Understand the Role of Six Sigma in the Energy Industry

The energy industry is a critical sector that plays a vital role in powering our daily lives. With the increasing demand for energy and the need to reduce costs and improve efficiency, it has become essential for companies in this sector to adopt effective methodologies to manage their processes and operations. One such methodology is Six Sigma, widely recognized for improving quality and reducing waste in various industries. In this blog, we will discuss the role of Six Sigma in the energy industry and how it can help companies in this sector to achieve their goals of reducing costs, improving efficiency, and enhancing customer satisfaction. Whether you are a professional in the energy industry or simply interested in learning more about this topic, this blog will provide valuable insights and information.

What is Six Sigma?

Six Sigma is a quality management methodology that aims to achieve near-perfection in business processes. Motorola developed it in the 1980s to improve manufacturing processes and reduce defects. The name “Six Sigma” refers to the statistical concept of standard deviation and the goal of achieving six standard deviations between the mean and the nearest specification limit, which equates to a process that produces only 3.4 defects per million opportunities.

The Six Sigma methodology is based on a data-driven approach that uses statistical tools and techniques to identify and eliminate sources of variability in a process. It is divided into two main phases: the Define-Measure-Analyze-Improve-Control (DMAIC) phase, which is used to improve existing processes, and the Define-Measure-Analyze-Design-Verify (DMADV) phase, which is used to create new processes or to optimize existing ones.

The Emergence of Six Sigma in the Energy Industry

The emergence of Six Sigma in the energy industry can be attributed to several factors. One of the main drivers is the need to improve operational efficiency and reduce costs. The energy industry is highly competitive and regulated, and companies are constantly pressured to improve their bottom line. Using Six Sigma, energy companies can identify and eliminate inefficiencies in their processes, leading to significant cost savings.

Another factor contributing to the emergence of Six Sigma in the energy industry is the increasing focus on sustainability and environmental responsibility. Using Six Sigma, energy companies can identify and eliminate sources of waste and pollution in their operations, which can help them meet regulatory requirements and improve their environmental performance.

Finally, the emergence of Six Sigma in the energy industry can also be attributed to the need to improve safety and reliability. The energy industry is a high-risk sector, and companies are under constant pressure to improve the safety of their operations and ensure that their systems and equipment are reliable and operate at peak efficiency. Six Sigma can help energy companies identify and eliminate risk sources in their operations, which can help improve safety and reliability.

Overall, the emergence of Six Sigma in the energy industry can be attributed to a combination of factors, including the need to improve operational efficiency, reduce costs, meet regulatory requirements, improve environmental performance, and improve safety and reliability.

Six Sigma Methodology in the Energy Industry

Six Sigma is a data-driven process improvement methodology aiming to eliminate defects and reduce process variability. The Six Sigma methodology consists of five phases: Define, Measure, Analyze, Improve, and Control (DMAIC). The problem or opportunity is defined in the Define phase, and project objectives are set. In the Measure phase, the current process is measured, and data is collected to identify the root causes of defects. In the Analyze phase, data is analyzed to identify the root causes of defects, and potential solutions are developed. In the Improve phase, solutions are implemented, and their effectiveness is verified. Finally, in the Control phase, the improvements are monitored and sustained to ensure that the process remains in control.

Six Sigma can be applied to various processes in the energy industry, such as production, distribution, and maintenance. For example, in the production process, Six Sigma can be used to optimize the yield of a refinery by identifying and eliminating the sources of variability in the process. Likewise, Six Sigma can be used in the distribution process to reduce the number of customer complaints by identifying and eliminating the sources of variability in the customer service process. Finally, six Sigma can be used in the maintenance process to reduce downtime by identifying and eliminating the sources of variability in the maintenance process.

The Six Sigma methodology has been widely adopted in the energy industry because of its ability to improve process efficiency, reduce costs, and increase customer satisfaction. By applying Six Sigma, energy companies can identify and eliminate waste and inefficiency in their processes, which can lead to increased productivity, improved product quality, and reduced costs. Additionally, Six Sigma can help energy companies to comply with regulatory requirements and industry standards, which can be critical to maintaining the company’s reputation and market position.

Overall, Six Sigma is a powerful methodology that can help energy companies to improve their processes and increase their competitiveness in the global market. By using Six Sigma to identify and eliminate sources of variability and waste, energy companies can significantly improve process efficiency, quality, and customer satisfaction, leading to increased profitability and long-term success.

Benefits of Implementing Six Sigma in the Energy Industry

Implementing Six Sigma in the energy industry can provide a wide range of benefits for companies in this field. Some of the key benefits include:

◉ Improved Efficiency and Productivity: Six Sigma methodology is designed to improve efficiency and productivity by identifying and eliminating waste in processes. This can lead to significant cost savings for energy companies and increased productivity and output

◉ Enhanced Quality Control: Six Sigma is focused on improving quality control and reducing defects. This can lead to improved customer satisfaction and help energy companies meet regulatory requirements and industry standards

◉ Increased Competitiveness: Companies implementing Six Sigma in the energy industry can gain a competitive edge by being more efficient, productive, and having higher quality control. This can help energy companies to win more business and stay ahead of their competitors

◉ Better Data Analysis: Six Sigma methodology uses data and statistical analysis to identify problems and improve processes. This can help energy companies to make better decisions and improve their operations

◉ Improved Safety: Six Sigma also focuses on improving safety and reducing risks. This can help energy companies to protect their employees, customers, and the environment

◉ Continuous Improvement: Six Sigma is a continuous improvement methodology, which means that energy companies can continue to improve and optimize their processes over time. This helps to ensure that the benefits of Six Sigma are sustained over the long term

Overall, implementing Six Sigma in the energy industry can significantly impact the bottom line and help companies improve their performance and competitiveness in the market.

Challenges in Implementing Six Sigma in the Energy Industry

Implementing Six Sigma in any industry can be challenging, and the energy industry is no exception. Some of the challenges that organizations may face when implementing Six Sigma in the energy industry include the following:

◉ The complexity of Processes: The energy industry is known for its complex and dynamic processes, which can make it difficult to identify and analyze the root causes of problems. This can make it difficult to implement Six Sigma methodologies effectively

◉ Resistance to Change: Changing how things have always been done can be difficult for employees. They may resist change and need to understand Six Sigma’s benefits fully. This can make it difficult to get buy-in from employees and to implement Six Sigma effectively

◉ Lack of Data: The energy industry often deals with large amounts of data, but this data may not be easily accessible or may not be in a format that can be easily analyzed. This can make it difficult to identify and analyze problems and implement Six Sigma methodologies effectively

◉ Limited Resources: Organizations in the energy industry often have limited resources and may need more personnel or the budget to implement Six Sigma effectively. This can make it difficult to train employees, collect and analyze data, and implement changes

◉ High-risk Environment: The energy industry operates in a high-risk environment where safety is a major concern. This can make it difficult to implement Six Sigma methodologies without compromising safety

Despite these challenges, organizations in the energy industry can still benefit greatly from implementing Six Sigma. By addressing these challenges and developing a clear implementation plan, organizations can improve their processes, reduce costs and improve overall efficiency.

Case Study of Six Sigma in the Energy Industry

One example of a Six Sigma case study in the energy industry is the implementation of the methodology at a natural gas power plant.

Background

The natural gas power plant had been experiencing high downtime and maintenance costs due to equipment failures. As a result, the plant management team realized they needed to improve their processes to increase efficiency and reduce costs.

Implementation

The plant management team brought in a Six Sigma consultant to help them identify and eliminate the root causes of their equipment failures. The consultant used a variety of Six Sigma tools, such as the DMAIC (Define, Measure, Analyze, Improve, Control) methodology, to analyze the data and processes at the plant. As a result, the team identified several key areas for improvement, including inadequate employee training, lack of proper maintenance procedures, and a need for more standardization in the plant’s processes.

Result

After implementing the changes the Six Sigma consultant recommended, the natural gas power plant experienced a significant decrease in equipment failures and downtime. Maintenance costs were also significantly reduced. Additionally, the plant saw an increase in overall efficiency and productivity. Six Sigma methodology implementation helped the plant improve its process and reduce costs.

Prospects for Six Sigma in the Energy Industry

The prospects for Six Sigma in the energy industry are very promising. Six Sigma is a data-driven process improvement methodology that has proven successful in various industries, including the energy sector. With the increasing demand for energy efficiency, cost reduction, and sustainability, Six Sigma is expected to play a crucial role in the energy industry’s future.

The need for efficient and streamlined processes becomes even more critical as the energy industry evolves and new technologies emerge. Six Sigma offers a comprehensive approach to process improvement that will help companies in the energy sector to meet these demands. The methodology can be used to identify and eliminate inefficiencies, reduce costs, and improve the quality of products and services.

Moreover, with the trend toward renewable energy sources, Six Sigma can help energy companies to better manage their transition from traditional to sustainable energy sources. It can assist in identifying bottlenecks in processes and finding opportunities for improvement, ultimately leading to more efficient energy production and distribution.

In conclusion, the prospects for Six Sigma in the energy industry are bright, and the methodology will continue to be a valuable tool for companies looking to remain competitive and improve their operations.

Recommendations for Energy Companies Looking to Implement Six Sigma

Implementing Six Sigma in any industry can be a significant change, and the energy industry is no exception. Therefore, companies looking to implement Six Sigma must keep a few key points in mind to ensure success. Below are some recommendations for energy companies looking to implement Six Sigma:

◉ Define Goals and Objectives: Before starting the implementation process, it’s essential to define clear goals and objectives. Companies should understand why they are implementing Six Sigma, what they hope to achieve, and what their end goal is

◉ Assign a Project Leader: Implementing Six Sigma is a massive undertaking, and it’s essential to have a dedicated leader to oversee the process. The project leader should have a deep understanding of Six Sigma methodologies and be able to manage the implementation process from start to finish

◉ Choose the Right Approach: There are several approaches to implementing Six Sigma, including DMAIC and DMADV. Energy companies should choose the approach that best suits their needs, depending on the nature of their business and the goals they hope to achieve

◉ Invest in Training: Six Sigma requires a significant investment in training, both for the project leader and the rest of the team. Energy companies should ensure their employees receive proper training in Six Sigma methodologies and techniques to implement them successfully

◉ Set up a Support Structure: Implementing Six Sigma can be challenging, and it’s essential to have a support structure in place. Energy companies should establish a support system that includes senior leadership, project teams, and other stakeholders to ensure successful implementation

◉ Track Progress and Results: Finally, tracking progress and results throughout the implementation process is essential. Energy companies should establish a system for monitoring and measuring the success of Six Sigma implementation to ensure that they are achieving the desired results

In conclusion, implementing Six Sigma in the energy industry requires a significant investment of time and resources. Therefore, companies need to be well-prepared, clearly understand their goals and objectives, and be willing to invest in training and support. By following these recommendations, energy companies can ensure a successful implementation of Six Sigma, resulting in improved processes and increased efficiency.

Source: invensislearning.com

Continue reading

Difference between DMAIC and DMADV

1. DMAIC :

DMAIC is a part of the six sigma program which stands for Define, Measure, Analyze, Improve, and Control. DMAIC is a business strategy used to figure out how to improve processes while controlling costs.  It’s an effective way for identifying the root cause of significant problems within your operations or the surrounding environment and then leveraging that knowledge to create a plan to solve it.

1. Define : First make sure that you have an objective in mind for what you want to improve and understand the problem as much as possible; this will help determine which process is being improved.

2. Measure : Next establish a baseline from which you can measure improvement by collecting data before any changes are made. This might involve making a control chart or collecting data from past projects/work because it contains groups of similar problems.

3. Analyze : After you have accumulated the data, analyze it to see what you can do to improve and get a baseline figure. This is a good time to use Control Charts and Pareto charts to see what improvements are occurring and which ones need more improvement.

4. Improve : At this stage you should begin making changes that will improve your process. One of the most important factors is changing system thinking in terms of getting rid of bad habits and creating new good habits. This part of the DMAIC cycle is for continuous improvement in your company and overall health of the system.

5. Control : After you have made all of the improvements, look at the data again to see how much it has changed and if there are any fluctuations or trends in your data. This will show you if you need to do more or less of something that you implemented.

2. DMADV :

DMADV is a part of the six sigma program which stands for Definition, Measurement Design Analysis Verification. These are general tools in the design process that we have found can be helpful to learn more about what is going on with your project.

The five phases are  :

1. Definition : The definition stage is when you establish a common understanding of how the design will be performed.

2. Measurement : The measurement stage is when you collect data that helps you understand how your project is unfolding.

3. Design : The design stage is when you leverage all of the information collected in order to make wise decisions about the next steps you may take. The information that has been collected can be seen as an opportunity to drive the project to a better outcome rather than being viewed as a source of risk.

4. Analysis : The analysis stage is when you take all of the information collected in step 2 and step 3, and perform an analysis on it to help you understand what is really happening with your project.

5. Verification : The verification stage is when you compare expectations of how you want the project to unfold with how the project is actually unfolding. By doing this comparison, you are verifying that your original intentions are being met.

When to use ?

1. DMAIC : DMAIC methodology is used when the product has already been released out of the company, however there are issues or the product deviates from the customer’s demand.

2. DMADV : DMADV methodology is used when the product is not built and is currently in the designing & planning process. It is also used when the product could not be optimized and needs to be rebuilt.

Difference Between DMAIC and DMADV :

DMAIC	DMADV
DMAIC is about the improvement and control process. It defines the business process. Find problem then solve it in a target of providing improved solution.	The design and verification process involves the redesigning of the whole process to come close to the requirement described by the customer.
It addresses the current processes of the project.	It addresses the design processes of the projects.
It works better with pre-existing projects.	It works better with new projects.
It is about minimizing the processes and correcting the errors.	While it is about preventing errors.
It uses quantitative tools.	It uses qualitative tools
DMAIC are usually considered for short term projects.	DMADV are usually considered for long term projects.
DMAIC gives specific solution.	DMADV is not the whole solution process, it is a part of the design solution.
It is about correction.	It is about prevention.
DMAIC controls future process performance.	DMADV verifies the design performance.
It is initiated from a problem.	While it is initiated from an innovation solution.

Source: geeksforgeeks.org

Continue reading

Lean Six Sigma Explained

If you are looking to improve the way you carry out processes in your business and change the way things work, Lean Six Sigma may be an ideal option.

Nearly forty years old, this methodology has helped thousands of organizations around the world speed up production, improve efficiency and help save money.

You may be wondering if it is right for your business and if so, how to go about becoming a qualified practitioner. If the answer to both of these questions is yes, we’ve put together this short guide to help you find out more about Lean Six Sigma.

The history of Lean Six Sigma

Lean Six Sigma was initially devised by Motorola in 1985. The methodology came into being as a way for the company to reduce manufacturing issues and increase efficiency.

In fact, ‘sigma’ is a measurement of variation. With Lean Six Sigma, you want to reduce the number of variations (or defects) in your processes, as well as eliminate any unnecessary steps. Sigma strives to achieve near-perfect output, high-quality and consistent improvement.

This methodology was initially used by companies in the manufacturing industry. However, Lean Six Sigma has now been adopted by a wide range of different sectors including IT, healthcare, marketing and banking.

If your business has processes in place, then the Lean Six Sigma methodology can be applied.

What is Lean Six Sigma all about?

Lean Six Sigma is about data gathering, looking at issues analytically and measuring data to see if the processes put in place are working.

It’s also about continuous improvement. Once a problem has been resolved, it still needs to be monitored to see if there is a better, more efficient way of carrying it out.

Lean Six Sigma requires buy-in and commitment from everyone in the organisation, from the Managing Director to those on the shop floor. Everyone across the company needs to be aware of what is being done and how even the smallest changes can have a significant impact.

A good project manager skilled in the Lean Six Sigma Methodology will rally people behind any changes that need to be made.

What is DMAIC?

DMAIC is one of the critical methodologies that Lean Six Sigma uses. It is utilised to find and eliminate defects in a process.

DMAIC stands for:

◉ Define – define the problem, as well as what and who is needed to solve it

◉ Measure – quantify the problem and establish a baseline so you can see how things are improving

◉ Analyse – identify what is causing the problem and how you can change or remove these issues

◉ Improve – solve the problem, implement the changes and check that any amends made are working

◉ Control – ensure that the problem does not return, and that the process continues to improve over time

The belts of Lean Six Sigma

Six Sigma has a lot in common with karate. Both require precision, dedication and knowledge.

With karate, you receive a new colour belt when you pass your exams. With Lean Six Sigma, although you do not receive a physical belt, the different grades are called ‘belts’!

People with no formal Lean Six Sigma qualifications are known as ‘white belts’. After this, there are three different levels of accreditation.

Lean Six Sigma Yellow Belt

This is a short introduction to the world of Lean Six Sigma – ideal for those who may not be managing change but are involved in supporting the people that are.

Learn more about our Lean Six Sigma Yellow Belt

Lean Six Sigma Green Belt

The next step on the Lean Six Sigma ladder, a Green Belt, is a good starting point for project managers. This course introduces them to the problem-solving frameworks that Lean Six Sigma utilises.

This is a lifetime certification and does not need to be recertified.

Learn more about our Lean Six Sigma Green Belt

Lean Six Sigma Black Belt

In karate, a black belt means that you have mastered all there is to know and are ready to lead others. The same applies to a Black Belt in Lean Six Sigma.

This advanced course teaches delegates how to drive change across the business as well as how to measure performance.

This certification needs to be renewed every three years.

You don’t have to complete Lean Six Sigma Green belt before you take on this accreditation, but it is recommended.

If you want to take things even further, you can study for a Lean Six Sigma Master Black Belt. This shows that you can not only lead others but are qualified to teach them in Lean Six Sigma methodology too.

Learn more about our Lean Six Sigma Black Belt

Why is Lean Six Sigma so beneficial?

Lean Six Sigma has the following benefits for organisations.

◉ It improves productivity

◉ It improves quality

◉ It increases customer and stakeholder satisfaction

◉ It ensures compliance with government regulations

◉ It reduces waste

◉ It reduces operating costs

◉ It reduces risk

◉ It reduces employee turnover

It also has benefits for you too! Holding a Lean Six Sigma Qualification can increase your salary and help you get promoted to managerial roles.

Source: itonlinelearning.com

Continue reading

What Is DMAIC?

DMAIC is an acronym for Define, Measure, Analyze, Improve, Control. DMAIC is the process improvement methodology of Six Sigma that’s used for improving existing processes.

DMAIC is pronounced: Duh-May-Ick.

What Is DMAIC?

DMAIC refers to a data-driven quality strategy for improving processes, and is an integral part of the company’s Six Sigma Quality Initiative. DMAIC is an acronym for five interconnected phases: Define, Measure, Analyze, Improve, and Control.

DMAIC Process Steps

Each step in the cyclical DMAIC Process is required to ensure the best possible results. The process steps:

Define the Customer, their Critical to Quality (CTQ) issues, and the Core Business Process involved.

Define who customers are, what their requirements are for products and services, and what their expectations are

Define project boundaries ­ the stop and start of the process

Define the process to be improved by mapping the process flow

Measure the performance of the Core Business Process involved.

Develop a data collection plan for the process

Collect data from many sources to determine types of defects and metrics

Compare to customer survey results to determine shortfall

Analyze the data collected and process map to determine root causes of defects and opportunities for improvement.

Identify gaps between current performance and goal performance

Prioritize opportunities to improve

Identify sources of variation

Improve the target process by designing creative solutions to fix and prevent problems.

Create innovate solutions using technology and discipline

Develop and deploy implementation plan

Control the improvements to keep the process on the new course.

Prevent reverting back to the “old way”

Require the development, documentation and implementation of an ongoing monitoring plan

Institutionalize the improvements through the modification of systems and structures (staffing, training, incentives)

Continue reading

Using DMAIC to Improve Another Improvement Process – CAPA

Six Sigma and its DMAIC (Define, Measure, Analyze, Improve, Control) methodology provide a structured process for solving problems and improving processes. For this project, our team used DMAIC to improve a problem-solving process used in the medical industry – the CAPA process.

Medical device companies are required to demonstrate compliance to the Federal Drug Administration (FDA) 21 Part 820.100 Corrective Action and Preventive Action (CAPA) to be able to sell medical devices in the United States.

The CAPA process at Medtronic complies with the regulations of the FDA and applicable international standards to address quality issues – device complaints, non-conformances and audit findings. The CAPA process is divided into three key phases and align with the DMAIC phases as shown in the table below.

CAPA Phase	What Happens in Phase	DMAIC Phase Correlation
Investigation	Determine root cause	Define, Measure, Analyze
Action	Take corrective action	Improve
Effectiveness	Verify the success of the corrective action	Control

Overview

In this project, the team of quality managers and engineers used a DMAIC process to improve the CAPA process. The existing process was complex, leading to several inefficiencies including rework of CAPA tasks and delays in getting the tasks completed on time. The CAPA owners faced several challenges in writing the tasks and needed guidance to complete their work.

The team gathered the voice of the internal customers by:

◉ Soliciting feedback from CAPA owners (people who were responsible for following the CAPA problem-solving process to resolve a specific problem)

◉ Performing KJ analysis or affinity diagramming to group the voices by common themes

◉ Prioritizing sets of customer needs and converting them to measurable requirements

The measurable requirements were flowed down, and concepts were generated using TRIZ (the Theory of Inventive Problem Solving – more on this later) and a concept was selected using a Pugh matrix. Risks were evaluated and the potential favorable or unfavorable impact was statistically modeled using Monte Carlo simulation.

Define

The Define phase began with the gathering of the voice of the customer. The team gave stakeholders of the CAPA process and quality managers a survey shown in Figure 1. The data gathered from this survey gave the team the insight to the customer needs: the CAPA process stakeholders needed guidance and examples to help with writing a new CAPA. This survey was a questionnaire in which individuals were asked to fill out what is going well with the CAPA process, what is not going well and what are the recommendations to improve the process. Each question was rated from 0 to 10, where 0 is the worst score and 10 is the best score.

Figure 1: Results of the CAPA Process Survey

To gather ideas on the type of platform to use to provide the guidance material to CAPA owners, a KJ analysis (similar to affinity diagramming) was used. This analysis was performed with the stakeholders of the CAPA process, which included CAPA owners and quality managers. The stakeholders brainstormed ideas based on the customer inputs. Ideas were written on sticky notes, organized on a white board and prioritized based on the key customer themes. (Figure 2)

Figure 2: Results of KJ Analysis

The KJ analysis identified the main theme to improve: Provide CAPA owners with easily accessible examples and templates to help with the CAPA process. Being able to quickly look up the examples and templates that will help the CAPA owners in writing their CAPA tasks.

This proposed solution took the form of a CAPA Portal: a web-based system that provides several templates and examples to complete CAPAs while meeting the compliance requirements and international standards.

Measure

Measurable requirements for the CAPA process are the timely completion with only a few rework cycles associated with completing a CAPA while meeting the FDA’s quality and compliance requirements. The CAPA Portal was developed with these three requirements for measurement:

1. CAPA disposition time: The total time to resolve a problem through the CAPA process

2. Number of CAPA rework loops: The number of times each step in the CAPA process must be repeated to fix issues

3. CAPA resolution time: The time that it takes to resolve a CAPA issue

These key requirements were flowed down from customer expectations using the House of Quality partially shown in Figure 3, and the team prioritized the requirements for the CAPA Portal. The prioritization involved assessing how well each measurable system requirement (left to right) could fulfill each customer requirement (top to bottom). If the system requirement could strongly improve meeting a specific customer requirement, an H for High was entered and assigned a relative value of 9. If there was a medium improvement, an M for Medium and a relative value of 3 was assigned. If there was a low improvement, then an L for Low and a relative value of 1 was assigned. The requirements were then flowed down to sub-system, component and lower-level component requirements.

Figure 3: House of Quality

For each column associated with each system requirement, the value of 1, 3 or 9 was multiplied by the relative importance (“Imp”) for that associated customer requirement and summed for the column. This resulted in high priorities for CAPA Portal (aka the “CAPA Playbook”) that would provide guidance along with Interface System Requirements and expectations for a CAPA Dashboard to summarize progress and results.

Analyze

Prior to implementing the requirements, it was necessary to identify the potential sources of failures that can lead to an ineffective CAPA. For the Analyze phase, FDA regulatory expectations and internal business expectations for effectiveness drove the team to understand sources of failure. Fault tree analysis (FTA) was performed to better understand what leads to a deficient CAPA record or an ineffective CAPA. The source of the failure? Deficient CAPA records are due to lack of access to guidance and inadequacies in training. Moreover, the analysis gave the team an insight on the causes that caused poor outcomes, as shown in Figure 4.

Figure 4: Fault Tree Analysis

From the FTA, a primary cause of ineffective CAPAs was related to lack to the to-the-point training material. This reinforced the team’s belief that the CAPA Portal must behave as a CAPA Playbook, providing step by step instructions with helpful examples and templates.

The requirement flow-down indicated that the user interface should make it easy for multiple CAPA owners to access and share information. Sharing and communication could enable more rapid resolution of the CAPA issues.

Developing a user interface involves tradeoffs. The TRIZ (Theory of Inventive Problem Solving) concept-generation approach provided a way for the team to dispassionately consider the tradeoffs and find an innovative solution to meet expectations involved in the tradeoff. The TRIZ approach converts tradeoffs into generic tradeoffs and recommends a small set of TRIZ principles that have been used to resolve that sort of generic tradeoff in the past, based on engineer and inventor Genrich Altshuller’s research of millions of patents. Our team used TRIZ to find solutions for the following tradeoffs.

Tradeoff 1:

◉ Feature to improve: Report out on CAPA metrics to increase productivity by 25 percent

◉ Undesired result: The user could be too overwhelmed by content to consume information

Tradeoff 2:

◉ Feature to improve: Provide a workspace for CAPA owners to fill out the templates

◉ Undesired result: Inability to download file due to slow speed

Tradeoff 3:

◉ Feature to improve: Make content available with templates and examples on a dedicated page

◉ Undesired result: Site unable to load all the content

From here, we identified three TRIZ principles (aka known solutions), which were applied to the CAPA Portal to address the aforementioned tradeoffs. The three principles applied to the CAPA Playbook were:

1. Principle of Universality: Allows a part of the system to perform multiple functions so other parts can be eliminated. This principle was applied to create dashboards and organize information to report out on CAPA metrics such number of open and closed CAPAs, CAPA age, etc. This solution addressed Tradeoff 1 to solve adaptability versus productivity of the CAPA Portal.

2. Principle of Preliminary Action: Allows pre-arranging the elements of the system so that they perform rapidly. This principle was used to attach notes to guide the user and files that serve as a CAPA template that users can access directly from the system. This solution addressed Tradeoff 2 to solve speed versus extent of automation of the CAPA Playbook.

3. Principle of Segmentation: Allows separating an element of a system into smaller interconnected elements. This principle was used to provide dedicated links on the CAPA Portal Interface to access the three key phases of the CAPA process: investigation, action and effectiveness. This solution addressed Tradeoff 3 to solve productivity versus reliability of the CAPA Playbook.

Using these TRIZ principles, the team was able to design the system interface and dashboard for the CAPA Portal.

Improve

Based on the user criteria for the CAPA Portal established in the Define and Measure Phases, a Pugh matrix was used to evaluate the strengths and weaknesses of the available systems – Sitebuilder, SharePoint, MAP AGILE and Confluence – and rated using S = Neutral or 0, + = add 1, and – = subtract 1 for each selection (Figure 5). The total score and the weighted total were then calculated to identify the system that had the highest score. The CAPA Portal was developed using a web-based system that can be shared with multiple users and can be used to easily access guidance material such as templates and examples.

Figure 5: Analysis Using Pugh Matrix

To ensure that the proposed CAPA Portal would meet the users’ expectations over a range of use conditions or noise factors, a P-diagram (parameter diagram) was used (Figure 6). It showed the interactions of the system, the inputs and outputs, the noise factors, control factors and error states. Error states from the P-diagram were evaluated further as failure modes through failure mode and effects analysis (FMEA). The FMEA helped the team analyze risks, prioritize risks and take actions to mitigate the risks. From this FMEA, the CAPA Portal was designed to anticipate potential error states and provide early warnings and direct users to mitigation through help options.

Figure 6: Parameter Diagram

Through implementation in the Improve phase of DMAIC, users of the CAPA Portal began accessing the new CAPA Playbook to guide them in their CAPA tasks. The value of this system was measured using the critical parameters of disposition time, rework and resolution time for each CAPA task.

The results from critical parameters measured over a period of May 1, 2019, to January 11, 2020 (before the deployment of the CAPA Portal), and January 12, 2020, to July 7, 2020 (after the deployment of the CAPA Portal).

◉ The number of rework loops in approving a CAPA task decreased by 61 percent.

◉ The total time in review and approval of a CAPA task decreased by 53 percent.

◉ The total time to resolve a rejection of a CAPA task decreased by 43 percent.

Control

The transition from the Improve phase to the Control phase of DMAIC typically includes overcoming resistance to change, implementation of control mechanisms such control charting, mistake proofing (poka yoke) and institutionalization.

Since the users and other stakeholders were engaged throughout this project, from gathering their own voices through being involved in generating and selecting concepts, there was little resistance to overcome. Rather, users were extremely receptive.

The control mechanism was provided by the CAPA Dashboard that was integrated into the CAPA Portal.  Poka yoke was integrated into the user interfaces and help systems. The CAPA Playbook was institutionalized within the original organization through documented and controlled processes; it is being used for all CAPAs, with its immediate feedback and control.

The results were rapidly shared with executives including vice presidents of quality and manufacturing operations. The executives were impressed by the project’s impressive results, and the executives requested that the CAPA Playbook and associated improvements to the CAPA process be replicated through other parts of the organization.

Continue reading

Case Study: Reducing Delays in the Cardiac Cath Lab

Cardiac catherization labs represent a significant capital investment for many hospitals. Realizing a return on this investment is increasingly challenging, given the introduction of advanced technologies and limitations in reimbursement. To meet the challenges and maintain fiscal health, hospitals pursue Six Sigma, Lean and change management techniques to improve throughput, maximize equipment utilization and increase efficiency.

New York-Presbyterian Hospital embarked on a comprehensive initiative aimed at improving throughput in the cardiac catherization labs at the Columbia University Medical Center, New York Weill Cornell Medical Center and Children’s Hospital of New York-Presbyterian sites.

The improvement initiatives at New York-Presbyterian focused on the various sub-cycle times impacting throughput – including case start time, room turnaround time and patient prep time. As a result of these multiple projects, the hospital gained 312 hours of procedure time without incurring any additional capital expense. An overview of one project conducted at Children’s Hospital of New York demonstrates how the Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) methodology provided the framework and tools to raise departmental productivity by improving first case start times.

The Define Phase

Improving first case start time was selected as a project by the Children’s Hospital of New York for several reasons. It contributed to a significant amount of lost productivity and failure of the first case to start on time was delaying subsequently scheduled cases. This variability in start time and lack of schedule predictability also was contributing to staff, physician and patient dissatisfaction.

A charter was developed and approved by senior leadership and a team assembled to lead the initiative. The charter provided:

• Project Scope – This established the parameters for the project. The start point of the cycle was patient’s arrival at the hospital and the end point of the patient’s entrance into the cath lab. The charter also described areas outside of the team’s scope, such as room turnaround time, which was the focus of another team.
• Business Case and Problem Statement – Baseline data indicated that 62 percent of the first cases were not starting on time representing 267 hours of lost staff productivity and unused procedure capacity annually.
• Goal Statement – A goal of 80 percent on-time starts was established.
• Team Members – The team for the project included the cath lab director, staff, cardiologists and anesthesiologists. The vice president of operations served as project sponsor and oversaw the work of the team.
• Timeline – A timeline including frequency of meetings, dates and times was agreed upon at the team’s first meeting and proved essential to keeping the project on track.

The project charter provided the team with focus and direction. The team then developed a map describing the current process.

Figure 1: High-Level First Case Start Process Map (Source: GE Healthcare and New York-Presbyterian Hospital)

Completion of the process flow map pointed to one opportunity for immediate improvement – streamlining the nursing assessment. One of the department’s nurses routinely calls patients the night before to reinforce pre-procedure instructions. Discussion during the process flow mapping exercise revealed some redundancy in the information gathered during this phone call and the nursing assessment completed the day of the procedure. The team agreed that initiating the nursing assessment during this phone call would eliminate duplicate data collection, and shorten the time needed to complete the assessment the day of the procedure.

The Measure Phase

The team used brainstorming and a fishbone diagram to identify all the potential contributors to delaying the start of the first case. Some of the factors identified included:

◉ Patient arriving on time

◉ Registration process

◉ Transportation

◉ Timeliness of patient prep

◉ Completion of assessments by the cardiologist, anesthesiologist and nursing

Figure 2: Brainstorming and Prioritizing Critical Xs (Source: GE Healthcare and New York-Presbyterian Hospital)

Data was then collected to identify those factors having the most significant impact on delaying the start of the first case.

The Analyze Phase

Regression analysis, a statistical tool used to model and predict the relationship between variables, revealed that the time in which the cardiology assessment was completed was a key driver in whether the first case would be completed on time. The R-sq adjusted value showed that it accounted for about 60 percent of the variation in the process. Here is a table showing the first case start data’s statistical analysis:

X	Test	Results	Statistically Significant?
Nurse	Test for Equal Variances	p=.725	No
Nurse	Moods Median	p=.583	No
Nurse	Regression	p=.762	No
Latest Assessment Time	Moods Median	p=.432	No
Latest Assessment Time	Test for Equal Variances	p=.132	No
Latest Assessment Time	Regression	p=.177	No
Anesthesia Yes/No	Moods Median	p=.710	No
Anesthesia Yes/No	Test for Equal Variances	p=.318	No
Oral Pre-Med Yes/No	Test for Equal Variances	p=.981	No
Oral Pre-Med Yes/No	Moods Median	p=.288	No
Anesthesiologist	Moods Median	p=.389	No
Anesthesiologist	Test for Equal Variances	p=.013	Yes
Anesthesiologist	Regression	p=.625	No
Patient Arrival	Test for Equal Variances	p=.909	No
Patient Arrival	Moods Median	p=.615	No
Difference vs. Card Assessment	Regression	p=.042	Yes
Time Patient on Table vs. Card Assessment	Regression	p=0.00	Yes
Difference vs. Anesthesia Yes/No	Regression	p=.532	No
Difference vs. Nursing Assessment	Regression	p=.658	No

Source: GE Healthcare and New York-Presbyterian Hospital

The Improve Phase

The team used this information to discuss and develop plans to ensure the cardiology assessment could be completed in a timelier manner. For example, since the cardiology fellow typically initiates the cardiology assessment, the director of cardiology explored other responsibilities and obligations that might be interfering with timely completion of the assessment. As part of developing a revised process, the team also completed a new process flow map indicating a target completion time for each step in the process that ultimately would lead to the desired case start time.

As shown in the table below, re-measurement of the process indicated a dramatic improvement in the number of first cases starting on time and a reduction in variation.

Data Categories	Baseline Data	Improve/Control Data
On-Time First Case Start	38 Percent	83 Percent
Baseline Z	1.44	2.47
Median	13 Minutes	0 Minutes
Mean	38.24 Minutes	6.33 Minutes
Standard Deviation	55.62 Minutes	22.4 Minutes

The Control Phase

Process control mechanisms were implemented to ensure the changes could be sustained, and that the gains achieved from improvement activities would not be lost over time. The control plan outlined the procedure for monitoring the critical X (completion of cardiology assessment) as well as the number of on-time first case starts. Regular reporting to the project’s executive sponsor reinforced the importance of the initiative and insured that changes would become imbedded into the organization’s culture.

Continue reading

Case Study: Surmounting Staff Scheduling at Valley Baptist Health System

Located in Harlingen, Texas, Valley Baptist Health System is a full-service, not-for-profit community health network ably serving the population of south Texas and beyond. The system is comprised of multiple organizations including Valley Baptist Medical Center, a 611-bed acute care hospital providing the number one rated orthopedics service in Texas, a state of the art children’s center and a lead level III trauma facility. The organization also serves as a teaching facility for The University of Texas Health Science Center.

In 2002, Valley Baptist Health System began to implement GE’s Six Sigma approach as a rigorous methodology for process improvement and a philosophy for organizational transformation. The adoption of Six Sigma at Valley Baptist fostered a revitalized culture that embraces the voice of the customer, breaks down barriers to change and raises the bar on performance expectations. Through this initiative, the team at Valley Baptist began to examine the most critical opportunities for improvement and select projects that would align with strategic objectives and produce measurable results.

As with most healthcare providers today, maintaining appropriate staffing levels and improving productivity are among the top concerns at Valley Baptist. During the initial wave of Six Sigma training projects, the team at Valley Baptist launched an effort to review and improve the staff scheduling process for one nursing unit in orthopedics. Within this particular unit, there had been a history of overtime and use of agency hours that did not seem to correlate with changes in patient volume. Patient census would fluctuate while staffing levels remained the same, and the higher hourly wage for overtime and agencies had begun to strain the overall labor budget.

The primary focus for this project was to improve the unit’s ability to responsibly meet staffing targets while protecting the quality of patient care. It is a challenge to reach that optimal level – avoiding overstaffing yet appropriately meeting daily needs. Paramount in this effort was the notion that targets would be met without adversely impacting customers. Patient satisfaction scores had to remain constant or increase, and this mandate was built into the project and measured through the use of upper and lower specification limits.

A cross functional project team was assembled including the chief nursing officer as sponsor, the assistant vice president from human resources, the nursing house supervisor, the nurse manager from the cardiac care unit, a representative from IT and a charge nurse. The introduction of any new change initiative can elicit skepticism, but since Six Sigma concentrates on fixing the process rather than assigning blame, once the approach was understood much of the skepticism subsided. Stakeholder analysis and other CAP (change acceleration process) tools helped to surface concerns and improve communication.

Also supporting this project were metrics to measure productivity for nurses and managers that had been introduced through the adoption of Six Sigma. The dual emphasis on productivity and quality provides a framework for offering cost effective care and aligns with the customer-centered mission at Valley Baptist.

Defining the Goal

During the Define phase of the project, the team concentrated on clearly identifying the problem and establishing goals. The nursing units in general had struggled to meet their staffing targets and were over budget on labor costs. For this project, the team decided to focus on one orthopedics nursing unit based on three criteria: the unit was not extremely specialized or unique so it offered the best representation of nursing as a whole; the manager was very supportive of the initiative; and this unit offered clear opportunity for improvement and results.

To understand the current scheduling process, the project team used the SIPOC tool to develop a high-level process map. SIPOC stands for suppliers, inputs, process, output and customers. Inputs are obtained from suppliers, value is added through your process, and an output is provided that meets or exceeds your customer’s requirements. SIPOC is extremely useful during process mapping.

Measuring and Analyzing the Issues

As they moved through the Measure and Analyze phases, the project team focused on data collection and the identification of the critical “Xs” that were impacting staff scheduling. Historical data was gathered from the payroll system to analyze regular time, overtime, agency use, sick time, vacation, jury, funeral leave and FMLA. They examined 24 pay periods for each data point. Fortunately, the team was able to extract the data they needed from existing systems and avoid manual data collection, which is more labor intensive and can increase the project timeline.

Given the availability of continuous data for the “Y” or effect and the potential Xs or causes, regression analysis was the tool chosen to help the team understand the relationship between variation from the staffing goals and vacation, FMLA, sick leave, overtime, agency nurse usage, and so on. Through regression analysis, they were able to determine that three critical Xs could explain 95 percent of the variation: agency use, overtime and census. The next step would be to understand underlying factors – data would point the team to interesting findings that disputed their original theories.

The Improve Phase

During the Improve phase, the team used many of the CAP and Work-out tools. Such acceptance-building techniques are key to success, since improvements introduce changes in process and human behavior. The team conducted a Work-out session to develop new standard operating procedures for better management of overtime and agency usage – critical drivers in staffing.

The chief nursing officer attended the sessions to underscore the importance of this initiative from a leadership perspective. The project team used the process map to indicate where they might have opportunities for improvement, and then conducted separate Work-outs on each area. They brought in nursing staff, house supervisors and other stakeholders to participate in the search for solutions.

Never Assume

This project furnished a classic example as to how Six Sigma can be used to either corroborate or dispel original theories. Management at Valley Baptist had initially assumed they were over budget on labor costs due to sick leave, FMLA, vacation and people not showing up, which would have naturally necessitated the additional overtime and agency hours. The data and analysis proved those assumptions to be incorrect.

This project translates to $460,000 in potential savings for one unit. Conservatively, if it were spread across the health system the savings could exceed $5 million.

It turns out there were several factors contributing to the staff scheduling challenges. One illuminating aspect to come from the Work-outs was the realization that nurses didn’t like floating in and out of units – this came up in every session. There were also issues with the staffing matrix which attempted to set parameters based on volume. Compliance was not ideal, and the matrix itself was based on data that was not completely current. Another complication was that maintaining information in the matrix involved labor intensive, manual processes that were difficult to control.

The central metric of this Six Sigma initiative was worked hours divided by equivalent patient days. Valley Baptist Health System defines worked hours as those hours during which an employee was actually working – including regular time and overtime, and excluding non-productive hours such as sick and vacation time. Equivalent patient days is the volume statistic utilized within the Orthopedics Unit. It is the typical patient days number adjusted to reflect short-term observation (STO) patient volume.

The team discovered the use of overtime was not always need-based. Units would regularly schedule 48 hours for each nurse, with the extra eight hours of overtime built-in as “traditional” usage. This became an accepted practice and although in theory, adjustments are supposed to be made when the patient flow is lighter, this was not happening. On the form used to submit data the nurses would have to guess what hours they might actually work. The matrix might indicate compliance, but the payroll data actually showed them clocked in for 14-15 hours instead of 12.

Another critical issue is that the nursing unit lacked appropriate mechanisms for shift coordination and handoff. There were two fully independent teams between the day and night shifts, and there was not a smooth transition between them. Part of the problem stemmed from a lack of written guidelines governing the overtime between shifts. Nurses would finish their regular 12-hour shift and stay on overtime to complete tasks rather than pass them on to the next shift.

Results and the Control Phase

The development of new standard operating procedures has clearly had a positive impact on the organization. This gave staff a plan they can follow and established accountability. The unit began a process for transition meetings between shifts. The outgoing nurse now takes the incoming nurse to the patient’s room, introduces them and provides a report on the current status and whether there are outstanding orders. In addition to improving operations for the hospital, this change has also been well received by patients, as reflected in rising satisfaction scores during the pilot.

The project on staff scheduling has led to an overall reduction in the higher hourly cost of overtime and agency use, and has translated to $460 thousand in potential savings for this one unit. Conservatively, if this project were spread across the health system the savings could exceed $5 million. It is also important to note that this project started at the 0 sigma level and increased to Six Sigma for nine consecutive pay periods.

“At Valley Baptist, we continually seek opportunities to improve productivity,” said Jim Springfield, President and CEO. “This focus is critical for our future success and ability to meet patient needs.”

To ensure results are maintained, managers use control charts and trend reports with data from HR, time and attendance and payroll systems. This provides real time information on productivity, tracking worked hours versus patient days to show alignment with targets on an ongoing basis.

Organizational and Customer Impact

The bottom line is that nurses, management and patients are all happier as a result of this project. With the pilot in the Control phase, Valley Baptist has held Work-outs to determine how they might broaden the SOPs and implement this approach across the system in all nursing units.

“Staff has become much more flexible. We initially encountered some resistance, but using the CAP tools and working through the process helped to create a shared need and vision.”

Leadership involvement and support turned out to be a significant factor in the overall success of the project. This initiative represented a major culture change from previous CQI and TQM approaches to quality improvement. All previous efforts had involved hard work and good intentions, but prior to Six Sigma, they lacked the framework and rigor to institute statistically valid long-term results.

The health system is moving toward autonomy through additional Green Belt and Black Belt training with projects, and through participation in a Master Black Belt course at GE’s Healthcare Institute in Waukesha, Wisconsin. This experience provides instruction and interaction that prepares the MBB to come back and teach within the organization.

“Coming from the HR side, it’s important for organizations to know it’s possible to change the way you’ve always done things, and that employees will adapt to a new approach. If you can overcome the stress surrounding change you can realize increased efficiency. This is a positive way to control staffing without employing slash and burn techniques.”

Irma Pye, senior vice president at Valley Baptist, attended a conference in Utah with other healthcare executives. When the issue of performance improvement and staffing came up, someone mentioned they’d attempted to do a project on this and it had failed because they couldn’t afford to alienate and potentially lose good employees. Irma spoke up and let them know that based on her own recent experience, you can indeed address this issue and it can work if it is approached in the right way using the right techniques.

“Usually, when you ask the department manager to trim labor costs they think it can’t be done because it will antagonize employees . . . they’ll either take a job somewhere else, or stay there with negative feelings which impacts morale. This approach was able to affect change, while avoiding issues of layoffs or pay cuts.”

Continue reading

Six Sigma Software Development Case Study

This article illustrates the Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) process using an organization that develops software packages as an example. The Six Sigma DMAIC approach to process improvement provides a powerful mechanism for improving the software process. Typical benefits will exceed costs within 6 to 12 months from initiation of a Six Sigma program for software development, and the on-going return will be very substantial – often a 15-25 percent reduction in software development costs in year two, with continuing reductions thereafter.

Project Selection

While the selection process precedes a project’s Define phase, identifying an initial general goal, there is a chicken and egg relationship with Define as that goal is better understood and refined. We have an initial idea of our goal, but we may need to do some of the Define work before we know if the scope is reasonable. Project selection brings out another important consideration not directly addressed by Define – it establishes the link between candidate projects and corporate strategy (in one sense, these are the top level Ys).

Figure 1: Linking To The Strategic Plan

The Define Phase

The first phase of a Six Sigma process improvement project, known as “Define,” has four steps:

1. Identify the customer and the critical to quality (CTQ) requirement that will be the focus of the project. The CTQ may also be referred to as the project Y [as in Y = f (x)].

2. Create the project charter.

3. Develop a high-level process map.

4. Define phase tollgate review.

As a general rule, the scope of a Six Sigma DMAIC project is limited so that the project can be completed within four to six months – this guideline avoids projects that try to “boil the ocean.” Experience clearly establishes that overly large projects have a high failure rate. Hence, it is often necessary to decompose the initial project objective into a series of lower level objectives that become individual projects. This may be referred to as decomposing the project Y.

The following diagram illustrates one way we might decomposed a “big” Y into a series of smaller, more manageable objectives that contribute to realization of the larger goal – in this instance, improving software related “capability” (to deliver projects on time, with predictable effort, and with an acceptable number of released defects). This decomposition raises the issue of project prioritization and selection – we have limited resources that can be devoted to improvements, so which shall we do first?

Figure 2: Narrowing Goals and Scope: Preliminary Project Selection

Let us suppose that you are in an organization the produces software packages for sale. If this is where you live you probably are most concerned with the software definition, design and construction path. If that is the case, the second level Ys might include time to market, total development cost per size and delivered quality in terms of defects. We recognize that there are potentially many factors that influence these outcomes, so we need to decompose further to get to a Six Sigma project of manageable scope.

Let us then suppose that recent experience has led you to believe that your ability to meet schedules is poor – which in turn suggests a third level Y. At the third level the CTQ objective might be something like the percent of project commitments delivered on time (or perhaps within some plus or minus window). Now we’re focused on something likely to be actionable in a reasonable time, and we have an initial idea how to measure success. As we investigate further we may decide to decompose to a fourth level (or more), so let’s take this decomposition process one more step. Let’s assume we are a decentralized organization with various divisions in different parts of the country – in that case we may elect to further narrow the scope of our project to a particular division. Typically we will look at data we have, such as the percentage of late projects for each division, as a way to select the division to tackle first. Assuming we are successful with the first division we can replicate the process to other divisions later.

Figure 3: Late Projects By Division

Once through this first step in the thought process we move to step 2 – creating the project charter. A charter will typically include:

◉ A business case: What is the expected payoff if you are able to improve the CTQ? Preferably this to be expressed in dollars whenever possible – some organizations are totally insistent on this point, some accept soft benefits.

◉ A problem statement: A succinct statement of the business problem that you are attempting to solve. Using the software company scenario above this might be something like “missed schedules are impacting customer satisfaction and are causing us to miss our revenue targets.” An organization concerned with deployment of purchased packages might describe the problem as “conversions that miss planned dates are causing unexpected budget increases that impact our profitability.”

◉ Goal statement: Here we want to state the objective in terms of the metric associated with the project Y. This could be stated as “improve the on-time project percentage from the baseline value of 62 percent to 90 percent within the next year.” (Notice this goal could be equally appropriate for an organization that buys and deploys software packages.)

◉ Project team: For example, the project leader would likely be a Green Belt. The project sponsor (Champion) is the VP of marketing, and team members will include the director of the project office, three software project managers and the director of quality assurance.

◉ Scope: For example, the project will concern itself with standard product development and deployment projects only, not custom software developed under contract.

◉ Financial opportunity: “Marketing surveys indicate that up to 10 percent of potential service contract revenue is lost due to late software deliveries. Subject to validation, the opportunity in our most recent fiscal year amounts to at least $800,000 per year.”

The Measure Phase

The Measure phase can be viewed as consisting of eight steps:

1. Confirm/refine the project Y (CTQ): We will investigate more fully how this will be measured, evaluate the validity of available measures and confirm our initial hypothesis as to the magnitude of the problem/opportunity.

2. Define performance goals or standards: Here we establish our target for improvement in the Y, setting a goal that is aggressive but attainable.

3. Identify segmentation factors for data collection plan: Here we identify factors that naturally segment our project Y (by project, product, organization, etc.) and Xs that are likely to influence the Y, and define how, when and where we will measure them. Generally speaking, factors that influence outcomes are of two types – things we can control and other factors (noise) that we can’t control. We are trying to understand what’s going on.

4. Assess and calibrate the measurement systems to be used: Are they reliable and consistent? How accurate are the data?

5. Data collection: We gather the needed data.

6. Describe and display variation in current performance: What are the distributions/ranges of X and Y values currently?

7. Containment plan: If the current process is in critical condition, what quick fixes could be put in place to reduce the bleeding until we devise a permanent fix?

8. Measure phase tollgate review.

Let’s work through each of these steps, continuing our focus on improving our project planning process.

Confirm the Project Y

We indicated in the Define phase that our goal for the Y was to improve our on-time project performance from the current baseline of 62 percent to 90 percent during the next year. To confirm or refine this Y we will need to probe a little more deeply into where this data came from, and what definitions it was based on. For example, what is the definition of “completion”? Does that mean the date the customer accepted the system? Or does it mean the date that the software team declared it was “finished”? There is often a big difference between these dates – the one that really counts is the customer’s date.

Define Performance Goals or Standards

After we collect some data that will allow us to confirm that the initial estimate of the on time rate (62 percent) is correct. We will re-examine the feasibility of our goal. Our initial targets calls for an improvement of about 50 percent, which seems reasonable as a stretch target for a Six Sigma project.

Identify Segmentation Factors for the Data Collection Plan

A review of professional project planning practices reveals certain attributes of effective planning processes. These attributes give some clues how we may wish to segment (or characterize) our projects for analysis. We might, for example, decide to investigate four controllable factors that appear to be associated with effective planning (recognizing that there may be other factors we haven’t yet identified):

◉ Short task durations

◉ Defined predecessor/successor relationships among tasks

◉ “Leveled” resources (making sure we had not planned 80 hour weeks for the team)

◉ Defined deliverables or end states for each task

There are also likely to be certain noise factors that we do not control – things that are simply aspects of the environment. Depending on the size of our organization and the number of projects we have completed in the last year or two, we may be able to segment or stratify the data in ways that will give additional insight into what is going on. We may, for instance, segregate the data according to the development or deployment group that did the work, or we may stratify according to the type of software project (e.g., business applications versus firmware that is embedded in a hardware component). It will often be the case that such segments will have different success rates.

Evaluate (Analyze) the Measurement System to be Used

In this instance our “measurement system” is probably largely our project management software system – something like Microsoft Project, for example. So, we will want to see if we can locate the plans that were developed and used for the recent set of projects that were used to determine the baseline performance of the Y. Assuming we find them (not always the case) we will want to interview the people responsible for updating and using these plans.

Do the plans reflect the work that was actually done? Were tasks added or deleted as the project progressed? Was the baseline plan saved so that we can accurately determine the promised completion date for comparison to the actual? How were project requirements changes handled? If the customer added significant new requirements during the project, was the baseline (target) date appropriately adjusted to reflect the change in scope? What rationale was used to make such adjustments? Does the customer agree that adjustments were reasonable?

The answers to questions such as these may lead us to make various adjustments to the data to make it more consistent and valid before we begin to analyze it. Sometimes we must face the fact that the data is so bad it is not usable without serious risk of drawing the wrong conclusions. The Six Sigma message is simply “understand the quality of the data before drawing any conclusions.”

An additional issue we deal with here is how to convert attribute information into something quantitative. There are a great many ways that might be done – here we offer one approach suitable to this situation. The attributes mentioned above are potential Xs that influence the schedule performance outcome – we believe that if we do a good job satisfying these attributes we will be more likely to deliver the project on time. Our hypothesis then is that high ratings on the Xs will be positively correlated with higher Y ratings.

One way we might approach determining if this hypothesis is correct would be to set up a scoring scheme by which we rate the Xs for each of our historical projects in order to see if higher attribute scores are indeed correlated with better schedule performance. Here is one example of how we might do that (many other reasonable approaches are possible):

Table 1: Possible Scoring System

Data Collection

Using this scoring scheme, our next step is to collect the X and Y data for each of the projects in our baseline sample. That might produce a set of data something like that shown below.

Table 2: X And Y Data Collection

Describe and Display Variation in Current Performance

Once we have this data we will want to display it graphically in order to see what, if any, relationship there may appear to be between our Y (schedule performance, defined as percent of plan, and our summary X, total score). We might produce similar graphs of the individual elements of the score.

This would gives us a result like the adjoining graph which shows us at a glance that there does appear to be a relationship between our X and Y, as suggested by the trend line – as the score increases (more of the attributes of a professional plan are present) we see that the our Y (percent of plan) improves – projects with professional plans seem more likely to be on time. But we also notice that there are some projects (those inside the circle) that don’t seem to fit the general pattern – these suggest that some other factor we have not yet considered may be impacting the outcome. We don’t have enough evidence yet to be sure there is a cause and effect relationship, but clearly the connection merits a closer look – that’s what we will do in the Analyze phase.

The Analyze Phase

Analyze is a seven step process:

1. Measure the capability of the existing process

2. Refine improvement goals

3. Identify significant data segments/patterns

4. Identify possible Xs

5. Identify and verify critical Xs

6. Refine the financial benefit forecast

7. Analyze phase tollgate review

Determine/Refine Measurement of Process Capability of the Existing Process

When we examine the data we have collected during the Measure phase we see that it reveals that using the customer’s dates we have a 20 percent on-time rate (our baseline), not the 62 percent figure we got from the software team. We can convert this information into one of the several available standard Six Sigma measures of capability (defects per million opportunities [DPMO]; z-score; sigma level; Cp; or Cpk).

We won’t go into the mathematics of these here (you can find more on this elsewhere on this site). In this instance we would probably choose Cpk (worst-case capability) as the most suitable choice, and we would find that the value we get is less than .2 – not very good! We would like to see a value of at least 1, and higher would be better.

Refine Improvement Goals

With this knowledge in mind, we may want to re-evaluate our goal. If the goal is to remain 90 percent that means we are targeting an improvement of 450 percent! While this may not be impossible, a single intervention is unlikely to produce a gain of that magnitude so we may wish to set a target that is more realistic and attainable in the near term, within the scope of our current project. When the gap is this large it is likely we will need a series of Six Sigma projects to close it – usually a better choice than one very large project. We may choose to keep 90 percent as our stretch target, but we should not be disappointed if our achievement on the first project is somewhat less – the payoff may still be very substantial.

Identify Significant Data Segments/Patterns

As indicated earlier, we could segment the data by software group or by software type – if we did so we would follow the pattern of analysis discussed here for each segment independently. In the interest of keeping this easier to follow we will focus on the single segment of data shown earlier – as already mentioned, we do notice a pattern in this data. Most of the project outcomes seem to be related to the planning best practices attributes reflected in the data we collected, but there are five ‘outliers’ that seem to be influenced by one or more other factors.

Identify possible Xs

Our observations about the pattern in the data lead us to the next step in our analysis. What other unidentified factors might explain the outliers we have observed? One of the factors influencing software project outcomes is the schedule itself – when we start with an unrealistic schedule, bad things often happen.

This gives us a clue that the realism of the planned schedule could be one of the factors that explains the outliers we observed. We can investigate that hypothesis by collecting an additional piece of information about each of these projects (i.e., how did the planned schedule compare to industry norms for similar projects)? One way we might answer this question would be to use one of the commercially available software project estimating models. Note that there are a number of complicating factors relating to use of these models that we won’t go into here – that topic will be addressed in a future article. For now, we ask that you accept our assertion that this can be done.

We gather this additional piece of information about each of the 20 projects and add it to our data table – we’ll call the new column “plan percentage” which we define as (actual planned duration)/(duration indicated by the estimating model). This means that when we have a value less than 100 percent our planned schedule was shorter than that given by the model – in other words, unduly optimistic. This results in a new table that looks like this:

Table 3: Updated Data Collection

Although we now have what seems to be a good candidate list of controllable factors, we may want to probe a bit deeper to see if we can discover the underlying whys. Why did we not break large tasks down into one-week segments? Why did we not define predecessor/successor relationships among our tasks? One of the Six Sigma tools, known as the 5 Whys, encourages us to probe deeply by asking why five times in an effort to get to the real root of the problem. To illustrate:

Why don’t we define predecessors?

– we didn’t know it was important

– why? – no training was provided

– why? – no training budget

– why? – manager didn’t think it important

This analysis tells us something about issues we will need to address to make an improvement stick.

Identify Critical Xs

With this data in hand we can determine which of these factors are the most influential in determining the schedule performance outcomes. One way we can do that is to use multiple regression analysis. Again, we won’t go into the details of how to do that, we’ll just go direct to the conclusions we can draw from such an analysis.

The conclusions we reach from analysis of this sample indicate that about 78 percent of the variability we see is explained by three factors (the Xs) – task duration, predecessors and plan percent. Hence our project (likely this is really two objects – one to deal with the planning process and one to deal with the estimating problem) will focus on actions we can take to improve our control over these Xs.

Refine the Financial Benefit Forecast

In order to determine what improvements like this would be worth to the business we might go back to the business cases for our sample project to make an estimate of the opportunity cost to the business resulting from the delays. To illustrate, we might find that the average first year business benefit expected from these 20 projects was $850,000. We know from our data that on average our projects are planned to take 15 months, and that on average we actually require 134 percent of the planned duration – hence on average we are five months late.

Given the average delay and the average first year business benefit we may estimate the opportunity cost as 5/12 times $850,000 ($354,000) times cost of money – at 15 percent the is about $55,000 per project, or over $1,000,000 total for our 20 projects if they could all be delivered on time. Our target is 90 percent on time, so we might reasonably expect a benefit of around $900,000 if that target is achieved.

As suggested above, it appears that we will really need two different projects to accomplish our goal, so we might say that our expected annual benefit for each project is actually $450,000, less whatever it may cost us to do the project. Experience indicates that we can do projects like this for far less that the expected benefit – $100,000 or less per project might be a typical cost.

The Improve Phase

We will continue our example on the assumption that we have decided to spin off the effort to improve our estimating as a separate Six Sigma project – we will follow that thread in a future article. Here we will focus only on the professional planning practices.

Improve is a 5-step process:

1. Identify solution alternatives

2. Tune/optimize variable relationships between Xs and Ys

3. Select/refine the solution

4. Pilot or implement the solution

5. Improve phase tollgate review

Identify Solution Alternatives

There appear to be three obvious options in the case – we could 1) train all of the people responsible for project planning on best practices, 2) assign mentors or coaches from the project office to review the draft plans and help project managers bring them up to the best practice standard or 3) use some combination of these options.

Tune/Optimize Variable Relationships Between Xs and Ys

In this instance no tuning is necessary – we see that there is a clear relationship between higher X ratings and better Ys.

Select/Refine the Solution

Here we will evaluate each of the solution alternatives with respect to applicable effectiveness criteria. In this instance we will consider the cost of each option, how effective we believe it will be, and perhaps the lead-time required to implement. Most likely we won’t have any real data about relative effectiveness, so we likely want to pilot two or more of the options to evaluate relative effectiveness. That leads us to the pilot step – after we have the results of the pilot we will make a final selection.

Pilot Test or Implement the Solution

We may decide to try each of the alternatives with a different team, and do a review at the end of two or three months to see how each of the pilots is working out. To do this we might, for example, score the plans these teams have produced using the same approach applied to our historical data. If one method shows a meaningful (positive) difference we most likely select that option if it is reasonably in line with the second best option with respect to cost and lead-time.

The Control Phase

The purpose of the Control phase is to make sure that our improvements are sustained and reinforced. We want to be sure we put in place all of the actions that will help the change be both successful and lasting.

Control can be described as a 5-step process:

1. Develop control plan

2. Determine improved process capability

3. Implement process control

4. Close the project

5. Control phase tollgate review

Develop Control Plan

The control plan will define how we will monitor the Xs and the Y, and what actions we will take if these metrics indicate we have strayed from our planned levels. We will also specify what action is to be taken if the metrics are off target.

In this instance we may decide that each project plan will be scored at the beginning of each project phase, and any that scored below a five on any of the Xs will be required to make changes to bring the plan up to that level. We might also indicate that we will require a special project review if the target for the Y is not met. The goal of such reviews will be to discover why the goal was not met, and to institute corrective actions as necessary.

Determine Improved Process Capability

Here we document the new level of performance of the selected success metric.

Implement Process Control

We have defined what we will monitor, who will do it and how often. In this step we simply execute our control plan.

Close the Project

Closing the project includes a formal transfer of responsibility from the Six Sigma team to the operational personnel who will sustain the process. As part of the closing process the team will archive all of the project records and data, and will publicize lessons learned and successes.

Six Sigma Process Improvement – Engaging the Team

Improving a process, like building character, can be done by the people involved, but not to them. Hence in Six Sigma we engage and empower the people who perform the software processes to plan and implement improvements themselves, with the guidance and assistance of Six Sigma specialists who are fully versed in software development best practices (both sets of knowledge are critical to success).

This requires a fundamental change in the way most software people view their jobs.

The Six Sigma DMAIC approach to process improvement provides a powerful mechanism for improving the software process. Typically benefits will exceed cost within 6 to 12 months from initiation of a Six Sigma program for software development, and the on-going return will be very substantial – often a 15-25 percent reduction in software development costs in year two, with continuing reductions thereafter.

In order to realize these gains it is essential to recognize that a significant cultural shift must occur. Achieving this cultural shift is best accomplished by providing Six Sigma training for all of the senior developers and managers in the software organization, with a mix of Champions and several levels of Six Sigma specialists (Yellow Belts, Green Belts and Black Belts) appropriate to the size of the organization.

Continue reading