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Manufacturing capacity planning is a method used to determine the maximum production rate possible at a facility or on a production line; analyze this rate against customer orders and anticipated demand, and create a plan to maximize actual output. This method may also be referred to as “finite capacity planning” because it helps manufacturers account for the actual limits of existing production resources as they develop production plans and schedules.
One purpose of manufacturing capacity planning is to ensure that production plans and schedules are realistic and do not exceed available capacity or breach any production rules or constraints. Working within maximum capacity limits, manufacturers avoid conditions that cause expedited scheduling, excess in-process inventory, missed delivery dates, and unhappy customers.
Another purpose of manufacturing capacity planning is to maximize production efficiencies. Just as capacity analysis and planning help companies avoid overcapacity issues, they also help ensure under-capacity conditions are minimized. That is, manufacturing capacity planning can be used to optimize production plans and schedules in a way that minimizes waste associated with idle machinery and personnel.
Manufacturing capacity planning requires data from all aspects of a production operation: supply chain capacity, inventories, staff qualifications, availability, the production capacity and maintenance schedule for each manufacturing machine or workstation, and more.
This complex demand on production capacity planning is easily handled by modern advanced planning and scheduling (APS) systems. APS systems treat production capacity planning as a dynamic process: instead of performing capacity analysis and generating production plans based on a static “snapshot” of the variables listed above, APS software accounts for changes as well as the impact each change has on other variables within the finite capacity planning scheme.
As a set of functions within advanced planning and scheduling software, manufacturing capacity planning features the following capabilities:
Analytical modeling — Manufacturing capacity analysis employs advanced algorithms to accurately track order and production variables and calculate the impact of changes on capacity planning.
Simulation modeling — The ability to run “what if” scenarios is a tool that enables manufacturing planners to determine the impact on workflow and productivity of variations in the allocation of resources, distribution, or sequence of orders, and so on.
Incorporating actual capacity — Initial capacity analysis results in reasonably accurate capacity estimates that account for not only the nameplate capacity but factors such as setup time, maintenance downtime, changeovers, cleaning operations, and more. It also accounts for systemic issues, like the time it takes for a work in progress to move from one processing station to the next. Variation due to human factors must also be part of the equation, such as differences in the time a set of tasks takes different operators. As actual production occurs, APS-based capacity planning revises these estimates with actual values. The more complex a production sequence, the more important this feature becomes. Estimates that are off by just a few percentage points may throw off synchronization and create significant wait time between production processes.
Identifying bottlenecks — Because it enables planners to visualize a production cycle and see where work-in-progress is stacking up, or where workstations are waiting to perform their operations, APS-based manufacturing capacity planning identifies production bottlenecks. The software accounts for such bottlenecks in plans and schedules, then adjusts as the manufacturer reallocates (or invests in new) resources to relieve each bottleneck.
Because it provides manufacturing planners insights into the actual workflow, work-in-progress, inventories, personnel, and more, manufacturing capacity planning enables plan and schedule optimization that helps boost productivity.
Increased resource utilization
More on-time delivery