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Custom Business Intel Layer for a BMW Dealership - Case Study

Banner image for the case study on Custom Business Intelligence Layer for a BMW Dealership in Denmark

Custom Business Intelligence Layer for a BMW Dealership in Denmark

Industry: Automotive

Service: Digital Engineering

Executive Summary

The supply chain of a BMW Denmark dealership was tangled in a web of disconnected data, leaving their team frustrated and hunting for answers. We built a Dealer Intelligence Platform, a single command center, that stitches BMW DMS data, supplier files, and forecasting models into a clear backorder dashboard. The result was real-time visibility over stock and service flows, fewer surprise warranty issues, and staff finally able to make proactive decisions instead of reactive firefighting.

About the Client

Our client is a BMW dealership in Denmark with a flair for shiny new rides and meticulously-certified pre-owned vehicles, servicing more than 10,000 BMW owners, delivering experiences beyond the ordinary both within sales and aftermarket. They leaned on BMW’s Dealer Management System (DMS) for their core transactions, but when management wanted the big picture, they found themselves lost in a maze of scattered data and missed insights.

Business Challenge

BMW provides robust OEM systems like DMS and Aftersales Online System (AOS), but these platforms focus on transactional operations they don’t provide the cross-system operational intelligence or custom analytics, but dealerships in Denmark operate in a tight, competitive market where premium brands are vying for both new-car buyers and a growing used-car audience. Online marketplaces like Bilbasen and DBA play an outsized role in how consumers shop for used vehicles, making inventory accuracy and speed-to-market essential.

Meanwhile, supply-chain and logistics problems that have persisted since 2020 continue to ripple through parts availability resulting in backorders and longer lead times. Our client was facing similar issues and were unable to see their parts pipeline in real time. Staff hours were spent chasing missing parts, reconciling spreadsheets, and piecing together fragmented email threads, instead of moving metal.

At our client’s dealership, problems looked like this:

  • Pulling together live views of sales, stock, and service data in one place was a headache.
  • Tracking true profitability per car is not easy when marketing promos, discounts, and repair costs all live in different silos.
  • The service team wanted to act before a row of warranties expired.
  • Reconciling supplier price lists and chasing after promotional emails was eating up precious hours.
  • Management tried aligning dealership targets with BMW’s KPIs, but their own performance metrics often slipped through the cracks.

Solution

Our team built a Dealer Intelligence Platform, a kind of “command center”, that sits on top of BMW’s DMS and any other data sources they use. No more hopping between apps and crunching spreadsheets late at night. The platform translates disconnected feeds into a single, actionable view for managers, sales leads, and service advisors.

Key Features

We built a secure lifeline right into BMW’s DMS, pulling sales, service, and customer info into their dashboard.
Automated processing of supplier price lists and promotional offers stored in Excel files, with normalization into a central database.
Using machine learning, our platform predicts demand spikes, seasonal trends, and how busy their service bays will be, in order to avoid surprises.
A slick React.js + Node.js dashboard brought all KPIs together, so department heads could finally see the full picture without bouncing between systems.
We ensured that management, sales staff, and service advisors viewed only relevant metrics.
Hosted on AWS, the platform easily flexes during big sales pushes, scaling up to handle surges in data.

Results

result_1

80% reduction in manual report preparation time

result_2

Faster decision-making, instead of wading through weekly Excel dumps, department heads got fresh insights every 15 minutes right on their dashboard.

result_3

Higher service department utilization through accurate forecasting of upcoming high-demand weeks.

result_4

Real-time gross margin tracking per vehicle became a breeze, factoring in marketing spends, hidden costs, and the small details that make a big difference.

Tech Stack

  • Frontend: React.js, Material UI for responsive dealer dashboards.
  • Backend: Node.js, Express.js for API aggregation; Python for data processing and ML forecasting.
  • Integration Layer: REST API and secure FTP parsers for DMS & supplier feeds.
  • Database: PostgreSQL for relational data; Redis for caching high-frequency queries.
  • Hosting: AWS EC2, S3, Lambda (serverless triggers for file ingestion).
  • Data Visualization: Apache Superset integrated into the dealer portal.

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