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Introduction to the Parametric_Quantitative Table

The parametric_quantitative table is designed to store and manage quantitative measurements and test results in a manufacturing process. This table plays a crucial role in the Serial system by capturing numerical data with associated metadata, enabling quality control, and supporting process analysis.

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Table Structure

The parametric_quantitative table is structured to efficiently store and relate quantitative data to other entities in the system. Here’s a detailed breakdown of its columns:
Column NameData TypeConstraintsDescription
iduuidprimary key, default uuidUnique identifier for each entry
company_iduuidforeign key, not nullReference to the company associated with the data
process_entry_iduuidforeign key, not nullLink to the specific process entry
unique_identifier_iduuidforeign key, not nullReference to the unique identifier (e.g., serial number)
valuenumericnot nullThe actual measured or recorded value
uslnumericnullableUpper Specification Limit
lslnumericnullableLower Specification Limit
is_passbooleannullableIndicates if the value is within specifications
is_discretebooleandefault falseSpecifies if the data is discrete or continuous
unittextnullableThe unit of measurement
dataset_iduuidforeign key, not nullReference to the associated dataset
created_attimestamp with time zonedefault now(), not nullTimestamp of when the entry was created

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Usage and Functionality

The parametric_quantitative table is designed to be a comprehensive repository for quantitative data in manufacturing processes. Here are some key points about its usage:
  1. Quality Control: The table supports quality control processes by storing both the measured value and the specification limits (USL and LSL). The is_pass field can be automatically calculated based on whether the value falls within these limits.
  2. Process Analysis: By linking quantitative data to specific process entries and unique identifiers, the table enables detailed analysis of manufacturing processes over time. This is crucial for identifying trends, process capabilities, and areas for improvement.
  3. Flexibility in Data Types: The is_discrete field allows for differentiation between continuous and discrete data types, accommodating various measurement scenarios in manufacturing.

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Notes

  • The table is designed with nullable usl and lsl fields, allowing for scenarios where only one or neither limit is specified for a particular measurement.
  • Example usage in TypeScript: 
    interface ParametricQuantitative {
  id: string;
  company_id: string;
  process_entry_id: string;
  unique_identifier_id: string;
  value: number;
  dataset_id: string;
  created_at: string;
  is_discrete: boolean;
  is_pass: boolean | null;
  lsl: number | null;
  usl: number | null;
  unit: string | null;
}
  • The created_at field allows for historical tracking and time-based analysis of measurements.
By leveraging the parametric_quantitative table, the Serial application can provide robust support for quantitative data management in manufacturing processes, enabling detailed quality control, process capability analysis, and historical trend identification.