Value Chain & Operations

Process performance control

Optimizing operating parameters to improve throughput, quality, energy use, and waste

How

SHAiPE: the LTPlabs framework

Set the Decision
- Define the process scope
  line, machine, plant, process step
- Clarify the decision level
  real-time control or tactical parameter setting
- Define optimization objective
  maximize throughput, minimize defects, energy and waste
Highlight what matters
- Identify key process variables and drivers
  machine parameters, environmental conditions, inputs
- Align on success metrics
  quality, throughput, energy consumption, waste
- Define operational constraints
  equipment limits, safety, production requirements
Augment with AI
- Identify critical variables and their impact on outcomes
- Model relationships between parameters and performance
- Enable scenario simulation and sensitivity analysis
- Data
  Sensor data
  Interventions or line changes
  Number and type of defects over time
  AI Model
  Results
  What parameters are crtitical to predict defects?
  What are the optimal bounds for these parameters?
Prototype your solution
- Prescribe optimal operating ranges and parameter settings
- Validate models on historical and real production data
- Run pilot on selected lines / machines and test recommendations against current operating conditions
Expand to scale
- Integrate into production systems and workflows
- Enable real-time monitoring and decision support
- Train operators and embed into operational routines
- Continuously monitor performance and refine models

Nutshell

times faster identifying the most common production defects

Improved process stability and consistency, and higher throughput and productivity
Reduced defect rates and improved quality, and lower energy consumption and waste
Better visibility on productivity drivers and root causes

Insights