What can you learn from using statistical learning methods in decision-making processes?
Statistical learning methods are a set of techniques that use data to learn patterns, relationships, and predictions that can help you make better decisions in industrial engineering. Whether you are optimizing a production system, designing a new product, or improving quality and reliability, statistical learning methods can help you discover insights, test hypotheses, and evaluate alternatives. In this article, you will learn what statistical learning methods are, how they differ from traditional statistics, and what benefits they can offer for decision-making processes.
Statistical learning methods are a branch of statistics that focuses on finding and describing the structure and complexity of data using algorithms and models. Unlike traditional statistics, which often rely on assumptions and parametric methods, statistical learning methods can handle large, noisy, and high-dimensional data sets, and use flexible and non-parametric methods. Some examples of statistical learning methods are regression, classification, clustering, dimensionality reduction, and ensemble methods.
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Statistics are a collection of data that has been pulled together in hopes of telling a story of what has transpired. Many times, data is collected for processes, equipment, output and issues. In businesses, it is rarely used to look at behavioral trends of employees. Yet the more erratic behavior becomes, the more erratic processes can be. There is typically a direct correlation to the behaviors of people and the behaviors of processes and equipment. Collecting behavioral data is something to consider when looking at using statistics for problem solving.
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Statistics is an established scientific method for identifying trends, deviations, and patterns that, if interpreted in a timely and correctly manner, direct efforts to correct processes, or even substantial improvements in performance with rational, scientific-based guidelines. If within your process, it is possible to combine the team's experience with results and statistical reports, the chances of success and correct decisions will increase exponentially.
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Statistical support allows strengthening appropriate decision-making processes. Statistical decision making process. Therefore, it allows the problem to be resolved more effectively and efficiently. It allows us to quantify reality and have the elements that allow us to analyze it. Description of the starting situation. Establishment of models and patterns of action. Detection of problems, differences, areas for improvement and cause-effect associations.
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Using basic statistical analysis of any process in manufacturing - the actual manufacturing processes or performance data on machinery, assembly operations are a rich source of information to guide improvements, focus efforts and investments (80/20), and measure the precision and repeatability of manufacturing processes. This area of focus is particularly important now because so many firms are turning to automation but overlook the HUGE impact process variability has on the success of automation projects. From my experience high variability and the lack of knowledge about the variability is the number 1 cause for the failure of automation projects....
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To learn without making mistakes... Statistics helps you to identify: patterns, trends, behaviors and/or deviations, as long as you'll draw a correct baseline. Hence if you define in a correct way the basis for the analysis you will get a significantly accurate answer about how to act based in hard facts & data.
One way to understand the difference between statistical learning methods and traditional statistics is to think about their goals and approaches. Traditional statistics aims to test hypotheses, infer parameters, and estimate confidence intervals using analytical and deductive reasoning. Statistical learning methods aim to learn patterns, relationships, and predictions using computational and inductive reasoning. Traditional statistics often uses simple and interpretable models that fit the data well, but may not generalize well to new data. Statistical learning methods often use complex and flexible models that may not fit the data well, but can generalize well to new data.
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Statistical learning methods aim to provide inferences based on the patterns of the data historically and predicted the future using that behavior, this is good for big data and if we wanted to forecast future trends. Unlike traditional statistics we focus on finding the significance between data sets to provide us insight on what to with the process improvement.
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From my perspective, statistical learning methods hold a clear advantage over traditional statistical methods in several key aspects: - can effectively navigate through high-dimensional, noisy, and non-linear data. - have ability to make predictions for entirely new data points, expanding their applicability beyond the confines of training data. - have demonstrated superior accuracy compared to their traditional counterparts, leading to more reliable insights. - As data sets grow in size, statistical learning methods maintain their effectiveness, seamlessly scaling to accommodate large-scale analyses.
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No deberíamos dividir la estadística tradicional de los métodos de aprendizaje estadístico como si fueran cosas completamente distintas. Ambas metodologías tienen su función, la estadística tradicional nos da una foto del escenario actual y/o a futuro y los métodos de aprendizaje estadístico nos brindan un “zoom” a dicha foto.
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Agree with the above statement and if we see in the broder way then statistical learning focuses on accurate results but traditional learning focuses on relationships between variables. Statistical learning methods can deal with very large data samples very easily but traditional learning will be difficult to handle large sample data.
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estábamos trabajando en la predicción de tendencias del mercado para un cliente clave. Optamos por implementar métodos de aprendizaje estadístico debido a la naturaleza dinámica y compleja de los datos. Necesitábamos no solo comprender patrones actuales sino también anticipar cambios futuros. Los modelos más flexibles y complejos del aprendizaje estadístico demostraron ser cruciales para la generalización precisa a nuevos datos, lo cual resultó esencial en un entorno comercial en constante evolución.
Statistical learning methods can be extremely beneficial for decision-making processes in industrial engineering, such as exploring and visualizing data to identify trends and outliers, building predictive models to forecast outcomes and optimize performance, finding and extracting relevant features and variables that can explain the behavior of a system or process, clustering and grouping data into meaningful categories, and combining multiple sources of data to enhance the accuracy of decisions.
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Statistics serve as a powerful tool, allowing individuals to interpret information visually through diverse graphs and tables. Following this, it facilitates the understanding of both normal and non-normal behaviors, shedding light on the reasons behind specific actions. Moreover, it equips us with the capability to predict future behavior with a reasonable level of certainty. Ultimately, by leveraging statistical insights, we can embark on a smarter and more informed decision-making process, perpetuating a continuous improvement cycle akin to the PDCA model
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In the decision-making processes, understanding the business problem is the most cruicial part of it. Backing it up with the statistical learning methods makes the understanding more solid and definitive because these methods help the problem-solver identify intricacies per se. When a problem is clearly defined, solutions become accurately fitting.
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The main advantage in implementing statistical learning methods in automotive or product based industry is that it will provide room for improvement in areas of demand forecasting, made to order technique, data visualization is an add advantage to compare and analyze the historic data and enhnace accuracy in predicting data.
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Statistical learning methods enhance decision-making by uncovering patterns in data, aiding in prediction, and optimizing variables for better outcomes. These methods bring objectivity to decision processes, automate tasks, and provide a data-driven foundation for risk management. By classifying data, personalizing decisions, and enabling continuous improvement, statistical learning contributes to more informed and efficient decision-making, fostering adaptability in dynamic environments.
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Statistics helps understand in which level of quality we are, so we can use it to improve any process and it works also as a KPI for quality management.
When utilizing statistical learning methods for decision-making processes in industrial engineering, there are several challenges that need to be addressed. Firstly, it is important to choose and apply the appropriate method for the problem and data at hand, considering the trade-offs between complexity, interpretability, and generalizability. Secondly, it is necessary to evaluate and compare the performance and validity of different methods and models, using suitable metrics and criteria. Thirdly, it is essential to communicate and explain the results and implications of statistical learning methods to stakeholders and decision-makers using clear language and visuals. Finally, one must address and mitigate the ethical and social issues that may arise from using statistical learning methods, such as bias, privacy, accountability, and transparency.
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There are several benefits for decision making but understanding the large data and design to make accurate predictions about data I feel gives lots of help to get the results. You can utilize software and scale the solution for any large data...and for many units.
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Os desafios incluem a necessidade de dados de alta qualidade para treinar os algoritmos corretamente e a interpretação complexa de resultados, pois os métodos de aprendizagem estatística podem ser caixa-preta, dificultando a compreensão exata de como uma decisão é tomada. Daí uma equipe preparada passa a ser um dos maiores desafios
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As I said, understanding and correctly using tools can help us make the right decision. We can implement software, speeding up treatment and generating more accurate information to support decisions, creating a competitive advantage.
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Selecting the correct method for decision making can be critical. Based on what needs to be presented to management a method should be selected to address these needs as well as ensuring that the methods can provide the information in a way that the audience can understand. From past experiences, employees who do not have a background or training in statistics, can have difficulty understanding results and why they support the decision that is being made. Any of the learning methods that have ability to present final data in either a simple data table or clear chart can help the audience understand what the decision criteria are but also allow them to drill down further if additional explanation is needed.
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Training intelligently and accurately defining what is sought in the analysis are the main challenges for achieving the desired results, regardless of the tools used.
If you're interested in using statistical learning methods for decision-making processes in industrial engineering, there are several steps you can take. Begin by understanding the fundamentals of statistics, probability, and data analysis, and how they relate to statistical learning methods. Then, explore the different types of statistical learning methods and their assumptions, applications, and limitations. Additionally, practice applying these methods to real-world problems and data sets in industrial engineering. Finally, stay informed of the latest developments in statistical learning methods and seek feedback from experts and peers.
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Essa é uma abordagem inovadora para um setor que ainda está atrasado em pelo menos 20 anos, em relação ao seus pares. No entanto, tem acelerado de maneira exponencial desde 2020.
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We are on a new level and evolution is knocking on our door. We have choices and mainly an improved study to choose the best practices and the best courses that can help us in this learning process. There is no ready-made recipe for the new tools that are being developed, but there is a consensus on what should be used correctly.
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Em forma de lista: 1. "Explore Recursos Online": Busque cursos e materiais online sobre aprendizado de máquina e métodos estatísticos. 2. "Prátique": Aplique os conceitos em projetos pequenos para ganhar experiência prática. 3. "Estude Casos": Analise casos práticos e projetos que utilizam métodos de aprendizagem estatística. 4. "Colete Dados Relevantes": Identifique problemas de decisão específicos e colete dados relevantes para esses contextos. 5. Selecione algoritmos que se adequem aos seus dados e objetivos. 6. "Ajuste Contínuo": Ajuste e melhore os algoritmos conforme necessário para otimizar os resultados ao longo do tempo. 7. Implemente os métodos aprendidos adaptando-os conforme as necessidades específicas do contexto.
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We are developing better tools together and we have made mistakes and successes along our journey. We have a lot to learn and this will always be a challenge.
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Machine learning >> statistical learning.
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While using algorithms and statistics to collect meta data to digest we must disconnect ourselves from the pure AI. It's the human experience and nuances that give this data relevance and can provide insights that AI is not capable of. AI works within the mathematical box that's created for it, where the human nuances can be interpreted by a much larger box of our reality. Discussion about results should always be a collaborative so complete understanding, evolution and progression can turn to wisdom
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