A packer strategy was critical to increasing production volume.
With product in high demand, one of the world’s most visible packaged goods manufacturing companies sought to increase production capacity by 20-25 percent. The system’s flaw, it was suspected, was a low-performing case packer. With ideas for alterations to the packer, the engineering team turned to Haskell to emulate the options to uncover the winning strategy.
Six ideas were on the table — which was the right solution?
The team pinpointed every subsystem of the packer in question, noting the timing of every cycle and dwell. The statistically processed data fed the true-to-life model that was connected to the machine’s PLC. Further bolstered by faster feed-in modification, the packer was put to the test.
Budgetary constraints ruled out a necessary upgrade.
The feedback was instant — success. The machine was being fed 30 percent faster. But, then the packer subsystems began to flag down to normal production rates. While they had successfully boosted throughput, unfortunately, short of a packer rebuild the line could not withstand the boost in volume. Due to capital constraints the client ultimately determined that the packer would have to wait and current production would have to suffice.