Ashika Gururani

Virtual Expert for Maintenance Executives using Gen AI

• Led end-to-end development of a multilingual Gen AI Virtual Expert for Maintenance Executives using an advanced RAG framework connected to internal documents, external sources, and SAP-integrated databases.
• Enabled capabilities such as morning summary, task prioritization, checklist recommendation for troubleshooting, task execution summaries, knowledge database extraction, and training link suggestions.
• Designed and delivered the architecture using Azure Functions, Azure OpenAI, Whisper AI, Azure Redis Cache, API Management, with the front-end UI built on Power Apps.

Intelligent Warehouse Optimization & PLC Automation – Global Utility Client

• Led the end-to-end delivery, strategized and deployed Intelligent warehouse slotting optimization algorithms for a global utility client, integrating AI techniques like Gen AI, seasonality, affinity analysis and constrained optimizationtechniques in Azure to improve storage efficiency by 20% and enhance operational throughput.
• Spearheaded the implementation of PLC circuit diagram reconciliation using RAG Framework for a global utility client, successfully deploying the solution on Azure.

Energy Efficiency Optimization for a Major Industrial Company in Asia

• Led the offline analytics workstream to optimize quality, specific power consumption, production throughput, and stable/efficient operations across multiple manufacturing units.
• Applied statistical modelling, regression analysis, constrained optimization, and process analytics to identify key drivers, improve energy efficiency, and enable sustainable operational performance.

Led a multi-year transformation program across 9 sites for a global mining major, delivering advanced analytics, spare criticality assessment, and reliability engineering initiatives to improve maintenance excellence and operational performance

• Spare Criticality Assessment: Developed and deployed ANN/XGBoost-based criticality classification models across 1,600+ assets improving inventory and spare stocking decisions.
• BoM Transformation: Strategized and performed BoM transformation for a 50-year-old mining operation by analyzing unstructured engineering documents using advanced analytics and text mining. Developed a consolidated Master BoM through segmentation and pattern detection.
• Reliability Engineering (RCM): Performed Reliability Centered Maintenance (RCM) incorporating FMEA and RCA to identify optimal counters and failure indicators across asset classes. Built a Weibull analysis–based reliability modelling template in to standardize replacement strategies, drive compliance, and ensure high equipment reliability.
• Asset Performance Optimization: Reviewed maintenance strategies based on RCM outcomes, delivering USD 28M in projected value through optimized maintenance intervals and reduced unnecessary replacements.

Asset Management for a Fortune Global 500 Company

• Performed user behavior and agent analytics for ServiceNow portal, identifying friction points and improving experience, leading to higher portal adoption and reduced resolution times.
• Led reporting strategy and as part of it responsible for articulating and implementing reporting solution for various work streams. As part of it defined KPIs and developed Performance Dashboards using BI tools (QlikSense, QlikView, Power BI) by integration with Data Lake, enabling leadership to track operational health and drive data-backed decisions.
• Conducted ML performance diagnostics to enhance auto-resolution, auto-assignment, and clustering models, improving accuracy and reducing manual triage.
• Analyzed Virtual Agent conversations to identify automation gaps and delivered recommendations that reduced incident volume and increased user satisfaction.

Additional Strategic & Digital Initiatives

• Managed HSE and WMS workstreams for a ME O&G Major, delivering successful change request releases with minimal defects. Demonstrated skills in risk identification and mitigation, effectively managing day-to-day operations, and overseeing a diverse team of 20. Proven ability to engage stakeholders, secure buy-in, and expand team capabilities, driving new revenue-generating opportunities and ensuring optimal performance across projects.
• Developed global jet fuel demand forecasting models integrated with Power BI, enabling leadership to forecast supply–demand gaps and optimize trading decisions.
• Built NLP-based Inspection Summarizer for O&G inspection reports, reducing manual review time by 70%.
• Developed predictive model for lube oil formulation using SVM regression and Monte-Carlo simulation. Formulation was optimized to obtain desired properties with minimum cost.
• Built ML models to forecast energy demand for a European electricity distributor, improving planning accuracy.

Business Development & Practice Leadership

• Lead for Data & AI vertical within the IAM practice, driving capability building, reusable accelerators, technical frameworks, and emerging Gen AI solutions.
• Contributed to sales pursuits through solutioning, AI architecture design, proposals, and business case development.

• Developed failure prediction model to predict the wind turbine bearing failure by classification of turbines on the basis of their performance and then extracting most significant features to build deep learning model.
• Developed RUL estimation framework for wind turbine bearing using Weibull distribution curve and neural network.
• Created anomaly detection models for stator temperature, generator currents, and turbine alignment.
• Built models to quantify power loss due to turbulence and icing, improving asset availability.

• Developed ML analytics for pressure spikes, rotor bow trips, bearing coking, and hydrogen consumption anomalies using supervised learning and physics-based modelling.
• Developed Gas Optimizer Tool to generate optimal GT schedules using constrained optimization thereby maximizing profitability with available fuel.
• Deployed ML solutions into Predix, achieving 65% failure mode analytics coverage across the fleet.
• Enhanced similarity modelling and anomaly detection techniques for combined-cycle plants.
• Developed Digital Twin models of Boiler and 3 Pressure HRSG Templates of Combined Cycle Plant.
• Supported global fleet RCA and troubleshooting involving GE Control Systems, HMI, and turbine operability.