REFLECTION- Learning Launch

CHALLENGE:-

As a part of a Mechanical Design Engineering team in a Research and Development centre (which  specialises in Special Purpose Machinery), I am accustomed to solve unique problems. The sole  purpose of our job is to conceptualise an idea to solve our customer’s problem, prototype or  conform the concept/theory developed and validate the prototype by rigorously testing it  ( subjecting the prototype to conditions it is likely to face during the course of the products life). In  order to solve a problem, we deem it necessary to understand the customer’s requirements. This is  done by means of a URS( User Requirement Speciﬁcation) which is provided by the customer. A  thorough study of the URS paints a clear picture of the customer’s wants and whether or not  achieving these is feasible or not.

As mentioned above, majority of the projects we encounter are unique, which require us to test out  new theories or concepts. Since there is uncertainty about the outcome of these concepts or  theories, LEARNING LAUNCHES is the tool we use to tackle the uncertainty. A learning launch is a  perfect way of failing fast and cheap, if possibility of failure in the initial attempts is high. These  constitute the reasons for us to select LEARNING LAUNCH as a design thinking tool to solve the  prevalent customer problem.

SELECTION:-

LEARNING LAUNCHES are essentially “fast and cheap” experiments. These two words suit  perfectly when one works in a Research and Development ﬁeld. As mentioned earlier the customer  provides us with a User Requirement Speciﬁcation. Based on the URS, collection of data relevant  to the customer’s problem becomes mandatory. The data includes:- • Literature review on relevant subject matter. • Consulting experts in the relevant ﬁeld. • Studying the ﬁnancial and manufacturing feasibility of the project.

Accumulation of the aforementioned data, facilitates a smooth execution of a LEARNING  LAUNCH.  Apart from practically testing the hypothesis, the LEARNING LAUNCH also paints a rough picture  of how the ﬁnal product might look like. It indicates whether ours and the customer’s thought  processes are aligned.

APPLICATION:-

Since conforming a prototype could be an expensive affair, we have purchased a rapid prototyping  machine. This machine uses additive manufacturing technique to build a product from scratch. The  raw material used for manufacturing the prototype via rapid prototyping is cheap. The machine can  be operated by a semi skilled labourer, which makes the operating it an economical affair.

The prototype built is scaled downed to half scale( half the size of the ﬁnal product). This helps  reduce time, cost and effort utilised in the process. The data generated from the rapid prototyped,  scaled down model of the product is then studied.

This data is then extrapolated to gauge the ﬁnal performance. Since the parameters studied during  this phase are independent of volume, extrapolating the data was generated is a right decision.

INSIGHT:-

The insights gained from the entire experiment are a by product of the data generated during the  learning launch phase. This data is then shared with the customer. A detailed study reveals that  there is a performance void between the results generated and the results expected by the  customer. The Learning Launch revealed that certain parameters affecting the performance of the  product were not taken into consideration. This could possibly be the reason for the performance  gap, which will be incorporated in the further learning launches till satisfactory results are obtained.

APPROACH:-

Before initiating the project, the team had studied various design thinking tools available to them.  Having brainstormed over which tool to implement, the team shortlisted Learning Launch. After  sharing the data generated from the Learning launch with the customer, the customer shared their  insights. Incorporation of additional features which weren’t mentioned within the initial User  Requirement Speciﬁcation, was one of the many insights shared.

The request, though made in the initial design phase, could change the the  performance affecting parameters of the product. Such a situation could have been avoided, if  the team decides to use VISUALISATION as its design thinking tool, prior to using a learning  launch. A virtual model of the product could be conformed as per the new requirements. This can  then be shared with the customer, and only after we have a GO AHEAD from the them, would be  start with the LEARNING LAUNCH. This would ensure the data generated from the next learning  launch is relevant, and could be constructively used to Launch the ﬁnal product.

