WFM Commons Newsletter
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WFM Commons has launched a comprehensive platform that integrates capacity planning, forecasting, scheduling, financials, multi-scenario planning, and an AI assistant. This solution streamlines workforce management by consolidating essential tools in one place, making it easier for professionals to optimize operations.
Read the full post on LinkedInA recent analysis revealed discrepancies between required and scheduled FTEs, highlighting that conventional weekly headcount figures mask deeper staffing issues. WFM professionals can utilize Smart Capacity Planning to pinpoint interval-specific staffing needs and adjust schedules dynamically for better service…
Read on LinkedInAdvanced Forecasting Studio allows you to test multiple forecasting models like NeuralProphet and LSTM on your datasets. This post reveals live test results and highlights how to select the best model for your unique series, aiding WFM professionals in making data-driven decisions for accurate forecasting.
Read on LinkedInThe new forecasting module within the Capacity Plan provides accurate volume forecasts and AHT analysis, streamlining capacity planning. Unlike typical methods, it assesses trends, seasonality, and the limitations of historical data to deliver reliable forecasts without unnecessary complexity. WFM professionals will…
Read on LinkedInThis post highlights the importance of accurately forecasting Average Handle Time (AHT) by utilizing a nesting headcount mix of tenured agents. WFM professionals will find value in the Capacity Plan, which simplifies this process without the hassle of validating assumptions.
Read on LinkedInWFM Commons' Capacity Plan uses interval scheduling to provide critical insights into your workforce needs. It highlights staffing inefficiencies, revealing understaffed and overstaffed intervals that traditional headcount metrics miss, ensuring you align agents where they're needed most.
Read on LinkedInIn a recent test, a seasonal naïve forecasting model significantly outperformed six more complex models, achieving an 8.9% MAPE compared to 14.4% for the best complex model. This reveals that WFM teams often select models based on perceived sophistication rather than accuracy. Learn how the right model choice can…
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