Inventory-based businesses face the same three questions every day: what to buy, how much to keep on hand and what price to charge. Each looks like an operational call, yet together they shape a large share of margin, working capital and service levels. Most of the time, they are answered with incomplete information, data that arrives late or the gut feel of whoever makes the decision.
Developed by TMS and sold in Chile by Grupo Intelsis, Predict AI applies artificial intelligence to demand, inventory and pricing decisions while connected to the existing ERP. This article covers its functions, how it fits into operations and the companies it is designed for.
Three daily decisions that shape your margin
When purchasing, inventory and pricing are decided at different times, by different teams and in different spreadsheets, the outcome is often contradictory: excess stock in some items and stockouts in others, in the same operation and the same month. Each gap erodes margin in its own way.
- Stockouts: the lost sale is only the visible part. Shoppers try another brand, distributors look for another supplier and relationships with key channels weaken.
- Overstock: idle capital, storage and handling costs, expiry or obsolescence risk and, eventually, markdowns to free up space.
- Misaligned prices: price lists reviewed too rarely leave margin on the table when demand heats up and slow turnover when it cools down.
None of these effects is inevitable, and all of them can be measured. If you work in food and beverage, it helps to understand the hidden cost of overstock and stockouts before comparing tools.
What Predict AI does
Predict AI is an artificial intelligence solution for companies whose operations depend on inventory. It recommends what to buy, how much to hold and what price to charge, supporting planning and efforts to prevent stockouts and excess inventory.
The models learn from the company's own sales and inventory history. Decisions stop relying only on last year's numbers or intuition and start reflecting what the data points to for the coming periods. Purchasing, planning and sales teams stay in charge: they review recommendations, handle exceptions and make the call.
Behind the solution sits Revenue Growth Management, a discipline that manages demand, inventory and price together so you sell better, not just more. If the concept is new to you, we cover what Revenue Growth Management is in a dedicated article.
Integration with the existing ERP
When evaluating the solution, establish how data will be obtained and what resources integration requires. Predict AI was developed to connect to the existing ERP.
- ERP connection: assess how to extract data from SAP, Oracle, Microsoft Dynamics, or another system, which permissions are needed, and how often to refresh it.
- Cloud services: define access, connectivity, and operational and support responsibilities between the company and its providers.
- Historical data quality: check the coverage and consistency of sales and inventory records before training the models.
- Adoption in stages: plan integration, data preparation, and recommendation validation before expanding use.
- Defined responsibilities: during assessment, confirm who handles integration, data quality, and support, and what involvement is needed from IT and business teams.
For SAP customers, there is an added architectural benefit. The ERP remains the system of record for transactions, while the analytical intelligence runs outside it. This is the same clean core principle behind SAP best practices: keep custom logic outside the ERP core, which makes upgrades and future migrations simpler.
Companies the solution is designed for
Predict AI was designed for mid-size and large companies with inventory-based operations. The best-fit industries are:
- Food and beverage, where shelf life and seasonality make every forecasting error more expensive.
- Consumer goods, with broad portfolios and frequent promotions.
- Specialty retail, with wide assortments and demand peaks.
- Distribution and import, where long replenishment lead times require early decisions.
Alongside the industry, assess the operating profile: SKU count, sales channels and how often demand, inventory and pricing decisions are made. These factors help determine the need for predictive analytics and the scope of the project.
How to prepare your company for the first step
An AI initiative for inventory and pricing works best when it starts small, with a clear owner and defined metrics. A few steps speed things up:
- Pick a scope with obvious pain: a category, channel or region where overstock and stockouts coexist.
- Organize the history you have: sales and inventory by item, covering the longest consistent period available.
- Set a baseline: service level, days of coverage, capital tied up in inventory and margin by category, measured before you start.
- Name the decision-makers: purchasing, planning and sales need to know who approves recommendations and how exceptions are handled.
- Scale based on results: once the first scope has been measured, extend the model to other categories and channels.
The role of Grupo Intelsis and TMS
Grupo Intelsis and TMS support adoption of the solution, including go-live, review of the first recommendations and methodological adjustments to the models for each business.
For Grupo Intelsis, an SAP Gold Partner, Predict AI completes an offering that runs from ERP infrastructure to artificial intelligence applied to decisions. When a project calls for data preparation or new use cases, our artificial intelligence practice joins the same work plan.
Let's talk about your operation
Today, Grupo Intelsis sells Predict AI in Chile. If your company wants to explore how AI can support demand, inventory and pricing decisions, talk to our team. We start by understanding your context, the data available and the ERP you run, and then recommend the path that best fits your reality.
