AI is increasingly being used to bring drastic changes in various fields, it can be implemented to automate the system for more efficiency and better performance. Supply Chain is one such field where the companies are trying to get an edge over their competitors by using AI to improve end-to-end supply chain management (SCM).
Supply chain management is the handling of the entire production flow of goods or services starting from acquiring the raw materials, manufacturing the product, distributing it to resellers or warehouses and then delivering the final product to the consumer. The ever-evolving customer expectations and the unprecedented pressures on global supply chains created by the COVID pandemic and the subsequent series of lockdowns and restrictions have now made it more necessary than ever to automate these supply chains.
Automated supply chain management can help reduce the manpower and help these companies to manage the huge amount of stock or inventory with proper management and supply system. It will also help them to operate with better efficiency and improve their operating margins.
Here are a few examples of how AI is already helping SCM operations -
• Improving Demand Prediction Accuracy — Using AI, organizations can make use of Machine Learning algorithms to predict changes in consumer demand as accurately as possible. These algorithms can automatically recognize patterns, identify complicated relationships in large datasets and capture signals for demand fluctuation. This will help in managing inventories and providing higher consumer satisfaction.
• Optimizing Routing Efficiency and Delivery Logistics — Route optimization is a critical component in logistics and transportation planning. It paves the way for timely deliveries and helps in lowering shipping costs. It can also help you calculate more precise delivery windows and thereby decrease the odds of a customer-not-at-home situation.
• Maintain Physical Assets — Smart machines make maintenance recommendations and failure predictions based on past and real-time data. This allows companies to do maintenance when needed and take vehicles out of the chain before performance issues create a backlog.
• Higher Consumer Satisfaction — Apart from on time deliveries and better demand prediction accuracy, AI can also be used to address customer queries and give them a quicker response. Chat bots can be used to provide 24/7 assistance, keep live tracking of products and also answer frequently asked questions using the data it is fed on.
• New Product Introduction Forecasting — New product introduction (NPI) forecasting is related to demand sensing. NPI forecasting aims to understand the future orders of new products that are yet to be launched in the market. AI model is fed information such as sales history, market & economic trends, external competition, product reviews etc. to generate the forecast for future products. Modern AI algorithms such as Long short-term memory networks (LSTMs) and Autoencoders are doing exceptionally well in generating NPI forecasts.
• Retain Talent — Data on employees such as their past performance and employment history can help AI understand the problems related to talent and predict the reasons that are causing employees to leave. This information can be further used to improve the working conditions of an organization.
AI in supply chain innovations is paving the way for a future where we can eventually expect to see AI-powered, autonomous vehicles used throughout supply chains. The data these platforms are mining and analyzing today will continue improving the cost and efficiency of an increasingly complicated global supply chain.
Contact us if you would like to explore solving any of your supply chain related problems with Artificial Intelligence.
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