Business Driver

Driven by business intelligence limitations faced by the CFO and her VP of Financial Planning & Analysis for a growing $250M Life Sciences manufacturer, our Client’s CIO challenged Trexin to rapidly and inexpensively leverage modern, innovative technologies to empower the business with data-driven insights in less than 2 months. From a financial planning and analysis perspective, the finance team’s access to consolidated enterprise data was constrained to periodically scheduled static reports in PDF format and Excel spreadsheets, severely limiting their operations decision support capabilities for data-driven decisioning, new initiative analysis, KPI identification & tracking, and scenario planning. Moreover, requests for new or extended data sets required the creation of a new static report by IT with an average turnaround of 1 ½ days, further slowing the pace and hindering the ability of the finance team to analyze operational data and make recommendations for tactical adjustments that had the potential to increase sales, speed reimbursements, and lift margins.

Approach

Working closely with our Client’s business and technology leadership teams, Trexin adopted an agile development approach based on 2-week sprints. The first sprint was focused on quickly initializing, configuring, and securing a cloud-based data and analytics environment and integrating it with our Client’s three primary operational systems and a legacy data warehouse. This was then followed by three sprints to extract and consolidate the individual data elements and make it accessible to the analytics application and business-user tools.

Results

In just 8 weeks, Trexin was able to provide the finance team with a modern analytics app and a consolidated set of enterprise data, an effort deemed so successful and cost-effective that it was immediately extended for another 4 weeks to include an expanded set of data. When the 3-month project was complete, the finance team had been fully empowered to create their own reports & dashboards, to conduct ad-hoc analysis (on-demand without IT assistance), and to dramatically improve their operations decision support capabilities with the future possibility of incorporating artificial intelligence and machine learning.

Unexpectedly, an immediate operational performance improvement also emerged. Because the consolidated data set was incorporated into a mobile operations dashboard providing near real-time insight into production and shipping status, leadership found that they could immediately ask questions and help troubleshoot challenges throughout the day rather than waiting for the production report to be emailed out by IT at 9:30 pm each night, which generally limited action until the following morning. As a result, shipment delays decreased, and the company was able to begin the new shipments 3 hours earlier each morning.