Supply Chain Analytics
Overcome Supply Chain inefficiencies by empowering functional users with actionable insights & recommendations
REQUEST CONSULTATIONThe Supply Chain Analytics Landscape
With growing Supply Chain inefficiencies, expectations around end-to-end visibility through digitization are on the rise. Building a sustainable and resilient supply chain for the future, with relevant and actionable insights powered by Machine Learning and AI, is critical for today’s Supply Chain leaders.
Supply Chain Analytics has become a mainstream focus area for various industries. The analytical tools that enable Supply Chain Analytics include Exception Analytics, Cognitive Alerting, Demand Forecasting, Prescriptive Recommendations, Optimizations, What-if Scenarios, and Digital Twins. The analytics and AI-powered Supply Chain has emerged as a distinctive competency for modern enterprises, a trend that will continue in the coming years.
How Course5’s Supply Chain Analytics Solutions Help
- End-to-End Supply Chain Visibility
- Cross-functional Collaboration
- Reduced Inventory Costs through Smarter Demand Sensing & Forecasting
- Service Level Compliance
- Better Customer Experience
- Significant reduction in Supply Chain inefficiencies
- Augmentation of Transportation & Logistics Responsiveness
- Smarter Sales & Distribution
See how you can go from supply chain analytics to business impact
Purpose-driven Solutions for Every Situation
Cognitive Alerting
Assess point and contextual anomalies with relevant explanations and early warning signals for functional and executive personas to respond in near-real-time.
Demand Sensing
Help your planners make short-term decisions based on recent events and demand signals from across the Supply Chain. Overcome latency issues associated with traditional time-series forecasting methods.
Multi-echelon Inventory Optimization
Maintain optimum inventory levels throughout the distribution network. Use what-if scenario engines to determine volumetric and financial effects. Automate model switching and account for actual demand variability using Machine Learning.
Order Lead Time Optimization
Collaborate with upstream partners on weekly demand forecasts (aggregate and granular levels). Optimize order processing workflow along with hub-and-spoke network operations.
Service Level Optimization
Drive categorization and prioritization of customer service elements by profitability tier and customer segment. Gauge affordability of service level actions through cost/benefit analysis. Optimize order processing workflow and hub-and-spoke network operations.
Maximizing On-shelf Availability
Leverage Nielsen and retailer-syndicated store and shopper-level data to optimize Supply Chain processes through JIT delivery, full truckloads of inventory (TLs), on-time shipment, forecast accuracy and efficiencies at distribution centers to maximize on-shelf availability.
Order Visibility & Customer Experience
Preempt order backlogs and last-mile delivery challenges. Deliver near-real-time order visibility to retail and D2C customers with reliable ETAs, and consistently invest in nurturing trust and loyalty among customers.
