Demand Forecasting and Planning in Retail: Datrics use cases

Do you have access to historical data and wonder how it can be easily used to predict future results? Your business may benefit from demand forecasting models and so can you. Make various business decisions such as inventory and replenishment management, product pricing, planning the manufacturing processes and even more. Demand forecasting models can help you in making well-considered, accurate data-driven decisions, while taking complex criteria and patterns into account.

Types of Demand Forecasting Techniques for Retail

Navigating the retail landscape requires precise demand forecasting to ensure optimal inventory management, resource allocation, and customer satisfaction. At Datrics.AI, we understand the importance of using the right forecasting model tailored to your specific needs. In this guide, we explore various methods, categorized by scale and time frame.

Macro-Level Demand Forecasting

This approach analyzes overarching economic trends, societal changes, and external market forces. These insights offer valuable perspectives on:

Portfolio Expansion: Identifying new markets or niches.

Market Intelligence: Gaining an upper hand through timely information on broader market shifts. For further insights into market expansion strategies, consider reading about AI in E-commerce: Use Cases & Examples.

Micro-Level Demand Forecasting

This technique zooms into specific segments, industries, or even individual product lines. It is highly customizable and can provide insights like:

Customer Behavior: For example, forecasting demand for organic skincare products among Gen Z consumers.

Regional Specifics: Assessing demand variations in different geographical areas. Understanding customer behavior is closely linked to the concept of Customer Lifetime Value (CLTV). Learn more about this in our article on Customer LTV in Retail and E-commerce.

Short-Term Demand Forecasting

Focused on a period of less than 12 months, short-term demand forecasting is essential for immediate business operations. It's particularly useful for:

Seasonal Promotions: Planning for holiday sales, Black Friday, or Cyber Monday. Inventory Management: Adjusting stock levels to meet daily or weekly demand. Effective inventory management is a key component of short-term forecasting. Explore our insights on this topic in the article Value of Inventory Management in Retail Businesses.

Long-Term Demand Forecasting

Looking beyond a one-year horizon, long-term forecasting helps in strategic planning. It can influence:

Business Expansion: Deciding on opening new stores or entering foreign markets.

Resource Allocation: Planning for workforce, machinery, and capital investments. For a deeper understanding of how machine learning aids in long-term planning, particularly in predictive maintenance, read our detailed article on Machine Learning for Predictive Maintenance.

Methods of Demand Forecasting with Datrics

Demand forecasting is more than just predicting future needs; it's using past data, the right methods, and tailoring it for each industry.

At Datrics, we utilize a combination of traditional and advanced demand forecasting methods:

Time Series Analysis: By analyzing past sales data, this method helps in forecasting demand trends over a specific duration. It’s particularly useful for businesses with considerable historical data.

Causal Models: Often, external factors like promotional events or holidays can influence demand. Our software recognizes these factors, providing more accurate forecasts.  For insights into how machine learning is transforming other areas of data analysis, such as credit scoring, read our article on Credit Scoring Using Machine Learning.

Simulation and AI Models: As one of the pioneering demand forecasting software, Datrics uses artificial intelligence and machine learning to constantly refine and improve predictions, especially in complex supply chain demand forecasting scenarios. Understanding anomalies in data is crucial for accurate forecasting. Learn about anomaly detection in our article on Anomaly Detection: Definition, Best Practices, and Use Cases.

Example of Demand Forecasting Methods

A local bookstore analyzes last year's holiday season sales to gauge demand for the upcoming Christmas period. Despite a nearby competitor, their monthly growth rate remains at 2%. Leveraging customer data, they identify best-selling genres and authors from the previous year.Their strategy involves boosting online and in-store promotions for these specific books. Based on the historical data and current growth rate, they anticipate a 6% increase in holiday sales compared to last year. Consequently, they plan to order an additional 4,000 units of inventory to meet the projected demand.This example illustrates how combining past performance and current growth trends can inform demand forecasting, enabling businesses to plan inventory and promotions effectively.

Why Demand Forecasting in Supply Chain is Vital

Effective demand planning and forecasting isn’t a luxury – it’s a necessity. It aids in:

Inventory Management: Avoid stockouts and overstock situations, ensuring that products are always available for customers.

Financial Planning: Forecast sales revenue accurately, helping in budgeting and financial projections.

Operational Efficiency: Streamline manufacturing and procurement processes based on accurate demand predictions.

At the heart of all these benefits is the aim to ensure customer satisfaction, and at Datrics, that's precisely what we offer with our state-of-the-art demand forecasting software. Experience the revolution in demand forecasting in the supply chain with us.

How to leverage from demand forecasting with is a web-based end-to-end data science platform that enables people with limited data science and machine learning background to work with data, extract meaningful insights and patterns and build advanced machine learning models without any need for code writing or installation. Moreover, you don't need to think about the model or infrastructure maintenance, we provide a fully managed environment to automate MLOps activities needed. If you do have extensive knowledge in data science and machine learning - you still can leverage our platform for this as well as to automate some routine tasks. Spend less time preprocessing and cleaning the data and more time working with the models.

We have created template solutions for particular issues to provide our users with more benefits. This means that you can take the "pipeline" which was created by our Data Science team, upload your data, do minor tweaks and have a working forecasting model. We decided to start with a demand forecasting use case to create our first template. The issue seems quite straightforward: you have the historical sales data and you would like to infer how the same product sales might behave in the future. In other words, knowing how the sales over the last year(s) you will be able to make predictions for the next week, month or quarter. This might have quite a few implications: you can manage your inventory better and avoid overstocking or running out of goods, you might optimize the prices if you know there are going to be major fluctuations in the amounts sold, or you just might be able to forecast your financial and sales KPIs and have data-driven assumptions for decision making on SKU or category level. Most of the applications fall under the planning activities umbrella category.

Which model is the best for demand forecasting?

There is a plethora of different models that can be used when trying to tackle demand forecasting issues. Some are more intuitively understandable, like using regression analysis in order to decide future behavior of data at any given point in time. Autoregressive moving average model (ARMA), and other generalizations and variations of it are widespread when doing time series analysis and forecasting, and demand forecasting in particular. Using those methods requires substantial knowledge of statistics and how the underlying models work. In a couple recent years, though, time series forecasting had been made drastically easier thanks to the Prophet algorithm. The algorithm works exceptionally well out of the box, handles missing values and outliers pretty well and requires virtually no knowledge of time-series analysis or algorithm fine-tuning. Thanks to Prophet, a person who has knowledge of his data and business domain can easily create powerful forecasts without actual hands on experience with time-series analysis on demand forecasting in particular.

That is why we recommend using Prophet as a first choice algorithm for demand forecasting. It is an algorithm developed by Facebook and aimed at forecasting behavior of time series. The key thing here is that you don't need to know under-the-hood dynamics, you can apply it in a few clicks, including advanced seasonality effects incorporation. It is robust to outliers and missing data and behaves quite well without extra fine-tuning required if you have enough historical data. For model evaluation, we will be looking mostly at Mean Absolute Percentage Error (MAPE), it basically tells you by how many percentages points your forecasts are off, on average. Do not try to chase the numbers, they are useful when comparing different models and give you a general feeling on how the model might perform. The actual thing you should focus on is benefits to your business because you are making informed, data-driven decisions, rather than trusting gut feeling and making decisions based on the assumptions.

The Future of Demand Forecasting Software

As industries evolve, so do demand forecasting methods. The journey from traditional statistical models to advanced algorithms like Prophet showcases the evolution in this arena. But what's next? With emerging technologies such as AI and ML, the potential for forecasting accuracy and ease of use is bound to grow. Companies like are at the forefront of this evolution, ensuring businesses have access to the latest and most effective demand forecasting software. By continuously adapting and refining our tools, we aim to help businesses make even more accurate and beneficial future predictions, keeping them a step ahead in their respective sectors.

Demand Forecasting in Supply Chain: No Advanced Skills Required

Understanding demand forecasting methods isn't always straightforward. For some industries, achieving accurate predictions is simple, while others face challenges. Don't get caught up in metric specifics. Instead, concentrate on the broader challenges and let our tool guide you to optimal solutions.

What is Demand Forecasting? A foundation in business analysis is all you need. There’s no requirement for intensive data science, ML, or coding knowledge. By understanding your business and the data driving your demand forecasts, you can easily import and preprocess data and then initiate a demand forecasting software model, all without a single line of code. provides a no-code analytics solution tailored for demand forecasting in the supply chain. With Datrics, you’re equipped with robust models designed to address outliers, understand trends, identify seasonality, and navigate various dependencies. This ensures your decisions are data-driven and informed. Moreover, users have the liberty to refine their data, experiment with diverse models, and evaluate metrics to pinpoint their ultimate forecasting model.

Explore TodayExperience the ease of premier demand forecasting software with Whether you're a newcomer or an industry veteran, our platform is designed to give you actionable insights. Dive in and unlock the power of sophisticated forecasting made simple.


1. What is demand forecasting in retail?Demand forecasting in retail involves predicting future customer demand using historical data, trends, and analysis to optimize inventory, pricing, and planning.

2. How does Datrics.AI enhance demand forecasting?Datrics.AI enhances demand forecasting by leveraging AI and machine learning models, including the Prophet algorithm, to provide accurate, data-driven predictions without the need for coding.

3. Why is demand forecasting important for supply chain management?Demand forecasting is crucial for supply chain management as it ensures optimal inventory levels, improves financial planning, and enhances operational efficiency, ultimately leading to higher customer satisfaction.

Do you want to discover more about Datrics?

Read more

Demand Forecasting and Planning in Retail: Datrics use cases

Demand Forecasting and Planning in Retail: Datrics use cases

Navigating the retail landscape requires precise demand forecasting to ensure optimal inventory management, resource allocation, and customer satisfaction.
How to reduce stockouts by more than 80% with Datrics

How to reduce stockouts by more than 80%: the new case for Profit Whales

Marketing agency works with a sophisticated ML-powered framework that predicts the supply and demand for Amazon's fastest-growing brands.
AI in E-commerce: Use Cases & Examples| Datrics

How AI Can Transform Your E-commerce Business

Explore AI's role in revolutionizing e-commerce with personalized shopping, logistics, and customer service. Discover growth-focused use cases.