How can you use data analytics to reduce manufacturing defects in Industrial Engineering?

Define the problem, somehow a unique process we must do. As operational engineer we have something like common sense that it is a problem. But when it come to statistical method become blur. So we have to combine several tools mixed from qualitatif and quantitatif value In order to define exact root cause. So the first step is make our environment convert to database so much more objective for having root cause problem

To reduce manufacturing defects in industrial engineering using data analytics: Data Collection: Gather data on production processes, equipment performance, and product quality. Include variables such as temperature, pressure, and machine settings. Data Cleaning: Ensure data accuracy by identifying and rectifying errors or missing information in the dataset. Descriptive Analytics: Analyze historical data to understand patterns, trends, and common factors associated with defects. Identify critical process parameters. Predictive Analytics: Use statistical models or machine learning algorithms to predict potential defects based on historical data. This helps in identifying areas of concern before issues arise.

In a well-integrated organization, data-driven departments analyze & interpret its input information and provide its output decisions and actions as inputs to action-driven departments, These action-driven departments then deliver services or products as outputs. This cycle is continuous, allowing for data-driven improvements at every stage. Therefore, to reduce manufacturing defects effectively, data analytics should be used to generate informed decisions, solutions, procedures, and actions. These should then be integrated throughout all departments, with the resulting output constantly monitored and analyzed for further improvement

Data analytics in industrial engineering can reduce manufacturing defects by collecting and integrating data from various sources, conducting root cause analysis, implementing predictive analytics for defect anticipation, optimizing quality control processes, managing supplier quality, providing continuous monitoring and feedback, optimizing manufacturing processes, detecting faults and diagnosing issues, tracking performance metrics and KPIs, and tailoring employee training based on data analysis. This approach enhances product quality, increases efficiency, and contributes to cost savings.

It is a very basic requirement to solve any problem to clearly define it in first place below are the some of the key points that needs to be taken care of using data analytics- - Quantify the problem using 5W2H - Define CTQ / Customer Requirement - Define Problem Objective / Goal - Develop Stakeholder Matrix - Develop high level SIPOC Chart to identify problem location - Develop communication plan

Data analytics is pivotal in minimizing manufacturing defects through process optimization. By employing sensors and data collection tools, we monitor key parameters, analyzing data to identify patterns and correlations, ultimately determining optimal process conditions. Predictive analytics enhances foresight, enabling proactive adjustments before defects manifest. In the final product, defects are identified and categorized using data analytics through real-time inspection techniques, such as computer vision and image analysis. Additionally, data analytics facilitates root cause analysis during defect occurrences, utilizing historical data to pinpoint patterns and correlations specific to defect types.

Key Considerations for Defect Reduction: > Focus: Defect-related data, process parameters, and environmental factors (if applicable). > Accuracy: Reliable sources, validation procedures, standardized methods. > Granularity: Appropriate level of detail and relevant temporal resolution. > Accessibility: Central repository, data management system, security measures. > Integration: Combine data from various sources, utilize analytics, build predictive models.

In the case of human errors that affect quality, it is important to keep a record of the errors that occur: type of error, workstation, product model, characteristics of the individuals making the error (experience, age, etc.), time of day, shift, cumulative work hours, day of the week. All these data may be necessary to perform an analysis of the possible factors that may be affecting human performance by increasing the probability of errors.

In Industrial Engineering, leveraging data analytics involves using advanced techniques to analyze and interpret data throughout the manufacturing process with the goal of reducing defects. The process begins with collecting data from various sources such as sensors and production logs. This data is then cleaned and preprocessed to ensure accuracy and consistency. Descriptive analytics is applied to understand historical data, identifying trends and patterns related to defects. Predictive analytics utilizes machine learning models to forecast potential defects based on historical patterns. Root cause analysis helps pinpoint the key factors contributing to defects, enabling targeted improvements.

Il convient tout d'abord de reconna?tre que les défauts de fabrication peuvent être causés par un certain nombre de facteurs : matières premières, conception, équipement défectueux ou mal réglé, erreur humaine, etc. L'analyse des données peut jouer un r?le clé dans l'identification des causes des défauts de fabrication en fournissant des informations approfondies sur les modèles, les tendances et les relations entre les variables. Elle contribue ainsi à une approche plus proactive et préventive de la gestion de la qualité dans l'ingénierie industrielle.

If quality problems are associated with human error, solutions should be aimed at improving the work environment: better tools, clearer and more accessible work procedures, use of error-proof systems (Poka-Yoke), reducing distractions and interruptions, improving visibility (lighting, contrast, brightness), reducing or eliminating time pressure, and optimizing work schedules and fatigue, among others.

Before full-scale implementation, conduct pilot tests or simulations of the proposed solutions. Gather data during the testing phase to evaluate the effectiveness of the solutions. Validate the results against predefined success criteria to ensure that the proposed changes meet the intended objectives.

Data analysis in industrial engineering can reduce manufacturing defects by identifying patterns and root causes of defects, optimizing processes through predictive maintenance, improving quality control, and enhancing decision-making with real-time feedback and predictive analytics.

Never be paralized by analysis,data are important but only a good monitor and analysis of data will give the the right database you need

Consideration should be given to whether the analysis was missing data to be collected. Some types of data are not collected in the quality records. For example, in the case of error-induced defects, the time of day when the error/defect occurs is relevant data. It is known that due to variations in circadian rhythms attention levels vary throughout the day. Likewise, the evening and night shift are more prone to errors.

Has the improvement cured the issue or changed it? Is there now a different problem due to the change? Its important to keep checking and gathering data.

- define what kind of defect you arr encountering. What is the scope. Where is it or is it not occuring - measure and analyze the defects. How big is the problem. Where is the problem. What is the financial impact to the company. What is the impact to the customer - improve based on data collected. Control. - Don't forget to monitor and do PDCA. - then go find another defect or pain point for continuous improvement