Case Study: Technology Investment

The Challenge

In a large company, engineers were considering a number of innovative manufacturing process concepts for a frequently used structural part. With the exception of one process, none of the processes had been used to manufacture this part at scale before. Furthermore, the processes were at different technical maturity levels. Consequently, the goal was to project production costs in order to decide between the alternatives.

Key questions the team sought to answer were:

• How much initial investment would each process require?
• What unit cost was expected based on the different inputs?

Our Solution

We projected respective production costs by creating manufacturing modules for each of the candidate processes in LineLab, as well as a design module for the part. Since the production processes were largely new inventions and proprietary to the company, we could not rely on our industry-standard process model libraries. Rather, we used inputs from the process design and engineering teams directly.

This input collection took a number of days, but we made extensive use of LineLab’s uncertainty features—accepting best, worst, and likely values for any input—thus easing the data-collection discussions. We could quickly start with initial inputs and add fidelity only where it truly mattered. This sped the overall modeling process and achieved swift buy-in from stakeholders.

Each model consisted of between 98 and 253 input parameters and between 125 and 323 output parameters. As a deliverable, we generated a results slide deck comprising cost distributions, recurring and non-recurring cost components, sensitivities, and production system parameters such as the number of workstations, utilization, and average wait time.

Outcome

Displaying the unit-cost probability curves in a combined graph allowed for an informed decision about the best path forward. It could be seen that one of the candidates not only had a low cost, but also very low uncertainty compared with the alternatives.

LineLab’s analyses showed the likely production cost of the lowest-cost option was 79.6 % less than one of the other alternatives; based on LineLab’s guidance, the ROI for the development program increased by a factor of 4.9.

LineLab’s production system results (e.g., machine counts) helped the engineering teams develop intuition for how scenarios would manifest at scale and informed cross-discipline discussions. Entering data into LineLab, solving, and pasting results into a management-ready slide deck was all done in hours—significantly shortening the timeline.