How do you analyze and reduce process variation?

In my experience it is really important to understand the process in order to improve it. If you do not map the process or define the problem first, it is difficult to quantify what the benefits will be. There is also a risk of losing sight of what you are attempting to improve, which may result in failing to reduce any process variation.

Walk the process to obtain current process map. Now define the steps/ processes you see, validate that each step is producing within the allowable variation. Correct this variation and validate process again, use PFMEA to help you detect failure mode/process step deficiency’s , where to look

"Emphasizing the pivotal role of analysis in reducing process variation, the initial step is careful planning. Key in this planning phase is selecting the right process and defining the criteria for this choice. It is essential to define the parameters guiding this process choice. Starting with an inappropriate process often leads to ineffective outcomes, culminating in no action or negligible benefits. Assessing the current state and envisioning the future state is vital for meaningful progress. Equally crucial is acknowledging process limitations. While improvements can enhance performance, it's crucial to recognize that you can't transform an apple tree into a mango tree, but you can certainly cultivate a sweeter variety of apples."

Standardize work is the bedrock toward reduction of in-process variation. I read somewhere that “Variation” is a synonym for risk, increased cost, missed deliveries, and loss of customer confidence. Root cause analysis stemming from process defects can become a nightmare when multiple variations of processes are in play.

Um ponto fundamental é conhecer, além das etapas do processo, todas as variáveis presentes em cada etapa que podem influenciar em seus resultados ou saídas, como velocidade, temperatura, umidade e press?o entre outras.

First, you define what the data will be, the collection period and the method of collection. If it is online (via PIMS or another historian), you must know the right way to extract it and separate good data from bad data. If the collection method is offline (observation, forms, interviews) care must be taken not to have a biased collection.

Data collection is important when trying to find the correct root cause of process variation. This happens when processes fail to follow a structured work flow. It's a leading cause of quality issues in my work area as people often tend to “find their own way” after learning a process. Standardization is a big driving factor at Bentley motors as a standardized process “should” achieve the same outcome no matter who it’s done by. When deep diving into quality issues I tend to look for the following; 1. Is it certain colleagues. 2. Are they working to process 3. Have they been trained to follow and work to process 4.Does the trainer, train people to work to process 5. Are the parts they are receiving to the correct spec

It is also important to identify the right data collection points and the measurement system. Depending on the process, there could be several sources and types of data that could be gathered from the process. Understanding what data is relevant and has impact on the effectiveness and efficiency of the process sets the stage for useful data for the process improvement. Again, using the right measurement system leads to gaining the right insights to the as is process and achieve the future state that is more efficient.

Process variation is a good application for AI in manufacturing. Collect data on output, machine parameters, quality records, operator records, supplier quality, etc. AI will identify changes coming in process variation and mean, as well as help to identify the root cause

Cuando analizamos datos de un proceso debe tenerse en cuenta que todo proceso sin excepción tiene variación. Un aspecto clave a tener en cuenta es visualizar si la variación de nuestro proceso es tal que tenemos una considerable cantidad de datos o resultados que caen fuera de los límites de especificación (tolerancias) y que significan un resultado o producto No Conforme. En caso de que efectivamente nuestra variación es significativa, deberemos primero identificar y separar las causas comunes de las especiales. Luego existen diferentes tipos de herramientas Six Sigma que aplicadas correctamente nos ayudarán a reducir la variación excesiva de nuestro proceso.

Understand the inputs and outputs of the process. Understand all the variables that affect the process/variable of interest. Look for correlations and use correlation coefficients to determine if the effect is small or big. Rank variables by the biggest to smallest impact on the process variance. Look for feedback loops. When you have all of this information infront of you, you then design ways to reduce the process variance.

Queue the six sigma playbook list of tools: - 5why - Ishikawa - Cause and effect analysis - Regression In all seriousness, talk to your SMEs, get an idea of what influences your process output, and test the assumption. Whether it’s as simple as a 5 why or as complex as a multi-factor DOE. Use the tool that is most practical and keep it simple where possible.

Ramesh, Your step 2 is necessary but for Step 3 I would load the data - as much as you can collect - into our parallel cordinate graph where you can see and analyze variability in a few minutes. You can also see various multivariable operating envelopes of your process and model those using geometry in minutes ready for use in real-time control. I don't know whether anyone has yet succeeded in modeling a full-size operating envelope using neural net (AI) methods but n-dimensional geometry is much faster, doesn't need more computing power than a typical desktop or laptop PC and is available now. I think it is a better choice for engineers working on process plant applications. See the PPCL website for more. Robin Brooks PhD chemical engineer

Your causal tree will identify variation in process parameters which in turn have root causes. Where possible put in place a permanent engineering solution in the following order: 1. Design out 2. Maintain out 3. Operate out (this can be a control feedback loop or operator instruction)

Agree, but you must refine before declaring your 'solution' a lot of ambitious engineering get a shot down and perhaps devalued if you declare a win before it actually works in production. Before rolling out a 'solution'. Dev/Ops is key! Coordinate with the operations for small scale testing and grow / refine your solution. The path to victory is winding.

Al final de todos los análisis, selección de ideas y demás, este es el paso fundamental que agrega el valor: ejecución. Sin esto, no pasará mayor cosa. De acuerdo a mi experiencia, tal vez lo más importante es tener personas con actitudes típicas de ejecución: capacidad para hacer que las cosas pasen, resolución de problemas (pensamiento #A3 ayuda mucho) y gusto por trabajar con retos.

Una de las soluciones clave para poder reducir la variación en los procesos es el estandarizar, la documentación de los procesos y su debido entrenamiento es clave para qué reforzándolo continuamente desde la operación y su liderazgo se pueda obtener un proceso lo más alineado posible al óptimo dise?ado, con la conciencia de que siempre existirá en la realidad cierto peque?o porcentaje de desviación.

Solutions are variable and maybe new methods, training, standardization, machine maintenance or can be more complex like chanage equipment, control environment (temperature, dust, etc.). Normally run the already mentioned tools are enough. It is important to follow a method to design the possible experiments to improve. I would say the knowlege of the factor that really influence the end result needs to be understood and possibly quantified.

Regardless of the path used, it will necessarily involve defining, manage and implement improvement actions. And, most importantly, it will be necessary to implement (or strengthen) the CONTROLS, making them more Preventive and more Robust (e.g.: Automatic Process Control, Poka Yoke, CONTROL Plan, Standardized Work, CEP for critical inputs). In parallel to the implementation of the most robust CONTROLS, place them in Visual Management, very close to the Executors of the activities, and carry out the necessary training (consider On The Job Training).

To monitor the results of solutions, you can use performance tracking tools, analytical dashboards, or regular reports to assess the effectiveness of your solutions. Make sure to define relevant key performance indicators (KPIs) to measure success.

In addition to having an excellent CONTROL Plan in visual management, very close to those performing day-to-day activities, with adequate training, Capability Studies (Zst = Sigma Level and DPMO) must be carried out with some frequency (weekly or monthly ), to evaluate how well (or not) the specifications are being met. And, if the DPMO level is not meeting what would be expected, investigate with statistical support and/or using FTA Fault Tree Analysis, generate and execute action plans and improve the identification of critical process inputs (vital X's) and continually improve CONTROL Plans.

Supervisar los RESULTADOS. Para saber si hay o no hay resultado, en los pasos anteriores debimos definir al menos un número (máximo 3) y una meta para ser capaces de acompa?ar el resultado, de lo contrario habremos trabajado sin norte. Sugiero que el acompa?amiento sea diario, y si se puede, con metas en tiempos más peque?os. En la industria lo implementamos con metas cada hora y en la construcción lo hicimos con dos metas diarias: una en la ma?ana, verificabamos a medio dia, y la otra en la tarde, verificando a las 5pm.

Monitor the results are normally neglected. It is important periodically verify if the process is still in control and keep a track if nothing new has entered the system - it can be new employee, new raw material supplier, etc. A data sheet should be constantly fille dup by the responsible for the process to capture anything that cna be used for a future investigation.

A KAIZEN mentality in all areas of the organization is ideal. This is easier said than done if the employees do no have a supportive Continuous Improvement process to share their ideas. How can this be established? Ask, create an idea suggestion program, listen to your employees on implementing small improvements that stacked provide significant cost savings and process flow improvements. Ultimately reducing leadtime to Customer.

The process variability, considering the process inputs (vital or critical X's), for continuous data (that which uses 1 equipment or 1 instrument/ device to obtain the data) will only be reduced (via standard deviation and coefficient of variation) if implemented more preventive and more robust CONTROLS. And, in this case, the best tool for this is the CONTROL Plan. In the CONTROL Plan, these more preventive and more robust controls are very welcome in the “CONTROL METHOD” column, such as: Automatic Process Control, Poka Yoke, Control Plan, Standardized Work, Timeline, CEP (for process inputs, with procedure for the Operator to act), Preventive Maintenance – Planned x Accomplished (on the machine).

You can apply all the 12-step methodologies you want, you will not succeed in making your processes capable unless you have engineers who understand, at some level, their physics and chemistry. If you don’t, you can’t anticipate the consequences of countermeasures. You need to either hire process engineers or give your existing team the opportunity to learn. Once you have this knowledge, knowing how to organize to solve problems, and data science will help. But be wary of methodologies. How you go about solving a problem should be based on what it is and the resources you have, not on a one-size-fits-all methodology.

DMAIC methodology is quite simple yet systematic approach to apply and adopt in all cases, wherein, it would required to measure, analyse and implement the solution for continuous improvement. However, the clarity on what & how data collection (check sheet) should be done is very important as it paves the path for ease in data analytics. Once the data gets analysed come up with a robust implementation plan and well identified responsibilities, and never forget to have periodic review mechanism unless it's not done.

In manufacturing applications, before measuring the current variation using standard deviations, ranges or histograms, make sure first that the process, the source or data, is under control. This means that the variation measured is only the natural variation within the process. It is also easy to confound data from different populations, and control charts may be one of the most useful tools to ensure this controlled state.

Design Of Experiments can be a valuable tool for reducing process variation. Once you have identified the process factors that can influence variation you can design an experiment. A good measure of process variation is Std Dev, so you will need a few replicates of each trial in your design to calculate this. The real trick is to use log(Std Dev) as your response. The Sweet Spot will be the factor settings that minimize log(Std Dev).

I have learned in most process issues there has been a lack of training and communication. Di they really understand the process and what is critical?