Intelligent Demand Forecasting and AI-enabled Sales & Operations Planning Solution

kiadó: Tredence Inc

Orchestrate & drive supply and demand consensus with AI-enabled sales & operations planning (S&OP)

Create a unified view for all stakeholders across the value chain, help them achieve demand consensus, match supply and demand, and leverage accurate forecasts to monitor its execution, so you can equip your supply chain to weather any future disruptions with sales and operations management. Demand forecasting solution is used to accurately forecast demand to optimize the planning of inventory, warehouse operations, and logistics.

Key challenges it can address:
  • Disruptions in operations & resiliency through greater visibility and consensus between demand, supply, and production capabilities that can help them proactively respond to change and plan ahead to save overhead and maximize ROI
  • Internal factors are missed while planning for labor, including tools and resources (number of plants, number of production lines, and open order items).
  • Lack of a centralized consumption layer to visualize the current state and record the shortcomings

How do we address your challenges?
Our integrated sales and operations planning solution helps you streamline operations by creating visibility into their impact in real-time.
    • Demand consensus: Build a robust demand plan that addresses the needs of all stakeholders.
      • Leverage top-down and bottom-up forecasts to plan better in sales, marketing, and operations.
      • Analyze any deviations between these forecasts to avoid disruption and seamlessly incorporate price interventions and promotions, so you can reduce inventory-related costs.
    • Demand-supply consensus: Leverage the demand-supply consensus module of our integrated S&OP solution to arrive at a consensus on your demand plan.
    • Execution monitoring: The execution monitoring module of our AI-powered sales and operations planning solution helps you tackle the deviations between demand and planned demand in real time.
    • The long-term forecast accuracy is improved using ML models by taking into account internal and external factors.
    • A safety pool is maintained to protect against variability. Open order data is taken into account while building the models.
    • A front-end dashboard makes the AS IS and TO BE states more visible.

Business Impact:
  • 23% Demand accuracy
  • 15% Carrier scheduling accuracy
  • 35% Reduction in planning hours
  • 4% Reduction in warehouse labor costs