October 1, 2021 | Divya Khare

Top 10 use cases for Machine Learning in Supply Chain

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Top 10 use cases for AI and ML in Supply Chain and Logistics :

Machine learning can help businesses improve supply chain management by making it more resilient to disruptions. Supply chains across the world are adopting Machine Learning to improve their processes, reduce costs and risk, and increase revenue. Here are 10 ways that you can leverage the power of ML in your supply chain. Machine learning in the supply chain can help retailers and distributors deliver transformational changes in their businesses. It can help them lower costs, improve efficiency, and enhance their customer service. 

According to surveys by PWC, AI is all poised to reimagine the in-store experience using robotic process automation, smart sensors and gears and connected devices. Supply chain management is eager to deploy this tool more than any other industry experts. Thirty-eight percent of retailers adopting AI and ML in their supply chain management are expected to see a growth in the coming time. 

Demand Forecasting

Let AI remove the guesswork in forecasting and avoid supply chain surprises. Demand forecasting in supply chain management plays a vital role in planning and implementing processes related to supply chain management leveraging AI to manage complex and unpredictable fluctuations in demand volumes.

Supply Forecasting 

Based on supplier commitments and lead times, the bills of material and PO’s data can be structured and accurate predictions can be made for supply forecasts. Balance your demand and transform your business needs to span the entire value chain. 

Text Analytics 

Data can be cleansed with text analytics to drive better decisions. Text analytics can be implemented with supply data, partner data, or shipment data to derive better insights from the supply chain.

Price Planning 

Leverage ML to optimize the increase or decrease in product prices based on demand trends, product life cycles, as well as stacking products with the competition. 

Inventory Planning

Machine learning can help you predict the demand growth for various products and services, such as apparel, furniture, and home appliances. It can also identify areas of the marketplace where there is an over-stocking problem. Automatically raises POs with suppliers based on shortages or future demand shortages by predicting both demand and supply to make sure you have the right products at the right time but are not overspending for excess inventory.

Inventory Price Balancing

ML can recommend products that are in excess and automatically reduce prices to clear inventory accordingly. ML uses historical data like past buying patterns to recommend products based on inventory positions. 

Stock Analytics 

Based on multiple structured and unstructured datasets, machines can now predict the cause of out of stock items or when those items will run out of stock more accurately than ever before so that you can plan shipments and delivery accordingly. Stock level analysis can help identify when products are reaching the end of their life cycles in the retail marketplace. It can also inform the pricing strategy of a given product.

Warehouse Management

Stock-outs at every level in the supply chain can be predicted. Understanding the root cause of stockouts and predicting accurate demand trends with better lead times from suppliers to reduce stock-outs. AI driven models help in programming autonomous vehicles and robots that are commonly used in warehouses. They help in receiving and transporting boxes.

Component Level Analytics 

Plan your supply on a component level with dynamic replenishment based on raw material planning. Machine learning provides business leaders with valuable insights that can help them make better decisions.

Production Planning 

Leverage IoT sensors and production automation mechanics to increase/decrease products and increase quality based on real-time customer feedback.

The rapid emergence and evolution of technologies such as artificial intelligence and machine learning have greatly contributed to the digital transformation of the supply chain. Experts believe these two phenomena are capable of delivering high-quality and cost-effective solutions for various industries.

Pluto7 has worked with more than 400+ customers solving their Supply chain issues. We have a team of experts who understand the customer’s needs and build customised solutions accordingly. 

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