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Vidhya Rathinavelu

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Vidhya Rathinavelu last won the day on May 26 2023

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    Cognizant Technology Solutions India Ltd.,
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    Manager - PEx

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Vidhya Rathinavelu's Achievements

  1. To create an effective Problem Definition Tree, a Black Belt should: Focus on specific and measurable outcomes. Prioritize actionable objectives. Maintain logical connections. Avoid overlapping or redundant objectives. Limit the number of levels. Use factual data and evidence. Iterate and refine as needed. When deciding at which level to stop and finalize project Y, a Black Belt should consider: Project goals and objectives. Stakeholder satisfaction. Time constraints. Budget considerations. Change in scope or organizational priorities. Clear identification of the problem statement and process improvements. Statistical significance. By considering these factors, a Black Belt can make an informed decision on whether to stop and finalize project Y at a particular level or continue with further improvements.
  2. The Sparsity of Effects principle is a fundamental concept in Design of Experiments. It states that, in any system, a vast majority of output's variability can be attributed to a small number of input factors. In other words, most inputs have either minimal or no effect on the outcome and only a few critical inputs significantly impact the variation. This principle can be helpful to researchers in Design of Experiments because it can help them to identify the most important variables to include in their experiments. By focusing on the variables that are most likely to have a large impact on the outcome, researchers can reduce the number of variables they need to test, which can save time and money. The principle of Sparsity of effects is useful in the following ways: Researchers can allocate their time and resources to investigate the vital few input factors responsible for most of the output variability rather than studying all possible factors. Instead of considering a whole lot of data, using this principle complex problem statements can be simplified by identifying and focusing on the most important factors. Knowing which input factors have little to no impact on the system outputs enables decision-makers to prioritize optimization efforts on high-impact factors and consequently make better-informed decisions. Sparsity in principle refers to situations where most elements are irrelevant or have minimal contribution to the overall solution or outcome. In cooking, the concept of sparsity can be illustrated through the idea of using only essential ingredients to create a flavorful dish. Consider preparing a simple pasta dish with garlic, olive oil, and chili flakes. These few ingredients, when chosen carefully and combined well, can create a complex and delicious flavor profile. Adding more ingredients like additional vegetables, cheese, etc may not necessarily enhance the dish. Sometimes, overutilisation of resources can even detract from the harmonious balance of flavors. Just like how an excellent cook like me , understands that sometimes restraint in using ingredients, can produce the best results, sparse solutions focus on the most significant components to achieve an optimal outcome.
  3. Levene's test is used to test if a specified number of samples, which have a normal distribution, have the same variance. This test is used to identify the homogeneity of the samples. During analyse phase, sometimes, we assume that the variances are equal for different sets of samples. Levene's test is used to validate this assumption. It is done before ANOVA. If Levene's test is failed, that is, if we are not able to validate that our assumption that variances are equal is not true, then ANOVA should NOT be conducted. Bartlett's test is also used to check if variances among multiple sample sets are equal. Levene's assumes that the data is normal, whereas for Bartlett's normality assumption is not a requirement. Levene's test is less sensitive to violations of normality whereas Bartlett's isnt. On basis of the data & the requirement, either of the test will be used.
  4. A Design Scorecard is a tool used in the design and development process to measure, validate, and communicate the performance of a design against predefined criteria. It helps designers, stakeholders, and decision-makers to align on priorities, identify potential areas of improvement, and make effective decisions about the design process. It can be used to identify areas where the design needs to be improved, and to track progress as the design is refined. By using this tool, teams can ensure that their designs meet established goals and overall project requirements. Design Scorecard in DMADV: 1. Define: In the initial stage of defining project goals and customer requirements, the Design Scorecard can establish key performance indicators (KPIs) that will align with the objectives. These KPIs will serve as benchmarks to track progress throughout the project. 2. Measure: In Measure phase, it helps track critical measurements related to customer requirements and specifications, existing process capabilities and performance standards. 3. Analyze: During analysis, the Design Scorecard supports analysis of potential root causes, by validating collected data and providing insights into gaps in performance that require attention. 4. Design: In this phase, the Design Scorecard helps to prioritise features and functions based on their alignment with KPIs and overall project goals, thereby supporting optimal resource allocation. 5. Verify: It tracks verification progress by comparing newly designed products or processes against the internal & external specification. Thus, highlighting areas needing improvement or modification before final implementation. Used case for a DMAIC project: Design Scorecard can be used in service industry for projects taken up to address the customer escalations. It can be used in Define phase to establish the targets, both internal & client targets. Comparing where the team is against the targets and to understand the differences. This then can It can be used in the analyse phase to prioritse the root causes.
  5. In real world, the data rarely follows a normal distribution. Data is affected by outliers and measurement errors, because of which IMR chart may not be effective in detecting changes, as these charts assume that the data is normal. In such cases, we can use charts for non normal data or we can transform the data, depending on the scenario and the data type. Data transformation techniques, such as the Box-Cox or Johnson transformation, can help stabilize variances and make the data more symmetric, allowing for more accurate interpretations and applications of control charts. Data can also be split into rational sub groups and then buliding control charts on each split data. Checking for process stability using transformed data helps to identify potential problems in a process. By transforming the data, we can highlight any trends or patterns that might not be visible in the raw data. This can help us to identify potential causes of variation. Then corrective actions can be identified to improve the stability of the process and prevent any problems from re-occurring. Example: A call center will monitor the waiting times in the queue. If the data is not normal due to variability, then the distribution will be a non normal distribution and IMR chart may not accurately detect the actual problem or the trends. If the wait time data can be transformed, then the analysis on the data will be more accurate and will help the call center to take effective decisions.
  6. A Way of Working (WoW) is a set of principles and practices that defines how an organization works. It defines the way people collaborate, communicate, and make decisions.A well-defined WoW can help organizations improve productivity, collaboration, overall performance and create a culture of continuous improvement by providing a framework for identifying and solving problems. Here are some ways that WoW can help create a culture of continuous improvement: It provides a framework of how work is done. It helps to identify and eliminate waste and inefficiency. It encourages cross functional collaboration and open communication. It supports the development of new ideas and innovation. It provides opportunities for training & development It ensures transparency and sets accountability To effectively implement WoW, organizations should focus on clear communication, training and coaching, and a commitment to ongoing improvement.
  7. A top-down diagram is a visual representation of a process, with the high-level steps/general steps at the top and the lower-level/more specific steps below. Top-down diagrams are mostly used in the software industry to show the architecture. They may also be used to show the steps in a manufacturing process or the flow of data in a database. To use a top-down diagram in a DMAIC project, we have to start by identifying the problem we are trying to solve. Then, brainstorm all of the possible causes of the problem. Once you have a list of potential causes, we can use anothe top-down diagram to drill down further Once the root cause is identified, action plans can be determined to fix it. Please see the attached diagram for the top down flow. A top down chart is used to: * Identify the overall goal of the project. * Break down the goal into smaller, more manageable tasks. * Track the progress of the project. * Identify any potential problems or risks.
  8. A probability plot is the graphical method for validating the distribution of data. It is a line plot of the data points against the theoretical values, which are the values of a cumulative distribution function, of a probability distribution. A probability plot can be used to assess the shape, location, Spread & outliers. It can also be used to compare the distributions of two or more sets of data. To create a probability plot, the data points are first arranged in ascending order. Then the cumulative distribution function is calculated and data is plotted. A well-fitting probability plot will have a straight line. If the data points are scattered around the line, then the distribution is not well-represented Some of the insights that we can get from a curve that deviates from a straight line are that, data: is not normally distributed. has outliers. may be skewed. Does not facilitate use of standard statistical methods to analyze the data. requires transformation or use non-parametric statistical methods. Regards Vidhya R
  9. Multi vari chart is a graphical representation from which we cannot draw statistical inferences. It is used to identify the multiple sources of common cause variations. We can check variations across piece to piece, variation on single piece and time to time using a multi vari chart Piece to Piece helps identify variations within a part of the product/process When we want to compare two different process/product, piece to piece can be used. Time to time variation is used to compare the variations between the different times of production Interpreting the Multi Vari chart: Means for each factor must be referred and the interaction of one factor with the other and the impact on the other can be understood by the studying the trend line that passes through the mean.
  10. The IS - IS not tool is a powerful problem solving tool used for identifying root causes by using a set of questions to identify the gap area. This analysis required detailing out the when, where, who, how and the extent of impact for a problem/concerns. It is a matrix where the above information are listed down and then against each of the input for the when, where, how, who and the imapct, it is determined if the particular factor "IS" impacting or "IS NOT" impacting. Once this is identified, if there is an "IS" impacting, then the factors that impact the problem/issue are isolated. This tool also helps in identifying patterns. For ex: If you are looking at errors and then you are using this IS-IS not analysis against each category of errors, the cause that you may arrive at may result in a pattern. Thus, this tool also helps in identifying the significant few.
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