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Understanding Applications

What are Applications?​

Applications in AI SCADA are pre-built functional modules that address common industrial automation needs. They provide ready-to-use solutions for complex production scenarios, reducing development time and ensuring best-practice implementations.

AI SCADA currently includes two core applications:

  1. Recipe Management: Store and manage production parameters for different products
  2. Equipment Statistics: Monitor equipment status, calculate OEE, and analyze downtime reasons

When to Use Applications?​

Applications are essential for:

  • Multi-Product Manufacturing: Quickly switch between different product configurations
  • Production Optimization: Track equipment efficiency and identify improvement opportunities
  • Quality Management: Ensure consistent production parameters across batches
  • Performance Analysis: Measure and improve equipment utilization and OEE
  • Root Cause Analysis: Systematically track and analyze equipment downtime

Recipe Management​

What is Recipe Management?​

Recipe Management allows you to store and manage production parameters (setpoints, formulas, configurations) for different products or production scenarios. When switching products, you can quickly load the corresponding recipe and apply it to equipment (PLCs, controllers, etc.).

Use Cases​

Paint Manufacturing:

  • Different paint colors require different ratios of pigment, solvent, resin, and additives
  • Store recipes for each color variant
  • Quickly switch recipes to adjust material feeding ratios

Food Production:

  • Different product flavors require different ingredient proportions
  • Store temperature, mixing time, and ingredient ratios for each recipe
  • Ensure consistent product quality across batches

Chemical Processing:

  • Different chemical products require specific reaction conditions
  • Store temperature, pressure, flow rate, and catalyst ratios
  • Minimize changeover time between products

Key Features​

Recipe Sets:

  • Group related recipes together (e.g., all coffee variants in one set)
  • Organize recipes by product line, equipment, or production area

Recipe Parameters:

  • Define parameters that vary between recipes (temperature, speed, ratios, etc.)
  • Link parameters to tags for automatic equipment control
  • Support multiple data types (numeric, boolean, string)

Recipe Operations:

  • Create, edit, delete, and duplicate recipes
  • Switch between recipes quickly
  • Apply recipes to equipment automatically
  • Read current equipment values into recipes

Two Usage Modes:

  1. Recipe Management Component: Pre-built UI component with all recipe operations
  2. Custom Interface: Build your own recipe interface using event-actions

Equipment Statistics​

What is Equipment Statistics?​

Equipment Statistics is a comprehensive monitoring and analysis tool that tracks equipment status, calculates efficiency metrics (OEE), and analyzes downtime reasons. It automatically collects data from equipment control systems and provides actionable insights for production management.

Use Cases​

Production Line Monitoring:

  • Track equipment running time, downtime, and idle time
  • Identify bottleneck equipment
  • Optimize production scheduling

OEE Management:

  • Calculate Overall Equipment Effectiveness (OEE)
  • Measure availability, performance, and quality rates
  • Set improvement targets and track progress

Downtime Analysis:

  • Categorize downtime by reason (planned maintenance, changeover, breakdown, etc.)
  • Identify top downtime contributors
  • Prioritize improvement initiatives

Shift Reporting:

  • Automatically generate shift reports with equipment status and OEE
  • Compare performance across shifts and days
  • Support continuous improvement activities

Key Features​

Status Duration Tracking:

  • Define equipment states (Running, Stopped, Fault, etc.)
  • Automatically track time spent in each state
  • Generate state timeline and duration reports

OEE Calculation:

  • Calculate OEE and its components (Availability, Performance, Quality)
  • Track actual vs. planned production
  • Monitor good parts, scrap, and cycle time

Root Cause Analysis:

  • Categorize equipment states (Planned Downtime, Changeover, Material Wait, Breakdown, etc.)
  • Record specific reasons for each state
  • Support manual reason entry for unidentified states
  • Drill down from state categories to specific reasons

Data Aggregation:

  • Aggregate data by shift, day, week, or month
  • Support custom classification fields (work center, line, equipment type)
  • Export data for external analysis

Application Architecture​

Recipe Management Architecture​

Configuration Phase (Development):

  • Define recipe sets and parameters
  • Create recipes with parameter values
  • Link parameters to equipment tags

Runtime Phase (Production):

  • Switch between recipes
  • Apply recipes to equipment (write parameter values to tags)
  • Read current equipment values into recipes
  • Manage recipes (create, edit, delete)

Equipment Statistics Architecture​

Data Collection:

  • Monitor equipment status signals (binary or integer)
  • Track production counters (output, scrap)
  • Record state changes and durations

Data Processing:

  • Calculate state durations
  • Compute OEE metrics (Availability, Performance, Quality)
  • Categorize states and record reasons

Data Storage:

  • Store detailed records (state changes, OEE snapshots)
  • Aggregate data by shift and day
  • Support historical analysis

Best Practices​

Recipe Management​

Recipe Organization:

  • Use clear, descriptive recipe names (e.g., "Coffee_Latte_Medium" not "Recipe_001")
  • Group related recipes in recipe sets
  • Limit the number of parameters to essential ones

Parameter Configuration:

  • Link parameters to the correct equipment tags
  • Verify parameter ranges and data types
  • Test recipe application in development before production use

Change Management:

  • Document recipe changes with version notes
  • Test new recipes thoroughly before production
  • Keep backup copies of working recipes

Equipment Statistics​

State Definition:

  • Define clear, mutually exclusive equipment states
  • Ensure every possible equipment condition maps to a state
  • Use standard state names across similar equipment

OEE Configuration:

  • Set accurate theoretical cycle times based on equipment capability
  • Update planned production time and quantity daily
  • Include all production (good + scrap) in output count

Reason Analysis:

  • Create a standardized reason library
  • Train operators to enter reasons promptly and accurately
  • Review and clean up unidentified states regularly
  • Use reason data to drive improvement initiatives

Data Quality:

  • Verify signal connections before going live
  • Monitor data collection for anomalies
  • Establish procedures for manual data correction when needed

Next Steps​

Now that you understand the Applications module, you can:

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