flexis Solutions - Shippers - Logistics 4.0 Concepts

flexis Inbound Pull

By eliminating decision-making siloes and offering tools for optimizing call volume and capacity usage, flexis’ Inbound Pull Module helps to streamline the logistics process so significantly that businesses stand to reduce logistics costs by up to 15%.

Rather than settling for the miscommunications and inefficiencies that result from siloized decision-making, companies that utilize the Inbound Pull Module can boost transparency and synchronization across different planning and logistics operations. The result is a more flexible, stable, and optimal process that turns fine-granular data on sales, inventory, and associated costs into actionable solutions for balancing storage and transport processing.

In this way, flexis’ solution dynamically calculates optimal call volumes in combination with the total transmission volume and respective load optimization on the transports, thereby achieving higher utilization levels, a higher proportion of direct deliveries, volume-optimized demand quantities in infrastructure-poor regions, and higher-traffic delivery models in well-connected areas.

With unique functionality at their disposal, users can:

  • Automate the control and stabilization of transports.
  • Find an overall optimization between transport and inventory costs.
  • Optimize utilization of storage capacity.
  • Use synergy potentials from intercontinental transport control.
  • Generationally improve end-to-end supply chain cost transparency
  • Develop medium-term transport forecasts to allow early securing of transport capacities.
  • (3PL/4PL) Optimize call-downs to reduce scheduling expenses.

flexis’ consolidation models adapt dynamically to production programs as well as changed purchasing conditions in the transport networks in order to achieve inbound optimized order quantities. The result is an eased burden for logistical planners and more efficient use of logistical resources for shippers working that elusive inbound sweet spot.



ToBe-TLC optimized Model