Case Study: Design Decision

In a Fortune 100 company, decisions had to be made about a large and costly key part. The design teams had conceptualized design alternatives with different manufacturing trade-offs: breaking the part up and adding an assembly step might bring some of the costs down, but would the assembly step lead to other issues that bring unanticipated costs? Various previous analyses did not reveal a clear winner, leading the team to favor the better-known, status quo design alternative. The goal was to provide a level comparison of predicted manufacturing costs for each of the alternatives.

We created a complete production cost projection based on the available design and manufacturing inputs. Design inputs were: exact dimensions including the stacking sequence of composite layers (ply book) and raw material cost. Manufacturing inputs were the types and sequence of processes, selected from LineLab’s built-in process models. Other inputs were the desired production rate, total program duration, number of shifts, and other financial inputs. From that, LineLab’s factory physics engine projected a scaled-up production operations scenario.

From these inputs, LineLab predicted the recurring and non-recurring costs for each of the design alternatives, as well as a number of intermediary output variables that the team could use to verify the models. Moreover, our result included sensitivity analyses for quantifying the cost drivers in design and production. We produced a cost vs. rate trade-study, showing the predicted unit costs for production anywhere between half of and twice the desired rate.

LineLab predicted that a production system for a design with a divided part would reduce costs by 32% over the existing approach. This was a significant result given the design team’s previous hesitance over the associated risks, and it empowered the project team to move forward with clarity. Furthermore, our sensitivity analysis uncovered important cost drivers and identified scenarios of design and rate that would reverse the optimum decision. Our rate hike analysis helped decision-makers plan the ramp-up of production and identify other favorable production rate scenarios.