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Causal AI for Manufacturing..
Path to industry 4.0
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
Work place 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 TRAIN. 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 raw material 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
Workers 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 management
A global Energy major implemented a Causal AI based Asset Performance Monitoring (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 remediation options.
This implementation reduces risks and preserves 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
Causal AI for Oil & Gas industry
Envisioning a green future
Causal AI can significantly advance the oil and gas industry’s efforts to enhance combustion efficiency and achieve decarbonization goals.
By precisely identifying the root causes of inefficiencies and emission sources, causal AI enables the optimization of combustion processes, ensuring that fuel is used more efficiently and emissions are minimized.
By leveraging these capabilities, the oil and gas industry can make substantial progress in improving operational performance, reducing environmental impact, and meeting stringent decarbonization targets.
Energy efficiency
De-carbonization
Advanced process
control strategy
Mass balancing
Prduction intelligence
Pipeline Leakage detection
Energy management & de-carbonization
A global oil & gas major implemented a Causal AI based energy optimization agent using Parabole’s TRAIN. Enhancing fired heater performance (energy efficiency, carbon emission and compliance) amidst varying fuel composition and changing process requirements was an unsolved challenge.
The AI agent utilizes causal knowledge to provide unit and equipment specific guidance for the operators and pinpoint the root causes of excessive energy consumption and carbon emissions from the heater.
The causal analysis identified a set of critical process variables from local, upstream and downstream equipment along with quantified setpoint range recommendations.
This implementation resulted in discovering sub-optimal air-fuel ratio and heater fouling as key factors driving poor combustion efficiency, leading to high fuel consumption and increased emissions (COX, SOX, and NOX).
Total economic benefit
2% – 5%
Improved combustion efficiency
5%
Reduction in carbon emission
15 seconds
Root cause analysis
Advanced process control
A Fortune 50 oil & gas major implemented a Causal AI based decision optimization engine to improve operational efficiency and environmental compliance using Parabole’s TRAIN.
The traditional siloed approach to optimizing operational processes (planning, scheduling, and APC) leads to suboptimal operational efficiencies. The isolated loops don’t take the real-word iteration of other process variables into consideration. The absence of plant condition knowledge in the planning and scheduling process, combined with the APC layer’s lack of visibility into supply constraints, results in unmet demand, revenue loss, and damage to brand reputation.
The AI agent integrates planning, scheduling and APC layer attributes to improve operational efficiency and environmental compliance. This implementation has reduced unscheduled asset downtime, significantly minimized productivity losses, revenue declines, and asset life-cycle costs.
Total Economic benefit
3%
Increase in yield
12%
Reduction in un-planned shut downs
6%
Reduction in product quality variations
Golden batch quality optimization
A Fortune 500 Biologics major implemented a Causal AI based root cause analysis system to optimize batch variations in bio-reactor’s yield, quality, and to minimize batch rejections using TRAIN.
The company faced significant challenges in maintaining consistent batch quality due to variations in batch cycle time and quality. The traditional siloed approach to optimizing production variables and replicating Golden Batch profile was sub-optimal.
The AI agent utilizes causal knowledge to continuously monitor batches against the “golden batch” profile. It provides guidance on input feed rate and specific process adjustments referencing the “Golden Batch”.
This implementation resulted in minimizing variations in batch cycle time and replicating the golden batch to enhance product quality. The easy to simulate process twin helps in understanding the complex interactions within production processes allowing for real-time simulation and analysis.
Total Economic benefit
8% – 20%
Reduction in annual raw material procurement cost
12%
Increase in recycled material usage
Faster
Selection of new suppliers
Pipeline leakage detection and remediation
Energy companies transport crude oil through hundreds of miles of pipeline network. They strategically place pumping stations in the pipeline network to sustain pressure and ensure a continuous oil flow. These pipelines traverse through potentially hostile areas susceptible to sabotage and are exposed to extreme weather conditions. As a result, energy companies often encounter pipeline leakages caused by natural and human factors.
Any such leakage, if not remediated promptly, results in significant operational losses and creates serious safety and environmental issues.
TRAIN’s leak detection agent autonomously formulates hypotheses, identifies flow anomalies, and localizes leak location in minutes.
Total Economic benefit
15 seconds
Root cause analysis
1.3%-2%
Maximize throughput
Simulations
Reduced Cognitive burden
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.