Reflection – Learning Launch

Challenge

I am an engineering manager responsible for product development in a high technology, capital  equipment industry.  My challenge was to understand as quickly as possible which of several design ideas  for a new product enhancement is the best choice. The new feature incorporates elements of machine  automation with some ergonomic considerations of the operator running the equipment.  The functional  requirements for the enhancement were decently understood, but cost budget of the design less so.  “As  cheap as possible” was the request from the product manager. The project also had a clear schedule that  a beta unit be retrofitted to equipment in the field in 5 months.  The most cost-effective idea had the  highest technical risk.  My group did not have enough design resources and my company didn’t have the  budget to pursue all the ideas to an advanced, beta-ready status.

Selection

I applied the Learning Launch tool to my challenge.  I needed simple, efficient experiments (“small bets”)  to determine which of several design proposals should be pursued in further detail with confidence to  avoid costly (dollar and schedule) changes in design direction later in the program.

In larger companies such as mine, the cultural pressure is to only test very complete designs, thinking it  saves time and money.  Unfortunately, this approach delays the quick learning necessary to refine the  product design.  This is a poor interpretation of “do it right the first time,” which really is about getting  the first shipment to the customer right the first time, not the first learning prototype.

Application

The initial emphasis was to test the ideas with the highest risk, but we decided to run a test on the less  risky options so there was a baseline reference for functional performance.  Having this baseline  performance of the most conservative approach helps deter any biased stakeholder from concluding we  should choose a higher risk option (e.g. let’s just go with the cheapest one) that fits his/her biased agenda  because there isn’t data that says it’s going to be any worse than the more conservative approach.  Also,  even the lowest technical risk option had enough complexity that the technical team believed it required  basic validation to provide confidence it was a viable option.

Schedule was a major issue guiding definition of the learning launches.  The various design options  consisted of 10 to 30 components involving a various mix of engineered, build-to-print parts and catalog  items.  The project schedule did not allow long lead-time procurement of build-to-print parts of the  complexity expected for the final design.  Although the final design was expected to utilize machined  metal and complicated formed sheet metal parts, the engineering team proposed minimum viable tests  utilizing readily available materials such as aluminum tubing, wood and plastic sheet. Parts were fastened  together with screws and industrial tape.  Representative concept parts could easily be fabricated from  these materials using hand tools, avoiding costly and time-consuming procurement from commercial  fabrication shops.  Several parts with complex geometry were constructed with a 3D printer.

Insight

We performed several learning launches on the design proposals using our simplistic models built from  easy-to-fabricate, easy-to-modify materials.  We were able to get the critical feedback of the different  design proposals to start the detailed engineering design in time to keep the project on schedule.

One important insight is the value of involving non-technical stakeholders in planning the learning  launches. A very typical approach to such a project by non-engineers is to demand an accelerated schedule  for a complete, production-ready design, and then demand an accelerated procurement schedule of the  design.  For projects with aggressive time lines, this approach often fails because it allows for no  learning/tweaking cycles and any changes will jeopardize the schedule commitment to the customer.  In  discussing the learning launch testing options with the stakeholders, the technical team realized  ergonomics was a major concern. The non-technical stakeholder’s initial insistence of highly detailed and  complete prototype was largely based on the belief that was the best way to evaluate serviceability and  ergonomics.  The technical team proposed simple 3-D wood/foamboard mock-ups to adequately illustrate  these concerns.

Approach

The learning launch tool was appropriate and very important to the success of this project.  But what  was reinforced in my mind is the importance of managing the mindset of all the stakeholders to accept  simple, approximate prototypes to get the necessary learning as early as possible.  Addressing this  mindset is especially important in a large corporate setting where various stakeholders (finance,  marketing, manufacturing) can easily fight simple learning launch experiments because it doesn’t easily  fit their metric-driven environment.  Because the mindset of all the stakeholders needs to be aligned for  a project to be successful, I think the use of the “Mind Mapping” and “Visualization” tools has important  application to achieve the necessary buy-in to allow the proper environment for rapid-learning  prototyping.

