Building an Analytics center for a big software solutions provider

The client is a leading software solution provider in supply chain management, manufacturing planning, retail planning, store operations and collaborative category management for customers across manufacturing, distribution, transportation, retail and services industries. In spite of being a leader in the supply chain solutions market, the client realized the need to stay ahead of the competition through analytics. While they were already working with service providers for intermittent help, they understood that true value lies in establishing their own analytics center to reap benefits of data sciences. As they were thinking through the right strategy for building analytics capability, there was a need for a nuanced approach in establishing and executing the center without affecting their core culture.

TheMathCompany started on this journey with the client as they realized the need for external help and facilitated them with hiring the right talent, enabling learning them through workshops, identifying the appropriate roadmap for capability enhancement and also establishing quick wins to gain confidence from business stakeholders.

Here are the key areas TheMathCompany helped the client with:


  • Assessment of the analytical maturity on proprietary benchmark tests showed that the customer was a novice when it came to analytics capabilities
  • Identification and publishing of the charter for the analytics center
  • Design of the analytical roadmap by function


  • Execution support for a forecasting solution that would help estimate revenue from the pipeline
  • Management support for teams to create churn model to identify and re-target customers at the risk of attrition
  • Creation of a next-gen supply chain system to predict risk in the supply chain
  • End to end prototype for a paints manufacturer including vision and execution


  • Recruitment of the right leadership for the analytics division with proven expertise in business development
  • Identification of the right mix of senior and junior data scientists required and establishing the team structure
  • Organization and execution of training programs to cover skill gaps including core analytics, right processes to be followed and latest trends in analytics
  • Running a workshop on ‘Automation - Why, What & How’ to improve overall team efficiency
  • Setup of an innovation hub to integrate analytics into product development