- Datafication of rapidly increasing data volumes and handling data complexity both from internal and external data sources.
- Reducing the latency of time-to-value for the products and services.
- Designing proprietary mathematical models to derive specialized insights to improve supply chain efficiency.
- Connecting various touchpoints in the supply chain to create a single source of truth across the supply chain.
- Identification of risks across the supply chain network to forecast and manage the exceptions created by vendors, customers, processes, planning etc.
Design data and analytics strategy to consolidate a wide range of inputs from internal data sources such as ERP, SCM, production and planning tools and external data sources such social, weather, blogs, customer records, web logs, ratings etc and derive consolidated signals to empower the supply chain analysts.
Architect smarter master data management to assimilate data from multiple disparate data sources of supply chain components through application of Machine Learning and Artificial Intelligence techniques to create an intelligent value chain that opens up new possibilities.
Deploy networks to connect, exchange and propagate data between the stakeholders of the supply chain especially between customer’s customer and vendor’s vendor thus facilitating strategic business engagements between organizations and derive insights from the data emanating across the extended network.
Architect a robust, scalable and compliant data and analytics processing infrastructure capable of of analyzing large diverse datasets generated across the entire spectrum of supply chain operations.
Build and deploy analytics stack from the organized data that continuously learns data from upstream and downstream components of supply chain to automatically suggest optimal operational decisions in Predictive Demand & Capacity Planning, Inventory Optimization, Sales & Operational Planning, Supply Planning etc.