Real-Time Service Performance Measurement for a Large Food Products Manufacturer
Service efficiency and KPI measurement is a critical element of any business’ value chain and organizations are beginning to emphasize on improved service efficiency to optimize costs and enhance overall customer experience. Learn how we delivered a scalable service performance management framework through a lean data architecture that can provide real-time analytics and insights on service performance metrics.
The Problem:
Mu Sigma worked with one of the world’s largest food product manufacturers to incubate Service Performance Management and scale it as a global program. The intent is to improve customer service position using data analytics to measure the actuals and identify opportunities for improvement.
The Mu Sigma Approach:
Upon deeper investigation of the problem space by using our unique New Art of Problem-Solving approach, it was evident that the legacy architecture that was in use had several layers leading to multiple dispensable technology hops. These infrastructural challenges were leading to delays in processing data which delayed consumption and insight generation.
The Impact:
A leaner data architecture helped eliminate redundancies and delays. The new logic built in PySpark were validated by comparing with the existing BW A leaner data architecture helped eliminate redundancies and delays. The new logic built in PySpark were validated by comparing with the existing BW reports to get a 99.9% match. Through the new solution design, Mu Sigma was able to implement a scalable performance measurement framework that enabled several positive outcomes for the client including:
• Increased ability to recognize causes of service loss
• Smoother reporting to all business stakeholders
Download the case study to know more