Logistics Know How

Supply Chain Forecasting: How to plan for expected and unexpected disruptions

Understand how to properly forecast your supply chain to improve your relationship with suppliers and ensure you’re booking the right amount of cargo to avoid unnecessary costs.

Curtis Doyle, 28 November 2022

31st May 2021

What you will learn in this article:

  • What supply chain forecasting is

  • Why supply chain forecasting is important

  • Different methods of supply chain forecasting

  • What makes supply chain forecasting difficult

As a business owner, you are constantly facing changing customer demands while having constant pressure to reduce costs and improve margins. Given the current situation in shipping with congestion, shortages of equipment (containers) for your ocean freight and the pandemic impacting many businesses around the globe, supply chain forecasting is now more crucial than ever.

What is supply chain forecasting?

Supply chain forecasting combines data from past supply with insights and understandings about demand, to help you make the best decisions for your business – whether it’s stock inventory, cargo booking, budget planning or expanding to new markets.

Analysing supply accounts for the bulk of supply chain forecasting. It involves looking at data about your suppliers to understand when you need to order products from them – whether they’re whole products or raw materials to be assembled further down the supply chain.

Analysing demand is also important to help you understand how much of your product your customers want during any given week, month or quarter. This is affected by a number of factors that can be predictable, like seasons and holidays, or unforeseen, like global events and natural disasters. Often such events can impact various transportation modes such as ocean freight or inland transportation.

Why is supply chain forecasting important?

Supply chain forecasting can play a major role in contributing to an efficient supply chain and a flourishing business:

  • Strategic planning – Businesses can be built or broken in the strategies they take around things like an expansion to new markets, budget planning or risk assessment. Forecasting gives you the insights to make these decisions wisely, ensuring your suppliers can meet your demand.

  • Staying on top of inventory – If you have a better understanding of the demand for your products in different markets, you can work more closely and easily with suppliers to maintain your inventory levels throughout the year. This keeps shortages to a minimum, which will make your customers happy, and keep warehouse fees under control without having to store unneeded stock.

  • Improved customer experience – Customer experience is set to define supply chains in the years to come. By being able to predict customer demand, you can manage your supply to ensure orders are fulfilled on time and you’re never low on stock. The result is a sense of trust between your customers and your business.

Methods of forecasting – Quantitative

There are two predominant methods to producing supply chain forecasting – quantitative and qualitative. Quantitative forecasting relies on historical data to predict future sales, making use of complex algorithms and computer programs to do so.

In quantitative forecasting, you might encounter the below methods. Each has its benefits and downfalls and should be considered carefully to determine their best use:

  • Moving average forecasting is one of the simpler methods of forecasting, which is based on historical averages. However, it treats all data equally and doesn’t take into account that more recent information may be a better indicator of coming trends than say, data from three or five years ago – and it doesn’t allow for seasonality or trends.

  • Exponential smoothing also considers historical data but does put more emphasis on recent data – as well as accounting for seasonality. This makes it ideal for short-term forecasts.

  • Auto-regressive integrated moving average (ARIMA) is a method of forecasting that is known for being highly accurate, but also very time-consuming and costly. It’s well-suited to forecasting up to 18 months or less.

  • Multiple Aggregation Prediction Algorithm (MAPA) is a newer method of quantitative forecasting which is specifically designed for seasonality – making it perfect for businesses producing seasonal items.

Methods of forecasting – Qualitative

In the event of historical data being hard to find, when launching a new product, for example, you need a new approach – and that’s where qualitative forecasting comes in handy. It relies on the insights, expertise and experience of industry experts – alongside more detailed research:

  • Historical analogies predict sales by assuming that the sales of new products will mirror an existing product that you, or a competitor, produces. While it can work in the long term, it’s not advised for short-term forecasting.

  • Internal insights are a ground-up approach to forecasting; using the insights and opinions of experienced staff members to inform predictions. As you might expect, it is not known for a high level of accuracy but is an option when quantitative methods aren’t possible.

  • Market research will be familiar to many businesses and is the process – sometimes timely and costly – of researching, surveying, polling or interviewing a specific demographic.

What is the best method of supply chain forecasting?

There is no one-size-fits-all approach to supply chain forecasting – and regardless of which you choose, you will never be 100% accurate – because at least some of the forecasting is based on assumptions; and there will always be unforeseen events that defy those assumptions – like a pandemic, for example!

However, while qualitative forecasting has its place in those situations where historic data is unavailable or unreliable, it is the consensus that quantitative forecasting is the strongest method. It uses concrete information and statistical techniques, which removes the risk of bias while producing clearer and more accurate results.

What makes supply chain forecasting difficult?

Supply chain forecasting opens up a world of opportunities for your business through the access it provides to insights on future demands, trends and supply information. But there are several factors that can disrupt the system.

  • Regulation changes and global events like COVID-19 – Changes in regulations between nations and continents can often disrupt forecasting as supply chains adapt to accommodate new laws and past data becomes a little less relevant. In the wake of COVID-19, emergency laws were passed around the world to close borders, stop travel and delay trade – the impact has been far-reaching and ongoing, with bottlenecks at borders and congestion at ports. Combined with Brexit, it’s easy to see how regulation changes can disrupt supply chains and supply chain forecasting.

  • Changing trends and consumer habits – While changing trends and habits are a constant in the world of supply chains, the unpredictability with which they change means they remain a threat to forecasting. For example, in the last year as the world closed down consumers went online and spent over $4.2 trillion dollars worldwide – while online shopping has only been growing, the sudden shift forced many small businesses to adapt quickly to avoid stock shortages or delays.

  • Seasonality and supplier lead times – Not accounting for seasonal and peak periods in supply chain forecasting will easily throw your forecasting off course. These periods in the calendar often impact ocean freight and should be planned months in advance – or you risk missing out on opportunities to capitalise on increased demand.

An important consideration when doing this planning is that different suppliers or manufacturer will have different lead times, not just based on the services they provide, but due to their own seasonal or holiday calendars. This makes building strong relationships with suppliers really important.

How you can stay up to date on all relevant supply chain news

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