Productivity & Quality Archives - Page 2 of 9

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

Manufacturing efficiency in discrete operations depends heavily on accurate data capture, classification, and real-time measurement of performance metrics such as OEE (Overall Equipment Effectiveness). However, inconsistent data capture, manual interventions, and unreliable PLC logic often lead to incorrect insights, masking true efficiency and impacting decision-making. This case study highlights how an AI–IIoT–enabled framework was conceptualized to address these challenges. By improving data accuracy, automating classification, and standardizing implementation across plants, the organization aimed to unlock true production visibility and operational efficiency.

Customer

A manufacturing organization with operations across forging, drilling, and injection moulding processes, facing challenges in efficiency measurement, data capture, and workforce usability.

Business Objective

Improve accuracy of downtime vs. changeover classification
Enable reliable rejection and rework data capture
Enhance production efficiency measurement beyond planned vs. achieved metrics
Strengthen PLC/IoT-based data capture for manual operations
Standardize IoT implementation across plants

Scope of Services

Design of AI–IIoT–enabled framework for manufacturing operations
Automation of downtime and changeover classification
Enablement of conditional rejection and rework data handling
Implementation of advanced efficiency metrics beyond basic production tracking
Enhancement of PLC/IoT logic with anomaly detection
Standardization of IoT data capture across multiple plants

Key Challenges Addressed

Misclassification of downtime vs. changeover due to flawed timestamp logic
Delayed and inaccurate rejection/rework data entry
Misleading efficiency metrics masking real production performance
Inconsistent pulse capture in manual drilling operations
Fragmented IoT adoption across different manufacturing units

Benefits

Accurate classification of production events and improved OEE visibility
Reduced dependency on manual data entry and intervention
Improved quality data accuracy for rejection and rework analysis
Better alignment of efficiency metrics with real production performance
Standardized and scalable IoT implementation across plants

Impact

Improved production accuracy and operational visibility
Enhanced workforce usability and reduced manual intervention
Better decision-making through reliable efficiency metrics
Foundation for scalable AI–IIoT adoption in discrete manufacturing environments

Introduction

Following a large-scale merger, a global media organization faced increasing pressure to improve cash flow visibility and financial efficiency. While revenues remained stable, poor working capital management limited liquidity and reduced the organization’s ability to reinvest and respond to market dynamics.

The challenge was not growth; it was unlocking trapped cash within existing operations.

Customer

A multinational media enterprise undergoing post-merger integration, dealing with fragmented financial processes and inconsistent cash management practices across business units.

Business Objective

Improve cash conversion cycle and free cash flow visibility
Identify and unlock working capital trapped in operations
Standardize financial processes across merged entities
Strengthen control over receivables and payables

Scope of Services

Working Capital Diagnostic

Conducted a structured assessment of accounts receivable and payable processes, identifying inefficiencies across the cash cycle.

Process Deep-Dive (Order-to-Cash & Procure-to-Pay)

Analyzed end-to-end financial workflows to uncover delays in collections and inefficiencies in vendor payment structures.

Opportunity Identification & Prioritization

Identified multiple high-impact levers to improve liquidity, including customer payment delays and suboptimal vendor terms.

Financial Visibility Framework

Designed a centralized tracking and reporting mechanism to monitor working capital performance across business units.

Transformation Roadmap & Governance

Established a structured execution plan supported by a program management office (PMO) to drive adoption and ensure accountability.

Key Challenges Addressed

Lack of visibility into real-time cash flow performance
Delayed customer payments impacting liquidity
Vendor payment terms below industry benchmarks
Fragmented financial processes post-merger
Absence of standardized working capital governance

Benefits

Improved Cash Visibility

Enabled leadership to track free cash flow and working capital performance in real time

Optimized Financial Processes

Standardized receivables and payables management across business units

Stronger Vendor & Customer Management

Improved control over payment cycles and contractual terms

Structured Financial Governance

Introduced accountability through centralized monitoring and execution frameworks

Impact

Identified opportunities to unlock $800M+ in cash benefits within two months
Improved cash conversion cycle across business units
Reduced delays in receivables and optimized payables structure
Strengthened financial control in a post-merger environment

Introduction

Telecom ESG transformation is becoming critical as operators face increasing pressure to reduce carbon emissions, optimize energy consumption, and align with sustainability regulations. A leading European telecom operator undertook a large-scale ESG transformation to build a future-ready, sustainable business model while maintaining operational efficiency.

Customer

A leading European telecom operator providing connectivity and digital services across global markets, managing large-scale network infrastructure with high energy consumption and strict regulatory requirements around sustainability.

Business Objective

Achieve net-zero emission targets
Reduce energy consumption across network infrastructure
Align operations with ESG regulations
Enable sustainable growth without impacting service quality

Scope of Services

ESG strategy design and roadmap development
Energy optimization across telecom infrastructure
Data-driven monitoring of emissions and energy usage
Integration of sustainability metrics into operations
Governance model for ESG tracking and reporting

Key Challenges Addressed

High energy consumption across telecom networks
Lack of real-time visibility into emissions
Regulatory pressure for sustainability compliance
Balancing cost optimization with ESG goals

Benefits

Improved energy efficiency across operations
Better visibility into sustainability metrics
Reduced environmental impact
Alignment with global ESG standards

Impact

45% emission reduction target achieved
Strong progress toward net-zero goals
Improved operational efficiency alongside sustainability
Enhanced brand positioning as a sustainable telecom provider

Introduction

Telecom operations transformation is essential for operators dealing with rising costs, fragmented processes, and increasing customer expectations. A leading Asia-Pacific telecom operator transformed its operations to improve efficiency, reduce costs, and enable scalable service delivery.

Customer

A leading Asia-Pacific telecom operator offering mobile, broadband, and digital media services, operating across multiple markets with complex service delivery models and high operational costs.

Business Objective

Reduce operational costs
Improve workforce productivity
Standardize processes across operations
Enhance service delivery efficiency

Scope of Services

End-to-end operations assessment
Workforce and process optimization
Implementation of standardized operating model
Performance tracking and KPI alignment
Automation-led process improvements

Key Challenges Addressed

High operational costs
Inefficient workforce utilization
Fragmented processes across business units
Lack of standardized KPIs

Benefits of Telecom Operations Transformation

Streamlined operations across departments
Improved workforce efficiency
Better cost control and visibility
Enhanced service delivery performance

Impact

70% productivity improvement
15% cost reduction achieved
Faster decision-making and execution
Scalable operating model for growth

Introduction

Global operating model transformation enables enterprises to standardize processes, improve governance, and reduce operational costs across complex, multi-region environments. Organizations operating in regulated industries often face fragmented processes, inconsistent compliance practices, and high run-the-business (RTB) costs. This case study highlights how a global enterprise transformed its IT and operations landscape by implementing a standardized, automation-led operating model. By combining AI-driven automation, process rationalization, and strong governance frameworks, the organization achieved scalable efficiency, compliance excellence, and financial optimization.

Customer

A global enterprise operating across multiple regions with a complex IT and operations landscape and strict regulatory and compliance requirements.

Business Objective

Transform global operating model across IT and operations
Standardize processes across portfolios and geographies
Improve operational efficiency and governance
Reduce RTB costs at scale
Strengthen audit and compliance posture

Scope of Services

Global IT and operations delivery transformation
Process standardization and SOP rationalization
Setup of Automation Center of Excellence (CoE)
Implementation of AI-driven operational automation
Risk, audit, and compliance governance frameworks
Global delivery hub and proximity support enablement

Benefits

Enterprise-wide standardized processes
Strong and consistent compliance posture
Improved agility and faster transformation cycles
Predictable and scalable operating model
Reduced dependency on external vendors

Impact

Reduction from 395 SOPs to 25 standardized processes
100% green audit compliance sustained over multiple years
Zero upfront financial commitment for transformation
Multi-market automation rollout across regions
Improved operational consistency and governance

Introduction

Cloud transformation is critical for cold chain logistics providers where infrastructure reliability directly impacts time-sensitive supply chains such as food and pharmaceuticals. Legacy data centers often limit scalability, increase operational risk, and hinder responsiveness to dynamic demand. This case study highlights how a global cold chain logistics provider adopted a cloud-first strategy to modernize its infrastructure. By leveraging hybrid cloud architecture and automation-driven migration, the organization improved resilience, ensured near-zero disruption, and created a scalable foundation for future digital operations.

Customer

A global cold chain logistics provider supporting temperature-controlled logistics, warehousing, and distribution for food and pharmaceutical supply chains.

Business Objective

Enable a cloud-first IT strategy
Improve infrastructure resilience and scalability
Reduce dependency on legacy data centers
Ensure zero disruption to critical operations
Support future digital and operational growth

Scope of Services

Migration of production and DR workloads to Microsoft Azure
Implementation of hybrid cloud operating model (MCOD)
Extension of cloud infrastructure across EMEA and APAC regions
Data center consolidation and migration
Infrastructure automation and digital twin modeling
Testing, deployment, and stabilization of cloud environments

Benefits

Improved infrastructure reliability and resilience
Faster response to operational and business needs
Standardized global infrastructure operations
Reduced operational risk for time-sensitive logistics
Scalable foundation for digital transformation

Impact

70% of workloads migrated to Azure
97% virtualization achieved
Near-zero downtime during migration
Near-zero data loss across systems
Improved operational continuity across global operations

Introduction

Digital logistics platform transformation enables enterprises to modernize legacy systems, reduce operational costs, and improve visibility across complex supply chain operations. Large logistics organizations often struggle with fragmented application landscapes, high run-the-business (RTB) costs, and limited end-to-end shipment visibility. This case study highlights how a global logistics company transformed its operations by building a next-generation digital logistics platform. By rationalizing legacy systems, standardizing processes, and integrating application and infrastructure support, the organization achieved significant cost savings, improved efficiency, and enhanced revenue realization.

Customer

A global supply chain services and logistics company headquartered in the United States, managing enterprise-scale freight forwarding operations and a large application ecosystem.

Business Objective

Reduce RTB costs across IT and operations
Improve end-to-end shipment visibility
Standardize and re-engineer business processes
Reduce incident volumes and support dependency
Establish integrated SLAs and KPIs across operations

Scope of Services

Transformation of core freight forwarding systems
Design and development of a next-generation digital platform
Rationalization of 170+ legacy applications
Creation of a unified enterprise data layer (single source of truth)
Application support services across 115 applications and 25 technologies
Infrastructure support and enterprise help desk operations
Stabilization and automation of support processes
SLA and KPI definition and implementation

Benefits

Significant reduction in RTB costs
Faster customer onboarding through standardized workflows
Improved shipment visibility across logistics operations
Reduced complexity through platform consolidation
Enhanced IT service reliability and predictability

Impact

$100M reduction in RTB costs
60% reduction in customer onboarding time
11% increase in revenue realization
20%+ reduction in ticket volumes
Improved operational efficiency across applications and infrastructure

Introduction

Global IT operations standardization enables logistics enterprises to eliminate fragmented processes and build scalable, automation-led delivery models. Transport and vehicle logistics companies operating across multiple countries often face inconsistent workflows, duplicated efforts, and limited coordination across regions. This case study highlights how a leading European transport and vehicle logistics company transformed its operations by implementing a globally integrated, automation-driven model. By standardizing processes and leveraging AI-driven monitoring and automation, the organization improved efficiency, reduced operational noise, and enhanced financial productivity.

Customer

A leading European transport and vehicle logistics company operating across multiple countries with globally distributed teams and support functions.

Business Objective

Eliminate ad-hoc and non-standardized operational processes
Drive automation-led transformation across applications and infrastructure
Enable a globally integrated operating model
Improve financial productivity through industrialized operations
Enhance coordination across regions and delivery units

Scope of Services

Application development, AMS, and infrastructure support
Management of 55+ enterprise applications
Multi-language EUC support across regions
Global integrated operations and automation delivery model
Proximity support across 13 countries
NOC and SOC operations implementation
Auto-ticketing and event-driven monitoring
AI/ML-based automation and incident optimization

Benefits

Standardized and consistent global operations
Reduced operational overhead through automation
Faster incident detection and resolution
Improved cross-region coordination
Better utilization of IT and support resources

Impact

Integrated infrastructure and applications delivery model leveraging synergies
MOM layer integrated with monitoring tools and AI-driven automation
Measurable financial productivity through automation industrialization
Reduced duplicate tickets and improved operational efficiency
Scalable global operations model supporting business growth