What is sales forecasting in 1C?
Sales forecasting in 1C is an analytical process that allows determining the future volume of demand for goods or services based on accumulated data. The 1C system usually contains a rich array of information: sales history, warehouse movements, customer data, returns, discounts, and seasonal fluctuations. It is this data that is used to build a forecast. The goal is to understand what, when and in what quantity will be in demand. This is especially true in a dynamic market where the slightest fluctuations in demand can affect profits or losses.

You can build a forecast in 1C manually, based on Excel and expert assessments, but this approach is not scalable. The introduction of AI makes it possible to use machine learning and time series algorithms to build accurate models that do not depend on the human factor and can analyze thousands of positions simultaneously.

Why is AI a new stage in sales management?
Artificial intelligence opens up fundamentally new planning opportunities. Traditional methods, such as linear extrapolation, work in conditions of stable demand and a small range of products. But modern business is faced with a huge number of SKUs, changeable customer behavior, and the influence of external factors — from weather to news.

AI models, including XGBoost, Prophet, and GPT, can take into account multiple variables and identify hidden dependencies that are hard for humans to notice. For example:

• automatic identification of seasonal patterns;
• taking into account marketing campaigns and their delayed effect;
• reaction to changes in prices and competition;
• the opportunity to “learn” from new data and improve forecasts over time.

Using AI in 1C makes forecasting not only more accurate, but also more efficient: you can recalculate models at least every day, adapting purchases and logistics to the real situation.

Who is it suitable for: retail, wholesale, production?
AI forecasting in 1C is universal and is applicable in most areas where you need to work with inventory or plan demand. For example:

1. Retail: It is especially important for chain stores to accurately assess how much product will be needed and in which region. Mistakes lead either to lost sales (no product) or to excess balances (capital freeze). AI can make forecasts by category, retail outlets, even time of day and day of the week.

2. Wholesale trade: Distributors work with large volumes of supplies and a variety of partners. It is important for them to understand not only final demand, but also time peaks in order to manage logistics. The 1C forecast helps to make purchases from manufacturers, reduce shortages and optimize inventory.

3. Manufacturing plants: It is especially important for plants and factories to know in advance how much production will be required in order to properly plan:

• loading production lines;
• purchase of raw materials;
• shift schedules for employees;
• logistics of finished products.

AI in 1C makes it possible to build a closed planning cycle: from demand forecasting to production targets.

Thus, forecasting with AI is not just a fashionable tool, but an almost necessary element of a modern business management system, especially in conditions of uncertainty and high competition.

Why do businesses need sales forecasting

Procurement planning
One of the main uses of the forecast is to accurately plan future purchases. If you know in advance what goods will be in demand and in what quantity, you can purchase them in advance, optimizing prices and terms of delivery. This helps to avoid shortages, especially during peak seasons, and to minimize logistics costs. Moreover, forecasting allows you to create “smart” orders — for example, directly to the right location or taking into account the preferences of a specific group of customers.

Managing balances
Effective inventory management is a key factor in business sustainability. The demand forecast allows us to maintain the required level of balances: not too high so as not to freeze working capital, and not too low so as not to miss out on sales. AI helps you see which items should be kept in large quantities and which ones should be kept only on order, based on their likely demand.

Optimization of logistics and production
The forecast affects not only the warehouse, but also the entire supply and production chain. If you know when and where the demand will increase, you can prepare in advance:

• logistic routes and transport;
• purchase of packaging and raw materials;
• utilization of production facilities.

As a result, costs are reduced, and the number of urgent orders and unplanned deliveries is reduced.

Reducing “dead residue”
AI forecasting makes it possible to identify products with reduced turnover even before they become “dead”. The system will tell you which items are losing demand and how much they really cost to keep in stock. This is especially important for seasonal goods, fashion collections, equipment and other products with a short life cycle. Knowing this, you can hold a sale on time, launch a marketing campaign, or change your sales strategy.

How AI forecasting works in 1C

Data sources: sales, nomenclature, warehouses, seasons
To build a forecast, the system analyzes data for a period of 6 to 24 months. The main sources are:

• sales history by day and week;
• nomenclature structure (categories, brands, SKUs);
• movement and stock balances;
• seasonal factors, including holidays, climate, consumer behavior patterns.

It is important to ensure the consistency and completeness of this data — without it, no model will give the correct result.

What the model analyzes: seasonality, trends, demand surges
AI models can take into account:

• a typical seasonal pattern (for example, sales growth in December or a fall in summer);
• stable trends — both upward (increasing interest in the category) and downward;
• local outbursts: reactions to sales, new collections, viral products;
• external factors: exchange rates, holidays, competitors' shares.

This data makes it possible to form not just a general forecast, but a dynamic model of customer behavior and demand systems.

What models are used

Linear regression is simple and fast, but is only suitable for stable categories with linear demand.

• XGBoost is a powerful model that can analyze complex dependencies and interactions between variables.
• Prophet (Meta) is a great library for working with time series and seasonality.
• GPT-4o is not used for mathematical forecasting, but for generating text analytics, conclusions and recommendations based on data.

Technical methods of implementation:

• Google AutoML Forecast is a cloud-based solution for businesses that need high accuracy without writing code.
• Azure Forecasting API, an analogue from Microsoft, integrates with Power BI.
• Python scripts are ideal for customization and deep control over the model.
• BI solutions with API to 1C — allow you to integrate the forecast directly into the working system: Power BI, Tableau, Qlik.

The right model and tool make it possible to implement predictive analytics in 1C with minimal costs and a quick return on investment.

Tools and methods of implementation

Let's move on to the most important block: which sales forecasting tools to choose and what implementation methods can be used. Sales forecasting tools using artificial intelligence can be roughly divided into three approaches:

1. visualization with analytics,
2. software processing
3. automation through no-code platforms.

Below we will take a closer look at each of them so that you can choose the most suitable way for your business to implement predictive analytics in 1C.

1. The combination of 1C and Power BI: Visualization+built-in forecasts

Power BI is a business intelligence tool from Microsoft that allows you to connect data from various sources, build interactive dashboards, and use built-in forecasting features.

How it works: In 1C, you generate reports, for example, with sales history for specific goods and warehouses. You can upload this data manually to Excel or set up automatic transmission via the OData protocol. In Power BI, you connect a source, visualize sales trends, and use the built-in forecasting features (based on linear regression) available in line charts.

In practice, this allows you to:

• See seasonal peaks and downs by product category
• Evaluate which items should be purchased by certain dates
• Get a quick forecast for 1-3 months ahead

Who is it for: for marketers, analysts and executives who value visibility and speed of decision-making, without deep immersion in programming.

2. A combination of 1C and Python

Python is one of the most popular languages in data analysis and machine learning. It offers a wide range of libraries (pandas, scikit-learn, prophet, xgboost) that allow you to build complex prediction models with high accuracy.

How does this work?

You export data from 1C in CSV or Excel format. Using a Python script, you clean and analyze data, train the model on historical sales, and then make forecasts. The obtained data can be imported back to 1C or visualized in a BI tool.

Why it's valuable:

• You can customize models to suit the specifics of a particular business
• Use advanced methods: gradient boosting, time series, cross-validation
• Assess model accuracy by metrics (MAE, RMSE)

Disadvantage: It requires technical training — at least a basic understanding of Python and machine learning principles.

Who is it suitable for: companies that have a data analyst or IT specialist in their team, or a willingness to hire an external contractor to customize scripts.

3. 1C+n8n+AutoML/GPT: automation without code

n8n is an open-source visual automation platform that allows you to assemble processes from blocks without the need for programming. In the context of 1C, it can act as a link between your system, cloud analytics services, and tools for interpreting results.

Example of the process:

1. n8n receives sales data from 1C on schedule via REST API or CSV upload
2. Sends them to AutoML Forecast from Google Cloud to build a forecast
3. Receives the result and processes it via GPT to output a text recommendation: for example, “demand for air conditioners is expected to increase by 34% in August”
4. Returns data to 1C or sends it to email, Telegram or Google Sheets

Why this is convenient:

• Does not require programming skills
• Allows you to quickly assemble and scale processes
• It's easy to add new blocks: visualization, notifications, CRM integration

Disadvantage: you need to first configure access to the API and understand the platform's logic, at least at a basic level.

Who is it for: small and medium-sized businesses that want to automate the forecasting process without hiring an IT team.

Collection and preparation of data from 1C

Regardless of the approach chosen, the key step will be to collect high-quality data from your 1C system:

• REST API — used in modern 1C configurations (for example, ERP or Trade Management). It allows you to receive reports, directories, documents in the form of JSON via HTTP requests. Good for connecting to external platforms.

• OData is a data exchange standard that allows you to “publish” reports and tables from 1C and connect them directly to BI systems (Power BI, Tableau, Excel). It is useful when you need to visualize large amounts of data without unnecessary development.

• SQL queries — direct access to the 1C database. It is used carefully, most often for reading. It is ideal for creating custom reports, merging tables and uploading large amounts of data.

The choice of tool depends on the company's objectives, the amount of data and the team's level of training. Power BI and n8n are suitable for quick pilot projects. For accurate calculations, use Python. The main thing is not to be afraid to experiment and build sales forecasting in 1C as a live, constantly improving process.

Step-by-step instructions: how to set up a demand forecast in 1C

AI forecasting in 1C is not just a theoretical possibility, but a technology that is actually being implemented. Below is a step-by-step instruction on how to go from initial data to a ready-made forecast that will be integrated back into 1C and used in operations.

1. Data preparation

The first and most important step is to collect high-quality information. To build a reliable forecast, you need to:

• Sales history: at least 6 months in advance, ideally 12-24 months. The more data, the more stable the result.
• Product classification: it is desirable to group the nomenclature by categories, brands, sizes, price segments, etc.
• Seasonal spikes: note periods of high demand (holidays, promotions, seasonality) so that the model can take into account their impact.

Important: the data must be structured without missing fields and exported to a format suitable for further analysis (CSV, Excel, OData).

2. Treatment and cleaning

Even the most complete data needs to be prepared:

• Removing emissions: sharp sales spikes due to a one-time order or operator error distort the forecast.
• Filling in gaps: if there are periods without information in the data, you need to carefully restore the values or mark them as exceptions.
• Normalization and filtering: for example, converting date formats, converting values to a single scale, removing duplicates.

You can perform these steps in Excel, Power Query, Python, or BI tools.

3. Model training

At this stage, the machine learning algorithm is connected:

• The choice of method: AutoML (Google), ARIMA, Prophet, XGBoost or even GPT for generating text recommendations.
• Setting parameters: training period length, forecast frequency (weeks, months), target metrics.
• Testing: The model is tested on historical data. If the forecast error exceeds the allowable threshold, the parameters are clarified.
• The result: a table with forecast values for the next period (for example, 1-3 months in advance), grouped by product, category or warehouse.

4. Implementing the forecast into processes

Making a forecast is half the battle. We also need to properly implement it into business processes:

• Import to 1C: using the REST API, external processing, or uploading an Excel file.
• Use of the forecast: in the procurement planning subsystem, budgeting, logistics, production planning.
• Setting up visualizations: using Power BI, Metabase or other BI solutions so that the forecast is understandable not only for analysts, but also for purchasing managers.

HowTo: How to connect Google AutoML to 1C via n8n

If you want to automate the forecast without programming, you can use the 1C+n8n+Google AutoML bundle:

• Preparing an upload from 1C: a report or processing is created that exports sales data to CSV.

Creating a workflow in n8n:

• Node to get a file or API data;
• Node HTTP Request to Google AutoML Forecast (pre-trained model);
• Node JSON Parse and filter results.

Backward integration:

• or uploading the forecast back to 1C (for example, via email and automated processing);
• or send it to Google Sheets, Notion, Telegram or corporate email for decision-making.

This approach is convenient if it is not possible to use in-house developers, but at the same time you want to get predictive analytics with high accuracy.

Use cases

Clothing retail chain
A chain of 25 stores has implemented AI forecasting based on 1C and Power BI. The system takes into account weather conditions, holidays and seasonality. The result is a 30% reduction in surpluses and an increase in the availability of key goods during hot periods.

Electronics distributor
A company that supplies components and gadgets uses AutoML to forecast demand from B2B customers. Based on the forecast, the volume of orders from manufacturers is adjusted and individual offers for wholesale customers are formed.

Furniture factory
The cabinet manufacturer uses Prophet and Python to build a 90-day forecast. The data obtained is used in forming production orders, loading sites and planning the purchase of materials. This made it possible to synchronize demand with production facilities and reduce unplanned downtime.

Possible mistakes and how to avoid them

AI forecasting in 1C is a powerful tool, but if configured incorrectly, it can lead to distorted results. Below are common mistakes and recommendations on how to avoid them.

Not enough data
A forecast based on 1-2 months of sales is a guess, not an analysis. A minimum of 6 months, preferably 12-24 months of history, is required for the model to be able to recognize trends and seasonal patterns.

The analysis period is too short
Even if you have a lot of data, you can't limit yourself to recent weeks. Otherwise, you will miss out on long-term patterns and risk retraining the model based on current volatility.

Improper data cleaning
A single release, such as a mass order from a corporate customer, can distort the forecast for months. It is necessary to carefully filter out anomalies, correct omissions, and normalize fields (for example, units of measurement).

Lack of feedback from users
Once the forecast is implemented, it is important to monitor how it corresponds to actual sales. Without this, you won't know where the model went wrong and how to refine it. A “control group” is a good practice — comparing real orders with those that would be made according to the forecast.

H3 Lack of documentation and transparency
If a business doesn't understand how the forecast works, it won't trust it. It is important to document what data was used, what model was used, and what parameters were set.

By avoiding these mistakes, you will not only increase the accuracy of the model, but also make the forecast a convenient and useful tool in your daily work.

Let's summarize

AI forecasting in 1C paves the way for companies of all sizes to more accurately manage demand and resources. It not only reduces costs and risks, but also makes work more predictable and strategically verified.‍

How to get started
You can start small: audit existing data in 1C, upload the sales history and try to build an initial forecast — even in Excel or Power BI. This will help you see the data structure and identify which indicators are missing.

How to test a model
Divide the available data into training and test samples. Make a forecast based on the first 80% and compare it to the remaining 20%. Use error metrics (MAE, MAPE, RMSE) to assess model quality.

Where to scale
After the forecast is successfully implemented in the sales and purchasing department, it can be scaled to other business functions:

• logistics: forecast traffic volumes and transport needs;
• finance: build models of revenue, receivables, cash gaps;
• HR: plan staff employment by workload periods.

Artificial intelligence in 1C is not a complex technology, but a practical tool that can produce results within 2-3 weeks after the pilot is launched. The main thing is to start.

If you want to speed up implementation, improve the team's skills, or delegate the entire task, you can always contact Start:Duck experts. We train, support and help implement turnkey AI solutions — from auditing and data collection to automating forecasts and integrating into 1C.

FAQ

Can GPT be used for forecasts in 1C?
Yes, GPT-4o can be used as an addition to classic models, for example, to generate text analytical reports, hints for buyers and automatically generate conclusions on forecasts.

Does a programmer need to know Python?
Not necessary. There are no-code solutions such as n8n, Power BI, AutoML from Google and Azure that allow you to build a forecast without writing code.

Which data is more important: sales or seasonality?
These types of data work together. Sales are the basis for the forecast, but the model will not be accurate without taking into account seasonal fluctuations. AI makes it possible to take both aspects into account at the same time.

Are there ready-made modules for 1C?
Yes. There are ready-made integrations with Power BI, modules for uploading via OData, REST API, as well as extensions based on external processing that make it easy to connect external analytics and visualization services.