The Challenge
Optimizing staff allocation with Machine Learning.
In the highly competitive world of retail, where customer experience and operational efficiency are paramount, Starbucks faced a common challenge – optimizing staff allocation. The ‘made-to-order’ nature of their business demanded precise resource allocation to ensure excellent customer service while maximizing revenue. Historically, resource planning was heavily reliant on the intuition of in-store managers, often resulting in overstaffing, customer dissatisfaction, and lost revenue. Starbucks needed a data-driven solution to revolutionize their staff optimization efforts.
The Action
We tackled Starbucks’ staff optimization challenge by building a precise demand forecast model
To tackle the challenge of staff optimization at Starbucks, we took the following actions:
Demand Forecast Model: We built a robust demand forecast model that predicted resource needs for each station, down to the hour. Leveraging historical data and advanced forecasting techniques, this model provided accurate predictions of customer demand.
Optimal Resource Allocation: Using the demand forecasts, we combined them with walking times and unit processing times to recommend the optimal number of resources required for each shift. This data-driven approach ensured efficient staffing levels aligned with actual demand.
Cloud-Based App: To empower Starbucks’ in-store managers and make the results easily accessible, we developed a customized cloud-based application. This user-friendly interface allowed managers to quickly access and implement recommended resource plans, simplifying the optimization process.
The Impact
A data-driven approach to staff optimization
Our data-driven approach to staff optimization delivered remarkable results for Starbucks:
Accuracy: The demand forecast model achieved an outstanding top-line accuracy rate of 95% on average. In-store managers now had access to highly reliable data for resource planning.
Cost Reduction: Implementing data-driven resource planning fundamentally changed and optimized staffing, leading to substantial cost reductions. This efficiency improvement positively impacted the bottom line.
Operational Efficiency: Deeper visibility into the efficiency of different stations and staffing optimization prompted strategic changes to in-store layouts. These changes, in turn, supported optimized delivery times, enhancing overall operational efficiency and customer satisfaction.
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