What Is Capacity Planning and How Often Should You Do It?

Capacity planning, an important element of manufacturing, refers to the process of matching production capacity with sales demand. Apart from manufacturing, this discipline is applicable to virtually every endeavor that faces constraints in meeting demand, and it includes service industries, retail and commercial sales, IT, government agencies and many more.

While the most common use of capacity planning is to schedule production capability to meet short- and medium-term demand, it's also an important element of long-term strategic and organizational planning.

In most organizations, formal capacity planning takes place only once a year. While this isn't ideal, the complex nature of the iterative process that considers sales demand, production capacity and material sourcing means that a capacity planning exercise may take several weeks. Due to this, and the need to be able to respond proactively to unexpected events, many businesses are turning to high-level analytical solutions to discover what capacity planning is and how to optimize capacity planning decisions.

What Is Capacity Planning?

Capacity planning is determining the ability of a production line, service department or function to meet a specified demand over a period of time. Inherent in this are actions required to adjust the capacity of the system to meet demand. This may include working additional shifts, outsourcing and investment in additional capacity.

The magnitude and pattern of the demand is first established. This, known as the demand forecast, is a detailed analysis of expected sales demand on a weekly, monthly and annual basis. Concurrently, the organization's production capability is established. Starting with theoretical capacity, management determines an effective capacity that takes into account factors such as downtime, servicing, legal and other constraints. Thereafter, factors such as available manpower and the number of shifts to be worked are applied to determine actual capacity.

This process, called rough cut capacity planning, matches overall demand to production capabilities. In an ideal world, production capacity would equal demand so as to minimize inventory costs. However, this is rarely possible due to inventory and production lead times, so in practice most organizations carry a certain level of inventory to act as a buffer between production capacity and peak sales demand.

The medium-term capacity plan normally aligns with the annual budget. This is typically supplemented by a short-term capacity plan that concentrates on meeting the immediate production requirements for the next period of time, which may be a week, month or quarter. Additionally, a long-term capacity plan focuses on providing future capacity as part of the organization's strategic planning process.

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History of Capacity Planning

When looking at capacity planning, it's useful to learn the background details. It's likely the first attempts at capacity planning occurred during the Industrial Revolution, and its importance grew as companies adopted mass production techniques in WWI. Initially, capacity planning was, by necessity, a clerical function performed by teams of industrial engineers who matched capacity of individual machinery and production lines to demand. However, the advent of computerized production planning in the last century opened up the potential for computers to do the number crunching, reducing the manual load.

It wasn't long before capacity planning became a part of materials resource planning (MRP) and ERP. Its primary focus was on the practical steps needed to provide a production capability to meet demand.

Modern Concepts of Capacity Planning

If demand was predictable and stable, then rough cut capacity planning would be all that's needed when the question of what is capacity planning arises. Unfortunately, that is not the case. There's intense competition between companies for market share, which sees enterprises continually searching for ways to reduce costs; at the same time product life cycles are getting shorter. Added to that, unpredictable global disruption such as weather events and trade wars threaten supply chains. The effect of this is that plans alter frequently and capacity planning techniques must adapt to these changing needs.

This means capacity planning needs to be more precise as well as be able to reflect both medium- and short-term realities. The time to prepare and review capacity plans needs to change from weeks and months to days if these plans are to be usable in a fast-changing world. Additionally, capacity planning should not only consider capacity but also production cost and optimization. Most importantly, capacity plans should be achievable and practical.

Complexity of Capacity Planning in Large Organizations

Capacity planning for a company that has a handful of production lines and relatively few product types is relatively simple, although even in this case the possible permutations are many. However, when tackling the question of what is capacity planning, the situation is infinitely more complex in large organizations making hundreds of different products, especially when production facilities are not product-specific. In this event, there may be thousands of possible permutations to consider, a task that's practically impossible with conventional capacity planning tools. Certainly, the time needed to produce even a reasonably precise capacity plan is likely to be measured in weeks or months. Added to that is the virtual impossibility of reviewing the capacity plan, except on an annual basis.

It's almost inevitable that capacity planning using conventional MPR and ERP tools is limited to achieving a workable arrangement that meets demand. Although the issue of what is capacity planning is easily answered, it's almost impossible to evaluate the answers to questions such as:

  • What's the lowest production cost?
  • How can we optimize production?
  • What represents the best product combination for each facility and production line?
  • How can the organization increase overall profitability while meeting demand?

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Optimizing Capacity Planning Using Intelligent Modeling

The core of the problem lies with the difficulty of assessing multiple options to determine a capacity plan that not only meets demand but which is optimized in terms of certain criteria, such as lowest overall cost, greatest throughput and on-time delivery. While MRP II and its successor EPR determine workable solutions, they don't incorporate the ability to optimize complex scenarios involving multiple production lines and products.

This was the dilemma facing Unilever. Despite having some of the best minds in the business, along with SAP and supporting supply chain software, the company was spending hundreds of hours trying to determine an optimum capacity plan. Unilever needed a better answer. The solution was to introduce a prescriptive analytics intelligent model together with optimization software to define the best solution meeting certain criteria.

At one time this meant the use of complex mathematical modeling, but fifth-generation drag-and-drop modeling techniques now simplify modeling processes. An organizational model is built that closely mirrors reality. Once the model is verified and calibrated, it's possible to determine the most favorable capacity plan and evaluate alternative scenarios in a fraction of the time needed with legacy supply chain planning software.

Moving Capacity Planning into the 21st Century

Building a capacity planning model using prescriptive analytics means it's feasible to constantly review and update the model. In Unilever's case, the company was able to move from an annual capacity planning exercise to performing it on a monthly basis. Additionally, the company was able to factor in existing constraints, which meant solutions were always feasible.

Apart from formal capacity planning, prescriptive analytics models allow organizations to respond proactively to unexpected events such as sudden change in demand, production outages and logistic issues. A further benefit of prescriptive analytics solutions built on a fifth-generation programming language is that it's easier to modify models to reflect changed circumstances than those developed with traditional algebraic mathematical modeling software that requires high-level coding skills. Additionally, line managers and planners find models developed using drag-and-drop techniques easier to understand because nothing is hidden inside complex computer code. Once the phrase "what is capacity planning" is interrogated, it's simple to move onto 21st century methods of setting up a successful model to help your company thrive.

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