Imagine how many people enter and exit supermarkets every day of the year: they buy hundreds and hundreds of very different products, and the shelves empty randomly, but it seems like a magic wand almost always replenishes them on time.
Certain items such as oil, water, or toilet paper have a very constant demand over time, so there must always be availability. However, many other products can vary drastically from one week to another or from one season to another. For example, the demand for ice cream is not the same in summer as it is in winter.
The performance that supermarkets are capable of managing and achieving every year is astonishing. But how do they do it? How do they manage all this to work so well? The answer is not that there are invisible magical elves who replenish the shelves every time a product runs out.
We are talking about one of the most impactful use cases of Artificial Intelligence: demand forecast. Do you want to know what it is and how it works? Keep reading because we'll tell you everything here!
What is demand forecast?
Changing product life cycles, customer loyalty (increasingly demanding), hyper-personalization, shorter delivery times, or strong market competitiveness generate difficulties and a lot of uncertainty in determining the margin between what is demanded and the stock.
In this changing and competitive environment, it is essential to be able to anticipate what will happen to take the right path. This is forcing businesses to react and adapt their production models. Organizations that want to not only survive but also make a difference in the market must have tools that allow them to plan their demand effectively.
Demand forecast is the technique used to calculate as accurately as possible the quantity of goods or services that customers will buy over a certain period of time, in other words, it is responsible for estimating the demand that will exist in the future.
One of the great keys to the success of companies is the ability to meet the demand for their products and services, that is, to have sufficient resources to supply the market. This is why this tool is so important, as it helps organizations to plan their production, manage their inventory, and better administer their resources. By anticipating demand, companies can make informed decisions about how many products to produce, how many to have in inventory, and set reasonable prices to satisfy customers without facing supply shortages.
Production and the supply chain are just one side of the potential of demand forecasting: workforce planning and hiring, marketing, as well as risk management or customer satisfaction can also be positively impacted with this powerful tool.
How do they work?
Advanced Analytics and Artificial Intelligence have become key business instruments thanks to which organizations are able to increase their productivity, consolidate their markets, and stand out from their competition. Big Data has ushered in a new era in which companies can leverage the enormous volume of data they generate to predict the future of their businesses and make better decisions based on that information.
Demand forecast works thanks to predictive analytics, one of the most common uses of Machine Learning. This type of analysis uses mathematical methods to forecast future events or results. Through an iterative process, the predictive model is developed using a set of training data and then tested and validated to determine its accuracy in order to make the best forecasts.
The ability to accurately forecast demand through predictive analysis allows the retail sector to reduce errors from 20% to 50%, according to McKinsey. Subsequently, lost sales can be reduced by up to 65%.
Within predictive models, demand forecast uses regression predictive models: those that can predict a specific value, in this case, the forecast of future demand. They use statistical techniques to establish a mathematical relationship between a variable of interest (known as the dependent variable) and one or more explanatory variables (known as independent variables), with the aim of predicting future values of the dependent variable. These complex algorithmic systems analyze historical sales data, trends, patterns, and many other relevant factors that we will see in the following section in order to forecast correctly.
Playing with demand factors
When predicting demand, it is necessary to take into account that many variables can influence these forecasts. Factors can be a seasonal trend that invariably returns on a certain day of the year, a more subtle factor inherent in the demand for similar products, or even a factor related to production capabilities that we can track internally. There are dozens (if not hundreds) of internal business indicators or external factors that can be taken into account to anticipate an increase or decrease in demand. We can highlight, for example:
The historical evolution of the sales of each product.
The time of year and special events (Christmas, Black Friday, etc.).
The type of product or service.
Consumer behavior.
Market evolution.
The local and global economic situation.
Regulatory regulations in the sector.
Playing with two or three factors on a spreadsheet may be feasible, but it quickly becomes impossible when parameters are multiplied. That's where AI models come in handy: to obtain the most accurate model possible while testing potential demand drivers over a relatively short period of time. The business questions that a demand forecast model can help answer can be very diverse, but here are some examples:
What will be the best sellers in three months?
Why are sales of some products decreasing?
To which areas should products be sent? In what quantities?
How should I adjust the product purchasing strategy?
What marketing channels can help drive purchases?
Depending on the questions you are trying to answer, the model can be built to forecast in daily, weekly, or monthly time intervals. Including a wide range of potential factors, choosing a specific time range, and being able to adjust the results with powerful parameterization are variables that must be taken into account in a powerful demand forecasting model.
How to calculate demand forecast
There are numerous procedures for predicting demand. In this article, we will learn about three fundamental types:
Qualitative forecasting:
They are based on subjective factors, such as conducting surveys of customers about their opinions on products, market trends, or predictions from experts in a particular sector. They are often used when historical data is scarce or unavailable. Qualitative forecasting is less precise than quantitative forecasting, but its implementation is faster and cheaper. This type of forecast merges as much data and information as possible to obtain unbiased estimates.
It is ideal for estimating the demand for new products on the market, although it must be rigorously developed due to the likelihood of obtaining more inaccurate results (due to the subjective nature of its sources).
Quantitative forecasting:
Quantitative forecasting is often more accurate than qualitative forecasting, but it can be more expensive and its implementation requires much time. It uses historical data, statistics, and other objective metrics that allow for fairly accurate results. It is related to market evolution to make sales estimates by conducting sales tests with pilot items and studying the results.
This type of forecast is often used for inventory planning, preventing short- and long-term sales, and for supply chain optimization.
Causal model:
By incorporating detailed information about the factors that influence demand variations over time, the causal model is the most accurate and complex demand forecasting tool, perfect for organizations seeking to predict their long-term demand. They are based on the in-depth analysis of sales history.
For example, a jewelry sales business could implement a causal model forecast considering factors such as their historical sales data, marketing budget, promotions, digital campaign results on social networks, their new stores, their competitors' prices, the weather, the general demand in their area, and even the unemployment rate in their locality.
Advantages of demand forecast
Predictive analytics-based solutions are the key to anticipating the future and choosing the right path. This achieves greater productivity in different departments and operations and reduces the company's operational risks. Let's take a look at the advantages of using demand forecast:
Improving inventory planning: By knowing future demand, you can spend less money on inventory purchase orders and storage. This ensures better product turnover, accelerates goods receiving and order preparation processes, and reduces storage costs.
Detailed tracking of inventory levels will allow you to replenish easily and forecast production and resource planning over time, meaning you can offer the products your customers want, when they want them.
Avoiding stockouts: Properly managing your inventory will guarantee that you have enough product on hand to meet your order demand, but not too much to avoid waste (avoiding lost sales and customers).
Effectively synchronizing your marketing and promotional strategies.
Designing tailored offerings: By understanding the behavior, purchasing habits, and desires of your customers, you can anticipate their needs and offer them what they want, thereby increasing their satisfaction.
Properly planning your sales and warehouse staff: Periods with higher sales peaks or high season will require sufficient staff to support this load and operate optimally. By forecasting demand, you can manage this well in advance.
Having enough space, tools, and machinery: Likewise, it will allow you to anticipate possible equipment failures to organize machinery maintenance in advance.
Cost savings: By having precise control of the necessary goods in stock.
Avoiding expiration and product deterioration in the warehouse.
Anticipating the state of the next cash flow: You can budget more accurately to meet supplier payments and other operational costs.
Improving risk management.
Streamlining the overall operation of company departments and decision-making.
Without demand, there is no business. And without a deep understanding of demand, companies do not know how to operate correctly. That is why demand forecast is such a valuable tool: it will allow you to make much more informed and informed decisions, from managing your inventory, optimizing the supply chain to the efficiency of your marketing campaigns. Customer expectations change faster than ever, so companies need a tool to accurately predict their demand.