Optimizing Sawmill Equipment

To determine technical feasibility, we started our engagement with a Discovery effort. This helped us reduce risk by uncovering viable paths forward and investing in shared learning. We removed the monster hiding in the riskiest assumptions through rapid prototypes and testing. We involved stakeholders from each part of the business early in the process. This was critical to understanding all risks involved and making the Discovery successful.

Using F#, we built a prototype based on images of lumber from our client. It was important to come up with a solution that would accommodate 15-25 boards per minute to meet industry demand.

We planned for all likely positions of the lumber after being grasped by the machine. This uncertainty introduced additional variables to the planning problem, each one making the problem exponentially harder to solve. 

Our prototype demonstrated that we could solve the problem in under 1.25 seconds, so we felt confident we could meet the 2 second goal. Not only was our tool faster than the old one, it also produced more optimal cuts.

Once we discovered the solution was technically feasible, we started story mapping the full solution to lay out the challenges ahead. We worked with our client to split the story map into multiple future releases.

We used a low fidelity prototyping technique called UI breadboarding to ensure we included the necessary elements of the UI. This helped us make sure we considered everything we’d need to build when forecasting the cost and timeline to build the full solution.