Case Study: Production Optimization

At a company with extensive production operations, the production team was analyzing an existing production system for potential cost-savings. At the same time, the team was tasked with increasing the production rate by 20%. Available inputs were high-level production data: timestamps for individual units before and after each process as well as the most notable workstation costs and the desired production rate.

We built an optimization-based model of the production system to find the minimum-cost configuration of workstations and tools to achieve the new production rate. We used unit time stamps to derive process time averages and standard deviation as inputs for our analysis. LineLab optimized inventory and equipment for the increased production rate and predicted other production performance indicators such as queueing times, machine utilization, and production variability. LineLab produced comprehensive sensitivity analyses pinpointing key manufacturing cost drivers. Using these analyses, we could immediately see that the cycle time variation of one particular process was a major driver of the overall production cost.

The results gave an accurate projection of production costs for the desired rate. What’s more, the analysis showed that by decreasing the variation in process time for one process alone, 1.4% of total unit costs could be saved ($6m/yr). This could be achieved by re-aligning instructions and incentives to promote consistent process performance rather than rewarding “record” cycle times. Consequently, no additional investment was necessary for this 1.4% improvement in costs, but just a change in work instructions. None of the existing, state-of-the-art tools had previously been able to identify this opportunity.