Capacity and constraints

Most manufacturing companies have a deep hierarchy of planning and scheduling processes that start with market forecasts and business plans, and end with machine and unit schedules. The higher levels are the responsibility of logistics, marketing, sales, and executive management, while the lower levels of the hierarchy are typically the responsibility of manufacturing operations.


Most manufacturing companies have a deep hierarchy of planning and scheduling processes that start with market forecasts and business plans, and end with machine and unit schedules. The higher levels are the responsibility of logistics, marketing, sales, and executive management, while the lower levels of the hierarchy are typically the responsibility of manufacturing operations. Manufacturing operations will become involved when actual production capacity is used in a finite capacity schedule. A finite capacity schedule takes into account limited resources and determines a schedule that does not exceed the resource limitations. The limiting resources are often equipment, such as a maximum throughput. However, limiting resources could also be raw material availability, storage space, or personnel availability.

Manufacturing IT’s responsibility is to maintain and export the predicted production capacity so that it can be used to generate a finite capacity schedule. In many production facilities, the capacity is constrained by a single resource for each production line, such as a machine. Such “bottleneck machines” are typical in discrete manufacturing and are usually product-independent. In these situations, the predicted capacity can be easily represented in a table of capacity per bottleneck for fixed time periods. The finite schedule time period, called the time bucket, can be hours, shifts, days, weeks, or months.

Time buckets

Consumable products may have time buckets of hours or shifts, while other goods usually run time buckets of days and weeks. The time bucket will often be a compromise between production personnel, who want long periods of steady production for high efficiency, and supply chain planning personnel, who want small buckets for maximum flexibility. Finite capacity scheduling systems often use theory of constraint (TOC) models and the drum-buffer-rope (DBR) method for fixed bottleneck problems. These are explained in an easy to read series of books by Dr. Eliyahu Goldratt ( ) and should be required reading for any manufacturing IT professional.

Process manufacturing often has “floating bottlenecks.” This means that the bottleneck resource can change based on the current product or product mix. For example, a single line may generate materials that flow into several downstream lines. In these situations, a more complicated scheduling method called process flow scheduling (PFS) is usually used. PFS uses a model of the physical process and may be optimized for minimum material inventory or for economic manufacturing run lengths, depending on the company’s business needs. Representing the predicted capacity in a PFS scheduled system can be complex and complicated. Because there is no single bottleneck machine, capacities must be maintained for each bottleneck machine in each part of a production line. Capacities must also be defined for different product mixes. This complexity usually requires a database or a set of tables, one table per product mix.

In both discrete and process manufacturing, the capacity information is also useful for operations management, providing a quick snapshot of committed capacity (the part of total capacity that is already committed to previously accepted production), unattainable capacity (the part of total capacity that is unavailable due to product mix, maintenance, or other reasons), and the available capacity that can be used for future production requests. Capacity information may also contain a confidence factor. For example, it may specify what production will be available at 95% confidence and what additional capacity may be available at 50% confidence. A confidence factor allows plant management to decide on the risk to take in accepting additional production requests.

Providing accurate and timely capacity information to business scheduling systems should be a goal of every production facility. Plants need to maintain a database of capacity information so that they can receive accurate and implementable schedules.

Author Information

Dennis Brandl is president of BR&L Consulting, Cary, NC, which is focused on manufacturing IT solutions. He is also chairman of the ISA88 committee. Reach him at .

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