Q 580.   What is Response Surface Methodology? How can this technique be used in Improve phase of a DMAIC project for process optimization?

 

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Response Surface Methodology

This is a statistical method also a mathematical technique which is generally used for optimizing the processes and utilizing the relationship between input variable and output response.

This method is generally used when there is complex and nonlinear relationship.

It is also a mathematical model which is used to approximate the relationship between an input and output.

 This method is broadly used in various fields like –

·         Engineering

·         Manufacturing

·         Chemistry

·         Biology

·         Social Science

 

This is very good tool used for below process

·         Process improvement

·         Product optimization

·         Decision making

 

This tool is very useful in DMAIC methodology. This tool is generally used in IMPROVE phase of DMAIC methodology.

Ø  This tool is useful when we want to identify key process variables such as – which are significantly impact the process performance, like -

·         Temperature

·         Pressure

·         Time

 

Ø  This tool is also used when we are doing any design of experiments (DOE). It also helpful when we are designing a series of experiments. It can explore the design space and gather the data and response.

Ø  This tool is also used in when we are conducting any experiments.

Ø  This tool also used when there is need of analyse the experimental data because it specify the relationship between input variable and the response.

 

With the help of this RSM technique in improve phase of DMAIC project. We can extensively optimize the process with understanding and can show the relationship between input variable and response.

Response surface methodology(RSM) uses experimental data to build mathematical representation of the relationship between input variable and output responses. The output response's for various combination of the input variable can then be predicted using this model. This allows the process to be optimized by determining the factors, that have the biggest effects on the output response. 

 

The best settings RSM can be used in the Improve phase of a DMAIC project to optimize a process. This can be done by running a number of RSM experiments to get the information on the output response for the various combination of the input variables. Once done, the relation between the input variable and the output response can then be described mathematically by using the data. To get the best settings for those factors, RSM model can then be used to predict the output response for various combinations of input variable

 

We can utilize RSM in DMAIC project's Improve phase by:

 

• Finding the variable with the biggest effects on the output response can be useful.
• Finding the best settings for these factors can be useful.
• Predicting the response of the output for various combinations of the input variables can be helpful.
• RSM can also assist in minimising the number of experiments that must be carried out

Response Surface methodology is a statistical and mathematical approach that can be used to design, improve, and optimize a process. It is beneficial for analyzing the situation when multiple independent variables impact the dependent variable or response. It helps to reduce the noise of an experiment which in turn and guarantees optimization. It consists of a response variable which displays observed outcomes of experiments which is also referred to as output.

It plays an important role with its involvement in new product design and development and also in upgrading the existing designs.

It is an essential and very robust technique for data manipulation and analysis of research data in order to acquire a quality result or improve it.

·        One of the main advantage of RSM is large volume of data can be obtained by performing limited number of experiments.   

RSM in Improve phase

1.)  All project or experiment decisions are clearly explained and outlined during this phase. Some of the decisions made under this topic include the research purpose, technique, and variables that may influence the outcomes. This procedure handles all the information required for the experimental strategy.

2.)  Once the experiments is defined clearly and the data is collected, we get an idea of how many experiments are to be performed and how to perform them.

3.)  After performing the experiment, the result is then compared and analyzed. The analysis of the outcome is compared to specific conclusions and defined research parameters/ the target or the expected outcome.

4.)  A test can be performed to see whether the actual performance of the product in service matches the improvement identified in the results. The test here allows in determining the research gap. After the gap is identified, various other steps are considered to improve the experiments and make it achieve the target determined.

RSM utilizes a number of surface visualization techniques. It visually assesses how various circumstances influence the answer. When a regression model is constructed as an outcome of interactions that involve multiple factors that predict, visualization helps to express the experimental results or answers more effectively. Some examples of graphical visualization tools known as response surface plots include effects, contour, residual or surface plots

Response Surface Methodology, RSM (also known as Response Surface Modeling) is a technique to optimize the response(s) when two or more quantitative factors are involved.

 

Response surface methodology optimizes set points of factorial variables such that responses reaches desired maximum or minimum value .

 

The dependent variables are known as responses, and the independent variables or factors are primarily known as the predictor variables in response surface methodology.

 

Example – if one wants to install an HVAC system in a hill station resort ,he need to estimate efficiency of the room.

This is done by recording different temperatures on different season ,and volume of the room.  

Benefit of applying RSM here is improving the energy consumption & there by reducing the energy cost of the resort.

Finding a range of temperature to operate is better , instead of operating at a particular point.

 

Moreover, keeping very cool in summer or very hot in winter would be very wasteful. Response Surface Methodology, RSM, is very useful to optimize variables/factors more practically as compared to just the statistical significance test for a particular point. How can we apply this here in this practical scenario of finding the optimum ambience as “Improve” phase is below case :

 

The below illustration shows, Human comfort is measured on a scale between 0 to 10 in a hill station resort, where 10 is the most comfortable. When does  the human feel most comfortable ?.This improvement project should be taken by the resort to see optimal usage of their equipments and save energy in line with " NET ZERO" vision because resort is situated in a environment regulated country & zone.They have to comply to stringent norms.

To fulfill The main criteria here is resort wants to be “NET ZERO “ as their vision on carbon consumption by 2027, they have taken a six sigma project and they can deploy their countermeasures in Improve phase using RSM. To  take different experimental trials to  optimize humidity and temperature for the best comfort, for which  the response surface is used as shown.

 

Response surface model used for finding the comfort level improvement of Humans vs Humidity & temperature combinations.

This was not an easy question to answer. It is pleasing to see several good answers. RSM is often associated with Box Behnken Design or Central Composite Designs, as these provide good models with limited runs (low cost). RSM also studies curvature in response surfaces very well. Gradient-based optimisation is key to RSM. The biggest benefit of RSM is that it requires limited experiments compared to full factorial experimentation. 

Avishi Mehta has mentioned most of these aspects; her answer is the best. 

(Some answers could not be approved because of AI-generated content.)