Capacity and Workforce Planning

easy to use and powerful

Strategic planning options and operations → Online productivity control

phenomenal google geographic data

Demand-oriented deployment of personnel based on current/planned orders

easy to use and powerful

Online load preview per department, team, and shift also available on mobile devices makes time-specific and dynamic bottlenecks visible

easy to use and powerful

Minimization of the organizational effort for reassignments

easy to use and powerful

Scenario calculation for the optimization of working time models and employee deployment

easy to use and powerful

Holiday planning based on the expected order volume and the resulting workload

Strategic Capacity Planning

  • Optimization of working hour models, staff number, and teams
  • Improvement of ergonomics in warehouse processes
  • Development of operational recommendations to handle sudden changes in demand
  • Optimization of buffer capacity for load peaks
  • Scenario calculations, e.g. changes in working schedules, change in order schemes of customers, mix of full time and part-time workers
  • Budget planning

As part of strategic capacity planning, the required workforce is determined, budgets are planned, and various scenarios are considered. Using a true-to-scale model and adaptable standard times and time studies, realistic process times can be detected in the digital twin. Based on the simulated storage tasks, the necessary personnel for each process in the logistic sequence can be realistically identified. This makes it possible to determine in the long term which workforce and shift model is suitable in which situations. Load peaks can be simulated and, for example, a mix of regular and temporary employees can be examined.

Mid-term Capacity Planning

  • Extrapolation of past scenarios and order pools for future periods
  • Volume planning, applying of alternative scenarios
  • Automatic identification of efficiency for estimating future staff demands
  • Holiday planning in cooperation with administrative systems
  • Shift scheduling
  • Prediction of the staffing required based on automatic scaling from comparable past periods

For medium-term planning, future demand is estimated based on expected demand. The aim is to make the most accurate planning possible based on information from the past. At the same time, the expected workforce needs are automatically calculated considering the forecasted sales volumes and order structures. From this, holiday planning can be generated, which is based on holiday requests from higher-level management systems. Approval is then granted on the basis of the expected capacity requirements. A similar procedure is used for foresighted shift planning. Shift structure and numbers are determined for a mid-term period.

Operational Workforce Planning (Online Productivity Control)

  • Bottleneck avoidance through constant, current load evaluation in the digital twin
  • Optimization of the available workload through automatic redistribution of employees between work areas
  • Consideration of, among other things, current orders, warehouse assignment, current staff availability and/or relocations, changes in allocation, and failure of system components
  • On-time delivery by determining the expected end dates of the orders
  • Re-prioritization of orders and relocation of staff in case of imminent delay
  • Balancing the capacity workload of the employees

A central element for operative workforce scheduling is current load balancing in the digital twin. Based on current orders and, if necessary, short-term forecasts, the expected workload is simulated over the day. This results in a division of the employees according to their abilities in the work areas. The expected completion date for each order is constantly monitored, so if a delivery is in danger of being delayed, it is possible to react in time by re-prioritizing orders and reallocating employees. In the case of newly arising circumstances, such as system failures, a reallocation can take place at any time. This can be done automatically. The constant transparency of the workload and the reduction of unused resources leads to an increase in productivity in the overall process.

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