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Production Defect Analysis Dataset
Data Science and Analytics
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About
This dataset provides valuable insights into factors influencing defect rates within a manufacturing environment. Each record contains various metrics crucial for predicting the occurrence of high or low defects in production processes. It is a synthetic dataset, generated for educational purposes, making it ideal for data science and machine learning projects. The dataset is imbalanced, with a focus on defect instances, and balancing may be required before applying machine learning techniques.
Columns
- ProductionVolume: Number of units produced per day. Data Type: Integer. Range: 100 to 1000 units/day.
- ProductionCost: Cost incurred for production per day. Data Type: Float. Range: £5000 to £20000.
- SupplierQuality: Quality ratings of suppliers. Data Type: Float (%). Range: 80% to 100%.
- DeliveryDelay: Average delay in delivery. Data Type: Integer (days). Range: 0 to 5 days.
- DefectRate: Defects per thousand units produced. Data Type: Float. Range: 0.5 to 5.0 defects.
- QualityScore: Overall quality assessment. Data Type: Float (%). Range: 60% to 100%.
- MaintenanceHours: Hours spent on maintenance per week. Data Type: Integer. Range: 0 to 24 hours.
- DowntimePercentage: Percentage of production downtime. Data Type: Float (%). Range: 0% to 5%.
- InventoryTurnover: Ratio of inventory turnover. Data Type: Float. Range: 2 to 10.
- StockoutRate: Rate of inventory stockouts. Data Type: Float (%). Range: 0% to 10%.
- WorkerProductivity: Productivity level of the workforce. Data Type: Float (%). Range: 80% to 100%.
- SafetyIncidents: Number of safety incidents per month. Data Type: Integer. Range: 0 to 10 incidents.
- EnergyConsumption: Energy consumed in kWh. Data Type: Float. Range: 1000 to 5000 kWh.
- EnergyEfficiency: Efficiency factor of energy usage. Data Type: Float. Range: 0.1 to 0.5.
- AdditiveProcessTime: Time taken for additive manufacturing. Data Type: Float (hours). Range: 1 to 10 hours.
- AdditiveMaterialCost: Cost of additive materials per unit. Data Type: Float (£). Range: £100 to £500.
- DefectStatus: Predicted defect status. Data Type: Binary (0 for Low Defects, 1 for High Defects).
Distribution
The dataset is typically provided as a CSV file (
manufacturing_defect_dataset.csv), with a size of 760.5 kB. It contains 17 columns and includes 3240 valid records for each variable.Usage
This dataset is ideal for data science and machine learning projects focused on manufacturing. It can be used to predict high or low defect occurrences, gain insights into factors influencing defect rates, and analyse production efficiency. Potential applications include developing predictive models for quality control, optimising production processes, and identifying key performance indicators in manufacturing.
Coverage
This is a synthetic dataset generated for educational purposes. It does not represent specific geographic locations, time ranges, or demographic groups. The data ranges for each variable are provided in the 'Columns' section.
License
Attribution 4.0 International (CC BY 4.0) license
Who Can Use It
This dataset is suitable for:
- Data Scientists and Machine Learning Engineers: For building and testing predictive models for quality control and defect prediction.
- Researchers: Studying the interplay of various factors in manufacturing efficiency and defect rates.
- Students: Learning about data analysis, feature engineering, and classification problems in an industrial context.
- Manufacturing Professionals: Seeking to understand and mitigate defect causes through data-driven insights.
Dataset Name Suggestions
- Manufacturing Quality Prediction Dataset
- Production Defect Analysis Dataset
- Industrial Defect Rate Dataset
- Manufacturing Process Optimisation Data
- Predictive Quality Control Dataset
Attributes
Original Data Source: Production Defect Analysis Dataset
QUALITY