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, and 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 were 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 to only the inputs and models which really mattered. This sped the overall modeling process and achieved swift buy-in from the many 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 cost and non-recurring cost components, sensitivities, and production system parameters such as the number of workstations, utilization, and average wait time.

97 ... 253
Input Parameters
125 ... 323
Output Parameters
Higher Profitability


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 associated with its result in comparison to the other candidates. LineLab’s analyses showed the likely production cost of the lowest-cost option was 79.6% less than one of the other alternatives, which had been a promising candidate in previous discussions; thus, based on LineLab’s guidance, the ROI for the development program was increased by a factor of 4.9. LineLab’s production system results, such as the number of machines, helped the engineering teams develop intuition for how the production scenarios would manifest when scaled and informed the dialog between the different disciplines. Entering input data into LineLab, solving, and pasting results into a management-ready Powerpoint presentation was all done in a matter of hours, significantly shortening the timeline.