ERP capacity planning and scheduling

Discussions on capacity planning and scheduling can be confusing because some mix up the terms, while others use them interchangeably...The truth is, they're two separate things that solve two separate problems.

1. Capacity planning keeps capacity available when needed.
2. Capacity scheduling optimizes available capacity. Planning is long-term; scheduling is short-term, usually a few days to weeks.

Production planning software blurs these lines, but the core issues remain separate. Understanding both helps determine if a company needs planning, scheduling, or both.

Capacity planning 

Sometimes referred to as Infinite Capacity Planning or Rough Cut Capacity Planning (RCCP), capacity planning focuses on whether future production plans are viable, not on the detailed sequencing of work. It's typically a medium to long-term activity and is generally easier to implement than detailed capacity scheduling.

Purpose and time horizon

Capacity planning exists to give early visibility of whether sufficient capacity will be available when needed. It highlights when additional capacity must be introduced, either permanently through new plant or equipment, or temporarily through overtime or extra shifts. Its primary value is foresight, not optimization.

Unlike capacity scheduling, it doesn't attempt to determine the exact order in which jobs will run. As a result, it can trade precision for speed, particularly because it is usually working with forecasts rather than firm orders. At this stage, directionally accurate answers are more valuable than false precision.

How capacity requirements are calculated

Capacity planning works by translating production volumes into time requirements. Forecast quantities are multiplied by standard operation times taken from routings or process definitions.

For example, if a forecast requires 1,000 units of a product in a given week, and each unit requires one hour in machining, fifteen minutes in painting, and fifteen minutes in packing, the resulting capacity demand for that week would be:

• 1,000 hours in the machine shop
• 250 hours in the paint shop
• 50 hours in packing

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It doesn't matter on which specific days these activities occur. Capacity planning is concerned with whether sufficient total capacity exists within the time bucket, not with daily or hourly feasibility.

Some RCCP approaches do attempt to offset early-stage operations into earlier periods based on lead times, but these remain approximations and should be treated as such.

Interpreting the results

Once all planned tasks are accounted for, the output is usually a report comparing required capacity to available capacity for each resource or work center. Utilization is shown as a percentage, making it easy to spot any overworked resources.

If there’s not enough capacity, the options to address it are limited:

• Increase capacity (additional shifts, overtime, or equipment), or
• Change the production plan.

This is where capacity planning feeds directly into capacity management decisions.

Data quality constraints

For capacity planning to be meaningful, two data elements must be broadly reliable: what will be produced, and how long it takes to produce it.

Many organizations lack accurate operation times. Historical timings may exist for costing or incentive schemes, but these are often poor representations of real operating conditions. Without usable time standards, even the best planning tools will generate misleading results.

Available capacity should reflect actual performance, not theoretical. A machine scheduled for 40 hours a week might only deliver 34 hours of usable output due to breakdowns and inefficiencies (ignoring this often leads to over-planning).

Changeover times also complicate matters, especially in sequence-dependent industries where it's difficult to model further into the future. Capacity planning typically absorbs these effects at an aggregate level rather than attempting to predict them to a tee.

Practical scope and limitations

Capacity planning is easier than capacity scheduling because problems rarely exist everywhere. In most factories, a small number of bottleneck resources account for the majority of constraints. Planning teams can focus their efforts on these areas rather than attempting to model the entire operation in detail.

Space constraints, inventory storage, and working capital often become limiting factors, particularly when building stock ahead of demand. Effective capacity planning, therefore, requires coordination across operations, finance, and sales (not just within production and in terms of time).

Capacity scheduling 

Often referred to as Finite Capacity Planning or Finite Capacity Scheduling, capacity scheduling focuses on how available capacity is used in the short term.

While capacity planning asks whether a plan is viable, capacity scheduling determines whether individual jobs can be completed on time given real-world constraints.

Purpose and time horizon

Capacity scheduling operates over days or weeks rather than months. By this point, options for increasing capacity are limited. New equipment cannot be installed quickly, and hiring additional staff is rarely feasible. Short-term overtime may be available, but often at a high cost.

The objective is therefore not to create more capacity, but to utilize the existing capacity as effectively as possible. This is where ERP scheduling software and smart scheduling tools are most commonly applied.

Sequencing and dependency management

Again, this may not be as easy as it sounds. Unlike rough-cut planning, capacity scheduling must account for the order in which work is performed. Even if sufficient total capacity exists in a given week, poor sequencing can make a plan unworkable. If assembly completes late in the week, the packing capacity that was available earlier cannot be recovered.

Scheduling must also respect operational dependencies. Some processes must occur in a fixed order, while others may be re-sequenced or run in parallel under certain conditions. Defining what is permitted, rather than what is typical, is essential for realistic schedules.

Overlap and flow considerations

Another key decision is whether operations can overlap. For example, packing may be able to start after a partial batch has been assembled rather than waiting for the full quantity. Whether this is practical depends on factors such as batch sizes, run speeds, material handling, and quality controls.

Overlap rules are rarely universal and often vary by product or process, making them one of the most difficult aspects of capacity scheduling to define and maintain.

Setup times and trade-offs

Set-up times are a major constraint in many manufacturing environments, even where continuous improvement efforts have reduced them. Sequence-dependent changeovers like flavour or colour changes, mean that minimizing total set-up time may conflict with meeting customer due dates.

Capacity scheduling cannot eliminate these trade-offs; it can only make them explicit. Decisions must be made about whether to prioritize throughput efficiency or delivery performance, particularly when capacity is constrained.

Data requirements and limitations

For capacity scheduling to work, the following must be broadly accurate and consistently maintained:

• Operation times and routings
• Resource capabilities and substitutions
• Sequence-dependent set-up rules
• Overlap and batching constraints
• Realistic machine and labor availability

While many ERP vendors are now pushing AI-driven or automated scheduling, their success still depends far more on data quality and organizational discipline than on optimization techniques.

Key takeaways/TL;DR

Capacity planning and capacity scheduling are two different things. While capacity scheduling systems (if implemented properly) can usually perform both roles, the job it's made for is more difficult to set up and run.

Leading ERP systems often include tools for both, but this shouldn't be confused with operational effectiveness. Many are technically capable of modelling capacity, sequencing work, and simulating alternatives, but they still depend on the quality of master data, planning rules, and the organization’s willingness to use the outputs to drive decisions.

It's most effective when used to test assumptions and expose constraints, rather than to produce a single 'approved' plan; it delivers the most value when it supports planners in making informed trade-offs, rather than attempting to automate judgment in complex or volatile environments.