, traffic situations and other important factors directly or indirectly impacting the delivery schedule

Machine learning is a direct application of artificial intelligence which enables a system to learn from data recorded from actions and experiences for better future experiences. Machine learning incorporates learning arising from the combination of different variables enabling better consumer experiences.

The logistics industry and its supply chain management are affected by high number of variables and uncertainties like inadequate area mapping or imbalance between demand and resources availability or vehicle breakdown or even the vagaries of weather. Determining innovative patterns in supply chain data through machine learning enabling excellent customer experiences can transform the prospects of most logistics businesses.

Some ways in which machine learning is positively influencing supply change management are:

1. Enhancing Last-Mile Delivery Experience

Matching delivery time with customer's convenience has always been a challenge in last-mile delivery. Before the modern technological interventions took place, it was a trial and error method for finding the addressee present at the time of delivery. The application of AI in logistics has reinvented the last-mile-delivery experiences. AI uses algorithms, patterns and predictive insights from large data sets to differentiate categories. For example, we use machine learning to identify the type of delivery address – whether it is office or home – and the system automatically figures out the best time to make the delivery attempt. This increases the likelihood of addressee's presence at the delivery address ensuring successful delivery and improving the customer experience.

ML also helps to keep the supply chain updated about weather forecasts, traffic situations and other important factors directly or indirectly impacting the delivery schedule. Incorporating all the variables for a best-case delivery schedule increases the likelihood of successful delivery and improves the customer experience.

Successful deliveries in first attempt mean on-time shipment completion which brings in cost economies in the whole supply chain process.

2. Identifying the Right Delivery Locations

Best of cartographers in the world cannot provide a minute up-to-date map with all possible addresses listed accurately. With net access and ecommerce penetrating the interiors and a continuously expanding habitable landscape, locating unstructured addresses is a tough job for delivery personnel. Indian addresses, where non-standardized, are hard to decipher and locate. Pin codes while helpful to some extent, cover large expanses where locating the ultimate door for delivery is a task cut out for our delivery boys. Supply chain management works with such inaccurate data daily.

Machine Learning especially comes in handy here. We look at historical delivery data and use machine learning models to triangulate the approximate geolocation where the address lies.

3. Enabling Field Staff to Take Smart Decisions

In the logistics industry, the on-ground variables are many and situations can change rapidly. A cyclone in Gujarat may require rerouting of shipments via different routes to different locations; a political rally in a locality may disrupt the availability of delivery personnel at the last mile hub or an unexpected surge in volumes from a client may choke certain hubs. There can be multiple resources to such situations. Using machine learning and advanced analytics managers can quickly learn best case and worst possible scenarios. It uses complex algorithms to suggest optimal solutions to field personnel for best decisions sans much error.

Machine learning and AI-based techniques form the foundation which will sustain the next-generation logistics and supply chain ecosystem in the market. ML is ideally suited for providing insights for improving supply chain management performance through better inventory planning, cost optimization, improvement in customer experience by eliminating fraud, reducing risk, and error free delivery management. It can also encourage the creation of new business models.