MANUFACTURING
It’s time to reinvent your manufacturing operations with end to end data solutions
Pain points in the Manufacturing industry
Unplanned downtime impacting production
How Acuvate’s Digital Services Framework Helps
Improve asset availability through forecasting early fault detection in critical components and conducting proactive replacements.
1. Devices
What Happens: Timeseries data from all critical devices, such as Engines, Turbines, and Compressors as well as maintenance information from ERP will be collected in this business process.
Impact:This step ensures that we collect the necessary data to build the solution.
2. Connectivity
What Happens: The data collected by these devices is transmitted in real-time using the most suitable connectivity technology, be it 4G, 5G, WIFI, LPWAN, or others, depending on the infrastructure, size and the critical nature of the data.
Impact: Ensures seamless and immediate data flow, also facilitating quick response to emerging issues before they escalate into downtime.
3. Edge Compute
What Happens: Data processing occurs close to its source (on-site or near-site), allowing for real-time analysis of health and performance. Together with 5G as transport, this minimizes latency in detecting potential faults.
Impact: Enables immediate identification and analysis of issues, reducing the time to action for maintenance teams.
4.Cloud Compute & Storage
What Happens: Aggregated and processed data from various sources is transferred to a cloud-based platform where it's stored and further analyzed. This centralized data management enhances the depth of analysis. All types of data are stored on a consolidated Data Platform for further analysis. For ownership reasons, it is critical that all data is stored on this Data Platform.
Impact: Facilitates comprehensive analytics for predictive maintenance, identifying trends that indicate potential failures.
5. Applications & Services
What Happens: Advanced AI-ML models analyze the cloud-stored data to predict early equipment failure and recommend maintenance activities. These applications turn data from various sources into actionable insights.
Impact: Transforms preventive maintenance into predictive maintenance, scheduling repairs before equipment fails.
6. Inform Decision Makers
What Happens: Executives and plant managers receive comprehensive insights into the overall health of manufacturing equipment, including performance metrics, risk assessments, and the impact of maintenance strategies on production efficiency.
Impact: Enables strategic oversight of maintenance operations, ensuring that decision-makers are well-informed about the status of equipment health and maintenance activities, thus supporting the goal of minimizing unplanned downtime and maximizing production uptime.
7. Support Decision Making
What Happens: The system generates detailed reports and alerts based on AI-ML analysis as input for Digital Twin application, suggesting specific components for inspection or replacement. These recommendations are prioritized based on the severity and impact of potential failures. Gen-AI and Extended Reality applications are also built to create simple and realistic workflows for decision-making.
Impact: Maintenance teams can make informed decisions quickly, focusing on the most critical maintenance tasks first.
Quality control on manufacturing lines need improvement
How Acuvate’s Digital Services Framework Helps
Automated quality control adopts machine learning with sensors to collect data and provide automated handling during abnormal production.
1. Devices
What Happens: Sensors and cameras are installed along the manufacturing lines, often using special frames, for real-time monitoring of product quality and to detect deviations from standard specifications.
Impact: Provides a basis for immediate detection of quality issues, enabling the collection of detailed data on every aspect of the production process related to quality.
2. Connectivity
What Happens: Utilizing high-speed and low latency connectivity technologies, such as 5G, the system ensures the instantaneous transmission of quality data from sensors and cameras to edge-based processing units for AI-MV analysis.
Impact: Facilitates the real-time flow of quality-related data, ensuring no delay in identifying and addressing quality issues.
3. Edge Compute
What Happens: Initial AI-MV processing of the quality data is performed at or near the data collection point, allowing for quick identification of anomalies or deviations from quality standards. This enables immediate action to remove this component from the production line.
Impact: Reduces response time to quality issues, enabling immediate corrective actions to be taken on the production line, thereby minimizing defective production runs.
4.Cloud Compute & Storage
What Happens: Data on production quality is further stored and analyzed in the cloud or on-prem enterprise data platform using advanced AI and machine learning algorithms, which assess patterns and trends in quality issues over time. The data platform is critical requirement to provide AI systems holistic enterprise data as well as for clarity regarding data ownership.
Impact: Enhances the overall quality control strategy by identifying root causes of quality issues and facilitating the development of long-term improvements in production processes.
5. Applications & Services
What Happens: AI-MV models process the aggregated data to automatically adjust production parameters or alert operators to take specific actions when quality deviations are detected. For real time processing, the step is executed on the edge on the data platform. Other non-time-critical elements are executed against the data on the enterprise platform.
Impact: Automates the quality control process, making it more efficient and reliable, and significantly reducing the rate of production errors and defects.
6. Inform Decision Makers
What Happens: Based on the tooling, high-level reports and dashboards are generated, offering executives and managers a comprehensive overview of quality performance, trends in production quality, and the impact of automated quality control measures.
Impact:Ensures that strategic decisions regarding quality control measures, investments in technology, and process optimizations are informed by comprehensive data and analytics, driving continuous improvement in product quality.
7. Support Decision Making
What Happens: The system provides detailed analytics and recommendations to production managers and quality control teams, highlighting areas for improvement and suggesting adjustments to production processes to enhance quality. These insights come from custom-built Gen AI, Digital Twin, XR (Extended Reality), PowerBI, or even email solutions integrated with the customer's business applications.
Impact:Empowers decision-makers with actionable insights to refine production methods, implement quality improvements, and optimize product standards.
Get in touch with an Acuvate Advisor today!
Talk to Advisors
Higher operational costs due to lower levels of automation
How Acuvate’s Digital Services Framework Helps
Maximize usage of robotics in various parts within manufacturing lines, data collection for remote monitoring, maintenance, etc.
1. Devices
What Happens: Robotics and IoT (sensor) devices are integrated across manufacturing lines for tasks like assembly, packaging, and quality control. Robotics is used to replace operator rounds, collecting relevant data and images from critical points in the plant. Data can also be collected from ERP and other software applications.
Impact: Enhances efficiency and precision while reducing dependency on manual labor, leading to cost savings.
2. Connectivity
What Happens: High-speed connectivity options (4G, 5G, Wi-Fi) are utilized to facilitate seamless communication between robots, sensors and AI-MV/MV applications.
Impact: Enables real-time data exchange and coordination, reducing operational delays and improving response times.
3. Edge Compute
What Happens: Edge is used for applications and workflows that need real time action and decisions. In this step, all data is consolidated on a single data platform. Edge computing capabilities are implemented to allow for immediate data processing close to data sources, optimizing robotic actions.
Impact: Minimizes latency, ensuring robots operate efficiently and respond quickly to operational needs.
4.Cloud Compute & Storage
What Happens: Operational data from robots, sensors and IoT devices is collected in the consolidated on prem or cloud-based Enterprise Data Platform. AI-ML/MV algorithms analyze this data in the cloud, offering insights into performance and optimization opportunities.
Impact: Informs strategic decisions by providing a comprehensive view of manufacturing efficiency and maintenance forecasting.
5. Applications & Services
What Happens: AI-ML/MV applications process the data to enable predictive maintenance, operational optimization, and quality control adjustments. Gen-AI solutions provide a single interface for queries as per user requirements. Results also to be included as part of Digital Twin when used.
Impact:Improves robotic automation capabilities, allowing for self-optimization and enhanced performance over time.
6. Inform Decision Makers
What Happens: Executives and plant managers are provided with detailed reports and analytics on the impact of robotic automation on operational costs and efficiency.
Impact: Supports strategic planning and resource allocation, ensuring investments in automation deliver maximum return on investment.
7. Support Decision Making
What Happens: Using Gen-AI results, managers receive data-driven insights and recommendations for process improvements, equipment maintenance, and investment priorities. Engineering and maintenance staff can be provided Digital Twin applications for this step.
Impact: Facilitates informed decision-making, focusing on areas with the highest potential for cost reduction and operational improvement.
Inability to move towards autonomous plants
How Acuvate’s Digital Services Framework Helps
Decisions will be increasingly made directly on the basis of the AI outcome, starting with low-risk decisions and evolving over time to high-risk decisions.
1. Devices
What Happens: Advanced sensors and IoT devices are deployed across the plant to collect real-time data on operations, environment, and machinery status. Other data sources include ERP, weather forecasting among other applications.
Impact: Establishes a data-rich foundation for AI to analyze, driving the initial steps towards plant automation.
2. Connectivity
What Happens: Utilizes high-speed connectivity and low latency (5G) to ensure seamless data transmission from devices to AI analysis edge or enterprise data platforms.
Impact: Guarantees instant data flow, critical for real-time AI-based decision-making and autonomous operations.
3. Edge Compute
What Happens: Where timing is critical, this step implements edge computing for immediate data processing at the source, enabling quick, localized decision-making by AI systems.
Impact: Minimizes latency, essential for real-time automation and for AI to make immediate, informed decisions.
4.Cloud Compute & Storage
What Happens: Aggregates and analyzes extensive operational data in the cloud, leveraging AI to derive insights and make predictions. All data is ingested on to a cloud based or on-prem Enterprise Data Platform, depending on customer needs.
Impact: Provides a scalable environment for comprehensive AI analysis, supporting complex, high-risk decision-making processes.
5. Applications & Services
What Happens: AI-ML-MV applications process operational data, making autonomous decisions for low-risk operations and gradually taking on higher-risk decisions.
Impact: Facilitates a controlled transition to autonomy, with AI reliably managing more critical plant operations over time.
6. Inform Decision Makers
What Happens: These are autonomous decisions providing direct setpoints back to the sources of the data and operators. Detailed reports and analytics on AI performance, decision outcomes, and areas for improvement are generated for operations review.
Impact:Enables strategic oversight of the transition to autonomy, guiding further investment in AI and adjustments to operational protocols.
7. Support Decision Making
What Happens: AI-ML-MV generated recommendations and decisions are immediately provided to operational teams for review and action
Impact: Builds trust in AI decisions among staff, ensuring smooth integration of AI into operational processes and decision-making.
Contact an Acuvate Advisor today!
Talk to Advisors
Siloed data leads to ongoing operational inefficiencies
How Acuvate’s Digital Services Framework Helps
Relevant, real-time data sources are aligned in enterprise data storage to enable accurate and timely AI based decision-making.
1. Devices
What Happens: Diverse data-gathering devices, including IoT sensors and smart machinery ERP, weather forecasting etc, are deployed across various operational areas to collect comprehensive, real-time data.
Impact:Breaks down data silos by aggregating data from across the enterprise, ensuring a holistic view of operations.
2. Connectivity
What Happens: High-speed connectivity technologies (such as 5G and Wi-Fi) are used to transmit data from these devices to centralized data storage systems without delay.
Impact: Facilitates the seamless flow of data, ensuring that information from disparate sources is promptly available for analysis.
3. Edge Compute
What Happens: Initial processing and analysis of data occur close to the source, filtering and prioritizing data for more efficient transmission and storage. However, edge is only used for time-crtical use cases and this pain point may not need edge.
Impact: Reduces bandwidth requirements and improves the efficiency of data storage by transmitting only the most relevant data to the cloud.
4.Cloud Compute & Storage
What Happens: Data is stored in a centralized cloud-based or on prem enterprise data platform, where it is further processed and analyzed by advanced AI algorithms. The data consolidation ensures maximum value from AI interventions.
Impact: Eliminates data silos by integrating data from across the enterprise, enabling comprehensive analytics and insights.
5. Applications & Services
What Happens: AI-ML-MV) applications analyze the integrated data to identify trends, inefficiencies, and opportunities for optimization. Gen AI creates simple query-based interface for business users and maximizes the impact of data on the enterprise data platform.
Impact: Transforms data into actionable intelligence, supporting informed decision-making and operational improvements.
6. Inform Decision Makers
What Happens: Executives receive detailed reports and dashboards that present a unified view of operations, performance metrics, and AI insights.
Impact: Provides leadership with the information needed to make strategic decisions, fostering a culture of continuous improvement and efficiency.
7. Support Decision Making
What Happens: AI-generated insights and recommendations are delivered to operational teams and decision-makers using Gen AI or Digital Twin applications, highlighting areas for improvement and suggesting specific actions.
Impact: Enables data-driven decision-making, directly addressing operational inefficiencies and guiding strategic changes.
Lowering number of HSSE incidents
How Acuvate’s Digital Services Framework Helps
With 5G-enabled sensors monitoring behavior around the plant to track, manage and predict potential occupational hazards in real-time.
1. Devices
What Happens: Automated camera sensors and other safety-specific IoT devices are deployed throughout the facility to continuously monitor for potential hazards, compliance breaches, and unsafe conditions.
Impact: Provides a comprehensive, real-time monitoring system that enhances the ability to detect and respond to potential HSSE incidents before they occur.
2. Connectivity
What Happens: These devices use high-speed connectivity (4G, 5G, etc.) to transmit real-time data and imagery to processing centers for immediate analysis, ensuring no delay in hazard detection.
Impact: Facilitates the swift transfer of critical safety data, enabling real-time responses to potential safety issues and enhancing overall safety protocols.
3. Edge Compute
What Happens: Where time critical we use edge-based data processing at or near the collection point to quickly identify potential hazards, compliance issues, or environmental risks, minimizing the time to action.
Impact: Reduces latency in hazard recognition and compliance monitoring, allowing for immediate corrective measures to prevent incidents.
4.Cloud Compute & Storage
What Happens: Data and imagery are also sent to the on-prem or cloud-based Data Platform for more complex analysis, long-term tracking, and trend analysis, contributing to predictive safety and environmental management strategies.
Impact: Enhances HSSE incident prevention through deep learning and AI analysis, identifying patterns that may predict future incidents and guiding preventive measures.
5. Applications & Services
What Happens: Specialized AI-ML, and computer vision (MV) algorithms analyze the data to detect anomalies, predict potential incidents, and recommend preventative actions such as alerting staff in real time not to go into certain areas. These applications can also ensure that operations are compliant with relevant HSSE regulations. This can be further scaled for various safety scenarios.
Impact: Transforms surveillance data into actionable insights for real-time hazard management and compliance assurance, significantly reducing potential HSSE incidents.
6. Inform Decision Makers
What Happens: Decision-makers receive concise, actionable intelligence on HSSE status, including real-time alerts, compliance reports, and predictive analytics on potential hazards, directly influencing strategic planning and resource allocation.
Impact: Ensures that leadership is fully informed about the HSSE landscape, facilitating proactive measures to enhance safety, security, and environmental stewardship across the plant.
7. Support Decision Making
What Happens: The system generates automated alerts and detailed reports on identified risks, compliance issues, and recommended preventive actions, supporting operational and HSSE teams in making informed decisions. Potential tooling includes Gen-AI for reporting and analysis purposes, Digital Twins for real-time visibility and action on all issues.
Impact: Empowers teams with data-driven insights to swiftly address potential safety hazards, ensure compliance, and implement strategic changes to prevent future incidents.