Target Audience

Small to medium-sized businesses (SMBs) with historical time-stamped data (e.g., sales, inventory) stored in Excel or similar tools.

Challenge

Businesses struggle to extract actionable insights from existing time-stamped data (e.g., sales trends, material costs) to predict future demand, inventory needs, or operational risks without requiring deep machine learning expertise.

Solution Approach

Use pre-trained foundation models (e.g., Nixtla TimeGPT-1, Amazon Chronos) to analyze historical data patterns and generate forecasts directly from Excel or other data sources, eliminating the need for custom model training.

Value Add

Enables SMBs to quickly derive predictive insights from existing data, reducing uncertainty in decision-making (e.g., demand planning, cost optimization) with minimal technical effort.

How to Apply This

Predicting the future is an important application of AI in various fields, for large enterprises and small companies. Therefore, there exist powerful approaches to leverage so-called time-series analysis solutions. Moving beyond Excel and applying AI on your spreadsheet data can be realized with little efforts. You can leverage the services from US-based cloud vendors, but also run local solutions where your data stays on EU-servers or is hosted in your own infrastructure.
You need to start with:

• •identify the quantity you want to forecast,
• •collect the relevant data that carries information about this quantity,
• •prepare the data based on the domain knowledge of your topic experts and data analysts' support and
• •run proof-of-concepts to identify how good your predictions can get.

Interested in predicting the future for your own company data? Reach out to me and book a free 30-minute feasibility call.

References

Various foundational models, tools like TimeGPT offer Excel plugins for seamless integration.

Read more here: https://www.infoworld.com/article/3543468/5-ways-companies-can-use-time-series-forecasting.html