Applying Data-Driven Insights Into Workforce Management
- With over 260 stores across the Nordics and employing 1400 passionate pet professionals, Musti Group found itself struggling with workforce planning and scheduling practices to keep up with the growing business
- Hundreds of individual store managers were in charge of store level workforce planning and scheduling practices – resulting that the overall WFM practices had become costly and inefficient
- Workforce structure had become inflexible, which had led to the vast usage of 0-hour and small hour contracted personnel. This caused dissatisfaction in personnel, created huge administrative burden for store managers and resulted in unnecessary complexity for HR department. Business was loosing millions wasted in inefficiencies and lost business potential.
- Started by framing the business challenges more in detail, creating hypothesis and collecting & prioritizing portfolio of use-cases.
- Conducted an initial business case study and developed understanding on how insights from data and analytics could help us solve the challenges – gained executive support and resources for moving forward with pilots
- Applied analytical models across workforce management practices to inform process owners and decision makers about quick wins as well as more advanced development opportunities further on the road
- Re-designed workforce management practices to enable the adoption of more advanced approaches for integrating insights from predictive analytics into workforce management and scheduling processes
- Applied machine learning models to forecast sales and predict workforce demand for optimizing scheduling
- Tested and validated analytical models and new supporting WFM practices in practice, inserted predictive optimization logic via API’s to existing workforce scheduling software