DMAIC Projects

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DMAIC is a project framework for working with Lean Six Sigma (LSS), which is based on both Six Sigma approach to systematic and quantitative approach to process improvements, and Lean [1]. A process following the Six Sigma approach aims to control the process in such a way, that no more than 3.4 defect per million possibilities, equal to all possibilities within 6 standard distributions (6σ). DMAIC is the framework used to work with process improvements, in order to reach the low failure rate. However, DMAIC can also be utilized to work towards other goals, such cycle time or customer satisfaction, as long as it is an existing process. [2].

DMAIC is an acronym for the five steps in the framework, called Define, Measure, Analyze, Improve and Control. The five stages consist of:

  • Define: in the define phase, what needs to be improved and for who is defined.
  • Measure: the measure phase is used to further understand and document the problem
  • Analyze: Based on the gathered data from the measure phase, the root causes to the problems are identified.
  • Improve: With the root causes identified, a solution can now be developed, tested and pilot implemented.
  • Control: The solution is first completely implemented in the important control phase. Following the implementation, it is paramount to monitor the effects, in order to ensure the change remains and works as intended.

This article describes the purpose, usage and limitations of using DMAIC in improvement projects. For creating a new process or redically changing an existing one, the DMADV framework is suggested instead.[1]

Contents

Big idea

Rephrase quantitative focus to data driven focus. Qualitative data can also be very important in DMAIC projects.

DMAIC is a data driven project framework, with a strong quantitative basis. This quantitative approach aims to ensure the project is operating based on facts and provides value. The value of a DMAIC project can be in multiple different ways. There is a big focus on the customer and their requirements, which is determined in the beginning of the project in the Voice of Customer (VoC). DMAIC projects can be executed continuesly and repeatedly in order to perfect a process.

Between each phase of the DMAIC project, a gate meeting is held with the relevant stakeholders. During this meeting, the current results of the project is presented for the stakeholders. If the stakeholders find the current results and proposed forward direction adequete, the gate is agreed as open and the next phase can begin. This is much like the Stage-Gate Process. This is done to ensure the final solution can work for the stakeholders and gain additional insights through cross collaboration. Another important aspect in DMIAC is co-developing with the users. As a project manager, it is important to ensure this co-creation takes place, as it supports the four most important values in the project management community. These values are responsibility, respect, fairness, and honesty. [3] By succesfully implementing a co-creation process and the four mentioned values, there is a higher chance of succesfully creating a functional solution that also remains implemented.


DEFINE
The define phase aims to set the scene for the project. Identifying and understanding the customer is done by defining the Voice of Customer (VoC). The VoC focus on the challenges of the customer. [4] There is often multiple stakeholders in the project and while defining the needs of the primary customer should be the focus in the VoC, it can benefit to also define VoC of the other stakeholders. This is to ensure the solution is optimal as possible.

Following the VoC, the Critical to Quality (CTQ) is defined. Defining the CTQ means to describe what is necessary to fullfill the VoC. This requires an understanding why the VoC is a problem and what could remedy this. It does however not describe a specific solution. To further scope the project, a SIPOC diagram can be used to aggregate already gathered information, aswell as cover angles not yet explored. SIPOC is an acronym for supplier, input, process, output og customer. While the SIPOC diagram is structured like the anagram, it is filled out from right to left. [1]

MEASURE

The measure phase is focused on defining the current facts of the situation. This means gathering the facts necessary to properly understand the complete situation and context. In order to gather the correct data and ensure it is gathered consistently, a Data Collection Plan is developed. [2] It is advantagous to build the data collection plan based on the SIPOC, as the SIPOC provides an overview of the process. There is a wide range of types of data, which can be collected and while DMAIC is designed to be used with quantitative, qualitative data can be very usefull. Qualitative data can provide insights into areas where it can be beneficial to gather quantitative data, it can assist in the solution development and it can help in creating an understanding of why some data is as it is. The selected data to collect should fit the type of project and associated goal. [2] Examples of quantitative data to collect can be; time consumption, error rate, units produced, but other types, such as customer satisfaction and employee morale can also be extremely relevant, depending on the context.

When it comes to collecting data, a range of sources should be considered. Some data can be gathered from existing data systems or simply by keeping count, while other types can benefit from evaluating video materials, completing questionaries, doing participant observations or alike. Data from participant observations or video material can often be quantified and thereby fit the DMAIC framework.


ANALYSE

When the data collection is complete, the data has to be analyzed. Depending on the goal of the project, there are multiple ways of doing this. Simply visualizing the data through various diagrams can be a good starting point, as it gives an insight into areas that call for further analyzis or discover patterns. When experimenting with different types of visualizations, it is important to consider the VoC by for example grouping different types of data to understand what provides value and what does not. In order to further understand the connection between various quantitative data sets, statistical analysis should be utilized.


A very used root cause analysis method is the Ishikawa Diagram, also known as the Fishbone diagram from its shape, wherein identified potential root causes and their influencers are structured.[2] This method can also combine quantitative data with qualitative data, as it can be combined in the diagram. Create a template. A pre-structured version of an Ishikawa Diagram is often used to ensure the typical areas of root causes are considered. This model is called The 7M's and refers to the seven significant areas starting with M: Management, Mother Nature, Measurements, Materials, Manpower, Methods and Machines. As with the Ishikawa Diagram, potential root causes to the problem are listed at each M.

In connection with the Ishikawa Diagram, it should be considered which cause is most significant. When doing so, the overall VoC should be in focus, as other root causes might seem relevant, but does not affect the VoC significantly.

IMPROVE

The improve phase aims to reduce the waste and increase the value added through reducing costs and processing time, and improve service to the customer. [2] The possible solutions should be explored through Ideation tools, much like in the third Double diamond section called Develop. Initial ideation methods include Brainstorming, ideation sketching and Worst Possible Idea.

The identified ideas can quickly be screened through an Impact effort matrix before proceeding to prototyping.


EDIT HERE
  • Conducting a range of experiments. Experiments can be through various types, from implementing physical prototypes to process changes. Should be based on the selected root cause.
  • Following the implementation of an experiment, the effects are monitored, in order to evaluate if the experiment has the desired effect.
  • How to monitor the effects.

CONTROL

  • The Control phase is key to ensuring the improvements stick.
  • Often not prioritized, as the change is now implemented, it is assumed that everything is fine. However, often small adjustments are needed to reach the optimal effects of the changes. In order to discover this, a process monitoring tool should be utilized.
  • For example a Pareto analysis.

Application

  • Utilizing DMAIC in practice. [5]
  • Practical example.
  • Connecting the different tools and monitoring the project with SIPOC, etc.



Method examples


CTQ: Insert example and context. Include how the CTQ should be measureable (two levels of CTQ, more descriptive, then a hard target).

  • Create figure showcasing VoC and CTQ.
  • Include details about Critical to Customer (CtC) from Six Sigma. Potentially model. Used and updated continually during the project.
  • Introduce SIPOC

Limitations

  • Less focused on qualitative aspects.
  • A DMAIC project is quite comprehensive and for smaller improvement projects, the A3 report might be better suited.

Annotated bibliography

  • Further reading about the principles of project management and ethics. [3]

References

  1. 1.0 1.1 1.2 Josefsen, R., Bækgaard, J., & Holme, K. S. (2014). Perfekte Processer. Praktisk Forlag.
  2. 2.0 2.1 2.2 2.3 2.4 Morgan, J., & Brenig-Jones, M. (2012). Lean six sigma for dummies: 2nd edition. John Wiley & Sons.
  3. 3.0 3.1 The standard for project management. (2021). A Guide To the Project Management Body of Knowledge (pmbok® Guide) – Seventh Edition and the Standard for Project Management (english) (pp. 21-60. Project Management Institute, Inc.
  4. 2021 International Six Sigma Institute https://web.archive.org/web/20220213113640/https://www.sixsigma-institute.org/Six_Sigma_DMAIC_Process_Define_Phase_Capturing_Voice_Of_Customer_VOC.php
  5. T. M. Shahada and I. Alsyouf, "Design and implementation of a Lean Six Sigma framework for process improvement: A case study," 2012 IEEE International Conference on Industrial Engineering and Engineering Management, 2012, pp. 80-84, doi: 10.1109/IEEM.2012.6837706.
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