The demand forecast affects the entire supply chain. By planning sales and having understanding of the future demand, you can not only ensure the right amount of products on the shelves, but also understand what activities need to be carried out with specific product positions in order to generate additional profit. In addition, this allows you to maintain a high level of availability of goods with minimal inventory.
The forecast of demand depends on many variables that require analysis: from historical data to external influences. It is quite difficult to take into account all these variables in a changing environment. Meanwhile, inaccurate forecasting entails an increase in unnecessary operating costs, such as logistics, warehouse or financial, and leads to lost sales. In this article, we’re explaining how to improve the accuracy of forecasting.
To improve the accuracy of forecasting in retail, you need to understand the main external and internal factors. Here are the main ones:
The methods of traditional manual analysis have long lost their effectiveness to mathematical models based on the use of artificial intelligence and machine learning. This approach not just involves using a certain formula, but improves the accuracy of demand forecasting, taking into account internal and external factors.
Forecast accuracy is key to inventory management, so each company must forecast demand for different periods of time. Depending on the goals and objectives of the business, there are several types of forecast:
Using the manual method for all the forecast types described above makes this process inefficient for a number of reasons:
To improve forecasting accuracy, retailers delegate complex mathematical forecasting calculations to intelligent systems that can automate this process and make it more efficient through the use of artificial intelligence and machine learning.
The forecasting efficiency and accuracy of these systems is affected by how often you enter new data that is relevant for the calculation. The more high-quality analytical data you provide to the system, the more accurately its mathematical model will work. So if you are just thinking about implementing a system that can improve forecasting accuracy, then we advise you to start working with historical data and demand-correcting factors right now.
What data is needed for the forecast? Let’s take SMART Demand Forecast as an example. This is a system capable of making demand forecasts for both permanent and promotional sales.
The recipe for the necessary data that retailers should structure and accumulate for a successful forecast is as follows:
The absence of some of the above data may affect the forecast accuracy. But this does not limit you in terms of implementing SMART Demand Forecast. To get more information about the benefits and capabilities of the solution, fill out the form.
With companies that are ready improve forecasting accuracy, we launch a pilot project that involves:
This is followed by a stage of full-scale testing of the solution, at which SMART Demand Forecast is integrated and deployed. The final step is the application of system algorithms for the entire range of products and points of sale. Depending on the wishes of the customer, in the future the project can be transferred to the SMART business specialists for technical control or implemented on the customer’s side.
A high-quality forecast is necessary for better control and management of the supply chain: inventory planning, improving the logistics component and enhancing customer experience. Other advantages of accurate forecasting include:
Therefore, an accurate forecast stimulates positive changes in many business indicators. A forecast based on artificial intelligence and machine learning takes into account more components affecting demand and speeds up the process of making managerial decisions in a company.
To get a personal consultation on SMART Demand Forecast, fill out the form.
Running a business today is not easy. Challenges, thrown at us by the reality one after another, significantly complicate this process, and it is sometimes extremely difficult to foresee the impact of unfavorable external factors on the market and consumer behavior. One of the most painful points for companies in recent years has been the shift in demand. A growing number of factors, from influencer posts to unexpected circumstances, are causing shoppers to change their buying behavior more frequently.
The problem is that these changes happen quite unexpectedly, and there is no magic tool that could foresee global situations that create risks for companies. But are there other methods to look ahead and adjust your business processes? In this article, we talk about demand forecasting, find out the importance of the process, and look at modern solutions that, based on machine learning algorithms and artificial intelligence, can improve your operational planning in a changing environment.
It is the process of estimating future demand through the analysis of historical data, information and the influence of additional factors. Effective demand forecasting provides companies with valuable information about opportunities in current and potential markets and helps managers make informed decisions about volume to order, product promotion and overall business strategy.
On the flip side, by ignoring this process, companies risk making wrong decisions in terms of product strategy and target markets. This in turn can create a lot of problems, such as increased storage costs, decreased customer satisfaction, and gaps in supply chain management. In short, the company either loses funds or does not receive them in full.
In general, the trend towards creating separate departments for forecasting demand in companies appeared in the late 80s of the last century. At first, in most cases, forecasts were based on simple statistical models and methods such as moving averages, exponential smoothing, or even instinctive judgment (colloquially called ‘gut feeling’). And then, with the development of technologies in the field of data storage and processing (Big Data), the demand forecasting process has undergone significant changes and has become an indispensable tool for businesses of different industries and sizes.
And if the demand forecasting software market in 2019 was estimated at $3 billion, by 2030 this amount is expected to be more than $14.5 billion (transparency market research). So, further on we’ll outline why you should pay attention to this topic and how demand forecasting can become part of your business processes.
Demand is the driver of all business. Not surprisingly, its analysis affects the efficiency of many company processes. Demand forecasting is never 100% accurate (only if it is a coincidence or fraudulent calculation), but it is necessary, because it affects the following:
