Demand forecasting systems are a very helpful tool for advanced process planning and for combating uncertainty in coverage forecasting. But what do we mean when we talk about uncertainty? Uncertainty is present in each and every business. It is the impossibility of knowing with certainty how a situation, a project or a business strategy will develop. The inability to predict the risks or the final consequences of the decisions that are taken.
In an ecosystem as complicated as the business one, it is essential to be able to anticipate what is going to happen in order to choose the right path. So what can a company do to reduce that uncertainty and make the best possible decisions? One of its most effective tools is the use of demand forecasting systems.
The digital revolution is transforming companies and the procedures they use to manage them. Advanced data analytics and Artificial Intelligence are becoming key business tools, thanks to which companies increase their productivity, strengthen their markets and position themselves ahead of their competitors.
Today’s amazing ability to have a huge volume of data available thanks to Big Data, gives companies the opportunity to use this data to design solutions based on Machine Learning techniques and thus foresee what is going to happen in their business and, consequently, improve decision making.
Sales can gain valuable information about their market potential and make the most appropriate decisions for their pricing strategy or profitability strategies. The ability to accurately estimate demand for individual products facilitates more efficient planning of operations. In addition to better management of associated costs (variable and fixed), investment plans and delivery times, and therefore their impact on the final price, which is passed on to the customer.
Predictive analytics: automatic conclusions, human decisions
It is essential that demand forecasting methods are efficient, inexpensive, accurate and adaptable to market behaviour. All these requirements can be taken into account in analytical models.
The most basic statistical analysis models have evolved into the more sophisticated versions of Machine Learning and Deep Learning. Automating the learning capabilities of algorithms, minimising prior human intervention on the data.
Predictive analytics provides insight into the future development and trajectory of basic indicators for the company. It provides invaluable information that helps companies to choose the best strategies according to their business objectives. However, although this method conveys valuable knowledge, it does not automate decision-making, which remains in the hands of people.
Demand forecasting utilities in different sectors
Strategic sectors such as the logistics industry, manufacturing or retail can benefit enormously from the use of demand forecasting systems.
Inefficient management of product stock (due to overstocking or stock-outs) or logistics planning are areas of great impact on profitability in the retail industry. Large firms in the retail sector – which have already started to make use of these resources – are improving their forecasting and, as a result, reducing safety stocks and reducing costs.
Likewise in the logistics sector, where the correct use of resources (fleet, personnel) and the planning of distribution routes have a direct impact on profitability; minimising operating costs, without jeopardising the quality of service, is key to sustainability. Intelligent and flexible management of orders, resources (drivers, vehicles) and routes is essential to meet delivery commitments. By using Machine Learning techniques, it is possible to accurately predict demand. It is also possible to manage and control inventory, optimising product distribution and replenishment in the network of commercial outlets.
For their part, manufacturing companies must make continuous decisions about the quantity and manufacturing times for each of their products, considering the limited resources available to them. By using demand forecasting systems, it is possible to increase efficiency and control over production, ensuring delivery times, optimising inventory management and, ultimately, providing better customer service to the manufacturing industry.
Benefits of integrating demand forecasting into business strategy
Solutions based on predictive analytics models are a key tool for anticipating the future and choosing the best way forward. This leads to greater efficiency in the different departments and processes and reduces the company’s operational risks. The advantages of using demand forecasting methods are numerous:
- Tailoring supply based on consumer buying habits.
- Improve inventory management, increasing turnover rates and lowering warehousing costs.
- Anticipate the state of the next cash flow. This means companies can more accurately budget for supplier payments and other operational costs.
- Know when staffing and other assets need to be expanded to maintain operations during production surges.
- Anticipate potential equipment breakdowns, taking into account data on their activity, so that machinery maintenance can be organised in advance.
- Detect fraudulent actions or movements and non-payments before they happen in order to implement procedures to prevent or mitigate them.
At decide4AI we provide our clients with all those instruments that contribute to the basis for making accurate and efficient decisions.
The high added value provided by our tools often becomes, on many occasions, the differential competitive advantage that allows our clients to consolidate their position in their respective sectors.