Supply Chain 4.0 Solutions
Bullwhip effect is one of the biggest challenges in supply chain management, where demand information gets distorted when traveling from downstream to upstream.
At ML Analytix, we believe that by harnessing the power of IoT data, we can provide solutions for solving this challenge. Our main expertise revolves around cutting edge machine learning algorithms that enable quick and efficient demand planning, inventory data modelling, reverse network design and end to end traceability. Some of our key technologies are following:
SCM.Demand – Our best in class tool which harnesses the power of Big Data to provide efficient demand planning outputs.
A good forecasting methodology is scalable, can process big data and is able to extract models from data. Feature extraction methodologies are deployed to solve the challenge of dimensions reduction handling exceedingly large amounts of data.
SCM.Cloud – We offer cloud Microservices for end to end traceability and visibility of your supply chain data.
From analyzing the historical trends to sales forecasting, resulting replenishments and MRP process, the whole cycle is interlinked with cloud at each step with back-end support for real-time POS and inventory data visible to all stakeholders in the value chain. Hence improving value chains through cloud and machine learning-enabled forecasting techniques for cost reduction through improved accuracy and communication.
SCM.Reverse – The first ever tool in the world using Machine learning technology to dynamically design your reverse supply chain network
Using artificial neural networks (ANN) to Conceptualize organic supply chain arising from consumer dissatisfaction or conclusion of product life-cycle
SCM.Warehouse – An Analytics based Warehouse and logistics design tool.
Warehousing and logistics presents the biggest expense in a supply chain. Using big data analytics and deep learning methods (DL), firms are increasing improving accuracy of operations in day-to-day logistics and transportation industry.