While Micropump, which manufactures small-volume, precision-flow, sealed pumps, has been in business for 46 years and has been owned by IDEX Corporation for 11 years, it is in the last several years that innovations in its product line have accelerated rapidly. The reason is because now there is more cash to finance research and development efforts. Interestingly, the cash is not from the benevolence of the parent company, but from the results of the Operational Excellence initiative that IDEX management began five years ago.
In 2001, Micropump was in need of revitalization. Market growth was lagging and operations were not improving rapidly. IDEX turned to Six Sigma and Lean (calling the program Operational Excellence) to provide the framework and philosophy to move Micropump forward. The IDEX guideline was to choose the best and brightest and dedicate them to full-time Black Belt roles. Micropump did just that. With less than 75 salaried employees on staff in Vancouver, the company could scarcely afford to dedicate two of its best employees full-time to Lean Six Sigma, but it did. It was a leap of faith because of the cost of adding two positions to the company’s head count. And it meant the Black Belts had to deliver.
But having the right people working on the right projects did not move the improvements ahead fast enough. President Jeff Hohman described a moment of frustration early in the Six Sigma effort. “I had these two high-powered and highly compensated engineers – really two of my best people – spending hours scrubbing data and building databases to get the information they needed for their projects.” Micropump had to deliver benefits from the Lean Six Sigma effort quickly, and it could not do it if the Black Belts were spending days at a time cleaning up data rather than analyzing it and acting on it.
The plant had been using statistical process control (SPC) since the mid-1980s and was an early adaptor of SPC software. The SPC software in use when Six Sigma was launched provided data about machining, molding and product testing processes in the shop, but extracting information to fuel the Six Sigma initiative was time-consuming. The company’s vice president of engineering, and one of the first Black Belts, Charlie Carr, described it this way: “Who knew how many ways there was for an operator to enter their name?”
With no way to limit the types of entries users were making, data errors like the inconsistent operator name were commonplace. Out-of control reasons could be entered free-form, making root cause analysis a painstaking effort. And finally, all data had to be moved into another application (even if no scrubbing was required) so that it could be imported into Minitab for further analysis. This meant data had to be handled multiple times before it was even ready for analysis. The process was just too expensive and error prone.
The first projects were focused on on-time delivery for customers. But on-time delivery data was not readily available. It had already become apparent that a new data acquisition method would be needed and that it would need to work with transactional processes as well as manufacturing processes.
Satisfied that SPC was a tool it needed to continue to use, at least in manufacturing, the company formed a project team to solve the data problem. There was a desire to know if one comprehensive solution could be applied across the business – in both manufacturing and transactions.
The team began by developing a list of critical-to-quality characteristics (CTQs) for process data:
◈ SPC must be used for process control in manufacturing – The company needed the ability to automate data collection and real-time alarms in all of manufacturing processes. The goal was to use existing quality data collection processes wherever possible. But the company wanted better support for automatic gaging, and more transparent data sharing. And process owners needed to be able to respond instantly to process shifts or special cause variation.
◈ The ability to accurately track transactional process performance – Team wanted to track manufacturing and transactional data at the same time, with the same system. While there clearly are differences between transactional and manufacturing data, there also are many similarities.
◈ A way to link information from many databases for use in operations – The company already had a lot of data in various databases. It needed a way to bridge these disparate systems.
◈ One source for process and product data – Once again, regardless of the source of the data, (dimensional, equipment performance, cycle times, defects, product testing), the company needed a way to reach it.
◈ Mistake-proofing of data – The vision was using current technology to eliminate operator data input errors. The company wanted the ability to use barcode scanning, pre-filled data fields, drop-down lists, etc.
◈ Real-time information about all processes – With a taste of how real-time data could help certain operations, the team figured it should span all processes.
◈ Ease of use by operators, supervisors, engineers and Black Belts – The idea was to get rid of a system that was cumbersome and difficult to use, thus making life better for everyone on the staff. Ease of use included having a system compatible with the statistical analysis software used by the company.
◈ Limited resources required for initial set-up and ongoing system maintenance – Finally, the team knew the company needed a system that required minimal on-going IT support and resources. The company was stretched too thin to place more demands on the IT staff.
The team then began considering options to meet company needs. Here are a few of the options considered:
◈ Hiring a programmer to create an application that could share information between current scheduling, SPC, engineering drawing and specification databases.
◈ Dedicating a portion of a Black Belt resource to data integrity.
◈ Investing in an enterprise wide knowledge-management system.
◈ Investigating the capabilities of different SPC software packages.
Hiring a programmer to create a custom application held some appeal because the team thought the company’s needs were pretty specialized, and because it wanted control of the solution. Initially it seemed like it might be more cost-effective, but as when the company’s core competencies and head count were fully considered, it was realized that the business of writing custom software was the best solution. Also, the team was aware of the typical high failure rates for IT projects. The clincher, however, the problem of maintaining a home-grown system. Team members had all seen clever home-grown software solutions implemented, only to see those systems hamper the organization as they became out-dated and unsupportable.
Dedicating a portion of a Black Belt’s time to data integrity seemed crazy to the team. While having clean, reliable data was essential to driving Six Sigma projects, the act of getting that data added absolutely no value to the business. So dedicating highly valued resources to a non-value-added activity was counter-intuitive. The team considered investing in training and developing people, other than Black Belts, to harvest the needed data, but the investment costs in lead time and training resources were considerable. And the bottom line was they still needed some kind of software and hardware to do the job.
As the team investigated the field of enterprise-wide knowledge management systems, it found some great summary reporting tools, but they all lacked several key capabilities that were essential to Micropump’s business. First, a large portion of company efforts were focused on manufacturing, while knowledge management systems were not. Coupled with that was a serious weakness in real-time statistical analysis capabilities. While most of these tools could tell when something missed a target, they could not identify a statistical shift in mean or a statistical trend in real-time. Nor did they readily interface to the company’s statistical analysis software. Finally, they did not help scrub the data. In that way, they really did not move the company beyond where it already was – spending countless hours massaging and scrubbing data for projects.
The last choice, commercially available statistical process control solutions, proved to be the best course of action for the company. The team learned that while these systems have a reputation for belonging on the shop floor, the good ones do that and more. In short, the company was able to find a system that met all of its criteria. That system has been deployed now for nearly four years, and it continues to provide clean, reliable data in real time so that continuous improvement can be deployed across Micropump. The company’s precious Belt resources now spend their time doing the work of continuous improvement instead of cleaning data or being a shadow IT department.
The entire DMAIC framework depends on the availability of reliable, quality data. In many companies, no provisions are made to ensure that key process information will be available when needed. The scenario experienced at Micropump early in its deployment is all too common – Black Belts designing redundant databases to capture process information for their projects, which stop when the project is closed. But now at Micropump, availability of data is a real priority. Micropump has been recognized as a leader within IDEX for its access to data for ongoing process control, for process analysis and to maintain the gains of its improvement efforts. Now other business units within IDEX are following Micropump’s lead and quickly gaining momentum.
During the last five years, the Operational Excellence program has yielded outstanding results at Micropump. The company integrated Lean tools with Six Sigma in 2003 resulting in a 30 percent improvement in on-time delivery while cutting inventory by more than half. The company is now extending the reach of Operational Excellence into the supply chain and to customers. Looking at the entire value stream has yielded even greater opportunity for improvement.
A significant impact on the company’s Six Sigma project cycle time has been noted as well. In the Define phase, projects are being scoped, prioritized and chartered faster than ever. Time required for the Measure phase has been reduced by an average of 10 percent, and implementing the Control phase is almost painless. The company is completing more projects and yielding benefits faster. Associates working on Operational Excellence project teams are seeing the results as well, and the momentum of the program has not slowed – it actually has accelerated.
Can the focus on building reliable data systems be given all the credit for Micropump’s success? Probably not. But the management team recognized the need to build a data infrastructure, and certainly deserves credit for enabling Six Sigma and Lean management.
But having the right people working on the right projects did not move the improvements ahead fast enough. President Jeff Hohman described a moment of frustration early in the Six Sigma effort. “I had these two high-powered and highly compensated engineers – really two of my best people – spending hours scrubbing data and building databases to get the information they needed for their projects.” Micropump had to deliver benefits from the Lean Six Sigma effort quickly, and it could not do it if the Black Belts were spending days at a time cleaning up data rather than analyzing it and acting on it.
The Quest for Clean Data
The plant had been using statistical process control (SPC) since the mid-1980s and was an early adaptor of SPC software. The SPC software in use when Six Sigma was launched provided data about machining, molding and product testing processes in the shop, but extracting information to fuel the Six Sigma initiative was time-consuming. The company’s vice president of engineering, and one of the first Black Belts, Charlie Carr, described it this way: “Who knew how many ways there was for an operator to enter their name?”
With no way to limit the types of entries users were making, data errors like the inconsistent operator name were commonplace. Out-of control reasons could be entered free-form, making root cause analysis a painstaking effort. And finally, all data had to be moved into another application (even if no scrubbing was required) so that it could be imported into Minitab for further analysis. This meant data had to be handled multiple times before it was even ready for analysis. The process was just too expensive and error prone.
The first projects were focused on on-time delivery for customers. But on-time delivery data was not readily available. It had already become apparent that a new data acquisition method would be needed and that it would need to work with transactional processes as well as manufacturing processes.
Satisfied that SPC was a tool it needed to continue to use, at least in manufacturing, the company formed a project team to solve the data problem. There was a desire to know if one comprehensive solution could be applied across the business – in both manufacturing and transactions.
The team began by developing a list of critical-to-quality characteristics (CTQs) for process data:
◈ SPC must be used for process control in manufacturing – The company needed the ability to automate data collection and real-time alarms in all of manufacturing processes. The goal was to use existing quality data collection processes wherever possible. But the company wanted better support for automatic gaging, and more transparent data sharing. And process owners needed to be able to respond instantly to process shifts or special cause variation.
◈ The ability to accurately track transactional process performance – Team wanted to track manufacturing and transactional data at the same time, with the same system. While there clearly are differences between transactional and manufacturing data, there also are many similarities.
◈ A way to link information from many databases for use in operations – The company already had a lot of data in various databases. It needed a way to bridge these disparate systems.
◈ One source for process and product data – Once again, regardless of the source of the data, (dimensional, equipment performance, cycle times, defects, product testing), the company needed a way to reach it.
◈ Mistake-proofing of data – The vision was using current technology to eliminate operator data input errors. The company wanted the ability to use barcode scanning, pre-filled data fields, drop-down lists, etc.
◈ Real-time information about all processes – With a taste of how real-time data could help certain operations, the team figured it should span all processes.
◈ Ease of use by operators, supervisors, engineers and Black Belts – The idea was to get rid of a system that was cumbersome and difficult to use, thus making life better for everyone on the staff. Ease of use included having a system compatible with the statistical analysis software used by the company.
◈ Limited resources required for initial set-up and ongoing system maintenance – Finally, the team knew the company needed a system that required minimal on-going IT support and resources. The company was stretched too thin to place more demands on the IT staff.
The team then began considering options to meet company needs. Here are a few of the options considered:
◈ Hiring a programmer to create an application that could share information between current scheduling, SPC, engineering drawing and specification databases.
◈ Dedicating a portion of a Black Belt resource to data integrity.
◈ Investing in an enterprise wide knowledge-management system.
◈ Investigating the capabilities of different SPC software packages.
The Analysis of Options
Hiring a programmer to create a custom application held some appeal because the team thought the company’s needs were pretty specialized, and because it wanted control of the solution. Initially it seemed like it might be more cost-effective, but as when the company’s core competencies and head count were fully considered, it was realized that the business of writing custom software was the best solution. Also, the team was aware of the typical high failure rates for IT projects. The clincher, however, the problem of maintaining a home-grown system. Team members had all seen clever home-grown software solutions implemented, only to see those systems hamper the organization as they became out-dated and unsupportable.
Dedicating a portion of a Black Belt’s time to data integrity seemed crazy to the team. While having clean, reliable data was essential to driving Six Sigma projects, the act of getting that data added absolutely no value to the business. So dedicating highly valued resources to a non-value-added activity was counter-intuitive. The team considered investing in training and developing people, other than Black Belts, to harvest the needed data, but the investment costs in lead time and training resources were considerable. And the bottom line was they still needed some kind of software and hardware to do the job.
As the team investigated the field of enterprise-wide knowledge management systems, it found some great summary reporting tools, but they all lacked several key capabilities that were essential to Micropump’s business. First, a large portion of company efforts were focused on manufacturing, while knowledge management systems were not. Coupled with that was a serious weakness in real-time statistical analysis capabilities. While most of these tools could tell when something missed a target, they could not identify a statistical shift in mean or a statistical trend in real-time. Nor did they readily interface to the company’s statistical analysis software. Finally, they did not help scrub the data. In that way, they really did not move the company beyond where it already was – spending countless hours massaging and scrubbing data for projects.
The last choice, commercially available statistical process control solutions, proved to be the best course of action for the company. The team learned that while these systems have a reputation for belonging on the shop floor, the good ones do that and more. In short, the company was able to find a system that met all of its criteria. That system has been deployed now for nearly four years, and it continues to provide clean, reliable data in real time so that continuous improvement can be deployed across Micropump. The company’s precious Belt resources now spend their time doing the work of continuous improvement instead of cleaning data or being a shadow IT department.
The Proof Is in the Results
The entire DMAIC framework depends on the availability of reliable, quality data. In many companies, no provisions are made to ensure that key process information will be available when needed. The scenario experienced at Micropump early in its deployment is all too common – Black Belts designing redundant databases to capture process information for their projects, which stop when the project is closed. But now at Micropump, availability of data is a real priority. Micropump has been recognized as a leader within IDEX for its access to data for ongoing process control, for process analysis and to maintain the gains of its improvement efforts. Now other business units within IDEX are following Micropump’s lead and quickly gaining momentum.
During the last five years, the Operational Excellence program has yielded outstanding results at Micropump. The company integrated Lean tools with Six Sigma in 2003 resulting in a 30 percent improvement in on-time delivery while cutting inventory by more than half. The company is now extending the reach of Operational Excellence into the supply chain and to customers. Looking at the entire value stream has yielded even greater opportunity for improvement.
A significant impact on the company’s Six Sigma project cycle time has been noted as well. In the Define phase, projects are being scoped, prioritized and chartered faster than ever. Time required for the Measure phase has been reduced by an average of 10 percent, and implementing the Control phase is almost painless. The company is completing more projects and yielding benefits faster. Associates working on Operational Excellence project teams are seeing the results as well, and the momentum of the program has not slowed – it actually has accelerated.
Can the focus on building reliable data systems be given all the credit for Micropump’s success? Probably not. But the management team recognized the need to build a data infrastructure, and certainly deserves credit for enabling Six Sigma and Lean management.
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