Centralized Energy Management: 2000+ Meters Connected, <1 Year Payback (Leading Pulp & Fibre Manufacturer)

@Faclon Team

October 18, 2025

2min read

About the Company

A flagship of one of the region's largest conglomerates with $23.3 billion market cap as of 2024, spanning sectors like cement, chemicals, pulp and fibre, and financial services. As a top global producer of viscose staple fiber and key player in chlor-alkali, linen yarn, and fabrics, the company operates 20+ manufacturing units across 14 manufacturing plants with 10,000 employees across all business verticals. The organization maintains commitment to sustainable growth and innovation, operating across 4 key manufacturing plant locations with significant energy consumption of 800 GWh per year to cater to growing manufacturing capacities.

Problem Statement

Critical surveillance and safety challenges in hazardous chemical operations

Insufficient legacy system support Previous energy management systems lacking effective support mechanisms and customization capabilities leading to user frustration and operational inefficiencies
Data integration limitations Significant issues with data disconnect and lack of advanced analytical tools impeding comprehensive specific energy analysis across manufacturing operations
Manual reporting burdens Extensive manual effort requirements for report generation and data collection increasing operational workload and reducing productivity across plant locations
Limited system coverage Previous deployments providing only partial visibility without downstream equipment monitoring affecting overall energy management effectiveness
Multi-site coordination challenges Need for standardized rollout across multiple manufacturing locations requiring centralized platform deployment and user onboarding
Scalability constraints Requirements for rapid scalability and smooth adoption across sites while managing hundreds of thousands of electrical feeders consuming substantial billed electrical energy

How Faclon Solved the Problem

I/O Vision: No-code computer vision platform for comprehensive AI-enabled surveillance:

Centralized Energy Management

Deployed Faclon's I/O Sense platform on Azure creating unified energy management system for all pulp and fibre manufacturing plants

Standardized Multi-Site Rollout

Implemented systematic deployment beginning with 2 major plants followed by additional manufacturing units ensuring consistent platform adoption

Advanced Data Integration

Established SAP HANA connectivity linking existing SAP systems for production tracking and energy analysis with comprehensive data acquisition architecture

AI-Powered Analytics

Integrated artificial intelligence capabilities including deviation analysis with ML-based benchmarking and SEC prediction for varying SKUs and machine performance assessment

Comprehensive Platform Features

Deployed I/O Matrix for swift electrical data correlation, dynamic SLDs for network visualization, actionable alerts for real-time anomaly detection

User Management System

Onboarded 200+ users across sites providing training and role-based access management with feature allocation ensuring effective platform utilization

The Outcome

Transformational results across key performance indicators:

2000+ energy meters connected

Complete integration of energy monitoring infrastructure across all manufacturing facilities enabling comprehensive data collection and analysis

5 plant locations digitized

Full deployment across multiple manufacturing sites ensuring standardized energy management approach and centralized oversight capabilities

$1.4 million savings opportunities identified

AI-driven analysis revealing substantial cost optimization opportunities through comprehensive energy consumption analysis

Less than 1 year payback achievement

Rapid return on investment through energy efficiency improvements and operational optimization delivering immediate value addition

Multi-level analysis capability

Implementation of 4-level energy mapping from main incomers to plant sections, processes, and machine feeders enabling granular consumption analysis

AI-powered optimization success

Deployment of machine learning benchmarking models analyzing historical data to predict expected consumption and identify deviation opportunities

Behind the Scenes

Technical implementation and strategic considerations for this deployment

Azure cloud infrastructure

Private cloud deployment ensuring scalability, security, and seamless access across all locations with automatic updates and centralized control

Multi-system integration architecture

Comprehensive data acquisition from 450-900 meters per plant using existing Modbus infrastructure, Historian systems, and Schneider converters with MQTT connectivity

I/O Matrix implementation

Advanced mapping tool enabling energy meter correlation from main incomers through 4 levels (plant sections, processes, machine feeders) with custom formula capabilities

AI analytics deployment

Machine learning algorithms analyzing historical performance data identifying most energy-efficient operational periods and predicting consumption based on production variables

Comprehensive reporting framework

Over 30+ automated reports generation capabilities with scheduling functionality, Sankey energy balance diagrams, and customizable stakeholder distribution

Alert and visualization system

Smart SMS/email notification system for 12 anomaly types including abnormal voltage, power factor issues, harmonics, and transformer overloading with dynamic SLD creation capabilities

TESTIMONIAL

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