English
Assigns demand nodes to supply points so that the resulting network has least possible network load.
SB SND Modules
Values Delivered
Geo Spatial Optimiser
Efficient Assignment of stores to branches
Cost optimisation
Improved service aspiration
Minimised salesman travel distance
Minimal last mile costs
Multi modal transport optimisation
Vehicle Route Planning Optimiser
Assigns the right vehicle for each supply x demand x product lane
Assigns the right vehicle to the right source x destination x product mix
Maximises VFR while minimising total Transport Distance & Network Load
Cost optimal milk run route
Respects Capacity, vehicle availability and expected service frequency constraints
Inventory Optimiser
Recommends the right inventory based on lead time, cycle time and service level
Recommend inventory norms based on the expected SLA and resilience/responsiveness needed
Evaluate the responsiveness of an existing inventory norm
Exploit demand aggregation benefits
Reduce Under Performing Inventory
Leverage existing supply network to recommend zero inventory branches
Business Capabilities
2M+
Retailer’s assignment handling capability
10000+
SKUs handling capability
200+
Production facilities
2000+
Inventory & warehousing facilities
20+
Times Period
500,000+
Lanes
Technical Capabilities
Polaris uses a Mixed Integer Linear Programming model (MILP) for solving its supply network design problems
20M
Continuous variables
25M
Constraints
4M
Integer variables
0.1% in 1HR
Optimality Gap of running
Benefits
$14.7M PA
Cost Saving per annum up-to
$15M
Inventory reduction per annum
1.1 Day
Last leg Lead time improvement
1.6MT
CO2 emissions reduced per annum
100000 sqft
Warehouse size reduction
15%
Reduction of effective Network length
10%
10% increase in Vehicle utilization
12%
Direct to customer shipment improvement