Capacity planning in manufacturing and distribution and fulfillment (D&F) operations refers to the process of evaluating anticipated production targets against current and future labor resource availability. In these competitive market sectors, growing labor shortages, rising wages and the unpredictability of the labor pool can quickly erode profit margins. The overarching goal of capacity planning is to make more accurate projections about how to meet incoming fulfillment or production demands while lowering the cost-per-unit produced.

To account for unexpected absences and inevitable attrition rates, many site managers are faced with the prospects of running behind in production schedules or extending hours into overtime to keep pace. But an overtime strategy can lead to a never-ending cycle of increasing labor costs just to meet daily production targets. For many operations, maintaining this tenuous balance of resources and production becomes a standard operating procedure (SOP).

But what if there were viable strategies for breaking this cycle and effectively preparing for unforeseen labor constraints? What if operations could test multiple scenarios to see which ones offer the most optimum labor and cost optimization approaches?

This white paper will examine the emerging science of capacity planning for labor and demonstrate how data-driven forecasting models can deliver measurable savings in labor costs and significant productivity improvements.