Demand Forecasting

Demand forecasting is the process of using predictive analysis software reading historical data to determine consumer trends to aid in assessing which products and services are in high demand, and which are becoming more obsolete. It differs from sales forecasting in as sales are limited not only by demand but also by availability of the goods sold. Read our guide on how modern AI techniques can provide powerful forecasting solutions.

This is a difficult question to answer. The accuracy of a forecast can depend on a great number of factors. These factors range from the variability of historical data to the measurement of external causal factors influencing your sales. To learn more about forecast accuracy read our blog article.

Intuendi’s demand forecasting software can seamlessly integrate with most ERP systems through APIs or indeed any other source of data through secure FTP file transfer. For further information, visit our integrations section.

Every business responds to demand and provides goods or services to meet this demand so potentially every business could benefit from demand forecasting.

This aspect of forecasting represents one of Intuendi’s most powerful functions rendering us unique in the demand planning landscape. We can automate and factor into your forecast any significant causal factor when it itself can be forecasted or predetermined. Some examples are calendar events, marketing budgets, COVID-19, or even marketplace metrics like Amazon rankings or competitor positioning.

Primarily sales history. Casual factor information and historical stock levels are also important but not necessary.

Yes, the tool is powerful enough to support hundreds of thousands of individual SKUs simultaneously. Imagine forecasting those numbers in Excel!

Determining the frequency of forecasting depends on the specific business and the effort necessary in the calculation. Intuendi can update your forecast daily using the most recent data available.

Intuendi can use both top-down and bottom-up strategies or even a hybrid approach. There are many different algorithms available and these can be combined and fine-tuned by our team of internal data scientists, to provide the most accurate forecast possible.

Absolutely yes. The forecast is automatically updated daily but can be manually updated at any time.

Training is provided continuously, both during onboarding and with the launch of new features or at the request of the client. For all plans, technical support for the Service is included in the Customer’s subscription fee. Customer Support is provided by Intuendi via e-mail during Intuendi’s ordinary office hours at support@intuendi.com. Intuendi aims to respond to the Customer’s questions within two business days but no guarantees on a specific response time are given. Intuendi only provides technical support to ensure the best usage of the Service (e.g. troubleshooting, user data generation issues, help in setting user accounts, etc.).

Absolutely. We have a long history of assisting businesses during extreme growth including changes in their distribution models, the addition of new sales channels or warehouses, and increasing the overall dimension of their catalog and business.

This is one of the most complex operations in demand planning. In the case of substitution products, it is very simple to assign the performance of the replaced product. In the case of completely new products, Intuendi’s powerful AI can analyze similarities between the new product and existing products based on any number of attributes, detect correlations in the performance of a set of products, and use this data to define a launch forecast.

Yes, we have both inventory management and purchase order suggestion modules included in our solution. Both of these modules are driven using Intuendi’s advanced forecasting techniques.

These are the most important questions you should ask yourself when choosing a demand forecasting software: Can the software be modeled to match the complexities of my unique business model? Are there any external causal factors that should be included in the model? Will the software assist me in reacting quickly to changes in the demand and health of my inventory? What if my business model, distribution structure, or outreach of my business changes? Can the software accompany me in these changes?