Real Lean Transformation

Improving Lab Performance with Six Sigma

Do you want to reduce lead times while improving productivity in your QC testing laboratory? Read the following case study to find out how.


This case study shows how a lean six sigma project at a leading global pharmaceutical company managed to reduce lead times while improving productivity in their QC testing laboratory using the DMAIC approach.

Lean and six sigma have been popular methodologies in manufacturing for years and are now beginning to migrate outside of their traditional manufacturing habitat. However there are challenges in implementing these methodologies in other areas. Laboratories are a taxing environment in which to implement lean and six sigma but creative adaptation of the techniques can deliver significant improvements in cost or speed.

Define Lean Lab Goals

Defining the goals of any lean laboratory project seems like a simple task, but it is critical to whether the project will be viewed as worthwhile by the wider company and top management. The goals of the project should be chosen to mirror those of the business. In this case the goals of the site were to reduce “End to End” cycle time of their products while keeping the cost per unit as low as possible. The goals of the lab project were picked to mirror these, i.e. to reduce the cycle time for testing and release of product and to do so as productively as possible.

Tools such as Pareto plots and value stream maps are useful in deciding where the focus of a lean laboratory project is to be. A Pareto analysis of the incoming laboratory workload revealed that the majority of the workload (85-95%) is driven by 2-3 products. In situations like this, the most benefit can be obtained by focusing on these products.

Product A and C were from the same product family, received the same tests and could be tested together at the same time. While Product B accounted for 19% of the sample volume it did not account for 19% of the labs workload as it only required two very simple tests, while A and C received nine different tests. The project team decided to focus exclusively on A and C as it accounted for 80-90% of the labs workload and was the main priority of the site.

Volume by Product Type

Fig 1: Volume by Product Type

The “As is” Process Map revealed a significant portion of the cycle time is due to the approval and release activities carried out after the batches were fully tested. As a result it was decided that approval activities would also be within the scope of the project.
 

Measure Lean Lab Performance

The Measure phase of the project was to establish valid reliable metrics to monitor progress towards the chosen goals. The lab already had in place metrics on cycle time. A look at the breakdown of cycle times for Product A showed a spread of times centred around 11-15 days which corresponded to the labs target cycle time of 15 days. 66% of samples met the 15 day target time while 33% of samples were late. The average cycle time was 14.8 days.

 
 Product A Cycle Times (Jan-Apr)
Fig 2: Product A Cycle Times (Jan - Apr)

Looking at resourcing in the lab, it was immediately striking that the vast bulk of the resources were occupied by one test; test x. The results of this test were required by a separate department to proceed with their process. As a result the laboratory heavily resourced this test with the aim of trying to test every sample every day. This was inefficient as it resulted in variable numbers of samples tested each day. For example, on one day, five analysts might test 12 samples and the following day they may only test 4, a 67% drop in productivity from one day to the next. A strategy was required that would be consistently productive without adversely affecting cycle times. To do this it would be necessary to control the number of samples tested each day.

Analyze Data

The Analyze phase of the project looked at all the available data to determine the best way to move towards the desired goals of the project. It was found that:

  • Daily the lab received between 1 and 17 samples resulting in an average of 7 per day.
  • Weekly the lab received between 25 to 45 samples resulting in an average of 36 per week.
  • The weekly incoming workload was much less volatile than the daily pattern (coefficient of variance 0.2 versus 0.6).
So, although it was not possible to predict how many samples would arrive on a given day, it was possible to say with reasonable certainty that over the week the lab would receive approximately 36 samples. It was clear that it would be possible to have some level of control over the number of samples tested if a weekly testing pattern was developed due to the smaller weekly variation. Next, the weekly average (or takt rate) for each test was determined. The number of samples for each test would be different as Product A received some tests that product C did not and vice versa.
 
Having analyzed and reviewed all of the data a clear strategy was decided upon. The lab would run:
  • A fixed, weekly repeating pattern of tests (a Rhythm Wheel).
  • Testing at the weekly average every week i.e. testing at the weekly takt rate.
  • Every test would be run every week.
  • Samples would be tested in FIFO (first in first out) order.
In reality a figure slightly above the average had to be picked in order to cope with the expected weekly volatility, deliver acceptable lead times and account for failures/repeat testing. It was obvious that to follow this strategy some test would have to be run more often. So as not to negatively impact productivity, it was decided to reduce capacity for some tests (e.g. test x) in order to take resources from those tests and use them to increase capacity for other tests so that the overall cycle time for each of the tests would be closer to each other (a batch is only as fast as its slowest test).

Improve Lab Productivity

To improve productivity and ensure consistent results the team developed standard work for each of our testing roles. The team set about identifying:
  • The optimum number of samples for one analyst to test in one shift.
  • The best order in which to perform test activities.
  • Any improvements that could be made to the process.
  • Long periods of inactive time that could be used to run other short tests.
  • How many times to run the test each week.
Because the system controls what tests occur each day it removes much of the unpredictability and volatility that individual analysts experience in day to day testing. This provides consistent results thus ensuring both productivity and consistently low lead times.
 
There was concern over what effect the rhythm wheel would have on lead times for test x, so it was agreed that before any changes were made the team would model the outcome for this test. Using actual data from the previous 6 months, the model showed that, 49% of samples would have been tested the day they arrived, 31% the next day and the remainder after two days. This was deemed acceptable by all affected process owners.
 
Advantages of the rhythm wheel:
  • It was vastly more productive than the old system, requiring only 40 FTE shifts versus 54, (a 26% improvement).
  • It removed the uncertainty around the equipment capacity and avoided equipment conflicts.
  • It removed a lot of the stress and scrambling from the daily testing routine for the analysts.
  •  Every test is run every week to ensure consistent and short lead times.
In the define phase the “As is” Process Map revealed a significant portion of the cycle time was due to the approval and release activities. To address this, the review and approval process was reengineered to remove this delay by operating to the laboratories testing takt rate and reviewing every batch every day.
 
Once all the changes were implemented average cycle times tumbled from 15 to 8 days. The overall laboratory headcount was reduced from 20 testing analysts to 15, a 25% productivity improvement.
 

Control Phase

The Control phase of the project was initiated to ensure that the lead time and productivity gains established from the project would not be lost or eroded over time. To ensure that analysts knew exactly what was expected of them and to ensure that the productive roles developed from the test templates were not diluted, the team designed set roles which clearly showed:
  • The activities required for the test role.
  • The best order in which to complete them.
  • Clear break targets.

The roles were very successful at sustaining the productive roles within the laboratory.

The KPI’s (key performance indicators) were printed and posted weekly to show exactly how the labs cycle time performance was. There was a definite moral boost to the lab to see the lab performance consistently ahead of their targets. Before the six sigma lean lab project 66% of samples were tested inside the 15 day target time. After the project was completed the target was changed to 10 days, and all samples were consistently tested within the target time, with an average lead time of 8 days. There was an annualised 3.9 fold return on investment for the project (ROI).

This blog post was written by Andrew Harte, Senior Consultant at BSM. If you would like further information on improving lab performance with six sigma please send an e-mail to Andrew Harte

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