Ant Colony algorithm has been applied to various optimization problems,\nhowever most of the previous work on scaling and parallelism focuses on\nTravelling Salesman Problems (TSPs). Although, useful for benchmarks and new\nidea comparison, the algorithmic dynamics does not always transfer to complex\nreal-life problems, where additional meta-data is required during solution\nconstruction. This paper looks at real-life outbound supply chain problem using\nAnt Colony Optimization (ACO) and its scaling dynamics with two parallel ACO\narchitectures - Independent Ant Colonies (IAC) and Parallel Ants (PA). Results\nshowed that PA was able to reach a higher solution quality in fewer iterations\nas the number of parallel instances increased. Furthermore, speed performance\nwas measured across three different hardware solutions - 16 core CPU, 68 core\nXeon Phi and up to 4 Geforce GPUs. State of the art, ACO vectorization\ntechniques such as SS-Roulette were implemented using C++ and CUDA. Although\nexcellent for TSP, it was concluded that for the given supply chain problem\nGPUs are not suitable due to meta-data access footprint required. Furthermore,\ncompared to their sequential counterpart, vectorized CPU AVX2 implementation\nachieved 25.4x speedup on CPU while Xeon Phi with its AVX512 instruction set\nreached 148x on PA with Vectorized (PAwV). PAwV is therefore able to scale at\nleast up to 1024 parallel instances on the supply chain network problem solved.\n