Causal AI for Manufacturing
Path to advanced manufacturing
Digital transformation in manufacturing enterprises, driven by causal AI, plays a pivotal role in building a resilient supply chain, connected work force, and achieving sustainability goals.
By applying causal AI, manufacturers can gain deep insights into the complex relationships between various operational factors, enabling precise identification of inefficiencies and next best alternatives. This technology facilitates the optimization of raw material purchase, streamlining order processing, reusability of recycled waste, reduce safety incidents, to achieve superior business goals.
Supply Chain
Procurement planning
Supply Chain
Order processing
Safety
Workers Safety
Asset performance
management
Product master
R&D
Procurement planning and optimization
A Fortune 250 consumer products company (CPG) implemented a Causal AI based procurement planning & optimization agent using Parabole’s TRAI N. The current procurement system focusses solely on supply chain KPI’s and neglects impacts on product recipe, machine reliability and logistics.
The AI agent applies causal knowledge to enable cross domain KPI optimization. It helps in identifying new suppliers, selecting the next best raw material alternatives, determining volume to get the most out of their annualized procurement plan that not only meets cost saving targets but also suits the mills, its machines along with achieving quality adherence.
Total economic benefit
8% – 20%
Reduction in annual procurement cost
12%
Increase in recycled material usage
Faster
Selection of new suppliers
Touchless order processing and optimization
A Fortune 250 consumer products company (CPG) implemented a Causal AI based order processing and optimization agent using Parabole’s TRAIN. The AI agent applies causal knowledge to solve novel issues. Orders are assessed against customer data, enterprise data, and supply chain data, facilitating autonomous order grooming and processing.
The implementation has significantly enhanced process efficiency, cutting order processing time from 3 days to 15 seconds and dramatically boosting the percentage of “touchless” orders from 10% to 91%. Additionally, the agent provides the customer solutions team with detailed order information and identifies previously un-noticed internal inefficiencies, leading to a 12% reduction in OTIF penalties.
Total economic benefit
3 days to 15 seconds
Order processing time
10% to 92%
Touchless orders
12%
Lower OTIF penalties
Employee health & safety
A Fortune 500 petro-chemical refinery implemented a Causal AI-based operational safety agent using Parabole’s TRAIN.
Preventing serious incidents begins with understanding “what causes what”. The goal was to increase the adaptive capacity and reduce the cognitive burden on the worker.
The operational safety agent integrates OT and observational data to perform instant root cause analysis and delivers contextual guidance to even less-experienced workers that improves their overall adaptive capacity leading to a safer workplace.
Total economic benefit
15%
Reduction in recordable incidents
20%
Reduction in Loss Time Incidence (LTI)
15 Seconds
Root cause analysis
Asset performance monitoring
A global energy major implemented a Causal AI based Asset Performance Management (APM) & optimization agent using Parabole’s TRAIN. The agent applies causal theory to efficiently monitor the health of the asset including early fault detection, root cause analysis, and faster remediation for safe and reliable plant operations.
Using cumulative knowledge of subsystem interconnectedness, process flow, engineering details, and operator experience, TRAIN’s APM agent autonomously formulates hypotheses, identifies root causes for the fault, and suggests remedial options.
This implementation reduces risks and preserves asset uptime by identifying production impacting events and reduce maintenance/asset life-cycle costs.
Total economic benefit
15 seconds
Root cause analysis
$17 million
Dollar saves
5 seconds
Prognostics query
Integrated product master
A global energy major implemented a Causal AI based product information master for design optimization and product portfolio management using Parabole’s TRAIN.
The agent applies causal theory to discover interconnectedness between products, systems, subsystems, process flow, and engineering information.
This implementation enables product designers to search and visualize product concepts, their groupings, their alternatives, and associated dependencies in few seconds resulting in faster search and improved design decisions.
Total economic benefit
100%
Search accuracy
12%
Increase in alternate component usage
Faster
Time to search decision tree
Contact us to schedule a demo
Learn more about how Causal AI can empower your teams to
- Maximize touchless order processing and reduce OTIF penalties.
- Optimize Procurement planning and maximizing mill throughput.
- Minimize shop floor incidents to ensure workers safety.
- Minimize energy consumption and achieve de-carbonization goals.
- Harmonize technical and customer facing information for product/system design optimization.