What is Demand Forecasting in Supply Chain?

Ever wish you could predict the future? Do the day-to-day operational requirements of your business make you long for a crystal ball? Tired of taking a reactive, rather than proactive approach to your inventory, but unsure how to proceed? You’re in the right place.

As the old saying goes: Failure to plan means you’re planning to fail, but to plan for the future, it helps to at least have an idea of what the future might look like.

We’ll level with you — while we’re experts in logistics, we’re not gifted with psychic abilities, and as much as it’d help (and make us lots of money) we can’t predict the future. We can, however, make well-educated guesses, and these data-driven insights are what’s referred to in the biz as demand forecasting.

Fancy learning more? Read on.

What is demand forecasting?

In layman’s terms, demand forecasting is a method that businesses use to predict customer demand for their products or services over a certain period.

According to The Institute of Business Forecasting, businesses focused on consumer products can miss their one-year forecasts by a staggering 20% on average, and a large chunk of these losses are rooted in issues like stockouts and overstocking — both of which are prevented by demand forecasting.

Demand forecasting isn’t perfect — 100% accuracy can neither be guaranteed nor expected, but it’s still an essential element of supply chain management, as the insights you gain can be invaluable in the decision-making process.

How does demand forecasting work?

Demand forecasting uses all sorts of sources to estimate future inventory requirements. Let’s examine a few:

Historical sales data

If you’ve been in the business for a while, you’re likely sitting on a treasure trove of past sales figures. This data is invaluable for spotting trends or patterns that can be used to inform your inventory decisions. For example, if you notice that your sales of a specific type of winter jacket spike every November, you can forecast higher demand for this item as the temperatures drop.

Market trends

By keeping your finger on the pulse of industry trends you can gauge the broader market dynamics that affect your business. Take the recent uptick in sustainability-focused brands — if competitors within your niche are loading up on planet-friendly products, that’s a good indicator that you should do the same.

Consumer behaviour insights

Are your customers impulse buyers or do they hold out for sales? Are their purchase decisions driven by emotion, or are they more concerned with the utility and practicality of your product? What time of year do they shop? What time of day do they visit your store? By getting inside your customer’s head, you’ll stand a better chance of understanding which products they’re more likely to purchase (and thus, which you’ll need to stock up on).

External factors

Finally, we have the wildcard of demand forecasting: economic conditions, such as changes in disposable income, interest rates, or inflation, can heavily influence purchasing behaviour. Think of the pandemic: many businesses that didn’t account for the drastic shift in consumer priorities faced unexpected demand changes. In fact, a report from McKinsey & Company indicated that 75% of consumers tried a new shopping behaviour during COVID-19, emphasising how unpredictable external factors can shake up your demand forecasts.

You might draw your insights from all of the above sources, or just one or two, but as always GIGO (Garbage In, Garbage Out) applies; the quality of data inputted will determine the accuracy of the forecasts’ output.

Methods for demand forecasting

Now you’ve got a broad idea of the types of data you can use in your forecasting, let’s examine the two main methods used to transform your data into actionable insights.

Quantitative forecasting

Quantitative forecasting is all about number-crunching and typically focuses on predicting future sales figures. It includes a number of different elements, including:

Econometric forecasting

This method uses statistical models to link factors like price, income levels, or GDP to demand. By analysing how these factors interact, you can forecast how demand will change as they shift. It’s a bit more complex but useful for businesses where the economy plays a big role in sales.

Trend projection

Trend projection is one of the most straightforward methods. You take past sales data and project it into the future, assuming the same patterns will hold. This works great for businesses that have a relatively stable market and consistent trends — if your sales have grown by 5% every year, for example, trend projection assumes they’ll keep growing at that rate.

Barometric technique

This method looks at broader economic indicators, like consumer confidence or stock market trends, to predict demand. Essentially, it uses these external factors to gauge whether demand will go up or down. It’s particularly useful if your business is heavily influenced by the overall economy.

Qualitative forecasting

If you’re short on hard data or dealing with brand-new products or unfamiliar markets, qualitative forecasting is your best bet. These methods rely more on subjective insights, rather than hard numerical statistics. Qualitative forecasting techniques include:

Market research

Market research is exactly what it sounds like — getting feedback from your target audience through surveys, focus groups, or interviews. This helps you gauge potential demand, especially when launching new products. If you're wondering how well that new product might sell, asking customers directly is a solid approach.

Sales force composite

Your sales team knows your customers better than anyone. In this method, you gather input from your salespeople, who provide their predictions based on their day-to-day interactions with customers. While it’s based on judgement rather than data, it can be surprisingly accurate if your team is in tune with customer trends.

Delphi method

The Delphi method takes a group of experts, asks them to provide their forecasts independently, and then refines those predictions through multiple rounds of review. This method is especially useful in uncertain or rapidly evolving markets

Once you’ve decided which sources to draw your data from, it’s then a case of processing it and using specialised software to calculate and generate the forecast.

Of course, if you’re already struggling to juggle the myriad responsibilities involved in running your own business, this might feel like a big ask; yet another plate to spin. If that’s the case for you, you may want to explore the second option: outsourcing your logistics operations to a third-party logistics provider like ourselves. You can read up on all the services we offer here.

Why is demand forecasting so important?

So why go to the effort of producing a detailed demand forecast? Is it worth trawling through all that data?

Of course, it is — here’s why:

⚙️ It boosts efficiency: Overstocking leads to wasted resources and storage costs, while understocking risks lost sales and frustrated customers. By predicting demand more accurately, you avoid these extremes, making your operations leaner and more cost-effective.

🗓️ It aids production planning: With a clear picture of future demand, you can ensure your production team has enough materials and resources at the right time. This prevents bottlenecks in the supply chain and eliminates the dreaded last-minute scramble to meet unexpected demand surges.

😊 It keeps your customers happy: Nobody likes to hear “Sorry, we’re out of stock.” Demand forecasting helps keep your shelves (whether virtual or brick and mortar) stocked with the products your customers want, precisely when they want them. By predicting buying patterns and seasonal trends, you can ensure that your hottest products are always available, meaning more satisfied customers, more profit in your pocket, and fewer sales for your competitors — happy days.

📊 It helps capacity planning: Capacity planning is all about ensuring you have the right amount of resources (think: manpower, machines, materials) to meet customer demand without overloading your system, and demand forecasting can help here, too.

The challenges of demand forecasting

We’ve waxed lyrical about why demand forecasting is a worthwhile pursuit, but it doesn’t come without its pitfalls. To provide a balanced overview, let’s discuss some of the challenges you may face, too:

Data quality: Remember when we mentioned GIGO (Garbage In Garbage Out)? If the data you’re using isn’t accurate or is incomplete, your forecasting results may be poor, and this could play havoc with your business.

Accounting for all the variables: Let’s take a look at just a few of the factors that affect demand:

• Price elasticity
• Weather
• Inventory availability
• Seasonality
• Promotions
• Product cannibalisation
• Supplier lead-time variability
• The economy

And the list goes on…

Accounting for all of these variables is tough enough, but you’ve also got to consider that none of them exist in isolation. If you’ve got a store with thousands of individual products across multiple channels, this could equate to millions of data points. As such, you’ve got to make sure your software is up to the task — manually analysing via a bog-standard spreadsheet is near-futile, and will likely cause more inventory-related problems than it solves.

Omnichannel data siloing: If your in-store sales data is on one platform and your ecommerce data is on another, your forecast might miss critical insights, leading to an incomplete picture. You also run the risk of miscommunications between departments, and because of the fragmentation, there’s a greater need for manual interventions during forecasting, creating more work and making it harder to scale. This is why integrating your ecommerce software with your inventory management system is so crucial.

Human bias in qualitative methods: Qualitative data can be invaluable, but it’s also prone to bias. If one of your top salespeople is overly optimistic about a new product launch, for example, their forecast might be skewed. Likewise, experts in the Delphi method may start influencing each other as they refine their forecasts, leading to groupthink. Accounting for these inaccuracies can be problematic, and, due to human nature, it’s impossible to remove bias altogether, which is why it’s essential to balance things out with quantitative data.

How to get started with demand forecasting

Hopefully, by now we’ve convinced you of just how important demand forecasting is. If you’re keen to get started, follow these three steps:

1. Understand that demand forecasting works best when it’s focused on its primary goal: predicting what, how much, and when customers will purchase. Think of it as the cornerstone of your supply chain planning. Other processes, like Sales and Operations Planning (S&OP), should support this function without overshadowing it. This way, everything can work in harmony.
2. Feed your forecasting tools as much (quality) data as possible. Demand forecasting thrives on information — both historical and current. Don’t just rely on past sales figures; dive into other sources like market trends, economic indicators, and even customer behaviour insights. The more diverse your data, the richer your forecasts will be. Modern inventory management software, especially those powered by AI and machine learning, can analyse vast and complex datasets, providing you with predictive insights that are invaluable for planning.
3. Take a strategic approach to budgeting and resource allocation. It can be tempting to stick with legacy practices, but adjusting your workflows for better demand forecasting will pay off. You’ll see improvements in profitability and risk management, not to mention a more streamlined experience for your supply chain team. Setting aside budgets and resources early in the process can help get everyone on board and ensure a smooth rollout.

Keen to implement demand forecasting in your own business but unsure if you’ve got the capacity, resources, or know-how necessary? Get in touch with our team today — we’ve years of experience in providing businesses of all shapes and sizes with bespoke supply chain solutions.