What is Demand Forecasting?

How much product do you need to stock for the next big holiday weekend? How much stock should you invest in for next season? If you want to know the future, look to the past. That’s where demand forecasting comes in. 

Demand forecasting ensures you have the right products in the right place at the right time. You can ensure you have enough products ready to meet demand without filling your warehouse with excess stock. That’s integral to keeping your customers happy and your bottom line healthy. 

In this article, we’ll explore what demand forecasting is, types of demand forecasting, demand forecast tools, and more.

What is Demand Forecasting?

Demand forecasting is predicting future demand for a product or service. 

Demand forecasting methods use predictive analysis based on historical data, current trends, market insights, expert predictions, and other data to determine the potential future demand for specific products during certain times. 

For example, using historical sales data, an ecommerce candle store owner knows that their biggest sales peaks are around Valentine’s Day and Mother’s Day so they can plan for high demand.

While you can’t predict the future with 100 percent certainty, the proper demand forecast tools and methods can help you make accurate estimations to inform business decisions.

Importance of demand forecasting

The importance of demand forecasting cannot be overstated. The biggest reason for demand forecasting is to enable businesses to optimise their supply chain. Accurate demand forecasting is critical for ecommerce businesses to plan production and stocking of goods to meet customer demand at certain times. 

If you aren’t forecasting demand and supply, you can overestimate or underestimate how much stock you need. You’ll either have far too much inventory sitting in the warehouse costing you money, or a short supply of products, which means you’re missing out on valuable sales and profit.

And that’s only the tip of the iceberg. 

Let’s dive into the many benefits of demand forecasting:

1. Understand seasonal trends: Diving into historical sales data will help you identify and understand seasonal fluctuations, which can better inform your business decisions. You will see peak holiday seasons and when demand dips, and can make decisions accordingly. For example, if you see that your sales drop in June, you can look at sales promotions to keep customers engaged.  

2. Prevent out-of-stock items: From the customer’s perspective, this is a top benefit of demand forecasting. Running out of stock damages the customer experience. If customers see an item is out of stock, they may not return. Demand forecasting can help you work out which products are most likely to be in demand at different times and what quantities you’ll need in stock, lowering the risk of running out of stock. 

3. Avoid over-ordering: You also want to avoid having a warehouse full of products you can’t sell. Over-ordering can damage your profit margins and cost you more in the long run. Space is a premium. You may need to sell the items at a discount or risk not selling them to free up valuable warehouse space for the next season.

4. Prepare for the future: While you can’t prepare for every eventuality (COVID-19, anyone?), demand forecasting helps you better prepare your supply chain and business for events you can’t control, whether that’s a natural disaster or a new competitor. 

5. Enhance customer experience: When customers come to your store and can buy what they want, you deliver a great customer experience. Customers are more likely to come back and buy more, which means a healthier bottom line. 

6. Grow your business: Demand forecasting helps you adapt and grow your business. You better understand trends, sales patterns and customer demand, which helps you make informed decisions that will grow your business. 

7. Save costs: Demand forecasting helps businesses make decisions that can improve their bottom line. For example, it enables you to assess whether your warehouses are adequately sized to meet demand. Oversized warehouses can lead to excessive costs, constituting a heavy fixed overhead burden. Another significant advantage of demand forecasting is that it saves you from unnecessary spending. You’re not overspending on SKUs that might become dead stock, and you’ll avoid paying surplus costs to store those items at a warehouse. Reduced costs mean more capital to invest in your business growth.

8. Avoid product waste: If your business works with time-sensitive or perishable products, they have a limited shelf life. The longer your goods go unsold, the greater their expiration risk, contributing to lost profits. Demand forecasting helps prevent overstocking, which helps reduce the risk of wastage.

The importance of demand forecasting in supply chain management

What is demand forecasting in supply chain management? 

Demand forecasting impacts every part of the supply chain, particularly warehousing and inventory management. It’s about ensuring you have everything in place — from warehouse space and infrastructure to employees, to fulfil customer demand at any given time. 

Let’s look at a common scenario: 

When a business prepares its forecast for a peak period, whether it uses a third-party logistics provider like eStore or handles it in-house, it must start with internal planning.

Along with assessing whether their warehouses are adequately sized to meet the demand, the business considers the infrastructure, including scanners, pallet racking, trolleys, and potential automation and robotics. 

If there needs to be more equipment to meet the forecasted demand on peak days, the business must order equipment in advance. For example, to ensure you have everything in place for Black Friday sales at the end of October, you’ll need to start the process in July, as equipment delivery often takes several months. 

Once warehouse size and equipment requirements are determined, the most valuable asset becomes the workforce. It’s crucial to ensure appropriate staffing levels and shift structures, transitioning from one shift to multiple shifts per day if necessary to manage the peak demand. 

Logistics revolves around planning. If you can use demand forecasting to inform your supply chain management, you will have a profitable and headache-free peak period.

Demand forecasting methods in the supply chain

No one can predict the future with one hundred percent certainty. However, different demand forecasting methods will help you get closer to the reality. Here are some different types of demand forecasting in supply chain management:

Active vs passive demand forecasting 

The simplest way of looking at demand forecasting is as active or passive demand forecasting. 

Passive demand forecasting is the simplest method. It depends entirely on your historical data, such as past sales data, to predict the future. For the most accurate results, you should use data from the same sales period as the one you want to predict, e.g. Use Christmas sales data from last year to predict Christmas sales this year. 

There’s no need to use statistical methods or study external factors and economic trends. Passive forecasting is a good model for stability rather than business growth. It assumes that this year’s sales will be approximately the same as last year’s.

Active demand forecasting draws on lots of different data points to assess future demand, including:

• Economic trends

• Historical data 

• Marketing campaigns 

• Expansion plans

• Market analysis e.g. growth projections

This is an excellent method if you’re planning for growth or are just starting out.

Long-term vs short-term demand forecasting 

How far in advance do you want to predict? Short-term demand forecasting considers the next year, while long-term forecasting considers periods of more than a year, which can help you understand and plan for bigger market trends. 

Macro vs micro demand forecasting

Think of macro and micro demand forecasting in terms of how far you zoom in. Micro demand forecasting zooms in to examine a specific customer segment or industry. Macro-level forecasting, on the other hand, zooms out and examines broader and more external market influences, such as the economy, consumer trends, and competition, and how these will impact market demand.

Qualitative vs quantitative demand forecast models

Another way of approaching demand forecasting is with qualitative or quantitative models.

Qualitative demand forecast models draw on experiences, expert opinions, and estimates. You will likely involve internal and external experts in your demand forecasting to analyse and make predictions. 

Three popular models in qualitative demand forecasting include:

• Delphi method: This includes sending a questionnaire to a panel of relevant experts, customers or business peers and analysing the feedback. 

• In-house experts: This involves recruiting an internal panel of “experts” who work like a focus group to review data and offer predictions. 

• Market research: Whether reading reviews, analysing competitors, or monitoring social media, market research can be a valuable demand forecasting method. 

Quantitative demand forecasting, also known as statistical demand forecasting, looks at existing company data, such as financial reports, sales, revenue, and website analytics. It then applies this data using algorithms and statistical modelling to estimate future demand.

Types of quantitative demand forecasting include:

• Regression analysis: is the go-to method for businesses. It is a way of mathematically sorting out which variables impact demand. For example, you take all your monthly sales numbers for the past three years and any data on the independent variables you’re interested in, such as the weather. Then, you plot them on a chart so you can see the relationship between your sales and the weather over the same time period.

• Time series analysis: Time series quantitative models analyse historical data to establish patterns and build models, and use them to predict future demand and sales volumes. Time series demand forecasting in retail also involves identifying and incorporating seasonal variations, such as holiday shopping peaks, into predictive models.

Why you should combine demand forecasting methods

With so many different demand forecasting methods, your prediction may differ depending on which model you use. That’s why it’s best to use multiple demand forecast methods for a well-rounded picture of your future sales. The key is to use the ABC rule: Always Be Curious.

You need to keep challenging the numbers. When you think you have a prediction, look at it again and ask yourself, is it what you thought it would be? Keep looking for different ways to analyse any data you capture, which also means making sure that you are capturing the relevant information in the right way. When you partner with a 3PL company like eStore, they can help you aggregate the data points you need to create accurate forecasts.

Factors Influencing the Customer Demand Lifecycle

Countless factors influence the customer demand lifecycle. Some factors are within your control, but many are not. 

Let’s take a look at some of the most influential factors:

1 Seasonality

Seasonality is the changes in your sales data that occur every calendar year. It is generally measured over one year and stays predictable. Seasonality can be caused by natural factors, like the seasons, or by human factors, such as public holidays, retail events (like the Boxing Day sales), or school terms. 

For any ecommerce business, it is critical to understand how product demand shifts based on seasonality. For example, an ecommerce business that sells camping gear will undoubtedly expect an uptick in sales leading into the summer season. 

But in some cases, seasonality can be more complex. For instance, an ecommerce business selling kitchen equipment might find it tough to pinpoint specific times of the year when sales will increase.

To incorporate seasonality into your demand forecasting models, you must identify the seasonal cycle and the seasonal factors affecting your demand. You can use time series analysis, regression analysis, or seasonal indices to adjust your demand forecasts for seasonality.

2 Competition

How many competitors do your customers have to choose from? Competition directly impacts demand, as more companies are vying for your customers’ attention. Demand will likely take a hit when a new competitor comes onto the scene or an existing competitor introduces new products. Using demand forecasting tools can help you plan and prepare.

3 Types of products

The types of products you sell can also impact customer demand. For example, if you sell high-priced luxury items, such as watches, there will be fewer repeat customers, so product demand will be more unpredictable, and forecasting demand will be more difficult. 

4 Location

Where your customers are located will affect their demand for products. People from different nations, cultures and climates have different needs and expectations, meaning a product line that’s flying off the shelves in one country may get no sales at all across the border. And when selling in different hemispheres, you have the added impact of seasonality.

5 Macroeconomic trends

Macroeconomic events have a big impact on customer demand. These could be changes in government policies or central bank decisions on interest rates, which can affect consumer spending power. The influence can vary depending on whether the central banks aim to stimulate or restrict economic activity. 

Typically, retailers can forecast about 12 months ahead. Still, sudden shifts in central bank policies, like unexpected interest rate hikes after prolonged stability, can disrupt forecasts and existing inventory plans. For example, any impact on disposable income and household budgets will inevitably affect the purchase of luxury or discretionary goods for the regular household.

By closely monitoring the situation, you can determine how deeply you should invest in inventory purchasing, especially during peak periods.

6 Weather 

If you’re an online fashion retailer, and summer is much cooler than average, you’re likely selling less of the summer range because people need shorts and tees less. Likewise, if you’re a winter sports company selling ski and snow gear, you’ll sell less if the winter months deliver little snow.

7 Global events

Global events, such as political events and natural disasters, can impact demand. For example, consumers may boycott products associated with certain countries during a political conflict. During a natural disaster, like a flood, consumers need products like torches, generators and water pumps. 

Some events, like floods and bushfires, happen with enough frequency that you can prepare to a degree. But other events will throw demand forecasting completely off course. The global pandemic is the best example of this.

COVID-19 shattered the typical demand forecasts that guide retailers in figuring out how much to order and where to stock inventory. Early during the pandemic, sudden lockdowns and the move to working and schooling from home caused panic buying of many products—toilet paper experienced an 845% spike in demand in 2020. Some items sold out instantly, while others didn’t. At the same time, consumers rapidly shifted to online shopping, meaning brick-and-mortar retailers didn’t need to stock as much inventory on physical shelves. 

The pandemic also caused some obscure trends that retailers could never have predicted. For example, there was a sharp uptick in mascara sales, with drops in lipstick sales during the same time frame. The cause was the mask mandates, which encouraged female consumers to draw attention to their eyes while they covered their mouths in public. Some retailers abandoned their forecasting resources and hunkered into “survival mode.” But the right demand forecasting tools may have been able to help protect their bottom line.

Using AI for demand forecasting

The retail landscape is constantly changing, becoming more volatile and fad-driven than in the past. So, how can you accurately forecast product demand when the commercial landscape continually changes? That’s where artificial intelligence comes in. AI helps businesses take demand forecasting to a higher level.

The growing role of artificial intelligence and machine learning can help retailers and ecommerce businesses draw on a wealth of real-time internal and external variables and create forecasts that allow them to stay ahead of ever-changing demand.

According to McKinsey Digital, AI-powered forecasting can reduce errors by 30% to 50% in supply chain networks. Better accuracy leads to a 65% reduction in lost sales due to inventory out-of-stock situations and warehousing costs decrease up to 40%. 

AI demand forecasting tools can integrate with all your data sources and factor in future sales predictions, current stock levels, potential supply chain delays, and any other demand or supply constraints to ensure your supply chain and inventory management plan is accurate.

AI tools can also help with demand forecasting for new products. Businesses often rely on historical sales data to accurately predict demand, but this can be impossible for new products with no sales history. Demand forecasting software with AI capabilities can be used to simulate a demand forecast model based on similar products, and then automatically update the model as soon as new data arrives.

You can also use generative AI to answer simple questions about trends that help you forecast demand. Questions such as, “What are the biggest market trends that may impact our demand forecasting?” This can strip away some of the complexity around using technology for demand forecasting. 

However, AI and human intelligence should not be mutually exclusive. AI-driven predictions may still require human analysis to determine an extenuating cause.

Demand Forecasting Tools and Software

Getting accurate results with demand forecasting can take time and effort. The best way to achieve demand forecast accuracy is to use demand planning and forecasting software. 

The importance of demand forecast software is evident: By incorporating demand forecasting software into your retail operations, you can tap into data, trends and predictive analysis to effortlessly make precise predictions. 

The best demand forecasting software uses advanced algorithms, statistical models, and AI-powered tools to analyse historical sales data, market trends, and other relevant factors, making trend projections and forecasting future demand for products. 

Here are some more benefits of the best demand forecasting software:

• Provides real-time insights into your demand forecast so you can make informed decisions. 

• Examines each product’s unique life cycle and identifies seasonal trends. It can reveal where and how demand for certain products is affected by holiday surges, like Black Friday or Easter weekend.

• Create forecasts for individual products or services or entire product lines. 

• Create forecasts for different periods, such as days, weeks, or months.

• Adjust your forecasts as new data comes in so your forecasts remain up-to-date and accurate.

Summary

Demand forecasting helps you make smarter business decisions, especially for supply chain management, and prepare your business for the future. It will ensure you have the right products in the right place at the right time to make sales and grow your business. The trick is to use a wide variety of data, a combination of methods, and the best demand forecasting software to make the most accurate predictions possible. 

Find out how eStore can help you create accurate demand forecasting and implement the best logistics plans to meet your customer demand.