Shippers often struggle to develop robust transportation policies because it’s difficult to evaluate the effect of uncertainty and variability on their network. The problem is averages rarely reflect reality, yet most analysts still rely heavily on statistical averages.
Manhattan’s transportation modeling software allows you to develop a policy that combines the best of both worlds—optimization modeling to make cost-based decisions along with the flexibility of a simulation framework to address supply chain variability and evaluate decisions over time. The technology also combines stochastic optimization and simulation, which enables analysts to incorporate known variability.
With Transportation Modeling you can:
- Utilize the same planning logic and optimization routines as the daily planning system for consistent results
- Build models and review results using a next-generation user interface including maps and other graphical reporting
- Manage various scenarios to quantify results and the impact of change
Optimize your network by accounting for:
- Facility location and network design: determine which DC should serve which store for which products
- Delivery schedule optimization: open/closed stores, disruptions, model surge demand, holiday season
- Delivery window optimization: when to deliver good to each store
- Static route generation: DC labor and handling costs and distance-based transportation cost
- Pre-paid versus collect: determine which vendors and rates should be converted from prepaid to collect
- Consolidation: determine which engine parameters to use, evaluate different modes and rates