In business or in our personal lives, we are used to making predictions and estimates about future events. For example, the estimated time taken to arrive at a friend’s house or the amount of food needed for a dinner for several people. Decisions made based on a forecast can make us arrive at the right time for a meeting or buy the right amount of food or not. With that in mind, we at Aquarela have prepared this article on demand forecasting, covering its concepts, importance for business and evolution over the years.
What is Demand Forecasting?
Demand forecasting, as the name already tells, is the process for obtaining estimates of a future demand of products and services. This is done with existing data that’s been collected and stored. Forecasts can be generated through mathematical models, which use historical data; by qualitative methods, such as management experience or expert opinion; or even a combination of both.Some of the questions we seek to answer when making such predictions are “when”, “where”, and “how much”. It is important to note that predictions are not targets, but rather estimates of what will happen. Forecasting results aid businesses in planning their decision making. Therefore, a good demand forecasting process needs to produce good estimates.
How important is Demand Forecasting?
Demand Forecasting is an important activity that influences companies from different segments, such as: retail, consumer goods, pharmaceutical industry, automotive electronics, heavy machinery among others.
Demand forecasting is used for business planning, as every plan involves estimates about this type of forecast. Thus, the predictions are of importance, as they enable managers to plan more assertive activities toward the strategic goals of the business. It is also useful for the tactical and strategic process of companies. Managers and decision makers utilize demand forecasts on their daily activities. These predictions can also be used as inputs for the sales and marketing teams to create insights into demand generation and organize their actions.
Businesses, in general, need to make forecasts about their products to prepare an effective plan in both short and long term. With that in mind, demand forecasts are crucial for companies, as they affect inventory planning, logistical planning, production planning, cash flow planning, hiring decisions, purchasing decisions, among others. Poor or absent forecasting can lead to bad decisions. So, without good demand forecasting, businesses would be poorly preparing themselves for the future events.
Benefits
Better forecasts can result in better service levels, customer retention, cost savings, waste reduction (excess inventory and unsold products). In addition, the need for emergency production to meet unexpected demand is relieved, because, with predictions, companies can plan and make decisions that allow a better response.
Not being prepared for the demand can cause incalculable losses, such as the reduction in market share. For example, when a customer needs a product, but can not find the desired brand, he can often find a replacement with a competing brand. Due to this, companies can no longer be reactive to meet demands for their products. Furthermore, the exponential growth of supply chains belonging to certain companies was only possible due to demand forecasting improvements. They invest so that they can plan better, because they know that it generates a financial return. It is important to know what is happening at points of sales to obtain a more assertive demand forecast.
How are forecasts horizons classified?
The demand forecasts can be sorted by what are called “future horizon” in the time it applies. The three classification categories in relation to the time horizon are:
- Short-Range Forecast: usually less than three months, but can be up to one year. Used for purchase plans, production scheduling, predictions of labor and production levels.
- Medium-Range Forecast: Usually three months to one year, however can be from one year to three years. Useful for sales planning, operation plan analysis, production planning and budget planning.
- Long-Range Forecast: Three years or more. This kind of forecasting can be applied to planning new products, business expansion, as well as research and development.
Short-range forecasts tend to be more assertive than long-range forecasts.
What are the main categories of demand forecasting methods?
The methods of demand forecasting fall into three basic categories, which are:
- Qualitatives: methods that work based on opinions data of managers, experts, sales staff, and customers questionnaires to estimate the quantitative value of demand.
- Time series: statistical approach that relies on historical data to forecast future demand. Through these methods, there is a recognition of seasonal trends and patterns.
- Causal models: quantitative method of demand forecasting that uses historical data together with independent variables, such as economic conditions, competitors’ actions and promotional campaigns.
Regardless of the category of demand, a prerequisite is that there is a pattern or relationship that can be identified and modelled.
Regardless of the category of demand, a prerequisite is that there is a pattern or relationship that can be identified and modelled.
Evolution of demand forecast throughout history
Organizations began creating demand forecasting departments and functions in the late 1980s. However, this subject was already explored in previous decades in academia and by segments of products that need different parts to be assembled. The beginning of demand forecasting in companies, for the most cases, consisted of simple statistical models , such as moving averages, simple exponential smoothing and judgment by instinct (“gut feelings”).
Technological advances in data storage and processing (Big Data) have positioned demand forecasting as one of the main actors of value generation for Supply Chain. As a result of these advances, demand forecasting is getting better and better.
Conclusions and Recommendations
Forecasting future demands is a challenge that companies have to face in order to be able to make decisions that allow them to compete by generating better supply chain results. Demand forecasting is an essential activity for business planning, as it results in several benefits, such as: reduced waste, better allocation of resources, increased sales and revenue. This way, it helps organizations to be in the right place, at the right time, with the right product.
Also read: AI for demand forecasting in the food industry
Keep improving your companies’ demand forecasting process and technologies to keep pace with this ever-changing world. Aquarela Tactics has a demand forecasting module that can help you with this journey to know in advance what to expect from the consumer market and optimize your decision making.
What is Aquarela Advanced Analytics?
Aquarela Analytics is the winner of the CNI Innovation Award in Brazil and a national reference in the application of corporate Artificial Intelligence in the industry and large companies. Through the Vorteris platform and the DCM methodology, it serves important clients such as Embraer (aerospace), Scania, Mercedes-Benz, Randon Group (automotive), SolarBR Coca-Cola (food retail), Hospital das Clínicas (healthcare), NTS-Brasil (oil and gas), Auren,SPIC Brasil (energy), Telefônica Vivo (telecommunications), among others.
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Author
M. Sc in Mechanical Engineering at Federal University of Santa Catarina (2020) with emphasis on wind power forecasting using artificial neural networks. Graduated as Mechanical Engineer at UFSC in 2016. Developed researches on solid mechanics and renewable energy during his undergraduation. In 2014 received a scholarship from Brazilian Government through Science Without Boarders Program to study Mechanical Engineering during one year at The Pennsylvania State University (PSU) in the United States. Renewable Energy was his focus at PSU. Interest on product management, artificial intelligence, data science and renewable energy.
