Industrial
High Performance, AI-powered Smart Manufacturing That Ushers In Your Next Era Of Growth
Harness AI for Industrial Manufacturing
The use of analytics across various stages of the production process is fueling the growth for manufacturing analytics. By improving decision-making capabilities, providing vital information, reducing operational costs, and simplifying the overall supply-chain logistics, manufacturing analytics empowers producers to address challenges across the production value chain.
The advent of the industrial internet of things (IIoT) will further aid the adoption of advanced data management techniques and increase the demand for process optimization. To gain a competitive edge, businesses must look past the hype of new technology and put in place future-ready strategies that can weather changes on the fly.
Challenges Affecting Manufacturing Efficiency
Efficient manufacturing processes are essential for the success of any business, but there are a range of challenges that can hamper this critical objective. Understanding and overcoming these obstacles is vital for organizations seeking to optimize their operations and achieve their business goals.
Inadequate Insights
Comprehensive data insights are critical for manufacturers to optimize operations and identify areas for growth. Without such insights, manufacturers may struggle to make informed decisions, leading to inefficiencies and missed opportunities for improvement.
Siloed Supply Chains
Disconnected supply chains can lead to communication breakdowns, delays, and increased costs for manufacturers. Coordination and collaboration among departments and supply chain partners are necessary to ensure efficient operations and timely delivery of products.
Unplanned Breakdowns
Unscheduled equipment downtime can cause delays, increased costs, and lost productivity for manufacturers. This can have a ripple effect on the rest of the manufacturing process, leading to further inefficiencies and impacting overall operations
Low Quality Control
Inadequate quality control measures can result in product defects, recalls, and reputational damage for manufacturers. Poor product quality can lead to lost sales, increased costs, and decreased customer loyalty, impacting the bottom line of the manufacturing organization
Get The Winning Edge With Valiance
Integrated digital solutions company with expertise in IIoT, cloud, computer vision, and AI/ML use cases
Offer predictive maintenance, asset monitoring, and energy efficiency to leaders in various industry verticals
Adept at implementing industry best practices and developing solutions to drive digital transformation
Powerful alliances with hyperscalers and software leaders who build effective, futuristic solutions
Trigger Growth With An End-to-End Solution
Mineral Reserves Estimation
Improve the quantity and quality of mineral exploration, identify areas with high mineralization potential and save exploration costs and time with AI algorithms, augmented vision technology and deep neural networks. We also use ethical mining to reduce the environmental and material imprint of the entire process chain.
Process Automation
Automate time-consuming manual processes like data extraction, uploading, and validation to increase efficiency , and optimize metals balancing by integrating production and lab information systems with accounting systems. Leverage content intensive activities by extracting information from equipment maintenance manuals, safety protocols, regulations, and vendor data.
Predictive Maintenance
Analyze high-resolution sensor and production data to discover anomalies and failure patterns, reduce downtime and improve safety. Predict equipment failure and alert maintenance beforehand. We also offer insight on variables that prolong the equipment's life and generate maintenance tasks and timetables.
Operational Efficiency
High CAPEX, mounting production costs and volatile commodity prices make it extremely challenging for the mining industry to maintain cost efficiency and productivity. Predicting failures before they lead to downtime or asset degradation is critical to increasing asset lifecycle and safety. AI enables mining companies to improve efficiency, enhance safety and drive positive ROI by leveraging data-driven insights.
Defect Detection & Asset Maintenance
Energy and utility businesses need to find faulty pipes/wiring/machines to ensure safety of employees and the environment. Breakages corrode investments and brand value. Defects discovered at the end of the energy production process can cause significant losses for energy firms. Prioritizing defect detection through AI image recognition, pattern recognition, and computer vision systems minimizes significant losses, extends asset lifecycle and, protects employees’ health and safeguards the environment .
Load Forecasting
By accurately calculating energy usage, it is possible to reduce operational costs, energy disruptions, and financial losses. AI-based load forecasting systems rely on essential variables such as consumption data, weather data, and seasonality to estimate how much energy is required to meet consumer demand.
Outage Detection and Prediction
Restoring distribution system outages can be a significant operational expense for utilities Typically, there is a very limited window of time to restore electricity before consumers and regulators demand answers. Valiance combines meteorological data and forecasting knowledge with client data sets to create ML prediction models that help utilities anticipate weather events, improve restoration times, and increase customer and regulatory satisfaction.
Operational Efficiency
Traditional energy supply chains were centralized around the power plant, whereas decentralized microgrids integrate renewable and nonrenewable sources to best serve end-users. AI can boost operational efficiency by anticipating when solar and wind energy will be most efficient. It can predict high energy demand and generate more money by selling stockpiled energy during low output.
Operational Excellence
The chemicals market is very competitive, volatile and fast-paced. Companies must optimize their equipment, manufacturing, and operations to retain a solid market position. Valiance achieves operational excellence through enhancing production, energy savings, product quality, yield, and customer satisfaction. Using digital twins, plant managers can assess how process control improvements affect processes and performance, and keeps the plant safe, efficient, and competitive without posing any risks to the current operations.
Smart Predictive Forecasting
Advanced analytics and ML/AI-enabled models help forecast how much raw material is required for chemical production. It also helps streamline supply chains, prevents overstocking, and enables alterations at every step of molecule development. It can also estimate material pricing so companies can plan ahead to reduce loss and speed up just-in-time production.
Defect and Anomaly Detection
Most modern companies utilize IoT to collect real-time equipment data and automate response time. Visual inspection identifies machine defects and anomalies. It also evaluates chemical products based on color, texture, stratification, and solidity. By eliminating faulty items from the assembly line before further production, organizations may minimize operating costs and item returns.
Health and Safety Compliance
Chemical manufacturing is one of the most tightly regulated industries, with national and international norms covering manufacture, handling, distribution, and HSE (health, safety, and environment). AI promotes safety by combining IIoT, real-time data, and advanced analytics. Sensors and machine-augmented data digitize operations and supply networks. When systems are well-integrated, AI can detect leaks and other environmental concerns.
Our Success Stories
Reducing Furnace Oil Consumption
A leading aluminum manufacturer focused on optimizing production processes to reduce fuel consumption and operational costs, particularly in their Twin Chamber Furnace (TCF) and holding furnaces.
Key Challenge
Reduce overall LSHS consumption from 90 liters per ton to 85 liters per ton, with a focus on reducing TCF consumption to below 65 liters per ton.
Solution
- Process Understanding: Collaborated with floor staff to understand and optimize furnace operations.
- Data Source Identification: Integrated and processed vendor data to calculate furnace efficiency.
- Exploratory Data Analysis: Identified optimal metal temperatures to enhance efficiency
Outcome
- Oil Savings: Achieved a reduction of 5 liters per ton, savings of nearly $100k per annum.
- GHG Emission Reduction: Reduced emissions by 5%, contributing to environmental sustainability.
Prediction of Mushroom Events in Smelting Process
A prominent player in the aluminum smelting industry, focused on optimizing production processes and enhancing operational efficiency.
Key Challenge
Predict the formation of carbon lumps, known as mushrooms or anode spikes, which increase cell voltage by approximately 200 mV and decrease operating temperature, causing inefficiencies.
Solution
- Data Collection & Analysis: Collected data from smelting pots and mushroom events, created detailed reports on pot performance.
- Predictive Modeling: Used anomaly detection to predict mushroom events, identifying the top 10 pots at high risk.
- Real-Time Dashboard: Developed a dashboard to display anomaly scores and top risk pots, with predictions recorded for verification.
Outcome
- Improved Operational Efficiency: Enabled timely adjustments, preventing operational disruptions and maintaining optimal thermal and electrical balance.
- Enhanced Current Efficiency: Leading to saving nearly $1.5 million per year.
- Cost Savings & Recycling: Avoided anode waste, reducing carbon needed for manufacturing.
Fibre Quality Parameters Prediction
A leading global producer of viscose staple fibers (VSF) and viscose filament yarn (VFY), holding significant market shares in both global and domestic markets.
Key Challenge
Ensuring consistent fiber quality across 8 quality parameters, with a key focus on critical parameters such as Oil pick-up, Whiteness, Splinter and Moisture. They needed a real-time monitoring system to predict quality parameters every 10 minutes to give recommendations to achieve the target quality parameters.
Solution
- Implemented a real-time data integration and analytics pipeline to predict quality parameters every 10 minutes.
- Developed soft-sensor health index monitoring to identify data or process drift.
- Real-time quality parameter predictions for optimal operational adjustments.
- Real-time recommendation for target quality parameters
Outcome
- Reduced prediction intervals from 4 hours to every 10 minutes.
- Achieved > 95% accuracy in prediction models.
- Enhanced quality management by providing timely insights and maintaining desired quality ranges.
