DMAIC is a problem-solving approach used in Six Sigma, a methodology for improving products and business processes. As you might guess, this article focuses on business process improvement. DMAIC stands for Define, Measure, Analyze, Improve, and Control. The system identifies and eliminates process defects to improve efficiency and effectiveness. The five steps of DMAIC are as follows:
- Define the problem and set specific goals and objectives for the project.
- Measure the current process to collect data and identify key performance metrics.
- Analyze the data to identify the root causes of defects in the process.
- Improve the process by developing and implementing solutions to address the root causes identified in the analysis step.
- Control the improved process by establishing ongoing monitoring and measurement systems to ensure the stability of the improvements over time.
The adherence to the original DMAIC structure was one of its key strengths. As a result, the projects followed a precise pattern. However, as is often the case, a great strength also becomes a weakness. For example, more accessible communication means more significant potential for hacking. More extensive and faster people carriers can potentially transport diseases faster than at any time in history. And the strength of the DMAIC structure caused blinding toward technics to strengthen the DMAIC process. Pratt & Whitney increased their project successes by incorporating process simulation in DMAIC projects. Here is some of what they learned.
Improve the MEASURE Phase
Pratt & Whitney, a leading manufacturer of aircraft engines and components, has improved its Measure phase using process modeling and simulation. This new approach creates a digital twin of the production process verifiable against the existing system. The digital twin allows the company to understand better the current system’s parameters, behavior, problems, and bottlenecks.
The dynamic nature of the model makes the digital twin effective for understanding the current system. The digital twin far surpasses static data and becomes a living replica of the system under study. The digital twin includes the following elements:
- A Logical Process Flow Diagram — This diagram shows the various steps and sub-steps involved in the manufacturing process, from raw materials to finished products. By replicating the process, it becomes easier to identify improvements.
- Resource Assignments — This involves analyzing the allocation of resources, such as machines and personnel – when, where, and how available the resources are. It is possible to reduce bottlenecks and improve overall process performance by making appropriate changes to resource allocation.
- Yield Data and Variance Characteristics – By incorporating variation statistics it’s possible to assess the quality and consistency of its manufacturing processes. By tracking yield data and identifying variance characteristics, the company can identify and address issues impacting the throughput of its products.
- Process Demand Profiles — This involves analyzing the overall demand for the products and the demand for specific components and sub-systems. By understanding the demand profile, Pratt & Whitney can better plan and allocate resources, ensuring that production can meet customer needs in a timely and efficient manner.
- Load Profiles — This involves analyzing the workload and resource utilization in the production process, identifying areas where capacity is being underutilized or where there may be bottlenecks. By understanding the load profile, Pratt & Whitney can optimize resource use and improve the production process’s overall efficiency.
- Idiosyncrasies of the production process — This involves identifying and addressing unique or unusual factors that may impact the production process, such as seasonal demand fluctuations or changes in supplier availability. By understanding and addressing these idiosyncrasies, Pratt & Whitney can better ensure the smooth and consistent operation of the production process.
Overall, the improved Measure phase used by Pratt & Whitney has allowed the company to understand current processes better. One past employee stated, “it’s infinitely easier to fix the things you understand.” In addition, process modeling and simulation allow one to see beyond the static data and understand the system’s behavior. Understanding the behavior “has improved our ability to make profitable decisions.”
With a model, changes to any parameter accurately ripple through the entire system showing the overall effect. There are no new calculations to perform. Instead, the model simulates the impact of the change in seconds.
The Analyze phase of the DMAIC process involves breaking down a subject into its parts and examining its relationships to identify patterns, connections, and underlying principles. This phase aims to gain insight and knowledge about the process to understand its essential elements and relationships.
In the context of DMAIC, analysis typically involves running the numbers to find patterns and gain understanding. A practitioner might observe the existing system or ask workers for their observations. However, making changes to a production line and seeing the results is not typically part of the analysis phase because it can be expensive and disruptive.
A digital twin allowed Pratt & Whitney to examine any aspect of a process in detail and see the behaviors and causes of any issues. As a result, they can make adjustments and see the long-term consequences of those decisions within seconds. This type of analysis is beyond the reach of traditional analysis methods.
The Improve phase of DMAIC now uses process simulation to evaluate potential solutions and identify the best one. When combined with the Analyze phase, the team members already know the process drivers and elements holding the system back. Simulation tests every improvement suggestion for how it affects the entire system.
The optimization feature in ProcessModel finds process resonance. Process resonance occurs when all aspects of the process are in harmony. With resonance, production is higher with less effort, which means it costs less to produce the required amount. This technique can be a “secret weapon” for optimizing a process.
The above diagram illustrates how process modeling and simulation support and improve the DMAIC methodology.
The Control phase is the final phase of the DMAIC process. It involves controlling the improved process to ensure that it is properly implemented and maintained and does not revert to old ways.
The focus of the Control phase is to implement the changes decided on during the Improve phase to position the new process for success. To find deviations from the plan, compare the digital twin to the process. Then, use corrective actions to bring the process back into alignment. Overall, the Control phase ensures that the improved system is well-implemented and sustained.
Process Simulation Simplifies Decision-Making Throughout the DMAIC Process
DMAIC is a problem-solving approach used in Six Sigma for improving business processes. Pratt & Whitney has improved its DMAIC process by incorporating process simulation. This modeling creates a digital twin of the production process, allowing the company to understand better the current system’s parameters, behavior, problems, and bottlenecks. Using process simulation, Pratt & Whitney has improved and simplified every aspect of the DMAIC decision-making proce
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