Flush-reduction feasibility for a lubricant blender
Can historical changeover data predict the flush a line needs? A feasibility scan on viscosity, calcium, colour and infrared results from a blender's highest-changeover filling line.
Challenge
A lubricant blender flushed between products on experience: the team lead set the quantity, every batch was tested after filling, and about a third of under-flushed batches failed on viscosity. Slop cost about 18 SEK per kilogram, and the 20-litre line with the most changeovers had the lowest first-time-right rate.
Solution
Arundo mapped the current practice with the plant's team, identified that viscosity alone does not explain failures but that viscosity, calcium, colour and the infrared fingerprint together could support changeover planning, and proposed a feasibility scan on the historical data to test whether required flush volume can be predicted and to size the return.
Impact
A decision gate on a Flush Optimizer pilot, with the return estimated on slop reduction, fewer failed batches and a better first-time-right rate on the high-changeover line.