Network Design Software Backed by Real Client Results

Supply chain network design software models and optimizes the structure of a supply chain: how many plants and distribution centers to operate, where to locate them, which customers each should serve, and which transport lanes and sourcing rules to use. TranslytiX runs this as a 4-phase process — Diagnose (map the as-is network and cost per lane), Design (model up to 5 scenarios plus system-recommended ones), Optimise (an OR algorithm minimises total logistics cost under service constraints), and Future State (project the design 5 years out). Real client engagements have delivered results such as a footwear manufacturer identifying 15% potential cost savings and an FMCD company improving 1-day service level from 63% to 78% while cutting logistics cost 14%.

Center-of-Gravity (CoG) analysis identifies the geographically optimal location for a warehouse or plant based on weighted customer demand and distance, minimizing total transportation cost. TranslytiX uses CoG to simulate both realigned networks (using existing facilities) and greenfield networks (unconstrained by current locations), then compares the resulting cost and service trade-offs. In one engagement for an auto-accessories manufacturer, seven different CoG-based scenarios were modelled and compared before selecting a new warehouse footprint, delivering 12% cost savings. In another, CoG modelling helped a footwear manufacturer determine that just 10 of 18 candidate warehouse locations were needed to serve 90% of demand.

Results vary by how far the current network has drifted from its optimal footprint, but TranslytiX client engagements have delivered concrete, measured outcomes: a footwear manufacturer identified 15% potential cost savings through warehouse consolidation, an auto-accessories manufacturer achieved 12% cost savings through Center-of-Gravity redesign, and an FMCD distributor cut logistics cost by 14% while simultaneously improving 1-day service level from 63% to 78%. A separate FMCG ice cream manufacturer reduced average secondary distribution distance from roughly 450 km to 180 km through network realignment. Because each scenario generates an automated business case — cost delta, service impact, and payback period — the expected savings are quantified before any change is implemented.

Both are possible with TranslytiX, but the platform is built to make network design a repeatable capability rather than a one-off consulting exercise. Scenario and simulation templates can be saved and reused for future network redesigns, warehouse expansions, or policy changes, and the Future State phase explicitly projects the design against demand growth and new geography over a 5-year horizon. The roadmap also includes dynamic network re-optimisation — automatically re-evaluating the network when demand patterns shift materially from baseline — so the design keeps pace with the business instead of going stale until the next periodic study.