Reflection – Learning Launch

Challenge

As an operations manager responsible for process improvement, I had a challenge to not only  improve product quality, but also reduce the cost of production. The scope of the project  included producing premium quality product, which apart from increasing customer  satisfaction would make it possible to charge a premium price to the end customers. The  options to improving quality included using only the existing machinery with minor  modifications allowed within a tight budget. The other big challenge was the huge cost of  failure. The products are very costly, and experimentation must be limited to few batches of  production. The project also involved feedback and acknowledgement from the entire value  chain including the key supporting functions.

Selection

The learning launch was crucial because the cost of failure was high. Our production and quality  team had numerous idea generation and mind mapping sessions. Sudden process changes  could have had de-stabilizing effect. Resources included inputs from industry experts,  experienced professionals, research papers and results from previously documented  experiments. Several years of machine data and failure modes were analysed. After rigorous  brainstorming sessions, the team finalized the parameters that were to be experimented with.

Application

For the first learning launch our focus was on attempting a successful production of the product  with the changed parameters. Documents (trial plans) were released and communicated across  the value chain. There was a possibility of full failure with the new setting of the system. Two  batches of production were tried. The trial was conducted in the presence of stakeholder  representatives. The processes and results were documented, analysed, and discussed with the  entire team. Feedback from the core operations team was incorporated.

The second learning launch was based upon the observations from the first trial. Revised trial  plans were communicated to all stakeholders. This time the trial was conducted over multiple  batches of production. This time the product was passed on to the next link of the value chain  and the quality and performance of the product till the end users was tracked.

More trials were conducted with various operations team. Following the success of the trials,  the optimal combination of operating parameters was finalized, and the standard operating  practices were updated. The product produced from the trial process was added to the  premium product category.

Insights

The learning launch tool helped the team make incremental decisions about the parameters to  be tried. Instead of failing big, we were able to take immediate course correction after each  trial. This led to low cost of experimentation. By having to revisit every parameter combination,  we were able to identify patterns and co-relations between the machine data during  production and the resulting quality of the products. By involving all the stakeholders during

the learning launch, we were able to buy-in the approval of senior leadership. The systematic  approach of the learning launch was helpful in forming prioritization rules and action plan for  further experimentation

Approach

The team’s focus was on identifying opportunities of improvement within the existing set up,  without having to make drastic changes that could hamper productivity. Since the cost of  experimentation was significant, the team focused on planning the trials during low sales  seasons.

The many combinations tried during the learning launches were documented and kept for  future references. Insights gained through the processes would pave way for continual process  and quality improvements.


Reflection – Learning Launch Challenge As an operations manager responsible for process improvement , I had a challenge to not only improve product quality , but also reduce the cost of production . The scope of the project included producing premium quality product , which apart from increasing customer satisfaction would make it possible to charge a premium price to the end customers . The options to improving quality included using only the existing machinery with minor modifications allowed within a tight budget . The other big challenge was the huge cost of failure . The products are very costly , and experimentation must be limited to few batches of production . The project also involved feedback and acknowledgement from the entire value chain including the key supporting functions . Selection The learning launch was crucial because the cost of failure was high . Our production and quality team had numerous idea generation and mind mapping sessions . Sudden process changes could have had de - stabilizing effect . Resources included inputs from industry experts , experienced professionals , research papers and results from previously documented experiments . Several years of machine data and failure modes were analysed . After rigorous brainstorming sessions , the team finalized the parameters that were to be experimented with . Application For the first learning launch our focus was on attempting a successful production of the product with the changed parameters . Documents ( trial plans ) were released and communicated across the value chain . There was a possibility of full failure with the new setting of the system . Two batches of production were tried . The trial was conducted in the presence of stakeholder representatives . The processes and results were documented , analysed , and discussed with the entire team . Feedback from the core operations team was incorporated . The second learning launch was based upon the observations from the first trial . Revised trial plans were communicated to all stakeholders . This time the trial was conducted over multiple batches of production . This time the product was passed on to the next link of the value chain and the quality and performance of the product till the end users was tracked . More trials were conducted with various operations team . Following the success of the trials , the optimal combination of operating parameters was finalized , and the standard operating practices were updated . The product produced from the trial process was added to the premium product category . Insights The learning launch tool helped the team make incremental decisions about the parameters to be tried . Instead of failing big , we were able to take immediate course correction after each trial . This led to low cost of experimentation . By having to revisit every parameter combination , we were able to identify patterns and co - relations between the machine data during production and the resulting quality of the products . By involving all the stakeholders during the learning launch , we were able to buy - in the approval of senior leadership . The systematic approach of the learning launch was helpful in forming prioritization rules and action plan for further experimentation Approach The team ’s focus was on identifying opportunities of improvement within the existing set up , without having to make drastic changes that could hamper productivity . Since the cost of experimentation was significant , the team focused on planning the trials during low sales seasons . The many combinations tried during the learning launches were documented and kept for future references . Insights gained through the processes would pave way for continual process and quality improvements .