• Inconsistent and inefficient manual maintenance notifications due to reliance on experience and manual data assessment.
• Time-consuming work order estimations involving manual budget calculations, timelines, and resource allocation.
• Repeated iterations and validations for work order estimates causing workflow delays for planners.
• Cumbersome SAP data extraction and analysis requiring multiple manual iterations and verifications.

• AWS tools enable seamless data ingestion, storage, and knowledge graph development.
• Generative AI automates workflows like notifications, estimations, and task planning.
• AI-powered analytics extract insights from SAP data and historical records.
• LLMs provide conversational AI for reporting and recommendations.
  
  

• Automation reduced manual input by 50% and work order generation time by 40%
• Improved accuracy minimized repetitive validations and streamlined SAP data extraction, cutting retrieval time by 60%
• AI insights and knowledge graphs enabled faster, data-driven decisions with greater confidence
• AWS architecture ensured scalable, secure handling of sensitive operational data

• Data scattered across multiple systems like PLCs and SCADA prevented a unified view of operations.
• Lack of real-time monitoring and adjustments led to variations in product quality.
• High energy consumption due to inefficient processes and resource utilization.
• Reactive maintenance practices resulted in frequent equipment failures and production delays.
  
  

• IoT Integration: Deployed IoT sensors and AWS services for real-time data and insights.
• Process Optimization: Improved workflows, quality, and energy efficiency using ML and monitoring.
• Scalability: AWS cloud enabled seamless growth and remote collaboration.
  
  

• Reduced batch cycle times by 20% and improved equipment utilization by 25%.
• Achieved a 30% reduction in unplanned downtime through predictive maintenance.
• Improved product quality consistency by 15% with real-time adjustments.
• Optimized energy usage, reducing energy costs and waste by 12%.

• • Understanding the scope (requirement): Current V/S Expected performance (IT-OT)
  • Whether or not the current infrastructure is I-4.0 ready
  • How the current data architecture looks like; Sensors, DCS/SCADA, etc.
  • Additional sensor Implementation for I 4.0 use cases
  • Whether the entity of Data Infra is OPC compliant/evaluating the communication protocols supported
  • Identification of IT-OT Data requirement Tech Stack
  • Designing ISA 95 compliant IT-OT architecture

  Domains: MES, Historians, Industry Data Ops, IT-OT integration, Cloud infrastructure, Unified namespace architecture (UNS), ISA 95, Asset hierarchy

Benchmarking few key performance metrics and Zone to Win- To define Vision, Mission, Goals, Digital Culture, Talent & Skills, Technology Leadership, etc.

• Identifying the pain areas in Process manufacturing
• Blueprinting Dx - Defining the Process Problem Statements – production/ process optimization, Maintenance, Quality, Safety, Sustainability
• Creation of business Case for operational benefits

Operations Areas: Manufacturing planning & schedule, Quality, Overall yield, Zero defects, Energy optimization, OEE, etc.

Develop Priority Matrix depending on the Digital Maturity Assessment, Availability of Assets, Process, People

• Prioritizing the Business problems based on the savings, requirements and challenges
• Identification of Dx Tech Stack(s) for implementation
• Implementation plan and risk mitigation strategy
• Change management
• Industry 4.0 training needs identification
• Cultural transformation roadmap

For e.g. Prioritisation matrix, Dx roadmap, Cultural transformation, Training

PMC for Implementation of Solution(s) – Continuous efforts for e.g., IT-OT integration, Sensor Placements, APC, APM, OTS, Digital Workforce, Others

• Develop Scalability Framework
• Implementation roll-out plan across different locations
• Continuous Improvement - Gap Analysis and Scope of Improvement – KPIs, New / Advanced Use Cases
• Evaluation of Usage of the solution implements
• Post implementation benefit analysis/ ROI calculations
• Identification of gaps in solutions that are implemented and rectification plan
• Identification of Training needs
• New Use Cases definition, New modeling framework, Advanced Analytics, etc.

PMC: Ensuring successful Dx implementation and post-implementation ROI’s

• Fouling causes inefficiencies and unexpected shutdowns, leading to production losses
• Reactive repairs and routine checks drive up maintenance expenses
• Time-consuming data collection and analysis delay decision-making
• Limited visibility into equipment conditions hinders maintenance prioritization
• Lack of intuitive tools for data queries, reports, and workflow recommendations hampers efficiency
•  

• AWS IoT services enable real-time data collection, monitoring, and contextualization, with Amazon S3 providing scalable and secure data storage.
• AI/ML models in Amazon SageMaker predict fouling.
• Amazon Neptune offers knowledge graphs and digital twins for contextual insights.
• Generative AI with Amazon Bedrock or custom LLMs supports automated reporting.
• AWS QuickSight delivers intuitive data visualization.
  
  

• AI/ML-driven fouling prediction and automated maintenance schedules reduced disruptions, cut costs, and extended equipment lifespan.
• Real-time insights, contextual analysis, and Generative AI streamlined decision-making, reporting, and workflows.
• AWS services ensured a scalable, secure platform for managing large volumes of industrial data efficiently.