What Is a Demand Forecasting Tool?
A Demand Forecasting Tool is a tool that can predict the demand for services or products based on existing data.
The existing data considered in demand forecasting tools include not only past sales records but also various factors such as weather, seasons, economic conditions, trends in competitors, and others. By appropriately utilizing demand forecasting tools, it becomes possible to make rational decisions regarding production planning and sales strategies. Demand forecasting tools are not only essential for streamlining and optimizing current business operations but are also useful for tasks such as assessing new market entries, planning human resource acquisition and facility investments, and improving business processes, including facility investments and fundraising.
Uses of Demand Forecasting Tool
Demand forecasting tools are used in a wide range of industries. Specific examples of industries include:
- Retail and service industries
- Food and beverage
- Real estate
- Logistics
- Manufacturing (industrial, food, and other general manufacturing)
- Agricultural production
- Leisure
- Taxi industry
- Call centers
For example, in retail stores, forecasting sales can optimize inventory and personnel placement. In the food and beverage industry, predicting sales quantities for each time period can reduce food waste and ensure proper staffing.
The taxi industry uses demand forecasting to efficiently dispatch vehicles to locations with high demand, sales, and rider needs. Call centers use inbound call forecasts to optimize staffing. In manufacturing, it helps prevent overstock due to excess production or stockouts due to excessive demand.
Principles of Demand Forecasting Tool
1. Overview
A demand forecasting system calculates and predicts future demand using statistical algorithms based on a large amount of accumulated existing data. Recently, machine learning, particularly AI, which trains on data to construct predictive models, has become mainstream.
2. AI and Machine Learning
Machine learning is a technology that allows machines, specifically computers, to automatically learn from data, discovering rules and patterns behind the data. Demand forecasting systems that use AI can handle complex patterns and trends more accurately than other tools because they can process large amounts of data quickly.
However, this technology is based on the assumption that “patterns and trends that have occurred in the past are likely to continue in the future.” Therefore, it may be challenging to predict the impact of unpredictable, one-time events such as disasters.
3. Specific Statistical Methods
Examples of major statistical methods used in demand forecasting tools include:
- Arithmetic Mean Method
A method that uses the average value directly from past performance data for demand forecasting. - Moving Average Method
A method that averages recent data with past performance data from the same period of the previous year to forecast demand. - Weighted Moving Average Method
A variation of the moving average method that assigns weights to specific data. - Exponential Smoothing Method
A method that calculates new forecast values based on past forecast values and actual data, particularly suitable for short-term forecasts. - Regression Analysis Method
A method that predicts future demand based on multiple variables with causal relationships. Appropriate for forecasting in complex situations.
Types of Demand Forecasting Tools
Demand forecasting tools, as mentioned earlier, are products used in various industries and purposes. Therefore, there are products specialized for specific purposes, such as retail sales forecasting and proper inventory management, as well as all-around products that cover a relatively broad range of fields. Additionally, some applications are linked to demand forecasting for automated ordering.
The purchasing model often involves monthly or annual subscription fees, with some products offering free trial periods. It is necessary to choose the most suitable tool based on its purpose and situation when using it.