Overview
Our client, a Fortune 500 subsidiary, collected sensor data from garbage trucks but wasn’t using it for insights. They needed a solution to improve fleet performance, reduce fuel use, and enable preventive maintenance. Using Power BI, NeenOpal built interactive dashboards to reveal performance metrics, patterns, and machine learning results like anomaly detection and clustering.
- 100+
- Active Vehicles on Road
- 1 sec
- Data Granularity
- 200+
- Instant Vehicle KPI Tracking
- 500+
- Events Per Vehicle Per Day
Customer Challenges
Organizing and processing the sensor data was challenging due to its complexity and inconsistencies, requiring significant effort to prepare it for analysis. Here are the key challenges:
Challenges in Sensor Data Integration and Quality
Each garbage truck had hundreds of sensors collecting data on wheel speed, positions, pressures, temperatures, and switch states. However, no insights were being gained because the data wasn’t synced and lacked a consistent framework. While all the sensor data was stored in a common database, the data had issues like inconsistencies, missing values, and required changes. To make the data usable for analysis, it needed to be synced, columns had to be combined, and other transformations were necessary.
Issues with Unsynced Data
As each sensor was capturing data at different timestamps, data points across trucks or across different timeframes were difficult to compare. With such misalignment, meaningful insights and comparisons were not possible to attain.
Unified Sensor Data & Reporting Architecture
An integrated system design that consolidates fleet sensor data, automates processing, and delivers actionable insights through advanced BI reporting for optimized operations.
Solutions
To address the challenges with sensor data, a comprehensive approach was implemented, focusing on synchronization, analysis, and visualization. Here are the key steps taken to transform and extract valuable insights from the data:
Correlation Analysis
We applied correlation analysis through Python to observe the interrelation between pairs of sensors. This helped the company determine how different components are performing across different trucks under different conditions.
Data Synchronization and Aggregation
NeenOpal came up with a data structure to synchronize the same timestamps among the sensor data. This would allow cross-truck comparison. We stored and processed data using Snowflake through data transformation using both Python and Snowflake SQL.
Power BI Reporting
We developed a series of interactive Power BI reports to help visualize the processed sensor data. These reports allowed users to compare sensor interactions between trucks, monitor individual sensor values and real-time data volumes, and track sensor frequencies and detect out-of-range values. They also provided insights into sensor-value distributions across trucks using kernel-density estimation and quantiles, along with timeline trends and comparative sensor values across various time aggregates such as seconds, minutes, and days.
Anomaly Detection
Machine learning algorithms like isolation forest, k-means clustering have been deployed to perform anomaly detection to identify out-of-range timestamps in the data as potential early signs of fault indicators to prevent failure.
Fuel Consumption Metrics
We developed custom algorithms that would calculate average fuel consumption per truck errand. This way, correlating different sensor data points, we could estimate fuel consumption during different errands, compare trucks, and benchmark against competitors.
Services
Snowflake
Python
Power BI
Machine Learning
Benefits
Improved Operational Efficiency
The synchronization of data and correlation analysis enabled better decision-making, leading to a 15% increase in fleet management efficiency.
Reduced Fuel Consumption
By understanding fuel consumption trends and comparing performance across trucks, the company optimized routes and operation patterns, achieving a 10% reduction in fuel usage.
Faster Anomaly Detection & Maintenance
The machine learning-driven anomaly detection feature allowed the team to catch potential issues 75% faster, preventing costly breakdowns and minimizing downtime.
Conclusion
By using Snowflake for data storage, processing, and analysis, and Power BI for reporting, the client gained real-time visibility into their fleet operations. Through remote sensing data synchronization, correlation analysis, and dashboard development, our client was able to track and optimize truck performance, fuel consumption, and maintenance schedules. Custom anomaly detection and fuel consumption metrics played a crucial role in enhancing operational efficiency.
FAQ
Find quick answers to common questions about this case study and our approach.
What was the primary objective of the fleet optimization project?
The main objective was to integrate sensor and telematics data to improve fleet visibility, reduce operational costs, and enable data-driven decision-making through Power BI dashboards.
What challenges were addressed in this solution?
The client faced fragmented data sources, limited real-time visibility into vehicle performance, and difficulty tracking fuel consumption and maintenance metrics accurately.
How did sensor data integration improve fleet operations?
By consolidating real-time vehicle data such as fuel usage, engine performance, and route tracking, the organization was able to optimize routes, reduce downtime, and improve overall fleet efficiency.
What role did Power BI play in the solution?
Power BI enabled centralized reporting, interactive dashboards, and performance tracking, allowing stakeholders to monitor key metrics and make informed operational decisions quickly.
Authors
Madiha KhanContent Writerin
Case studies
Related case studies
Building a Validated Conversational Sales Analyst on Amazon Quick for a Managed IT Provider
Our client, a managed IT services provider delivering all-inclusive computer, internet and help desk support to small and mid-sized businesses, already monitored sales opportunities across eight funnel stages in an established Power BI dashboard. Answering deeper questions about stage duration, win rates and trends still meant stacking filters and interpreting the resulting measures. NeenOpal extended that environment with a conversational AI Analyst Agent built on Amazon Quick, connected to the client's opportunity stage funnel dataset in Amazon RDS. Teams now ask questions in plain language and receive summaries, charts and the supporting SQL, cutting the time to a routine answer by 75% and the number of analysis steps by 60%. The depth sits in the foundation: a curated opportunity-stage data model, documented business rules, and answers reconciled with the existing Power BI dashboard month by month and filter by filter, which matched on every indicator tested. Power BI stays exactly where it was.
Read case study
Tableau Dashboard Development for a US Insurance Organization: 4 Data Sources Unified
Our client, an insurance organization with field teams spread across every US state, partnered with NeenOpal to replace a manual, spreadsheet-driven reporting process with a consolidated Tableau dashboard environment governed by row-level security. Operational data lived in four separate systems covering Airtable, Salesforce, Google Analytics 4, and Google Search Console, and bringing them together meant exporting and stitching the data by hand, which left leadership without a single reliable view of performance. NeenOpal consolidated all four sources into an Azure SQL Server layer, reviewed and corrected the data quality issues that surfaced along the way, and delivered three Tableau dashboards across seven screens that refresh automatically every day. Row-level security was implemented across all fifty states and the independent districts, driven by an entitlement model the client maintains through a single Google Sheet, so every representative sees the states they are responsible for and nothing more.
Read case study
Pavel Motors Automates Vehicle Repair Estimation-to-Quote Process with Claude
For automotive service businesses, moving from a repair estimate to a customer-ready quote quickly is critical, yet the process typically means manually reviewing emails and attachments, extracting repair details, checking insurance eligibility, preparing the quote, and responding to the customer. Pavel Motors partnered with NeenOpal to transform this using Claude Cowork, building an AI automotive repair quote automation solution that connects intake, document understanding, business rules, quote generation, and customer communication: when an estimate arrives by email, Claude Cowork processes it along with its attachments, extracts the relevant vehicle, repair, cost, customer, and insurance information, applies Pavel Motors' business rules, and generates a customer-ready quote while giving customers greater transparency by identifying supported insurance providers and showing the estimated amount eligible for claim coverage. NeenOpal delivered the working automation in 5 days, giving Pavel Motors a scalable way to reduce repetitive administrative work, accelerate quote turnaround, and provide a more consistent customer experience.
Read case study
Unifying Multi-Region Sales Pipeline Analytics with Power BI and Power Query
Our client, a global healthcare company with commercial operations across Africa and Western Europe, partnered with NeenOpal to build a unified sales pipeline dashboard spanning more than six regional teams across two continents. Pipeline data was managed in isolation by each region, reported in inconsistent formats, and pulled from different systems including Salesforce and SAP, leaving leadership without a consolidated view of opportunities, orders, and revenue. NeenOpal designed and delivered a centralized sales pipeline dashboard in Power BI, powered by an automated Excel and Power Query ETL layer, that standardizes data from every region into one live view. The solution tracks each opportunity from lead to shipped order, benchmarks performance against multiple internal targets, forecasts revenue from existing customers, and lets non-technical users refresh the entire report in one to two clicks.
Read case study
Unifying Eight Disconnected Business Systems into Centralized Power BI Dashboards
Our client, a managed technology services provider (MSP), partnered with NeenOpal to replace a fragmented, siloed reporting process with a centralized Power BI dashboard environment. Business-critical data was scattered across multiple disconnected systems, from the CRM and PSA to the service desk, client surveys data, and network monitoring tools, leaving leadership without a single view of sales, customers, operations, or the workforce. NeenOpal designed and delivered a centralized Power BI reporting solution that unifies data from all of these sources into an automated, daily-refreshed analytics layer on AWS. By ingesting each source, staging it in a PostgreSQL warehouse, and modeling it into governed Power BI dashboards across every business function, the team turned manual, spreadsheet-bound reporting into interactive, self-service insight the client can filter and act on in seconds.
Read case study
Microsoft Fabric Migration with Row-Level Security for Multi-Client Analytics
Our client, a B2B financial wellness and HR technology company, where we have worked on their data that delivers reporting to multiple business customers, partnered with NeenOpal to consolidate a fragmented reporting stack into a single, secure analytics platform. The client's finance and user-engagement data lived across MongoDB, AWS RDS, Google Sheets, and HubSpot, and was surfaced through standalone Power BI dashboards built on AWS. Serving each downstream client meant securely embedding a per-user filtered dashboard for external users logging into the client's portal, and app-owns-data embedding at that scale requires dedicated capacity. On shared, standalone Power BI the team had worked around this by building and maintaining a separate dashboard and semantic model for every client, which increased infrastructure cost and slowed delivery. NeenOpal designed and delivered a four-week Microsoft Fabric migration that unified every source into one governed semantic model with dynamic row-level security. By rebuilding the pipeline on a medallion architecture inside a Fabric Lakehouse and enforcing access with a single Power BI security role, the client replaced many duplicate dashboards with one unified report that automatically shows each user only the data they are permitted to see.
Read case study