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Has it ever happened to you that you go to a store or supermarket but you can't find the product you want or need?
This means that the company was unable to anticipate market needs properly.
Therefore, in this article, you will learn what demand forecasting is and how you can calculate it.
Demand forecasting is a company's ability to estimate how many products or services customers will buy in a given period of time.
This allows companies to analyze how much they need to buy raw materials and other inputs related to the production or marketing of their products.
To calculate it, companies usually use variables such as:
You can calculate it in your company, you can do it with different analytical models that we will explain later. But in my experience, these tend to be limited and do not integrate external factors, so forecasts are less accurate.
Other companies use a demand planning and inventory management software such as Datup, who specialize in using deep learning (AI) and can integrate sources of information that a company on its own could not.
One day we may face a container crisis and the following week weather phenomena may boost the consumption of certain products. Therefore, companies need to anticipate market needs by correctly forecasting demand.
Its importance is that it becomes the fundamental input for your company's operations, directly impacting service levels and supply to meet demand.
By projecting aspects such as: what products they require, quantity, time and place in an optimal way, we meet customer needs.
5 of the main benefits of having an accurate demand forecast are:
Because of this great relevance it makes for companies' strategy and because this forecast becomes the basis for the decisions that the company will make, such as what investments to make to meet the growth rate.
Increasingly assertive forecasts are required, since deviations affect inventory failures and therefore your service levels or, on the contrary, oversupplies that capture your cash flow.
There are several factors to consider when forecasting demand and because of this there is great complexity in this process. Therefore, it is becoming increasingly important to have advanced analytics tools that help you improve your accuracy.
To make this possible, the starting point is to identify your sources of information, whether internal and/or external.
Starting from your internal sources, it is key that you can integrate your historical data and complement it with your marketing campaigns and feedback from key actors such as your collaborators in S&OP sessions, in addition to customers and suppliers.
In addition, you must identify that other factors may affect your demand including seasonality, weather, demographic factors, market trends, sector information, market research, relevant news, competitive analysis, are some of the factors that can affect your forecast depending on the industry in which you work.
This demand calculation can be carried out using qualitative or quantitative methods, in some cases and to increase accuracy, companies use mixed models.
The choice of the ideal method for your case may depend on the information you have, the industry or product to be analyzed and the accuracy required. So let's talk about methods.
Here we can highlight market research, which allows us to know consumer preferences, tastes or behavior.
Another commonly used qualitative method is expert opinion, where specialists are used who, based on the experience and knowledge of a particular industry, help to forecast demand.
Linear regression seeks to analyze the relationship between demand and one or more independent variables, such as price, advertising, economic factors, and others. It is based on finding correlations or causalities.
When it is desired to involve multiple independent variables, companies resort to multiple regression analysis to obtain a more complete picture.
One of the most used in this category are moving averages, where in a given period based on historical averages you can calculate average demand, in such a way that it will allow you to identify and smooth out fluctuations in demand.
It is also common to use exponential smoothing, which is similar to the moving average, but weights are assigned to historical data to give greater relevance to the most recent information.
Finally, in this category there is the decomposition of time series, where it is divided into components such as trends, seasonality and error to make more accurate projections.
This forecasting method allows you to align your vision of various strategic areas within the supply chain, such as sales, marketing, operations and finance.
It allows us to take into account, for example, negotiations that are being carried out with customers, marketing campaigns, and other factors that may affect the area of operations and the levels of service to customers.
Artificial intelligence models represent a great advantage over traditional models in the generation of demand forecasts in terms of time and volume of information processing and increased forecast accuracy.
Although there are several artificial intelligence methods for processing demand forecasts, such as: supervised and unsupervised learning, machine learning, deep learning, neural networks, natural language processing, clustering, among others.
Some of the most used are machine learning models, which undergo supervised learning so that, based on historical data, they can learn from patterns and make future projections.
Neural networks are also used to analyze a large volume of data and complex variables to generate millions of scenarios and prioritize the best results.
One of the main recommendations for improving demand forecasts is to be able to implement artificial intelligence models, due to their ability to process large volumes of information and to identify trends and patterns in a short time.
This allows companies that make use of this type of tool to make cost-effective decisions. In addition, with a model that is constantly fed back to give you better results.
It's important that you don't limit yourself to just one method for forecasting demand, since each product and combination you're analyzing behaves differently. Therefore, if you make use of a single forecasting method, they probably have a large number of references with big errors.
Being able to automate processes will free up space so you can dedicate yourself to tasks that add more work to your company. More time analyzing, executing strategies and reviewing results, instead of investing time in information processing.
Nowadays, having a good forecast of demand represents a great challenge for companies because there is increasing complexity generated by the environment.
The COVID-19 pandemic represented major challenges for companies worldwide, where it became increasingly important to talk about BANI or VUCA contexts.
Therefore, the main mistake you may be making is thinking that you can continue to forecast demand with the same process that used to work for you in the past.
However, due to its importance and impact on the business, we want to help you identify some common errors among companies that want to implement or improve their planning and demand forecasting processes.
The first thing to ensure is to have at least one reliable source of historical data, usually the ERP is the main source of data.
Depending on the method you use, the amount of story required may vary.
Understand if you should complement your main data source with other complementary sources such as, for example, a WMS, MRP, CRM, Excel files, and others.
Depending on your type of product and industry, external factors may affect you to a greater or lesser extent.
If you don't understand and integrate factors such as weather, holidays, economic and political issues, among other factors, you may be experiencing large deviations in your forecasts.
Subjectivity is one of the mistakes that companies fall into most frequently. If you don't consider an objective basis for your decision-making, you could probably be having overstocks or inventory failures.
If you already have a demand forecasting process, but you're not taking into account what your strategic areas, customers and key allies are saying, they're probably experiencing a big deviation.
How much do you track deviations at the general level and by reference?
The lack of follow-up to the errors you are having makes companies more reactive than proactive and preventive. Because they usually take action when the problem is profound and it usually takes more time and money to solve the problems that arise.
A company that sells cleaning products and cleaning services has annual revenues of 70 million dollars, more than 2,000 references and 5,000 customers. They have managed to improve their level of service and lack of stock, reduce logistics costs, anticipate suppliers, close new strategic customers and lower inventory levels, achieving a return on investment in three weeks.
Benefits
Looking at the various methods for predicting demand, you're probably wondering which one to use in your company. You could implement traditional statistical or collaborative models.
But without a doubt, based on our experience and speaking with hundreds of supply chain companies, the ideal option today are Artificial Intelligence solutions that are integrated into your business.
With their ability to rapidly process large volumes of data and detect complex patterns, AI models outperform legacy techniques in accuracy and agility. They positively impact the supply chain in a shorter time at a competitive cost.
Automatic prediction with machine learning is continuously fed back and improved. Projections go from being a manual challenge, to a world-class systematized process that enables unique competitive advantages against companies that do not adopt it.
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