Supply chain capacity planning—manage demand volatility

July 1, 2022 Anastasiya Malinovskaya

In today’s dynamic world and highly competitive market, supply chains seek to achieve customer satisfaction. Customers are more demanding than ever; they expect low-cost, on-time deliveries, and reliable service tailored to their needs.

Effective supply chains meet customer needs and remain profitable while non-optimized supply chains can undermine customer satisfaction and loyalty. The latter can also hamper profitable growth.

The critical functions of supply chain management are managing information and production flows. So, production and capacity planning are vital for ensuring supply chain efficiency.

In this post, we explore what capacity planning is from a high-level, strategic perspective and look at examples of how to account for throughput and storage capacity in a supply chain.


  1. Capacity planning in a supply chain
  2. Accounting for capacity in different supply chain software
  3. Example 1. Strategical capacity planning
  4. Example 2. Tactical capacity planning
  5. Conclusion

What is capacity planning in a supply chain?

Companies do capacity planning to determine if the throughput of a manufacturing facility is enough to fulfill demand. It is used to schedule production for short- and long-term planning.

To understand whether a manufacturer meets forecasted demand and calculate capacity, they have to consider the number of machines, staff size, product mix, efficiency, and other aspects. Usually, manufacturing companies do this kind of detailed capacity planning at the facility level.

However, at the supply chain level, managers should consider average and actual capacity while planning logistics operations. So, here we’re going to focus on how to account for the average capacity of a production facility and distribution center in a supply chain.

How to account for average capacity in different supply chain tools?

When you are planning to optimize your supply chain and consider capacity, choosing the most suitable tool (software) is important. The right choice will save you time and money.

The traditional option has been spreadsheets. There are many templates that you can find on the internet, but they are usually very complex and require manual work. Moreover, the immense amount of data and time that are needed could also increase the risk of making a mistake.

A more efficient option is to use software for supply chain design and analytics, such as anyLogistix. With it, you can run an optimization experiment and consider pre-built parameters and constraints, such as transportation and facility-related costs, demand fulfillment, throughput limits, etc.

Not only is anyLogistix more user-friendly than using spreadsheets, with map visualization and pre-built tables for data input, you also have several possible scenarios to choose from, for example, you can focus on profit.

To understand how anyLogistix helps resolve supply chain problems caused by demand fluctuations, let’s look at two capacity planning examples – one strategical and one tactical.

In the webinar recording below, SimWell shows how to incorporate capacity planning into your supply chain strategy featuring the said examples:

Example 1. Strategic capacity planning: how to account for constantly growing demand overflow

Baseline model and problem identification

Let’s say in anyLogistix you have a model of a supply chain in the North-East USA and you’re selling copy machines.

In this model, distribution centers (DCs) have maximum throughput – a maximum quantity of units that they can outbound per month. Inbound units for DCs come from a factory, so you need to set maximum throughput for the factory too (the number of units that it can produce per month).

After uploading the data to anyLogistix and configuring the supply chain model, you can run a Network Optimization experiment for five virtual years and see if there are any capacity issues. This is your baseline solution.

anyLogistix model of a supply chain in the Northeastern USA: baseline

anyLogistix model of a supply chain in the Northeastern USA: baseline (click to enlarge)

Investigating the problem

You can see that DCs in Detroit and Columbus serve the wrong customers because there’s already another DC (Philadelphia) in that area that efficiently supplies them. So, by improving this situation we could reduce transportation costs and increase our profit.

When we look at constraints in our model, we see that the Philadelphia DC has reached its capacity every year, and every year the overflow increases. With such progression, in five years this DC will be struggling.

How can we resolve this problem?

Locate potential DCs and choose the best location

Using a Greenfield Analysis (GFA) experiment, also called center of gravity analysis, you can determine the optimal location for new DCs in terms of transportation cost.

Determine the optimal locations for new DCs to serve this customer group

Determine the optimal locations for new DCs to serve this customer group (click to enlarge)

Assuming the new DCs have the same max throughput as the current ones, the GFA experiment has suggested three new DC locations.


As your supply chain is facing the problem of reaching the throughput limits, let’s assume you want to include not one but two more DCs. A Network Optimization experiment will help you determine which two DCs out of the potential three will be the best in terms of profit.

With the two locations suggested by anyLogistix you can increase the supply chain’s profit.

anyLogistix model of a supply chain in the Northeastern USA: solution

anyLogistix model of a supply chain in the Northeastern USA: solution (click to enlarge)

Example 2. Storage capacity planning: how to account for seasonal demand overflow

Let’s now look at supply chain capacity planning from a tactical viewpoint – should we rent, purchase, or build a new DC?

Baseline model and problem identification

Again, assume you have a supply chain. Firstly, as in the previous example, you configured your supply chain model in anyLogistix and ran a baseline scenario to see the current state.

anyLogistix baseline model of a supply chain: how to account for seasonal demand overflow

anyLogistix baseline model of a supply chain: how to account for seasonal demand overflow (click to enlarge)

You can see that your supply chain is profitable but doesn’t fulfill all demand because your manufacturing facility has reached its storage capacity in certain months over five modeled years.

To solve this capacity problem, you can expand the facility by adding more storage space or rent a storage nearby to cover seasonal demand surges.

Solution to the capacity limitation problem

You can pinpoint these two options (rented and extended facilities) on a map in anyLogistix and run a Network Optimization experiment. The best option that the software suggests, in terms of profit for a 5-year span, is to rent additional storage space.

High-level and detailed capacity planning in supply chain

When you are looking for ways to increase the efficiency of a supply chain you should do it holistically, otherwise improving one component, for example, production capacity, might jeopardize another.

We’ve showed you examples of how to do capacity planning from a high-level, strategic perspective. To design and optimize a supply chain, we’ve considered average capacity. However, for a detailed and precise capacity calculation you can use a more effective technique – supply chain simulation, as it captures the system’s dynamics.