Customer
A leading industrial safety equipment manufacturer seeking to modernize and standardize its finished goods supply chain across multiple manufacturing units.
Technology
Oracle Fusion, Supply Chain Digitization, Barcode & QR Code Traceability, Warehouse Management, Business Process Mapping
Business Objective
The client sought to digitize finished goods supply chain operations and establish a standardized, traceable process across manufacturing units.
Key objectives included:
- Digitize finished goods supply chain operations.
- Eliminate manual planning processes.
- Standardize packaging workflows across manufacturing units.
- Improve inventory visibility.
- Enable end-to-end batch traceability.
- Streamline warehouse and dispatch operations.
- Establish a scalable foundation for digital supply chain transformation.
Scope of services
BXI Technology conducted detailed process discovery and supply chain transformation activities across two manufacturing units, including:
- Detailed process discovery across manufacturing units.
- End-to-end workflow mapping from sales order to dispatch.
- Business requirement documentation.
- Current-state gap analysis.
- Future-state supply chain process design.
- Oracle Fusion integration strategy.
- Warehouse, packaging, planning, and dispatch process definition.
- Barcode and QR-based traceability framework.
- Business process mapping and standardization.
Benefits
- Standardized supply chain processes across manufacturing units.
- Reduced manual intervention in planning and operational workflows.
- Improved inventory and packaging visibility.
- End-to-end batch and pallet traceability.
- Enhanced warehouse and dispatch efficiency.
- Improved process governance and operational consistency.
- Established a scalable roadmap for digital supply chain transformation.
Impact
BXI Technology designed a future-ready digital supply chain framework integrated with Oracle Fusion, transforming fragmented manual operations into standardized, traceable, and digitally enabled processes. The solution strengthened operational visibility and process governance while establishing a scalable foundation for intelligent warehouse, packaging, and dispatch management.
Customer
A leading global manufacturer of industrial safety products.
Technology
Digital Twin, Agentic AI, Zoho IoT, PLC/CNC Integration, SCADA, Edge Gateways, Industrial Sensors, Data Historian, Analytics Dashboards
Business Objective
The client sought to create a scalable digital manufacturing environment by improving shop-floor visibility, machine utilization monitoring, production tracking, and operational intelligence.
Key objectives included:
- Improve real-time shop-floor visibility.
- Accurately monitor machine utilization and production counts.
- Automate downtime classification.
- Strengthen batch-to-furnace traceability.
- Enhance Overall Equipment Effectiveness (OEE).
- Establish an Industry 5.0-ready digital manufacturing environment.
Scope of services
BXI Technology designed and implemented a Digital Twin and Agentic AI architecture integrated with Zoho IoT across diverse manufacturing equipment and processes.
The engagement included:
- PLC and CNC machine integration.
- Integration with furnaces, laser cutting, grinding, drilling, stamping, welding, and injection moulding equipment.
- Sensor retrofits and edge gateway implementation.
- Industrial protocol integration and real-time data acquisition.
- KPI dashboards and operational analytics.
- Predictive analytics and AI-driven monitoring.
- Batch traceability and batch-to-furnace tracking.
- Standardized IoT architecture across diverse machines.
- Implementation without disrupting existing machine operations.
Benefits
- Real-time visibility into machine status, production counts, downtime, rejects, and cycle times.
- Improved batch-to-furnace traceability.
- Enhanced OEE monitoring.
- Automated operational insights.
- Standardized IoT architecture across diverse manufacturing equipment.
- Scalable foundation for predictive manufacturing.
- Readiness for Industry 5.0 initiatives.
Impact
BXI Technology established a unified Digital Twin and Industrial IoT framework that transformed fragmented shop-floor data into actionable operational intelligence. The solution enabled centralized monitoring, improved production transparency, strengthened quality traceability, and created a scalable foundation for AI-driven manufacturing optimization across multiple production processes.
Customer
A leading global email infrastructure provider seeking to streamline Microsoft 365 provisioning, tenant onboarding, license administration, and customer domain management through a standardized and scalable service delivery model.
Technology
Microsoft 365 Business Basic, Microsoft 365 Administration, Tenant Management, License Management
Business Objective
The client sought to streamline Microsoft 365 license provisioning and lifecycle management while enabling faster tenant onboarding and customer activation.
Key objectives included:
- Accelerate Microsoft 365 tenant creation and onboarding.
- Simplify license provisioning, modification, and renewal management.
- Establish a centralized process for license administration.
- Standardize customer domain onboarding.
- Improve turnaround times through structured provisioning processes.
- Create a scalable operating model to support business growth.
Scope of services
BXI Technology delivered a standardized Microsoft 365 provisioning and license management framework covering:
- Procurement and provisioning of Microsoft 365 Business Basic licenses.
- Tenant creation and customer onboarding.
- Centralized ticket management for adding, deleting, and modifying licenses.
- Microsoft 365 license renewal management.
- Customer domain onboarding.
- Standardized provisioning workflows with defined service turnaround times.
Benefits
- Faster tenant provisioning and customer activation.
- Simplified Microsoft 365 license lifecycle management.
- Centralized license administration.
- Improved operational efficiency.
- Scalable customer onboarding processes.
- Faster service turnaround.
- Enhanced service responsiveness through structured ticket management.
Impact
BXI Technology established a standardized Microsoft 365 provisioning and license management framework that enabled the client to efficiently onboard customers, streamline license administration, and support business growth through a scalable and automated service delivery model.
Customer
A leading financial services organization specializing in investment banking, wealth management, and asset management, operating through multiple legal entities across India.
Technology
Microsoft 365, Microsoft Entra ID (Azure Active Directory), Microsoft Exchange Online, Microsoft Teams, SharePoint Online, OneDrive for Business, Microsoft Intune, Microsoft Security, PowerShell Automation
Business Objective
The client sought to simplify Microsoft 365 licensing and workplace operations across multiple legal entities with different renewal cycles and decentralized administration. The objectives were to:
- Consolidate Microsoft 365 licensing into a single renewal cycle.
- Simplify license administration across multiple business entities.
- Provide expert Level 2 and Level 3 support for workplace infrastructure.
- Strengthen identity, tenant, and cybersecurity management.
- Establish a scalable managed services model for long-term IT operations.
Scope of services
BXI Technology delivered an end-to-end Microsoft 365 managed services engagement covering:
- Enterprise Microsoft 365 license procurement and renewal management.
- Consolidation of multiple license renewal schedules into a unified annual renewal framework.
- Level 2 and Level 3 support for Microsoft 365 administration.
- Active Directory and Microsoft Entra ID administration.
- Tenant management and governance.
- Workplace, network, and cybersecurity support.
- Advanced troubleshooting, PowerShell automation, and identity management.
- SLA-driven managed support through a centralized service desk.
Benefits
- Centralized Microsoft 365 governance across all business entities.
- Simplified license lifecycle management and administration.
- Improved operational efficiency through standardized support processes.
- Faster issue resolution with dedicated L2 and L3 technical expertise.
- Enhanced identity management and security posture.
- Better visibility into Microsoft 365 operations and support performance.
- Predictable, scalable, and streamlined IT operations.
Impact
BXI Technology helped the client establish a centralized Microsoft 365 operating model by standardizing licensing, administration, and managed support across multiple business entities. The engagement simplified IT operations, strengthened governance and security, reduced administrative overhead, and created a scalable foundation to support the organization’s future growth and digital workplace initiatives.
Introduction
Banking institutions operate in high-availability environments where system downtime and delayed incident resolution directly impact customer experience and business continuity. High incident volumes during peak business hours, duplicate tickets, and manual intervention reduce operational efficiency and increase risk. This case study highlights how a banking institution improved resilience through automated healing, intelligent ticket analysis, and service recovery mechanisms. By enabling event correlation, automation, and proactive monitoring, the organization significantly enhanced system stability and operational efficiency.
Customer
A large-scale banking institution managing high-volume IT incidents across application and infrastructure environments with 24×7 support requirements.
Business Objective
- Improve IT resilience through automated healing and recovery
- Reduce high incident volumes during peak business hours
- Minimize SLA violations and improve response times
- Eliminate duplicate and redundant tickets
- Shift from reactive to proactive IT operations
Scope of Services
- Heat map–based incident analysis across time and business hours
- Identification of peak-hour incident patterns and workload spikes
- Ticket classification and automation probability analysis
- Detection of duplicate and parent-child ticket patterns
- Design and implementation of automated healing workflows
- Enablement of event correlation and alert suppression
- Establishment of 24×7 Integrated Command Centre
Key Insights from Analysis
- 17,600+ incidents analyzed
- 75% incidents occur during business hours (9 AM–6 PM)
- High-volume incident drivers:
- Password issues (22%)
- Account issues (19%)
- Connectivity issues (17%)
- Configuration issues (16%)
- Significant duplication and parent-child ticket patterns observed
Detailed Findings
- High dependency on manual ticket logging and resolution
- Lack of event correlation leading to duplicate tickets (~400–500 cases)
- Inefficient prioritization affecting response times
- Repetitive issues (password, access, configuration) ideal for automation
- High operational load during peak hours impacting service quality
Benefits
- Reduced duplicate and redundant ticket volumes
- Faster incident detection and response
- Improved SLA adherence and service reliability
- Better prioritization of critical incidents (P1/P2)
- Enhanced operational efficiency and workload management
Impact
- 30.7% automated resolution achieved
- Up to 75% automation potential for password-related issues
- Significant reduction in manual intervention
- Improved service recovery and incident handling speed
- Strong foundation for resilient, scalable IT operations
Introduction
Insurance providers operate in highly customer-centric environments where service speed, accessibility, and reliability directly impact customer trust. High volumes of support tickets, SLA violations, and manual intervention often lead to delays and poor customer experience. This case study highlights how an insurance provider transformed its support operations through self-service enablement, automation, and workload optimization. By restructuring IT support processes and introducing intelligent automation, the organization improved service efficiency, reduced operational effort, and enhanced customer trust.
Customer
An insurance provider managing high-volume application support operations across multiple channels, including web, voice, email, and automated alerts.
Business Objective
- Improve customer trust through faster and seamless support
- Reduce SLA violations in response and resolution
- Optimize support workload across L1, L2, and L3 teams
- Enable self-service and automation-led support
- Reduce dependency on manual intervention
Scope of Services
- Ticket data analysis across time, volume, and channels
- Incident vs service request classification and optimization
- SLA compliance analysis (response and resolution)
- Skill-based workload and demand analysis
- Identification of automation and self-service opportunities
- Implementation of BOT, RPA, and auto-healing use cases
- Enablement of self-help and self-service platforms
Key Insights from Analysis
- 3,100 total tickets analyzed
- ~96% tickets converted to incidents (2,988) → poor classification
- SLA violations:
- 527 response breaches
- 589 resolution breaches
- Majority tickets originated from web (2,289)
- High dependency on manual support across channels
Workload & Skill Observations
- Operations contributed 45% of total ticket volume
- Finance & Supply Chain accounted for 44%
- Top skills in demand:
- Oracle EBS (44.9%)
- .Net/C# (20.7%)
- Oracle 4GL (19.7%)
- Strong opportunity for L3 → L2 → L1 shift-left model
Detailed Findings
- Poor ticket classification between incidents and service requests
- High volume of P3 tickets (78%) indicating inefficiency in prioritization
- SLA response violations higher than resolution → process gaps
- Lack of structured service catalogue and self-service adoption
- Repetitive issues (data updates, training, access issues) suitable for automation
Benefits
- Reduced manual ticket handling through self-service
- Improved SLA compliance and response efficiency
- Better workload distribution across support levels
- Enhanced visibility into support operations and performance
- Improved customer experience and trust
Impact
- 48.11% of tickets identified for automation/self-service impact
- 37% overall effort optimization achieved
- Significant reduction in repetitive support workload
- Improved SLA adherence and faster response times
- Enhanced customer satisfaction through seamless support experience
Introduction
Automotive enterprises operate complex IT environments across datacenters, SAP, MES, and enterprise applications. High volumes of manually logged incidents, false alerts, and delayed resolution impact operational efficiency and system reliability. This case study highlights how an automotive leader transformed its IT operations using predictive self-healing and automation. By enabling event correlation, alert suppression, and automated resolution, the organization significantly reduced manual intervention, improved system stability, and enhanced operational efficiency.
Customer
A leading automotive enterprise managing large-scale datacenter operations, enterprise applications, and manufacturing systems across global operations.
Business Objective
- Reduce manual ticket logging and operational overhead
- Minimize false alerts and improve monitoring accuracy
- Reduce P1/P2 incidents impacting critical systems
- Enable predictive and automated incident resolution
- Improve IT operations efficiency and reliability
Scope of Services
- Datacenter and IT incident pattern analysis
- Event correlation and alert suppression design
- Automation of service requests and incident resolution
- Predictive monitoring across SAP, MES, and infrastructure
- Self-healing workflow enablement across IT environments
Key Insights from Analysis
- 51%+ issues logged manually → major inefficiency
- False positives increased up to 19%
- P1/P2 incidents driven by:
- SAP security issues
- MES engine failures
- SAP HCM downtime
- Backup failures
- Automation potential identified across service requests and incidents
Detailed Findings
- High dependency on manual incident logging and handling
- Lack of effective alert correlation leading to noise
- Inefficient prioritization causing delays in critical incidents
- High recurrence of issues across SAP, MES, and infrastructure
- Significant automation gaps across EUC, DC, and network
Benefits
- Reduced manual intervention through automation
- Improved monitoring accuracy with alert suppression
- Faster incident detection and resolution
- Improved system stability and uptime
- Enhanced efficiency across IT operations
Impact
- 44% of processes identified as automatable
- 40–50% automation potential across service requests and incidents
- Significant reduction in false alerts and operational noise
- Reduced P1/P2 incidents across critical systems
- Improved operational efficiency and service reliability
Introduction
Manufacturing operations rely heavily on efficient IT support across infrastructure, applications, and core services. Rising ticket volumes, poor classification, and lack of structured service management create inefficiencies, slow resolution, and increased operational costs. This case study highlights how a cement producer transformed its IT operations by combining self-service enablement with automation and process standardization. By improving service catalogue design, governance, and automation readiness, the organization enhanced efficiency, reduced operational load, and improved service delivery.
Customer
A cement manufacturing enterprise managing large-scale IT infrastructure, applications, and support services across plant operations.
Business Objective
- Reduce rising IT ticket volumes and operational load
- Improve service efficiency through self-service and automation
- Standardize ITSM processes and governance
- Enhance response and resolution times
- Enable scalable and cost-efficient IT operations
Scope of Services
- Ticket baseline and trend analysis across incidents and service requests
- ITSM process alignment (incident vs service request classification)
- Service catalogue design and digitization
- Business priority and IT severity standardization
- Automation opportunity identification across IT domains
- Integration of incident classification, governance, and workflows
Key Insights from Analysis
- 36,107 total tickets analyzed
- 31,255 incidents vs 4,852 service requests (heavy incident skew)
- 2025 ticket volume already reached 75% of 2024 within 5 months
- Incidents surged to 80% of previous year volume
- IT core support demand increased by 10% YoY
Detailed Findings
- Process Issues (47%) → Lack of structured classification and ITSM governance
- Security Issues (18%) → Need for compliance, SOX alignment, and governance
- Hardware Issues (10%) → Gaps in lifecycle and service catalogue alignment
- Software Issues (8%) → Need for digitalization and automation
- Network Issues (7%) → Performance and monitoring gaps
Benefits
- Improved ticket handling through structured ITSM processes
- Reduced manual intervention via self-service enablement
- Better SLA adherence through prioritization and governance
- Improved visibility into IT operations and performance
- Enhanced scalability of IT support operations
Impact
- 20%–24% automation potential identified
- 40% automation opportunity in security-related issues
- Clear segregation of incidents vs service requests
- Reduced dependency on manual support processes
- Improved efficiency across IT infrastructure and applications
Introduction
Manufacturing plants depend on stable IT systems across EUC, SAP, network, and application environments to ensure uninterrupted production. High ticket volumes, manual intervention, and delayed resolution directly impact plant uptime and operational efficiency. This case study highlights how a cement manufacturer transformed its IT operations using AI-driven self-healing and automation. By analyzing ticket patterns, standardizing processes, and enabling automation at scale, the organization significantly improved efficiency, reduced incidents, and enhanced plant uptime.
Customer
A cement manufacturing enterprise managing large-scale plant operations with high IT dependency across EUC, SAP, network, and application environments.
Business Objective
- Reduce IT ticket volumes and operational load
- Improve plant uptime and operational efficiency
- Minimize SLA breaches and turnaround time
- Enable automation-led IT operations
- Improve service quality across IT environments
Scope of Services
- Baseline ticket analysis across EUC, SAP, network, and applications
- Ticket classification and severity alignment
- Service catalogue rationalization and digitization
- Automation opportunity identification and implementation
- AI-driven event correlation and self-healing enablement
- ITSM process standardization and optimization
Key Insights from Analysis
- 11,586 total tickets analyzed (Jan–Aug 2025)
- Ticket volume increased by 33% in recent months
- Majority tickets categorized as Moderate severity (10,771)
- EUC accounted for 6,412 tickets (largest contributor)
- Significant inefficiencies in ticket classification and prioritization
Detailed Findings
- Process Issues (27%) → Misclassification and lack of structured ITSM taxonomy
- EUC Issues (51%) → High dependency on manual support and outdated service catalogue
- SAP Issues (47%) → Need for lifecycle alignment and better business integration
- Hardware Issues (17%) → Gaps in service catalogue and storage/EUC alignment
Benefits
- Improved ticket handling efficiency through automation
- Reduced manual intervention in recurring incidents
- Faster incident prioritization and resolution
- Better SLA adherence across IT services
- Improved visibility and control over IT operations
Impact
- 48.33% overall automation potential identified
- 47% efficiency potential in process-related issues
- 29% efficiency improvement opportunity in EUC
- 19% efficiency opportunity in SAP
- Reduction in manual ticket handling and operational load
- Improved plant uptime and IT service reliability
Introduction
AI-based clinical decision support enables healthcare organizations to improve diagnosis accuracy, identify risks early, and enhance patient outcomes. Healthcare providers managing patients with complex medical conditions often struggle with fragmented data, delayed insights, and challenges in early risk detection. These limitations can lead to missed diagnoses and inconsistent treatment outcomes. By leveraging AI-based clinical decision support powered by deep neural networks, healthcare organizations can augment clinical expertise, improve decision-making, and deliver more accurate and timely care.
Customer
Healthcare organizations managing patients with complex medical conditions.
Business Objective
- Improve prognosis and treatment planning
- Enable early identification of high-risk patients
- Enhance diagnostic accuracy
- Support clinicians with data-driven insights
- Reduce missed or delayed diagnoses
Scope of Services
- AI-assisted medical diagnosis enablement
- Risk identification and patient stratification
- Clinical recommendation support systems
- Classification and categorization of patient data
- Integration of AI models into clinical workflows
Benefits
- Improved accuracy in diagnosis and treatment decisions
- Augmented clinician expertise with AI-driven insights
- Early detection of high-risk patients
- Better clinical decision support
- Enhanced quality of patient care
Impact
- Improved patient outcomes
- Reduced missed or inaccurate diagnoses
- More proactive and preventive care delivery
- Increased confidence in clinical decision-making
