How can you use DOE to reduce variability in your manufacturing process?

DOE is all about gathering data and analyzing it to make better decisions. Sounds complicated, but it’s actually nothing else but trying how to get the best results with the least effort and costs

Thrilled to share our journey toward enhancing Guardian Bikes' manufacturing strategy. We're exploring the feasibility of moving frame manufacturing to the USA ???? through a Design of Experiments (DOE). Key Focus: Materials: Durability, weight, and cost. Processes: Efficiency and cost-effectiveness. Suppliers: Domestic vs. international options. Labor Skills: Workforce expertise analysis. Regulatory Compliance: Meeting USA requirements. Our DOE prioritizes frame durability, production cost, lead time, and quality control to optimize manufacturing parameters. Stay tuned for results that highlight the benefits of bringing frame manufacturing to the USA, aligning with our commitment to quality, efficiency, and innovation!

Clear identification of your problem is crucial before conducting any experiments. Failing to do so limits your options and may result in outcomes that differ from your expectations. Imagine inputting a black stone and expecting a gem as output—it’s essential to clearly define your problem to achieve the desired results. Understand which parameters are affected by the issue and their historical performance. If the problem persists, consider Project A3. If you previously achieved success but now face setbacks, opt for problem-solving A3. This guidance aims to assist proactive problem solvers.

This is the most critical step in Design of Experiments. This sets the foundation for the entire DOE process and by clearly defining what you aim to achieve through the experiment, you can determine the factors to be tested, the appropriate levels of those factors, and the response variables to measure. This leads to meaningful and actionable results. Often when we are in problem solving mode, we utilize our intuition and experience and jump to conclusions on the cause versus utilizing a scientific approach that can help us remove our bias.

DOE can refer to different things depending on the context. In the context of business and manufacturing, it often stands for "Design of Experiments," which is a systematic method used to determine the relationship between factors affecting a process and the output of that process. If you have a different context in mind, please provide more details.

In a DOE you want to know the proportion of the variation in the process output that may be explained by the variation in the input variables. Before conducting a DOE, be sure your measurement system is not adding excessive variation to your study; otherwise, this can lead you to wrong conclusions. Typical MSA techniques are good to cover this aspect: Gage R&R (ANOVA, Type-1...), Linearity and Bias studies are common techniques to assess precision and accuracy. Depending on the objective of your study, define also how the output (Y) data will be analyzed in function of the experimental design: the Y average, range, standard deviation, etc. Translate the statistical conclusions into practical solutions and validate the improved state.

Primordial el entender la situación actual, ir a ver, ir y preguntar que está sucediendo en el proceso que está siendo analizado. Nunca se debe de comenzar a generar "ideas de mejora" sin saber que es lo que se tiene y lo que acontece.

Example: For the car manufacturing process, a fractional factorial design might be used to study the effects of assembly line speed and temperature on the quality of the paint finish.

Objective: Optimize the strength of the adhesive when joining plastic parts. Factors: Adhesive type (A, B, C) Curing temperature (30°C, 40°C, 50°C) Curing time (1 hr, 2 hr, 3 hr) Answer: Adhesive strength (measured in N). Experiment: Using a 3x3x3 factorial design, we performed 27 tests varying the factors. We analyze results to determine the optimal combination that maximizes adhesive strength in widget manufacturing.

A utiliza??o do Design of Experiments (DOE) representa uma estratégia eficiente para otimizar pesquisas e experimentos. Ao reduzir o número de testes necessários, o DOE economiza tempo e recursos financeiros, destacando-se pela abordagem sistemática e planejada. Além da economia, o DOE visa aprimorar a precis?o dos resultados, assegurando informa??es confiáveis para embasar decis?es. Identificar intera??es entre fatores é crucial, permitindo o desenvolvimento de estratégias para minimizar variabilidades. Em suma, o DOE n?o apenas simplifica procedimentos experimentais, mas também eleva a qualidade e confiabilidade dos resultados, destacando-se como uma ferramenta valiosa em diversas áreas.

To improve your CI projects, I highly recommend Minitab for analysis, especially with DOEs. We had 2 statics classes in our Clemson IE department that taught Minitab. We also heavily used it at Crayola in collaboration with Lehigh University. It’s worth the licensing fees!

Enumere los factores del proceso en los que confía que tienen un mayor impacto, y tenga en cuenta que menos será más, en este caso. No es conveniente que intente analizar una multitud de factores a la vez; un par de ellos a la vez será suficiente. Como parte de este paso, también hay que dejar claro qué respuesta del proceso (resultado) se va a analizar. También se recomienda tener en cuenta las interacciones de los factores en juego. Aproveche que el DOE lo hace posible, en lugar del enfoque tradicional de “probemos y veamos”. Determinar la interacción de los factores que más da?o hace a la eficiencia del proceso será de gran valor.

In the DOE for widget manufacturing, the key factors are adhesive type (A, B, C), curing temperature (30°C, 40°C, 50°C) and curing time (1 h, 2 h, 3 h). Variation in adhesive strength is expected with each factor. The interactions between them are critical. Optimization seeks the combination that maximizes resistance without compromising efficiency. The replication of experiments validates results, allowing improvements in quality and manufacturing efficiency. This strategic approach highlights the relevance of the DOE in process optimization.

Reducing the number of experiments: DOE can be used to reduce the number of experiments that are required to obtain the desired information. This can save time and money. Improving the accuracy of results: DOE can be used to improve the accuracy of the results that are obtained from experiments. This can help to ensure that the information that is used to make decisions is reliable. Understanding the interactions between factors: DOE can help identify interactions between factors, which can also contribute to variability. This information can be used to develop strategies to minimize the impact of these interactions.

Run several batches of car production by altering the assembly line speed and temperature in the paint booth, then measure the quality of the paint on finished cars.

In my opinion, the optimal sequence for conducting DOE is: -Begin with analyses like the Fishbone diagram and Hypothesis testing to identify key variables influencing the outcomes. -Prioritize and assess the variables based on their potential impact on the results. -Develop a plan for multiple experiments, manipulating prioritized variables systematically. -Execute planned experiments and record results. -Apply statistical analysis techniques or use software tools like Minitab to process collected data. -Interpret the analyzed results to understand the strength and significance of variables' impact. -Utilize insights derived from the analysis to support informed decision-making, optimizing processes based on influential factors.

Control factors (e.g. die temp) are things you can control. Noise factors (Outside humidity) are things you can't control. There are 3 strategies: 1. Shainin? RedX? to find the main factor 2. Six Sigma (Classic) DOE to find impact of factors 3. Taguchi methods to make process insensitive to noise factors. Suppose plastic moisture content impacts injected part size. RedX? will find a way to control moisture content. Six Sigma will find ways to adjust controls for various moisture. Taguchi will find settings that are insensitive to the moisture content. Each method has advantages and disadvantages. An expert trained in all 3 methodologies can be an asset.

This is a very useful tool that need to be applied on cases where the size of the prize justifies its use. In other words, the benefits of any improvements captured through DOE application, can easily fund all the experiments and more. Also, the sole application of DOE will derive a lot of process understanding to those involved which is priceless. They will learn what variables have greater or less impact on the final parameter of interest. All in all, a great tool to be leveraged.

Hi Below is the DOE Steps Identify Possible Factor and Design screening DoE or if you have historically data than analyze and validate correlation with response using regression, Hypothesis test etc. Once Factors and Level identified that design DOE carefully use replicate, repeat to handle noise factors. Conducted trails carefully keep an eye on assumptions and measure response, ensuring measurement system R&R. Analyze DoE and predict Optimize settings. Take a trial run and validate results based. Do hypothesis tests to prove your decision statistically. If results are good do standardization.

Understanding Factor Interactions: DOE goes beyond simply identifying individual factors and reveals how these factors interact with each other. Understanding these interactions is crucial for optimizing the process and minimizing variability. Optimizing Process Parameters: DOE provides a systematic approach to determining the optimal settings for the identified factors. This optimization leads to a reduction in variability and improvement in product quality.

SPC could be expensive and is not the choice for process control. Be open to consider other systems such as mistake proofing, compare cost and evaluate general pros and cons. Keep it simple...

As a result of your DoE, you should have an idea of the expected variation once you have optimised the object/process. Therefore the process control should be on the input variables. With proper understanding, early detection on one or more of the input variables can prevent from drifting outside the process window, and an adjustment on one or more of the input variables can be done without having to produce something considered as out of control from the output point of view. If possible, the variation in one input variable can be used as a signal for one or more input variables to be automatically adjusted to ensure process stays in control. This is the intelligence you should be able to get from a carefully conducted and analysed DoE.

DOE is absolutely critical in SPC out of spec treatment as analyses of such points are practically crucial for the overall control / improvement potential. Clear and accurate out of the limits analyses can also be decision making points during customer or 3rd party audits.

Once the top 2 - 3 factors are identified from the DoE experiment, most effort can be spent on controlling these two factors. Investments and expensive machinery spent on these 2 factors will give sufficient return on the investment.

Documentation and Continuous Improvement: Document the entire DOE process, including settings, results, and any unexpected findings. Use the knowledge gained to continuously improve the manufacturing process.

DOE is systematic approch for screening of critical factors from noncritical and to assess the effect of critical material attributes (CMA) & critical process parameters (CPP) that responsible for process variability on critical quality attributes (CQA)

DOE helps make things in a way that's more steady and reliable by testing and fixing important factors in how stuff is made. It figures out what things matter most and makes the process better. After trying and checking, we find the best way to make things the same way every time.

DOE can help to identify and eliminate sources of variation that are not visible or easily detectable.?For example,?DOE can be used to identify variations in raw materials,?equipment,?or operator technique. DOE can help to quantify the impact of different factors on process variability.?This information can be used to prioritize improvement efforts and make informed decisions about process design. DOE can help to develop robust processes that are less sensitive to variations in input factors.?This can reduce the need for rework and scrap,?and improve overall product quality.

You must have buy in from the employees that are operating these processes! This takes a significant investment in their technical understanding in the process they are operating! They need to understand what they are trying to accomplish with that part of the process. They need to understand why it’s important for the next part of the process. They need to understand the “key factors” that should be measured to insure their part of the process is delivering the right product to the next part of the process. Once this is in place Statistical process control of these key factor measurements can be put in place. Trying to do this without the upfront investment in the people running the process will quickly fail! This is my experience!

By systematically applying DOE, you can identify the critical factors affecting variability, optimize process conditions, and implement changes that lead to a more controlled and stable manufacturing process. This approach helps in achieving consistent and improved product quality while reducing variability.