Lean and Six Sigma generally tend to be viewed as methodologies that are most successfully applied in a manufacturing environment. In the last several years, however, companies also have had notable results in transactional and administrative processes. Recently Medtronic, a global leader in medical technology, has successfully adapted Lean Six Sigma to apply even to a wholly automated electronic IT process.

The Problem and Identifying a Solution Approach

As with many companies today, Medtronic is constantly working to improve its global supply chain. One part of that supply chain, a tool known as the daily “pipeline visibility report,” an electronic Kanban signal between distribution centers and production facilities run by many of the company’s business units, was not reliably being completed by the deadline of 3 a.m. U.S. CST. This delay caused shipping and production decisions to be based on non-current inventory levels. At a strategic level, this result inhibited the use of a daily replenishment model (with lower inventory), created short-term risk of stock-out and oversupply, and forced supply chain and information technology (IT) teams to spend time to diagnose and fix frequent issues.

This situation had been a known issue for some time; however, the solution had eluded the process owners. The problem was brought to a Black Belt in the global business solutions organization, who began investigating whether the DMAIC process and the Lean and Six Sigma tools could be appropriately and effectively applied to address it.

After consultation with Lean Six Sigma mentors and colleagues, the Black Belt reviewed how the problem might be addressed by an automated IT process and made the decision to address the problem using the DMAIC roadmap. The goal of the project was to increase the capability of providing the pipeline visibility report (Kanban signal) by the required deadline for production start on a daily basis by 75 percent by April, 2006.

Application of the Methodology

With a lead time goal, the Lean methodology was a solid fit for the project. The challenge was in navigating through DMAIC deliverables in a completely automated IT process. This would require some creative approaches. In the Measure phase, capability was easily defined and calculated using recent historical data through passive data collection. One advantage that was quickly discovered was that due to the electronic nature of the process, there was a wealth of passive data immediately available to be extracted. Adapting a value stream map (VSM) and sequence of events model to the electronic process proved more difficult. The project team moved forward with an adaptation of the VSM that was more conducive to understanding the multiple dependencies of the different electronic components in the process. Leveraging an off-the shelf software tool, the team was able to create a visual map of the steps in the process which included the supplier systems and the electronic delivery of the product to the final customers. The VSM was then updated to include data mined from the systems to understand and provide a visualization of where the duration of processing and delays were found and ultimately to understand the critical path of completion of product to the customer. Leveraging the VSM as a guide, specific program components of the process were then analyzed to a sequence of events level. As with the VSM, the team adapted an alternative method of producing a sequence of events. Each of the steps in the VSM could be systematically broken down into sub-routines executed by the software. Once again, the availability of passive data allowed simple creation and measurement of the sequence of events.

Leveraging the Data to Find Solutions

After the team had determined the best format of the tools that could be leveraged to produce the Measure phase deliverables, the project progressed in a manner similar to any other solution-focused Lean project. As part of the Analyze phase, possible improvements were identified and ranked against their potential impact and effort required to implement. Leveraging all the data that was gathered and presented in a visual environment allowed the project team to direct its effort toward improvements that included:

• Removing VSM steps from the product delivery critical path that were required by other processes but not required for delivery of the product of the process.
• Updating the dependencies of the process steps to allow for parallel processing where possible.
• Moving and combining process steps to reduce processing times.
• Removing unnecessary and redundant processing.

In addition, the team was able to further increase on time delivery of the product by working with both the suppliers, in this case, the supplier information systems, and the customer, to understand the entire value stream. Working with supplier systems, the team was able to identify “staging” areas of data where processes were not receiving data as soon as that data was available, causing significant lead time increases. Working with the customers, the team was able to get a clearer understanding of the customer requirements and put more robust communication vehicles in place to ensure the customers were always aware of when they should expect to see the final product.

The biggest learning for the IT team was found in taking the measurement data extracted from the systems and performing comparisons to the “tribal knowledge” measurements that were identified prior to the data extraction. As part of this exercise, the most critical key X was identified. A specific IT job that was expected to take less than one minute was taking a median time of 100 minutes to complete. This measurement led the team to understand where the greatest delays in processing the data existed and allowed the team to create a work around solution.

Results of the Lean Sigma Project Event

The project was extremely successful. The table below offers an overview of positive results. These results were achieved by application of DMAIC during a six-week project life cycle. In addition to the results shown, the IT team members gained an appreciation for leveraging the data sources that existed to help research and resolve issues. The control plan created as part of the project was easily adapted and owned by those team members integral to the initial solutions provided as part of the project.

Overview of Project Results
Metrics	Original	Goal	Actual: One Month Later	Improvement
Successful On-Time Delivery	14%	25%	83%	592%
Median Lead Time from Target	+84 Minutes	No Goal	-36 Minutes	120 Minutes
Median Lead Time of Host IT Groups Process Jobs	90 Minutes	No Goal	39 Minutes	57%

Conclusion: Creativity and Courage

The key take away from this exceptionally successful project was that Lean Six Sigma and DMAIC can be applied in many areas where it might be assumed that the methodology does not fit. It seems clear that this problem-solving methodology can be applied to any process that leaders equipped with creativity and courage will take it.