AI-powered demand planning software for smarter forecasting

New product planning is challenging because there is little or no sales history.

Flowlity addresses this challenge by combining human expertise and artificial intelligence.

The solution allows the use of analogous (substitute) products or market data to build an initial demand forecast for a new product. Furthermore, Flowlity provides forecasts even for references with very little history by relying on intelligent algorithms capable of generating trends from partial information.

Concretely, the planner can manually adjust the initial demand assumptions for a new product in Flowlity (for example, based on the launch of a similar product), then the AI refines these forecasts on the fly as soon as the first actual sales data arrives. This hybrid approach ensures that new product launches are taken into account in the supply plan, avoiding stockouts during launch while avoiding overstocking a product whose success is still uncertain.

Flowlity therefore ensures agile planning of new products, highly appreciated in retail as well as in B2B trading where range renewals are frequent.

The quality of historical data is a key factor for reliable forecasts.

Flowlity therefore offers a data cleaning process upstream of modeling.

Concretely, this involves identifying and correcting anomalies in your sales or consumption history. For example, we detect outliers (an exceptional sales spike due to a promotion or an input bug), missing or inconsistent periods, and we handle them appropriately. Cleaning involves several steps: standardizing units and formats, removing or smoothing outliers, and imputing missing data if necessary. As a general approach to data cleaning describes, it involves "identifying and correcting errors, filling in missing values, and putting the data into a consistent format" before analysis.

To do this, Flowlity uses business rules (e.g., ignoring zero sales during a factory shutdown) and algorithms: for example, a statistical method can replace an abnormal peak with a value more representative of the trend.

Furthermore, our demand forecasting AI is capable of integrating external data (market trends, weather, etc.) and detecting breaks in the history to avoid biasing forecasts. In practice, during onboarding, our teams assist you in auditing your history: we identify unreliable data with you (for example, a reference whose coding changed during the year) in order to adjust or exclude it.

This cleansing phase ensures that the forecasting model is working on a sound basis.

Finally, since Flowlity is a learning solution, the cleansing is continuous: over time, the algorithm learns new behaviors and can rule out future anomalies on its own.

You will of course retain control over validating or adjusting any processing of historical data.

Yes, these elements are an integral part of the data taken into account.

Flowlity allows you to configure supplier calendars, i.e., your partners' working/non-working days. For example, if a supplier is closed in August or only delivers from Monday to Thursday, the supply plan will automatically take this into account: no deliveries will be scheduled outside of their time slots.

This avoids unnecessary overstocking or waiting for impossible deliveries.

Similarly, the solution manages product launch and end-of-life dates. Our algorithms integrate the item lifecycle: you can indicate that a new product starts on a certain date (with a possible ramp-up profile) or that an existing reference will be obsolete from a certain date.

Flowlity “tracks product lifecycles (new products, end of life, etc.)” and adapts forecasts and recommendations accordingly.

For example, as an end-of-life approaches, the tool will gradually reduce replenishment proposals and then stop generating them beyond the end date, to avoid unsold items. Conversely, during a launch, Flowlity can use analogies (similar products) or market data to initialize the forecast, so as not to start from scratch.

In short, calendar constraints – whether they come from suppliers or your product cycle – are well managed by Flowlity. This ensures realistic planning aligned with operational realities.

Yes, Flowlity integrates promotion planning into its demand forecasting capabilities.

The solution allows you to take into account increases in demand related to promotional campaigns or price changes, in order to adjust forecasts and inventory accordingly. You can enter upcoming promotional events (sales, promotions, sales operations) so that Flowlity's AI can anticipate increased demand and offer tailored sourcing recommendations.

This allows supply chain managers in retail and B2B distribution to ensure that inventory levels are optimized to meet promotional sales peaks without creating excess inventory after the fact. Flowlity helps avoid stockouts during promotions while limiting post-event overstocks, which improves product availability and customer service rates during these critical periods.

Dive deeper

To find out how Flowlity can adapt to your promotional specifics, please request a personalized demo.

Feedback shows significant gains thanks to Flowlity, both in forecast accuracy and in inventory reduction and service rate improvement.

On average, our customers observe up to 60% inventory reduction and a 50% improvement in product availability by leveraging our solution.

For example, La Redoute was able to reduce its average inventory of packaging consumables by nearly 50% in one year of use. On the forecasting side, Flowlity continuously improves demand reliability.

During a deployment at Saint-Gobain, consumption forecast reliability reached 95.4% (measured by comparing it to actual sales at 3 months) and stockouts decreased by 27.6%, while lowering inventory levels by 11% compared to previous practice. These operational results translate into a rapid ROI:

Thales estimates the return on investment for Flowlity at less than 18 months.

Other clients, such as the Lemoine Group, aimed to reduce their inventory by €1 million and achieved this goal faster than expected, largely thanks to Flowlity. In addition to the figures, the organizational benefits are worth noting: planners save time (fewer emergencies to manage, more reliable planning), which allows them to focus on higher value-added tasks. The service rate improves, increasing customer satisfaction and revenue (fewer sales lost due to stockouts).

In short, with Flowlity, you can expect:

• less inventory,
• fewer stockouts,
• more sales – and therefore financial and operational optimization of your supply chain from the first year of use.

Indicators such as inventory turnover, OTIF (On Time In Full), and service level are seeing significant improvement thanks to the increased reliability of forecasts and the continuous optimization of supplies.

Many solutions are available on the market, and can be sorted out by size, key industry served, type of solution or even technology.

We find more relevent to focus on tech matters as performance, expected ROIs and integration conditions vary accordingly.‍

Legacy ERP-Based Solutions

• Examples: SAP APO (now SAP IBP), Oracle Demantra
• Strengths: Deep integration with enterprise systems
• Weaknesses: Rigid, slow to implement, high total cost of ownership

Cloud-Native & SaaS Platforms

• Examples: Anaplan, o9, Kinaxis, ToolsGroup
• Strengths: Scalable, real-time collaboration, AI/ML-driven forecasts
• Often include digital twins, scenario planning, and real-time analytics

AI-First and Disruptive Startups

• Examples: Flowlity, Lokad, Relex, Netstock, Fivetran + Snowflake (composable stack trend)
• Strengths: Most advanced algorithms on the market, agentic AI, agile deployments, plug-and-play APIs. The product is rapidly evolving with users' needs, to reachfast ROIs and high performance
• Focus on automation, lean planning, and “lights-out” supply chains. Also includes AI agents, scenario planning

Demand forecasting is the process of estimating future customer demand using historical sales data, market trends, seasonality patterns, and external factors. It helps businesses plan production, manage inventory levels, and allocate resources effectively to meet anticipated demand while avoiding stockouts or excess inventory.

Modern demand forecasting increasingly relies on machine learning and AI algorithms that detect complex patterns — such as promotional effects, weather impacts, and economic indicators — that traditional statistical methods often miss. Accurate demand forecasting forms the foundation of effective Supply Chain planning, enabling companies to make data-driven decisions about procurement, production scheduling, and inventory allocation across their entire product range.

Demand forecasting is one component of demand planning. It focuses on projecting future sales volumes using historical data, statistical models, and increasingly, machine learning algorithms that detect patterns like seasonality, promotional effects, and trend shifts.

Demand planning takes these forecasts and turns them into concrete action. It involves adjusting forecasts based on business knowledge, coordinating with procurement and production teams, and building consensus through processes like Sales and Operations Planning. In simple terms, demand forecasting answers "how much will we sell?" while demand planning answers "how do we prepare the Supply Chain to meet that demand?"

Demand planning is a cross-functional process that goes beyond simple forecasting. It combines statistical demand forecasts with market intelligence, sales input, and business context to build actionable plans that align procurement, production, and inventory with expected customer demand.

The goal is to have the right products, in the right quantities, at the right time — meeting customer needs while optimizing working capital and service levels. Effective demand planning requires collaboration between sales, marketing, and Supply Chain teams, supported by demand planning software that automates data processing and delivers reliable, AI-driven forecasts.

AI learns from patterns in sales history, seasonality, promotions, and external signals to generate more accurate demand forecasts than traditional statistical methods. Machine learning models continuously improve as new data arrives, detecting correlations and trends that humans might miss — such as cannibalization effects between products or the impact of weather on purchasing behavior.

This leads to better inventory decisions, fewer stockouts, and reduced excess stock across the Supply Chain. AI also automates time-consuming tasks like data cleaning, anomaly detection, and baseline forecasting, freeing demand planners to focus on exceptions and strategic decisions rather than routine data processing.

Absolutely. Flowlity's AI-powered demand planning software for SMBs is cloud-based, fast to deploy, and designed to replace manual planning in spreadsheets. SMBs gain better control over stock levels, avoid overstock and excess inventory, and automate critical replenishment decisions — without needing data science teams.

In practice, small and mid-size businesses often see the most dramatic improvements because they move from basic spreadsheet forecasting to AI-driven models that detect seasonality, promotions, and demand variability automatically. The result is fewer stockouts, less excess inventory, and planning teams that can focus on strategic decisions instead of data preparation.

Not anymore. Compared to legacy supply chain planning systems that require costly on-premise infrastructure and lengthy implementation projects, modern AI-powered demand planning software is delivered through cloud-based SaaS platforms, making it far more accessible for SMBs.

Solutions like Flowlity reduce the total cost of ownership by automating forecasting, inventory optimization, and replenishment decisions — eliminating the need for dedicated data science teams or expensive consulting engagements. The pay-as-you-go SaaS model means smaller companies can access the same AI forecasting technology as large enterprises, with faster deployment timelines and lower upfront investment.

For SMBs, AI improves forecast accuracy by automating what is usually done manually in spreadsheets. By combining demand sensing, sales data, and real-time signals, AI detects patterns linked to seasonality, promotions, and demand volatility that smaller teams don't have time to analyze.

Unlike traditional tools, AI continuously learns from new information and adjusts forecasts automatically, helping SMBs make more granular, reliable decisions despite limited resources. This means that even a planning team of two or three people can achieve forecast accuracy levels that previously required dedicated data science resources or large enterprise planning tools.

Ecommerce brands with demand volatility, multiple sales channels, or large SKU assortments benefit significantly from demand planning software. Accurate forecasting and inventory optimization help reduce stockouts, excess inventory, and manual planning effort across the entire product catalog.

For ecommerce specifically, demand planning software addresses challenges like flash sales, seasonal peaks, marketplace channel variability, and rapid product turnover that make manual forecasting unreliable. AI-driven tools can process signals from multiple sales channels simultaneously, helping brands maintain optimal stock levels without over-investing in safety stock — especially important for businesses scaling their catalog beyond what a small planning team can manage manually.