FAQ – Flowlity

Flowlity uses AI to generate optimal purchase orders based on probabilistic demand forecasts, dynamic inventory targets, and actual supplier lead time data rather than static reorder points. The engine works at the SKU-supplier level, so every recommendation reflects the specific demand pattern, variability, and lead time reliability of that item and source.

Routine replenishment orders that fall within defined policies are calculated, validated, and sent automatically without manual review. Orders that cross defined thresholds — unusual margin impact, elevated supply risk, volume anomalies, or new supplier conditions — are flagged as exceptions for planner review, with a clear explanation of why the threshold was triggered.

This covers the full cycle from order suggestion to supplier dispatch, including order confirmations and change management. Planners stop processing the 80% of orders that are routine and focus on the 20% where their expertise makes the difference.

Demand sensing has a direct and measurable impact on inventory performance. By detecting demand accelerations early, it triggers timely replenishment to prevent stockouts. Equally important, when demand decelerates, it prevents over-ordering that leads to excess stock, markdowns, or waste — a critical issue in food, beverage, and perishable goods.

The net effect is a tighter alignment between inventory levels and actual market demand: higher availability with less total stock. Saint-Gobain, for example, achieved a 9.25% reduction in inventory levels with Flowlity, because the system continuously recalibrates the optimal stock position based on real-time signals rather than static safety stock rules.

Demand sensing solutions ingest a wide mix of internal and external data:

Internal signals: point-of-sale and sell-through data, open customer orders, shipment and delivery data, real-time inventory levels across the network, promotional calendars.

External signals: weather data, economic indicators, social-media trends, competitive activity, local events and holidays, market-index movements.

The key is frequency and granularity. Demand sensing works best when data is refreshed daily or more frequently, and when it operates at the individual SKU-location level rather than in aggregated categories. Flowlity's platform ingests all of these signals through pre-built ERP connectors, meaning companies do not need custom data pipelines or dedicated data-engineering resources to start capturing value from demand sensing.

Flowlity incorporates demand variability directly into its core probabilistic models rather than treating it as a correction applied after the fact. Every forecast comes with a confidence range, so replenishment decisions account for how uncertain demand is, not just what the point estimate says.

On top of that, the platform can simulate the expected impact of promotions on future demand — drawing on past comparable promotions, price elasticity signals, and seasonality to estimate the uplift at the SKU-store level. Planners can pre-position inventory ahead of the promotion and avoid the twin failures of stockouts during the peak and overstocks at non-promoted locations.

The result is a replenishment strategy that adapts proactively rather than reactively, maintaining service levels during the periods of highest uncertainty — when it matters most.

Mid-market and enterprise Supply Chains rarely operate from a single location. Central warehouses, regional distribution centers, retail stores, and specialized hubs for spare parts or e-commerce fulfillment all coexist — and planning decisions at one node cascade through the others in ways that location-by-location tools simply cannot see.

Yes. The platform is designed to optimize inventory across complex networks, including multiple warehouses, distribution centers, and stores. By leveraging multi-echelon optimization, it ensures that inventory is allocated efficiently across all locations.

Instead of every site carrying its own protective buffer, the engine allocates inventory to the echelons where it delivers the best risk-adjusted coverage. This is particularly valuable for retailers and distributors operating across many locations and regions, where local-only planning leaves significant inventory and service-level gains on the table every week.

Most traditional replenishment tools were designed for a more stable world. They rely on fixed rules, static parameters, and simplified assumptions about demand.

In that context, they typically work with fixed reorder points, static safety stock levels, and periodic planning cycles.

This approach creates a rigid system that struggles to adapt when conditions change. As demand becomes more volatile and Supply Chains more complex, these limitations quickly lead to stock imbalances and inefficient decisions.

Flowlity takes a fundamentally different approach. Instead of applying predefined rules, the platform continuously adapts decisions based on real-time data and probabilistic models. Replenishment is no longer driven by static thresholds, but by a dynamic understanding of demand, risk, and constraints.

This results in several key differences.

First, decisions are adaptive rather than fixed. Inventory levels and replenishment quantities evolve continuously instead of being recalculated periodically.

Second, planning becomes predictive rather than reactive. By anticipating variability, companies can act before issues occur instead of correcting them afterward.

Third, the scope expands from local optimization to end-to-end Supply Chain performance. By combining replenishment with approaches such as multi-echelon inventory optimization, Flowlity ensures that decisions made at store level remain aligned with the entire network.

Finally, the user experience changes. Instead of manually reviewing large volumes of data, planners work in an exception-based environment where attention is focused on what truly matters.

The result is not just better replenishment. It is a more resilient, more efficient, and more scalable Supply Chain.

Store replenishment is the layer where planning promises meet on-shelf reality. Every fixed rule, every static safety stock, every weekly batch process shows up eventually as a stockout or a markdown. Flowlity's approach starts from that observation and builds a very different operating model.

Flowlity enhances replenishment by combining AI-driven forecasting with dynamic inventory optimization. Instead of relying on fixed rules, the platform continuously adapts decisions based on real-time data, helping companies reduce stockouts while optimizing inventory levels.

Working at the SKU-store level, the probabilistic engine surfaces only the exceptions that actually require a planner's attention, which shifts teams from manually reviewing thousands of replenishment orders to focusing on the few where the stakes are high. The measurable outcomes are straightforward: fewer stockouts, lower total inventory, and faster reaction when promotions or demand shocks hit a specific location.

In practice, Promotion Management Software connects commercial decisions directly with Supply Chain execution.

Teams can design promotion scenarios and immediately simulate their impact on demand, inventory, and service levels. Instead of working with disconnected tools, both commercial and Supply Chain teams rely on the same data and projections.

With platforms like Flowlity, this process becomes continuous. Forecasts are updated dynamically, risks are identified early, and decisions are adjusted before issues occur.

The result is a shift from reactive firefighting to proactive control.

Sales alone don't tell the full story.

A promotion can increase revenue while actually destroying value — for example by shifting demand earlier or cannibalizing other products.

To measure performance properly, companies need to understand what truly drove the results:

• Did the promotion generate incremental demand?
• What was the impact on margins?
• How did it affect inventory levels?

With integrated Dashboard & Analytics, teams gain this level of visibility. They can identify what worked, what didn't, and continuously improve future promotion strategies.

Yes — and this is where modern Promotion Management Software creates immediate value.

Instead of committing to a single plan, teams can test multiple promotion scenarios in advance. Discount levels, campaign duration, product selection — each variable can be adjusted and evaluated before launch.

More importantly, these scenarios are not evaluated in isolation. They are assessed based on their impact on demand, inventory, and service levels.

This allows companies to move from intuition-driven decisions to controlled, data-driven planning.

In Flowlity, material planning is driven by AI-powered demand forecasts combined with probabilistic inventory models.

Rather than relying on fixed safety stocks and static reorder points, Flowlity continuously recalculates material needs based on evolving demand signals, supplier lead times, and inventory positions across the network.

This means replenishment suggestions are always aligned with the latest Supply Chain conditions — not based on assumptions made weeks or months earlier.

Flowlity also provides planners with clear visibility into which materials are at risk of shortage and which have excess coverage, enabling more targeted and confident planning decisions.

The result is a material planning process that adapts dynamically, reduces manual intervention, and supports better purchasing and production decisions.

Modern MRP software improves Supply Chain planning by replacing rigid, rule-based logic with intelligent, data-driven approaches.

Traditional MRP systems rely on fixed safety stocks, static lead times, and manual forecast adjustments. Modern solutions use AI-powered demand forecasting, probabilistic inventory models, and real-time data integration to generate more accurate and adaptive material plans.

This allows Supply Chain teams to respond faster to demand changes, supplier disruptions, and inventory imbalances — without constantly reworking plans manually.

Modern MRP software also provides better visibility across the Supply Chain, helping planners understand not just what materials are needed, but when, where, and why — enabling smarter purchasing and production decisions.

Adoption is the single biggest factor determining whether a MEIO project delivers value — a sophisticated model that planners ignore is worth less than a simpler one they actually use. For that reason, a MEIO solution has to be transparent, intuitive, and aligned with how planners already work day to day.

Flowlity is designed around that principle. Every recommendation comes with an explanation of which signals drove it — demand trend, variability, lead time, inventory coverage — so planners can see the "why" behind each suggested order. Routine decisions are automated, exceptions are clearly flagged, and planners always remain in control of the final call.

The result is a tool teams trust and rely on, rather than one imposed on them from above.

Production planning software gives planners visibility into available manufacturing capacity across machines, production lines, and facilities — often consolidating data that is otherwise scattered between ERP, MES, and spreadsheets. That consolidated view is what makes it possible to identify potential bottlenecks and capacity conflicts before production actually begins.

By incorporating capacity constraints directly into the planning process, Supply Chain teams can build production plans that remain feasible, rather than plans that look good on paper but break down on the shop floor. Overloaded lines, missed changeovers, and last-minute expediting usually trace back to plans that ignored a binding constraint.

The practical benefit is more stable production operations: fewer emergency rescheduling cycles, better on-time delivery performance, and a smoother flow of materials through the factory — all while using existing capacity more efficiently.

Effective manufacturing production planning software should provide the visibility and flexibility required to manage modern Supply Chains.

Key features typically include demand-driven production planning, capacity constraint visibility, inventory-aware planning, and scenario simulation capabilities.

Advanced solutions also provide real-time plan updates as Supply Chain conditions evolve, allowing planners to adapt production strategies quickly.

Collaboration capabilities are also essential. Production planning software should allow different Supply Chain teams — including Demand Planning, procurement, and operations — to work from the same data and planning scenarios.

These features help ensure production plans remain both realistic and adaptable.

It sounds counterintuitive, but reducing inventory and reducing stockouts come from solving the same problem: poor inventory positioning.

Most companies try to avoid stockouts by adding safety stock everywhere. The result? More inventory… but still shortages in the wrong places.

DRP software takes a different approach. Instead of protecting each warehouse individually, it optimizes inventory across the entire network. It continuously analyzes:

• demand signals (by location and SKU)
• current stock levels across all nodes
• lead times and supply constraints

Based on this, it determines where inventory is actually needed, not just where it happens to be.

In practice, this means:

• less excess stock sitting in low-demand locations
• better availability where demand is real
• earlier detection of potential shortages
• smarter transfers between warehouses instead of emergency orders

For example, companies like Camif have reduced stockouts while scaling their operations, and others like Plum have significantly lowered inventory value — not by cutting blindly, but by placing stock where it creates value.

The key is simple: not more stock — better distributed stock.

DRP software is used to plan how products move across a distribution network. It helps companies decide how much stock is needed at each location, when to replenish, and how to rebalance inventory between warehouses when local demand shifts faster than the plan.

In practice, a DRP tool takes demand forecasts, current inventory positions, in-transit stock, and lead times as inputs, then generates a time-phased replenishment plan for every SKU × location combination. The goal is simple: maximize product availability while minimizing total network inventory.

Modern DRP software like Flowlity adds AI-driven forecasting and probabilistic inventory targets on top of this — so replenishment decisions reflect actual demand variability rather than static reorder points that quickly drift out of sync with reality.

Yes. Flowlity fully incorporates your operational constraints into its replenishment calculations. You can configure minimum order quantities (MOQ), lot sizes, purchase units, and even full-truckload constraints. The planning engine ensures that all these rules are respected in order proposals.

For example, if you have a minimum order of 100 units or a truck that only leaves when full, Flowlity applies these constraints and groups requirements accordingly. The Orders module aggregates requirements by supplier and ensures that conditions such as minimum quantities, lot multiples, or truck-load thresholds are correctly enforced.

As a result, generated supply orders are realistic, ready for execution, and aligned with your real logistics constraints. By avoiding fragmented or suboptimal orders, you optimize transport and storage costs — a critical lever for distributors and manufacturers managing high-volume inbound flows across multiple suppliers.

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Flowlity is an all-in-one platform covering the full Supply Chain planning cycle — from demand forecasting to operational execution — powered by probabilistic artificial intelligence. Its main features work together as a single decision system rather than as isolated modules.

Demand planning

Flowlity generates reliable sales forecasts using your historical and external data. Its probabilistic algorithm produces several scenarios, identifies the most likely one, and provides a confidence interval so planners can see uncertainty rather than hide it.

Inventory optimization

The platform recommends optimal stock levels for every SKU-location and calculates dynamic replenishment thresholds, alerting on imminent stockouts or overstocks.

Supply Planning

Flowlity generates replenishment proposals that respect MOQ, lot sizes, lead times, and capacity constraints, and can lift product availability by up to +50% while reducing excess.

Production Planning

For manufacturers, Flowlity builds capacity-feasible production plans, integrates bills of materials, and decides where to position buffer stocks to maximize on-time delivery.

S&OP and reporting

Real-time dashboards feed the monthly S&OP cycle, aligning demand, supply, and finance around one shared view of stock coverage, shortage projection, and capacity.

Supplier-customer collaboration

Flowlity offers a collaborative space to share forecasts, needs, and order confirmations with your partners — streamlining communication and reducing uncertainty along the chain.

Pricing & Promotions Management

An AI-powered Pricing Optimization & Promotions Management software simulates pricing and promotional scenarios, visualizes their impact on volumes and margins, and keeps pricing decisions aligned with supply capacity.

Overall, Flowlity automates up to 95% of planning activities while keeping planners in control — delivering inventory reductions of up to 60% and higher service rates. Contact us to see it applied to your scope.

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Flowlity’s calculation of supply recommendations is multi-criteria.

Our stock sizing and ordering algorithm takes into account the following parameters:

• ‍Forecast demand (and its variability) over the replenishment horizon – from your history, commercial forecasts, AI demand sensing, etc. ‍
• Current and ongoing stocks – available stock, supplier orders already placed, customer orders in portfolio (to calculate a net requirement). ‍
• Supply lead times – supplier or production lead time, to cover the period until the next delivery. The longer the lead time, the more safety stock the algorithm must provide. ‍
• Target service level – you can define a service rate (e.g.: 99% on product A, 95% on B, etc.). Flowlity calculates the corresponding safety quantities by probabilizing the risks of shortages. ‍
• Supplier variability – the reliability of delivery times or quantities. For example, if a supplier is unreliable or subject to delays, the system can increase the buffer stock. ‍
• Purchasing constraints – MOQ, batch size, pallet packaging, order frequency, etc., as mentioned above. Orders below the MOQ will not be placed and the required multiples will be respected. ‍

By combining these parameters, Flowlity calculates when and how much to order for each item and stock point, so as to avoid both stockouts and overstocks.

In short, the recommended quantities are the result of a multi-parameter optimization integrating your service objectives, anticipated demand and all your logistics and supplier constraints.

Flowlity and DDMRP (Demand Driven MRP) share a common goal:

To better position buffer stocks to absorb uncertainties and avoid the bullwhip effect in the supply chain.

However, their methodological approaches differ significantly.

DDMRP is a deterministic method that defines stock buffers at fixed decoupling points and adjusts these buffers mainly according to predefined rules (green-yellow-red colors based on consumption, for example). This works well for products with relatively stable demand, but can show its limitations on products with high volume volatility.

Flowlity, on the other hand, adopts a dynamic and probabilistic approach: the solution continuously calculates optimized safety stocks based on updated consumption forecasts and uncertainty assessment via AI.

In practice, Flowlity will dynamically adjust your buffer stocks based on detected risks (sudden increase in demand, supplier delays) rather than sticking to a fixed buffer size until the next review.

This is a “flow-driven” approach where buffers are recalculated frequently thanks to forecasts and early detection of variations, whereas classic DDMRP often provides for a more spaced periodic review. Note that Flowlity also identifies critical decoupling points in the chain (as recommended by DDMRP) in order to decouple demand and supply in the right places, but:

The difference is that these points are managed in a more intelligent and adaptable way thanks to machine learning.

In short, Flowlity takes the spirit of demand-driven while adding the power of AI to improve responsiveness.

Companies that find DDMRP too rigid or manual will appreciate Flowlity's ability to automate the recalculation of parameters (buffers, replenishments) on a continuous basis.

Moreover, according to Flowlity, pure DDMRP "finds its limits" on highly volatile products - this is precisely where Flowlity's AI approach makes the difference by better absorbing uncertainty.

Yes, we can support the ABC/XYZ classification of your items.

Flowlity knows how to prioritize high-value/fast-moving products and low-impact products to adapt planning strategies.

ABC analysis classifies SKUs by importance (e.g., A = 20% of products representing 80% of the value), while XYZ analysis classifies them according to demand regularity (e.g., X = regular demand, Z = highly fluctuating demand). Combining the two (9-box matrix) yields categories such as AX (critical products, stable demand) or CZ (low-value products, erratic demand).

Flowlity can integrate these segments:

For example, apply finer replenishment frequencies to A items with stable demand, and more agile methods for highly volatile C items. In practice, our calculation engine uses a lot of data (value, variability, lead times, etc.) to automatically prioritize SKUs, which amounts to dynamic classification.

We also provide reports that highlight items by class, so you can fine-tune your parameters (higher target service level for A, etc.). This approach helps you "prioritize resources, optimize inventory levels, and improve forecast accuracy" for each product category.

In short, Flowlity integrates ABC/XYZ principles into supply chain optimization to treat strategic and less critical products differently, for better overall inventory control.

Absolutely.

Flowlity offers a S&OP simulation module ("Tactical") that acts as a true digital twin of your supply chain. You can test different scenarios by adjusting, for example, service levels, supply parameters, or the composition of your product portfolio. The idea is to be able to assess the impact of strategic decisions or market changes without risk, before implementing them.

For example:

You can simulate what would happen if you increased the safety stock level on a category A, or if a key supplier extended its delivery time.

Our solution thus allows you to simulate the inventory and purchasing strategy by adjusting service levels, or even to test the parameters of supplier agreements (MOQ, delivery frequencies). Thanks to this scenario analysis, you can make your decisions (stock vs. service arbitrations, investment choices, etc.) with full knowledge of the facts.

This is a valuable asset for S&OP and risk management:

You can visualize the effect of sales growth, a supply disruption or a product launch on the entire chain, and thus be proactive in your action plans.

Absolutely.

Flowlity provides reliable, up-to-date data that can fuel your S&OP process and facilitate decision-making during meetings between sales, operations, and management.

The platform provides a consolidated view of forecast demand, projected inventory levels, and supply needs, allowing for a single, quantified reference during S&OP stages (demand review, supply review, etc.).

In addition, Flowlity integrates a real-time management module similar to IBP (Integrated Business Planning): ready-to-use dashboards, indicators, and reports provide instant visibility into the health of the supply chain. This helps align operations with strategic objectives—for example, by simulating different scenarios (surge in demand, major supplier delays) to assess their impact on the supply plan and finances. Flowlity doesn't replace your existing S&OP processes, but complements them by adding a layer of analysis through AI and simulation.

You can test hypotheses in Flowlity (product launch, promotional campaign, logistics incident) and then discuss the results in S&OP meetings with confidence. Thanks to Flowlity, S&OP becomes more responsive and accurate, which is an asset for medium to large companies facing volatile demand.

Flowlity was designed as a collaborative platform connecting the various links in the supply chain, particularly between a client (manufacturer, distributor) and its suppliers.

In concrete terms, the solution can act as a trusted third party where suppliers and customers share information transparently.

For example, suppliers can access (via a portal or dedicated interface) consumption forecasts or supply needs that concern them, and thus better anticipate upcoming orders. Similarly, Flowlity allows suppliers to track performance (on-time performance, reliability) and trigger alerts in the event of potential delays.

This shared visibility helps quickly adjust plans: if a supplier reports a capacity constraint or a longer lead time, Flowlity readjusts recommendations to avoid a disruption downstream.

In addition, the platform offers the possibility of collaborating on supply plans: online validation of order proposals, exchange of comments, jointly approved modifications, etc. This eliminates the need for multiple email and Excel file exchanges and makes supplier-customer relationships more reliable. For both B2B distribution companies and manufacturers, this improved collaboration means fewer unforeseen events and a more agile supply chain.

Because Flowlity is a cloud-based solution, your partners can easily connect—under the control of your administrators—to share this data.

Contact us to learn how to implement supplier collaboration on Flowlity in your context.

Implementation timelines vary based on data maturity and Supply Chain complexity. Enterprise platforms can take 6–12 months or more. Flowlity is engineered for fast time-to-value, and real-world deployments prove it.

Plum Living, a 45-person interior design company, went live with Flowlity across 630 SKUs and 2 warehouses — and achieved a 21% inventory reduction at go-live. Supply Caddy, a Flowlity Lite client, generated its first AI forecasts instantly after signing and was fully operational in under two weeks.

Typical mid-market deployments take weeks rather than months, thanks to pre-built connectors for major ERP systems and a cloud-native architecture that eliminates heavy IT infrastructure requirements. The deciding factor is data readiness — if your organization already tracks orders, sales, and inventory at the SKU level in a structured system, the foundations for demand sensing are already in place.

The timeline for MRP software implementation depends heavily on the solution chosen and the complexity of the Supply Chain environment.

Traditional MRP modules embedded in ERP systems often require long implementation cycles — sometimes 6 to 18 months or more — due to customization, data migration, and system integration requirements.

Modern, cloud-based MRP software like Flowlity is designed for faster deployment. Because these solutions work alongside existing ERPs rather than replacing them, implementation timelines are significantly shorter — often measured in weeks rather than months.

Flowlity’s approach focuses on rapid data integration and incremental rollout, allowing Supply Chain teams to start improving material planning performance quickly without waiting for a full system overhaul.

Implementation timelines vary widely depending on the solution architecture. Traditional MEIO tools, typically built on heavy enterprise planning suites, can take 6 to 18 months and often require dedicated data science resources to configure and maintain the models.

Modern AI-driven platforms like Flowlity are designed for faster deployment and much quicker time-to-value. Pre-built ERP connectors, a planner-centric interface, and a cloud-native architecture let mid-market companies go live in weeks rather than months.

Plum Living, for example, rolled out Flowlity across 630 SKUs and 2 warehouses, achieving a 21% inventory reduction at go-live. The key prerequisite is data readiness — if inventory, orders, and sales are already captured at the SKU-location level, implementation moves fast.

Implementation time depends heavily on the type of solution. Traditional ERP-based DRP modules typically take anywhere from 6 to 18 months to go live, because they require deep configuration, custom data integrations, and often a parallel change-management program involving several teams.

Modern cloud-native solutions like Flowlity are engineered for a very different timeline. Pre-built connectors to major ERP systems, a planner-centric interface that requires little training, and a SaaS architecture that eliminates infrastructure setup mean most mid-market deployments go live in weeks rather than months.

Plum Living, for example, went live across 630 SKUs and 2 warehouses and achieved a 21% inventory reduction at go-live. The deciding factor is data readiness — if orders, sales, and inventory are already tracked at the SKU level, the rest moves fast.

Flowlity was designed to deliver rapid benefits at a controlled cost.

First, in SaaS mode with our Flowlity Lite subscription, you don't have to finance heavy infrastructure, a simple subscription and light integration are sufficient.

Second, concrete results generally allow you to quickly justify the investment.

For example:

Saint-Gobain estimated a return on investment in less than 18 months after observing a 27.6% drop in stockouts and an 11% reduction in inventory thanks to Flowlity. Similarly, La Redoute reduced its packaging inventory by nearly 50% on average after implementation—significant savings.

To compensate for the lack of IT resources, Flowlity offers comprehensive support:

Our teams can handle a large part of the integration (ERP data extraction, configuration, etc.), which frees up your technicians. For example, we can start with a Proof of Concept using your existing data: during one project, a client tested our tool on 6 months of supplier history and was convinced by the reliability of the forecasts and the inventory reduction achieved in a few weeks.

In short, even with a tight budget and busy teams, you can initiate the project on a small scale and gradually ramp up. The rapid gains in performance (improved customer service, freed up time for suppliers, etc.) will make the investment profitable and appreciable from the first months.

During a Flowlity project, your teams are obviously involved, but Flowlity is committed to minimizing the internal workload thanks to its turnkey support.

Here are the main roles and implications on the client side:

‍Project manager / Internal sponsor

‍It is important to appoint a project manager on the company side (often a Supply Chain manager or an IS supply project manager) who will be the main contact with Flowlity. Their role is to coordinate internally (mobilize the right people, validate business decisions) and monitor progress. This role is typically expected on any project, with a moderate workload (a few meetings per week). ‍

IT team (DSI)

‍Since Flowlity is a SaaS software, the IT effort is mainly focused on extracting data from your source system (ERP, WMS, etc.). Your IT teams will need to provide Flowlity with the necessary data (usually via automated exports, SQL views, or API opening). Flowlity offers standard connectors for common ERPs, which significantly reduces development work. The IT department must also ensure that security and compliance are respected. In short, the IT department is called upon at the beginning of the project to connect the systems, then on an ad hoc basis to validate the flows and updates. ‍

Supply Chain business experts / Key users

‍These are your planners, demand planners, pilot suppliers who will work with Flowlity. Their involvement is crucial to configure the business rules in the tool (for example, validate product families, supplier groupings, constraints such as MOQ or supply frequency) and especially to test and validate the recommendations. During the acceptance phase, they are asked to compare Flowlity's proposals with their current practices, identify gaps, and provide feedback. This step allows us to adjust the tool so that it matches the reality on the ground. Typical involvement is a few hours per week for these key users during the heart of the project. Once the tool is in production, these same people will be the main users of Flowlity on a daily basis. ‍

Supply Chain / Demand Planning Management

‍Management plays a sponsor role (providing the impetus and objectives – e.g. “reduce stock by 20% without degrading service”) and change management. It is important that management leads the project, communicates internally on the expected benefits, and supports the process change. Concretely, this involves regular updates, possibly participating in a demonstration at the end of the project to mark adoption. Flowlity, for its part, takes care of most of the technical and methodological work: configuration of the AI, advanced settings, proper functioning tests. Our business consultants will guide your key users to define the right parameters (e.g. forecast freeze horizon, replenishment policy, etc.) based on best practices.

In short, your teams bring the business knowledge of your company, and Flowlity brings the tool and supply chain expertise. The project is a close collaboration, but one that's calibrated so your employees can continue to perform their daily tasks at the same time. Many of our clients are reassured by the light internal footprint: no need for a full-time army; just a few key people are enough to successfully implement the project.

Our methodology includes short, effective workshops so as not to monopolize your resources—we'll explain all this to you in detail at the project launch.

Yes, Flowlity offers a POC (Proof of Concept) as part of its commercial approach.

We understand that it is important for a company to concretely validate the benefits of the solution before fully committing.

A Flowlity POC generally takes place over a few weeks and allows us to test the solution on a small but representative scope. For example, we can choose a selection of items or a product category, in one or two pilot warehouses, with a few suppliers, in order to simulate the complete operation (from forecasting to procurement recommendations).

How does it work?

1. We define the POC objectives together (e.g., verifying that we can reduce stock by X% on family Y while reducing stockouts, or observing the savings in planning time on a given process). Your teams provide the basic data for the chosen scope (sales history, current stock, current orders, etc.).
2. The Flowlity team configures the tool for this scope and then delivers the results to you in the form of a report and a feedback workshop. Specifically, you'll be able to view the forecasts generated by Flowlity's AI, safety stock recommendations, and replenishment proposals, and compare them to your current practices. Often, this POC highlights quick wins: for example, identifying obvious overstocks or preventing a future shortage that the previous method hadn't anticipated. The POC is also an opportunity for your users to experience the Flowlity interface for the first time. They can log in, explore the dashboards, and understand the logic of alerts and recommendations. Their feedback is valuable and is taken into account to refine the configuration if we decide to pursue a widespread deployment. In terms of investment, a POC is offered at a reasonable cost and in a time-limited format, to facilitate your decision. The goal is to give you confidence in the solution.
3. If the POC results are conclusive (which is generally the case), you can then extend to other areas by capitalizing on what has been learned. If not (for example, if the POC revealed unforeseen constraints), you have the freedom to reevaluate the project. In any case, it's a win-win situation. Many of our current clients have gone through a POC that has convinced operational teams and management thanks to quick wins demonstrated in real-life situations.

If you are interested in a POC, contact us, we can define a scenario adapted to your specific challenges and propose a rapid action plan.

The duration of a Flowlity project can vary depending on the scope and complexity of your supply chain, but on average, we find that projects are much shorter than traditional supply chain tool implementations.

Typically, an initial Flowlity deployment on a first scope (for example, a product family or a pilot warehouse) takes between 8 and 12 weeks. During this period, the main steps include: project scoping and objective definition (1-2 weeks), data integration from your ERP into Flowlity and initial testing (4-6 weeks), parameter adjustments and user acceptance (2-3 weeks), and then go-live on the defined scope.

For a global deployment (multi-warehouses, multi-subsidiary), the project can extend over a few additional months, often divided into waves. For example, after a successful initial pilot implementation, other product categories or other sites are rolled out, which can bring the overall project to 4 to 6 months in total.

This remains very fast compared to traditional APS solutions.

Flowlity relies on an agile approach:

We deliver concrete results quickly, then we scale up. This way, your teams quickly see the benefits (for example, a reduction in inventory or better availability on the pilot scope), which creates buy-in to continue.

Note that thanks to Flowlity's SaaS architecture, production launches are simplified (no software deployment on your servers, just a connection to the data). Our experience shows that even large companies (several hundred thousand references) have been able to have an initial set of operational Flowlity recommendations in around 3 months.

Of course, each project has its specificities: if your data requires significant cleaning work or if you decide on a very broad functional scope from the outset, this can extend the duration a little. But in all cases, Flowlity's objective is to deliver value as soon as possible.

A concrete example: Sport 2000, a retailer, started its collaboration with Flowlity on an S&OP angle and saw results within a few months. Similarly, other industrial clients were able to see rapid gains. We can provide you with detailed timelines and examples during a personalized exchange.

Because Flowlity integrates with existing ERP, WMS, and data systems through pre-built connectors, companies can start generating measurable value quickly. Improvements in inventory levels, service rates, and planning efficiency are typically observed within weeks of deployment rather than the 12-to-18-month timelines common with traditional planning suites.

Plum Living, a 45-person interior design brand managing 630 SKUs across 2 warehouses, achieved a 21% inventory reduction at go-live with Flowlity, and subsequently grew that improvement to a 38% reduction in inventory value over time as the AI continued to refine its recommendations. Other customers see service-level gains of several percentage points within the first quarter of use.

The deciding factor for speed is data readiness: if sales, orders, and inventory are already captured at the SKU level, value flows fast.

Yes — especially for companies that are scaling and starting to feel the limits of manual planning.

At this stage, complexity increases quickly: more SKUs, more channels, more frequent promotions. Spreadsheets and disconnected tools become difficult to maintain and often lead to costly errors.

Promotion Management Software helps structure planning, improve visibility, and reduce risk without requiring heavy implementation projects.

For mid-sized companies, it's often the fastest way to gain control over growth without adding operational complexity.

Promotional periods are among the highest-stakes moments in the Supply Chain: demand can triple overnight, customer expectations peak, and every stockout is both a lost sale and a direct hit to brand trust. Getting promotions right operationally is therefore as important as getting them right commercially.

Yes — provided it is connected to Supply Chain planning.

Stockouts during promotions usually happen because demand and inventory are managed separately. Promotions increase demand, but Supply Chain teams don't have enough visibility to anticipate the impact.

By integrating promotions into Demand Forecasting and Store Replenishment, companies can anticipate demand peaks and prepare inventory before the promotion starts.

The result is simple: better product availability when demand is highest, without overstocking elsewhere.

That integration is what unlocks the shift from reactive to proactive: the system models the expected uplift from pricing depth, past comparable promotions, and seasonality, then right-sizes the pre-build at the SKU-location level. For retailers and distributors running frequent promotions, this is usually where the biggest service-level and margin gains come from.

Promotion ROI improves when decisions are based on real impact — not assumptions.

Instead of launching promotions and analyzing results afterward, companies can forecast demand uplift, simulate different scenarios, and evaluate both financial and operational outcomes before execution.

The key difference comes from connecting promotions to Supply Chain planning. When promotions are aligned with Demand Forecasting and inventory constraints, companies avoid the hidden costs that typically destroy ROI: stockouts, overstocks, and last-minute operational adjustments.

It's not just about driving more sales — it's about making sure those sales are profitable and sustainable.

Flowlity's dashboard is designed to provide Supply Chain teams with immediate visibility into the most critical operational indicators.

The dashboard summarizes key metrics such as inventory levels, stock coverage, and demand trends, allowing planners to quickly assess the overall health of their Supply Chain. Time-series visualizations help teams understand how these indicators evolve over time and detect emerging risks.

When deeper analysis is required, Flowlity's Analytics module provides additional capabilities, including data quality monitoring, operational alerts, and cross-site performance analysis.

Because the dashboard is directly connected to planning modules such as Demand Forecasting, Inventory Management, Supply Planning, and S&OP, insights generated in the interface can immediately translate into operational decisions. This integration allows organizations to move seamlessly from visibility to action and continuously optimize Supply Chain performance.

Supply Chain dashboards help reduce inventory by improving visibility on demand variability, stock dynamics, and replenishment decisions.

When planners have access to accurate and real-time indicators, they can identify situations where inventory levels exceed operational needs. This makes it easier to adjust safety stock levels, optimize replenishment strategies, and avoid unnecessary stock accumulation.

Dashboards also help teams detect slow-moving products and demand fluctuations earlier, allowing them to adapt procurement and production plans accordingly.

Combined with predictive analytics and demand forecasting tools, dashboards provide the insights needed to balance product availability with efficient inventory management.

Yes. Production planning software helps manufacturers align production volumes with real demand signals and current inventory positions, rather than with rules of thumb or outdated assumptions. That alignment is usually where the biggest inventory wins come from.

When production plans are built on stale demand data, companies tend to produce more than necessary to protect against uncertainty — and that buffer ends up as slow-moving finished goods or component stock. By integrating demand forecasts, real-time inventory availability, and production constraints into a single planning view, manufacturing production planning software allows companies to produce much closer to actual market demand.

The net effect is a measurable reduction in excess inventory while service levels are maintained or improved. Customers like Saint-Gobain have used this approach to lower inventory levels by more than 9% while improving service to 97%.

Companies typically achieve significant improvements in both inventory efficiency and service levels.

In most cases, results include 20% to 40% reduction in inventory, improved product availability, and fewer stockouts — without increasing operational risk.

This is possible because multi-echelon inventory optimization does not simply reduce stock. It redistributes inventory more intelligently across the Supply Chain, placing it where it absorbs variability most effectively instead of duplicating safety stock everywhere.

As a result, companies can reduce excess inventory while maintaining — or even improving — service levels.

For example, organizations like Danone, La Redoute, and Plum Living achieved substantial inventory reductions while improving operational performance using Flowlity.

Artificial intelligence significantly improves production planning by allowing Supply Chain teams to analyze complex data and anticipate disruptions earlier.

AI-powered production planning software can detect patterns in demand fluctuations, supplier performance, and inventory movements. This allows planners to identify risks and opportunities that traditional planning tools may miss.

AI also enables faster scenario simulation. Instead of manually testing multiple planning options, planners can evaluate different production strategies quickly and choose the most resilient plan.

By using AI to support production planning decisions, manufacturers can improve forecast accuracy, reduce operational disruptions, and build more agile Supply Chains.

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.

Excel and ERPs are valuable tools but have limitations for advanced planning.

Two out of three companies still use Excel in their supply chain, but this reliance results in manual and error-prone processes.

An ERP manages daily transactions, while Flowlity provides dedicated planning intelligence: automating 95% of repetitive tasks, detecting anomalies, and proactively adjusting them. With Flowlity's AI, your historical and MRP data is converted into probabilistic forecasts and optimized recommendations.

In practical terms, this means fewer Excel sheets to update and more informed decisions.

By adopting a specialized tool like Flowlity, you move from reactive management to proactive optimization of your supply chain, resulting in reduced inventories and a better service level.

Flowlity complements your ERP, it doesn't replace it.

A newly deployed ERP manages your master data (orders, inventory, etc.), and Flowlity plugs into it to provide a layer of intelligent optimization. Our solution integrates with existing ERPs to enrich planning: for example, it uses the ERP's MRP data and cross-references it with predictive algorithms to offer dynamic inventory and alerts in case of risk.

IT integration is simple and fast:

Flowlity is a cloud platform, connected via API, which allows for rapid deployment without disrupting your existing systems.

In practice, our customers find that Flowlity helps them "regain control over ERP parameters", for example by optimizing reorder points or stock security levels. Even if your ERP is new, you can therefore accelerate its ROI by adding Flowlity to reduce excess inventory and avoid stockouts.

Flowlity seamlessly interfaces with your environment – “integrates with ERPs to reduce disruptions” – without excessive workload for your IT teams.

Yes. Flowlity is a user-friendly SaaS solution that doesn't require a large IT team to run.

The tool has been designed to be easy to set up and simple to use: our customers confirm it is "user-friendly, easy to implement" and "very easy to use", while providing effective inventory control.

For a small supply chain team, this means you can be up and running quickly, without lengthy training or advanced technical skills.

What's more, Flowlity isn't a black box: the results are understandable by business users, allowing your team to make better decisions independently.

In short, even a small team can benefit from advanced planning without increasing its workload, and focus on what matters most – analysis and optimization – while Flowlity automates the tedious tasks, making it one of the best tools for small businesses.

Flowlity offers two products with different billing models:

Flowlity Lite‍

A fully Plug & Play SaaS solution billed as a monthly subscription.

No implementation fee.

The price depends on the number of SKUs and number of users.

Flowlity Enterprise‍

A fully integrated solution billed with a subscription + one-time implementation fee.

The implementation fee covers data setup, ERP/API integration and personalized onboarding.

The subscription price depends on the number of SKUs or inventory value, depending on your business.

Contact us to book a demo and discuss your scope. We’ll provide a tailored proposal based on your needs.

In most Supply Chains, order management and demand planning live in different tools — and often in different teams. Orders get placed against reorder points set months ago, demand signals evolve continuously, and the gap between the two is absorbed as excess stock or as stockouts. Flowlity is built to close that gap by design.

Yes — this is a core differentiator. Unlike standalone order management tools that rely on static reorder points, Flowlity connects every purchase order to its AI-powered demand forecasts and inventory optimization engine. This means order quantities and timing reflect actual expected demand, forecast confidence levels, and safety stock targets rather than fixed rules set months ago. The result is orders that are consistently right-sized, reducing both stockouts and excess inventory.

This tight coupling is what allows purchase orders to stay right-sized as conditions change, rather than drifting out of alignment between planning cycles. Planners no longer spend their time manually reconciling forecast updates with reorder parameters: the system does it continuously, and flags only the orders that require a human decision because a threshold has been crossed.

This is one of the first questions IT and finance leaders ask when evaluating Flowlity, and the answer shapes the entire implementation approach. Replacing an ERP is a multi-year transformation — Flowlity's value is delivered on a very different timeline, precisely because it sits on top rather than underneath.

No. Flowlity complements ERP systems by adding a decision layer on top of existing processes. While the ERP manages execution, Flowlity provides advanced planning capabilities that improve the quality of replenishment decisions.

The two layers exchange data automatically through pre-built connectors, so planners get better decisions without any disruption to existing ERP workflows or the teams who depend on them. The ERP continues to capture what has happened and what needs to be executed, while Flowlity decides what should happen next — which products to replenish, in what quantities, and when.

Artificial Intelligence significantly enhances the capabilities of Supply Chain dashboards by transforming them from simple reporting tools into predictive decision-support systems.

AI models can continuously analyze operational data to detect patterns and anomalies that would be difficult to identify manually. For example, algorithms can identify unusual demand spikes, forecast potential stockouts, or detect inconsistencies in data flows.

AI can also generate probabilistic demand forecasts, providing planners with a range of possible scenarios rather than a single prediction. This helps organizations better manage uncertainty and adapt inventory strategies accordingly.

In advanced planning platforms, Artificial Intelligence can also support scenario simulations and generate recommendations that help teams prioritize the most impactful actions.

Traditional DRP and AI-powered DRP solve the same problem — balancing network inventory against expected demand — but in very different ways. Traditional DRP systems, usually embedded inside an ERP, are built on fixed rules such as static safety stock levels and reorder points, deterministic forecasts that produce a single "best guess" for demand, and planning cycles that run weekly or monthly.

This works reasonably well in stable environments. But as soon as variability increases — promotions, new product launches, supply disruptions, channel shifts — these systems become rigid and require constant manual adjustments, which is why planners so often end up back in Excel.

AI-powered DRP like Flowlity replaces static rules with probabilistic demand models and continuously adapted inventory targets. Instead of one forecast, the system works with a range of likely outcomes and confidence levels, then flags only the exceptions that require planner attention. The result is fewer firefights and more reliable service levels.

As a modern cloud-native application, Flowlity is highly scalable and can handle large volumes of data without performance loss.

We support mid-sized companies with a few hundred SKUs as well as large groups managing millions of references and complex logistics networks. For example, at a multi-brand distributor, one of the configurations processed included "more than 1.2M SKUs." This complexity was successfully modeled by Flowlity. Our customers such as Sport 2000, EDF, and Saint-Gobain use Flowlity across large scopes, which illustrates its robustness.

In terms of volume, the platform can absorb years of sales history, hundreds of thousands of order lines, and daily stock updates without any problems. The cloud architecture (hosted on secure servers) dynamically allocates the necessary resources based on the load: distributed computing, optimized database, etc. So, even if your item or database doubles in size, the solution adapts. In addition, the calculation frequency is adjustable: forecasts and plans can be recalculated daily, or even in real time for certain indicators, without clogging the system. In short, whether you have 100 SKUs or 10M SKUs, Flowlity can manage them with the same efficiency.

Scalability has been proven by our production customers, and we continue to optimize performance to process ever more data (for example, integrating IoT data, real-time logistics flows, etc. in the future). So you can evolve with confidence, the solution will support the growth of your business in volume and complexity.

Any other questions?

If this FAQ has not covered all the points you would like to clarify, do not hesitate to contact us.

Our teams will be happy to answer your specific questions, provide you with concrete use cases, or support you in your thinking about optimizing your Supply Chain.

Flowlity attaches great importance to support and change management, because implementing a new planning tool is above all a human project.

Our project methodology is structured but flexible, inspired by best practices in Supply Chain project management:

‍Scoping phase

‍We start with scoping workshops to understand your current planning process, your pain points and your objectives (KPIs to improve, specific constraints). During this phase, we co-construct with you the deployment plan, the functional and technical scope, as well as the project team (on your side and Flowlity's side).

Data integration

Very quickly, we move on to the integration part. Our data experts work with your IT department to connect Flowlity to your systems (via data export, API, etc.). We use test datasets to ensure that everything is consistent. This step is done in parallel with the next phase. ‍

Configuration and prototyping

‍Rather than waiting until the end to show something, we set up a working prototype as soon as possible with your data. This allows us to validate the first forecasting and optimization results in Flowlity. We adjust the parameters (calculation periods, demand segmentation, stock policy, etc.) in collaboration with your key users. Rapid iterations allow us to achieve a satisfactory scenario. ‍

Training and user testing

‍Once the tool is configured on the target scope, we train your end users. The training is concrete, on your data, so that they can immediately project themselves. Then, we launch a test phase where your planners use Flowlity in parallel with their old system for a cycle (a few weeks). They compare the decisions proposed by Flowlity and their usual decisions, and give us feedback during regular updates. This double run phase is crucial to gain confidence. ‍

Phased production

‍When everyone is comfortable, we formalize the switchover: Flowlity becomes the reference tool for the scope, and the old operating mode is stopped (or kept as a backup at the beginning if necessary). We remain very present during the first weeks of production to help refine if necessary and ensure success. ‍

Post-deployment monitoring

‍After the go-live, the project is not abandoned. Flowlity offers regular monitoring (for example, monthly steering meetings) to verify the achievement of objectives, analyze indicators (stock reduction, improvement of the service rate, etc.), and process any new requests. In addition, as the solution evolves, we keep you informed of new features that could be useful to you. This methodology is led by an experienced Flowlity project manager, with the support of data and supply chain consultants. It is adapted according to your constraints: for example, if you prefer a big bang across the entire scope, this is possible (although we recommend gradual implementation). If you have periods of high activity (seasonal peaks) where you need to take a break, we take this into account in the planning. The idea is to work hand in hand with your teams, gradually transferring skills. At the end of the project, your users are autonomous on the tool, and our support teams take over for any future assistance.

In short, the implementation of Flowlity is a structured, collaborative, and results-oriented process, where each step aims to ensure that the tool integrates perfectly into your processes and that your teams adopt it enthusiastically.

Customer testimonials are available to illustrate our project approach and the successes achieved – do not hesitate to consult them or contact us for further details.

Yes – it’s even one of Flowlity’s founding principles: providing AI that can be explained and understood by the humans who use it.

We know that in the Supply Chain, planners and managers need to trust a tool’s recommendations, and this requires understanding the “why.”

Flowlity was therefore designed not to be a black box, but rather an educational tool as well as a decision-making tool.

Concretely, how does this manifest itself?

In the Flowlity interface, each forecast and each recommendation is accompanied by explanatory elements. For example, if Flowlity recommends ordering 500 units of item X for next month, the user sees the breakdown of the expected demand: seasonality, trend, promotional effect, etc., depending on the case.

The tool also displays a confidence interval around the forecast (for example: central forecast 500, with a low scenario at 450 and a high scenario at 560), which gives an idea of the uncertainty. This allows for the justification of calculated safety stocks. Furthermore, Flowlity provides alerts and justifications. For example: "Risk of shortage in 15 days on this product because recent demand exceeds forecasts by 20%." Or: "Inventory reduction proposed on this item, because its turnover rate has decreased over the last 3 months." Technically, Flowlity's AI uses machine learning models (including deep learning), but the complexity is hidden behind a simple interface.

Ensemble learning techniques are also favored, which smooth out predictions and avoid aberrations. And above all, Flowlity sees itself as an assistant: the user always has the option to review a decision. If they don't agree with a recommendation, they can modify it (for example, order a little more or a little less), and the system will take this feedback into account to adjust in the future. It's a virtuous learning loop where the human retains final control. During training, we insist that users understand how the tool works.

Without revealing all the algorithmic details, we explain the main principles (probabilistic forecasting, dynamic buffer calculation, etc.). Very quickly, planners see that the tool reacts as they would in many cases, but better because it reacts more quickly and integrates more data. For example, the tool can detect correlations between products that humans would not have seen – but it will display “30% increase in anticipated demand for product A because it is correlated with that of product B on promotion”. This kind of explanation makes AI tangible.

Finally, on the question of technical transparency, Flowlity is open to discussing its approach:

We publish white papers and articles on our approach (e.g., use of probabilistic vs. deterministic forecasts). Our goal is not to mystify the algorithm, but to make the supply chain smarter collectively. Flowlity users become better at their jobs because they learn from AI feedback. Many report that after a few months, they have a better understanding of their supply chain dynamics (seasonality, impact of promotions, supplier behavior) thanks to the visibility the tool provides.

In short, Flowlity's AI is transparent, explainable, and human-friendly. It's a companion that informs your decisions instead of arbitrarily replacing them. This philosophy increases trust and adoption of the solution within Supply Chain teams.

If you'd like to see in practice how Flowlity presents its recommendations and what explanations are provided, we invite you to book a demo where you can judge the tool's clarity for yourself.

Yes, Flowlity is fully GDPR (General Data Protection Regulation) compliant and attaches paramount importance to the security and confidentiality of its customers' data.

Here are the main aspects to consider:

‍Personal data and GDPR

‍In the context of a Flowlity project, the data handled is mainly supply chain data (products, stocks, sales history, etc.) which is rarely personal. However, if some indirect data contained personnel (e.g. supplier contact names, delivery addresses, etc.), Flowlity contractually commits to comply with the GDPR. Concretely, this means: consent and information on the data collected, data minimization (we do not process unnecessary data), right to be forgotten and return/destruction of data in the event of contract termination, etc. Flowlity can provide upon request a Data Processing Agreement (DPA) which details these commitments, including any subcontractors (cloud host for example) and storage locations. ‍

Secure hosting

The Flowlity solution is hosted exclusively on Microsoft Azure, with all customer data stored on Azure data centers located in France, ensuring full data sovereignty. These data centers offer guarantees of high availability, redundancy and physical security. Access to the servers is strictly controlled and monitored. In addition, Flowlity segments data by client: each client has its own isolated database, to avoid any mixing or leakage of information from one client to another. ‍

Encryption

‍All communications between your system and Flowlity are encrypted (SSL/TLS) to prevent any interception (listening) of data in transit. Similarly, data stored in the Flowlity database is encrypted at rest to protect against any illegitimate access. For example, if backups are made, they are encrypted. ‍

Access controls and authentication

‍Flowlity implements strict access controls. Your users access the platform via secure accounts (strong password authentication, with the possibility of SSO/SAML if you wish to integrate it with your corporate directory). Rights can be managed by roles to ensure that everyone only sees the data that concerns them. On the Flowlity side, only authorized people (for example, the project manager or the support team) can access your environment, and only for maintenance or assistance purposes, with your agreement. These accesses are tracked and limited. ‍

Security protocols

‍Flowlity follows industry IT security standards. We regularly carry out security audits and intrusion tests via external firms to verify the robustness of our application. The development of new features requires code reviews, particularly on everything related to data access. Flowlity has implemented a vulnerability management policy (security monitoring, regular updates of third-party components, etc.). The company is aiming for relevant security certifications (e.g. ISO 27001) as it grows, and is already applying best practices in its internal organization. ‍

Backup and continuity

‍Your data on Flowlity is backed up regularly, and the platform has disaster recovery mechanisms in case of a major incident. This ensures that even in the event of a failure, your data would not be lost and the service could quickly restart on a secondary infrastructure. These points are part of our SLA (Service Level Agreement) commitments. In terms of confidentiality, Flowlity undertakes to never share or use your data for purposes other than your project. The data you entrust to us remains your property. If you decide to leave the service, your data will be returned and then deleted from our systems after an agreed retention period. To summarize, security and compliance are pillars at Flowlity, because we work with sensitive clients (industry, distribution, sometimes defense like Thales mentioned in the press).

Whether it's GDPR compliance, technical security, or contractual confidentiality, everything is in place to ensure your information is treated with the highest level of protection. (We can provide you with our complete security and GDPR compliance documentation during our discussions, and involve our security experts to answer any specific questions your CIO or DPO may have.)

The Flowlity solution is available in French, English, Spanish and Russian (soon in German, Chinese and Japanese).

Flowlity's integration with your existing information system is designed to be simple and fast. ‍

Flowlity is ERP-agnostic, which means it can connect to any type of ERP or database, whether SAP, Odoo, Microsoft Dynamics 365 (AX/NAV), Sage, or even more specific systems.

Several integration modes are possible depending on your preferences and technical capabilities:

‍Connectors and APIs

‍Flowlity has secure RESTful APIs that allow you to send and receive data. If your ERP can call web services or if you use an integration platform (middleware type), it is possible to synchronize data (orders, stocks, item references, etc.) via these APIs. For example, you can call the Flowlity API to push daily sales history, and in return retrieve replenishment suggestions to integrate into the ERP. This mechanism is robust and real-time.

‍Flat files / Secure FTP

‍For contexts where you prefer to exchange via files, Flowlity supports the automated import/export of CSV or XML files. You can schedule file deposits (via secure SFTP) containing the necessary data, which Flowlity will ingest at a defined frequency (daily, hourly, etc.). Similarly, Flowlity can generate output files (for example, the list of orders to be placed) that your ERP will consume. This method, although less modern than the API, is often quick to implement because it does not require complex development on the ERP. ‍

Native connector

‍For some common ERPs, Flowlity offers pre-developed connectors or ready-to-use scripts. For example, with SAP, we have standard extractors (via IDoc or queries on tables) to retrieve needs and stocks. This reduces integration time since many standard fields are already mapped. In terms of data exchanged, Flowlity generally requires as input: sales or consumption history, stock data, the item repository (with supplier lead times, MOQ, etc.), possibly customer orders in backlog and current supplier orders. And as output, Flowlity returns: demand forecasts, supply proposals (quantities per item/supplier/date), target stock levels (recommended safety stock, etc.), as well as indicators (stock coverage, alerts). The exchange can be adjusted according to your use cases.

The important thing to remember is that the integration effort is minimized with Flowlity.

Many of our customers are operational very quickly because we reuse their existing extractions or standard connectors.

Flowlity supports your IT department during this phase and provides complete documentation of its APIs and formats. Furthermore, Flowlity's platform is highly available and designed to handle large volumes of data, ensuring that even file-based integrations of several thousand lines run smoothly.

Finally, from an ERP perspective, Flowlity is non-intrusive: it acts as an overlay without requiring any major changes to your ERP processes. For example, if you use SAP, you can continue to create your purchase orders in SAP, simply because they will have been calculated by Flowlity upstream.

This smooth integration philosophy facilitates acceptance by the IT department.

During the pre-project phases, our technical experts will be able to assess with your team the best integration strategy for your environment to ensure a smooth transition.

AI-driven supplier order management delivers the highest impact for mid-market companies in retail, wholesale distribution, and manufacturing that manage a large number of SKUs across multiple suppliers. These organizations typically have lean planning teams that spend too much time on routine orders, leaving little capacity for strategic work. Companies managing seasonal demand, long or variable supplier lead times, or complex multi-location replenishment see particularly strong results — often achieving significant inventory reductions while improving service levels.

Absolutely — and this is precisely where Flowlity operates every day. Historically, demand sensing was accessible only to large enterprises with dedicated data-science teams and multi-year implementation budgets. Today, Flowlity makes demand sensing accessible to mid-market companies through a plug-and-play architecture that connects to existing ERP systems without heavy IT projects.

Flowlity's clients range from 45-person companies like Plum Living, a digital-first furniture brand managing roughly 1,000 SKUs, to industrial manufacturers like Magotteaux and multi-category distributors like Ravate. What they share is the need for AI-driven responsiveness without enterprise-tool complexity. For even smaller teams, Flowlity Lite offers an accelerated path to AI forecasting with minimal setup.

Key criteria for mid-market adoption are: availability of transactional data (orders, sales, inventory), willingness to trust AI-augmented recommendations, and a solution that does not require a team of data engineers to maintain.

Unlike traditional planning tools that rely on deterministic models — one forecast, one fixed inventory target — Flowlity uses probabilistic Artificial Intelligence to model demand uncertainty explicitly. Every SKU at every location is represented by a range of likely outcomes and a confidence level, so inventory decisions reflect real variability rather than a single best guess.

This probabilistic engine continuously adapts inventory targets as new data arrives, without requiring manual re-parameterization.

Combined with fast deployment through pre-built ERP connectors and a planner-centric interface built for mid-market teams rather than large data science groups, the approach delivers measurable results quickly: higher adoption from planners who can see the "why" behind each recommendation, and more resilient Supply Chain planning compared to legacy systems that struggle as volatility increases.

B2Wise is a DDMRP-native tool that follows the methodology strictly, while Flowlity offers a more flexible, AI-enhanced approach to supply chain planning.

Comparative analysis: B2Wise vs. Flowlity

AI-enhanced forecasting vs order-driven DDMRP

B2Wise relies on demand-driven buffers that react to actual orders — a solid approach, but one that adjusts only after demand has changed. Flowlity adds a proactive layer: AI-powered forecasting that anticipates demand shifts and automatically adjusts target stock levels before disruptions hit. This is especially valuable for products with seasonal patterns, promotions, or erratic demand.

End-to-end scope vs inventory-focused

B2Wise focuses on DDMRP buffer management and replenishment. Flowlity covers the full supply planning cycle: from demand sensing to inventory optimization, S&OP, supplier collaboration, and strategic simulations. This means fewer tools, one shared data model, and better cross-functional alignment.

Flexibility: choose your method per SKU

One of Flowlity's key differentiators is the ability to combine DDMRP and forecast-driven planning at the SKU level. Some products benefit from demand-driven buffers; others need proactive AI forecasting. With Flowlity, you don't have to choose one methodology for your entire portfolio — you can mix and match based on each product's characteristics.

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In short: B2Wise is ideal if you want a strict, purist DDMRP implementation. Flowlity is the better fit for companies that need flexibility, AI-driven forecasting, and broader functional coverage beyond just inventory buffers.

Flowlity differs in multiple ways:

AI at the Core

‍Built from the ground up with AI and automation, unlike traditional APS solutions based on manual tuning and linear models.‍

Modern UX‍

Simplified, intuitive interface that supports high user adoption vs complex legacy screens.‍

Agile Deployment‍

SaaS-based, modular rollouts deliver value in weeks—not the long waterfall projects required by older APS tools.‍

Continuous Innovation‍

Flowlity updates frequently with new features, while legacy tools evolve more slowly and require manual upgrades.

In short, Flowlity is ideal for organizations looking for faster ROI, ease of use, and advanced automation in a modern planning environment.

Both Flowlity and Lokad are data-driven, but they diverge fundamentally:

• Code-based vs Ready-to-use: Lokad requires custom scripting in its Envision language, whereas Flowlity offers an out-of-the-box AI platform.
• ‍Automation vs Human-in-the-loop: Lokad emphasizes prescriptive automation; Flowlity augments human planners with explainable AI.‍
• Transparency: Flowlity prioritizes interpretability with confidence intervals, visual decomposition, and scenario simulation.‍
• Deployment: Flowlity is faster and easier to deploy (SaaS), while Lokad requires more technical configuration.

Summary: Lokad is a powerful, technical platform for expert users. Flowlity is a plug-and-play AI tool that empowers planners directly without needing a data science team.

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Colibri focuses on tactical S&OP and demand planning, often at aggregated levels (family/category), while Flowlity:

• Covers both tactical and operational levels, down to SKU/day-level replenishment.‍
• Uses advanced AI and probabilistic forecasting to automate and improve accuracy.‍
• Optimizes inventory directly, with order suggestions and safety stock recommendations.‍
• Supports wider collaboration, including external partners.‍
• Aligns execution with strategy, offering one consistent platform from planning to action.

In short: Colibri is ideal for setting up a lightweight S&OP process; Flowlity goes further by aligning high-level planning with real-time execution.

Relex is a powerful platform with deep capabilities in the retail sector, particularly for merchandising, store assortment, and promotions.

However, for B2B distributors or midsize wholesalers:

• Faster Implementation: Flowlity can be deployed in a few months versus a year or more for Relex.
• Focused Simplicity: Flowlity avoids retail-specific complexity and focuses on core planning needs (demand, supply, production).
• Supplier Collaboration: Flowlity enables real-time collaboration with suppliers—a critical need for B2B distribution.
• Total Cost of Ownership: Flowlity offers a more flexible and affordable SaaS pricing model.
• User Experience: With a more intuitive and streamlined interface, Flowlity is easier to adopt and operate daily.
• Pricing & Promotion Management: Flowlity now includes an AI-powered Pricing & Promotions module, enabling retailers and e-commerce players to simulate price/promo scenarios and measure their impact on sales and margins.

Conclusion: Relex is great for large-scale retail chains; Flowlity is purpose-built for agile, collaborative B2B planning with rapid ROI.

Slimstock is a well-established vendor known for its Slim4 solution, widely used across Europe for inventory optimization. It offers solid statistical forecasting features, but its approach remains grounded in traditional methods (manual rules, static safety stocks).

Flowlity vs Slimstock: a quick comparison

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Flowlity, by contrast, is a next-generation AI-native solution that differs in several ways:

• AI-driven vs Traditional Logic: Flowlity uses machine learning and probabilistic forecasting to dynamically adjust forecasts and stock levels, reducing manual effort.‍
• Broader Functional Coverage: While Slim4 focuses mainly on demand forecasting and stock optimization, Flowlity also supports supplier collaboration, production planning, and S&OP integration.‍
• Cloud and Collaboration Native: Flowlity is built for real-time, multi-stakeholder collaboration in the cloud, allowing suppliers or remote warehouses to interact directly.‍
• Modern UX & Fast Deployment: Flowlity provides a user-friendly interface designed for today’s planners, with rapid deployment in just a few weeks.

In short: Slim4 is proven for traditional approaches, but Flowlity offers a more innovative, automated, and collaborative experience for digitally mature organizations.

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

Flowlity stands out for its technological expertise, high level of automation and optimization, seamless integration, and fast, measurable performance.

Optimal inventory

The global research and advisory firm Gartner has named Flowlity a Cool Vendor 2025, highlighting its innovative and impactful approach, capable of transforming current industry practices, particularly through the use of artificial intelligence.

Tactical

‍In addition, Flowlity is the only player on the market to offer a dedicated module – S&OP Tactical – designed to determine the best strategy to achieve your objectives through personalized data-driven simulations and scenarios.

It enables companies to identify the optimal inventory strategy to meet their goals using advanced simulation capabilities.

Order-to-cash (O2C) covers the sales side: from receiving a customer order to invoicing and payment collection. Supply order management sits on the buy side: it manages purchase orders sent to suppliers to replenish inventory and fulfill demand. While both involve order workflows, they serve opposite ends of the Supply Chain. Companies need both to run smoothly, but the planning intelligence required on the supply side — connecting orders to forecasts, safety stocks, and supplier constraints — is fundamentally different from O2C process optimization.

Traditional MRP (Material Requirements Planning) calculates what materials are needed based on bills of materials, production schedules, and on-hand inventory. It generates order suggestions in a deterministic way: given these inputs, here is exactly what to buy. The logic is proven but rigid — it does not account for demand variability, supplier reliability, multi-echelon inventory positions, or real-time signals from the rest of the Supply Chain.

Supplier order management software takes MRP outputs further by applying AI-driven optimization on top of them. Routine orders that fit within policy are automated end-to-end; exceptions — orders that exceed margin, volume, or risk thresholds — are surfaced to planners for review.

The result is a step up in supply intelligence: raw material requirements become risk-adjusted, supply-aware purchase decisions instead of unchecked MRP output that planners rework in Excel.

Procurement and order management operate at two different layers of the supply flow. Procurement is the strategic function: sourcing suppliers, running RFPs, negotiating commercial terms, qualifying and managing supplier relationships over time. It answers the questions "who do we buy from?" and "on what terms?"

Order management focuses on the execution layer — generating purchase orders, tracking fulfillment, managing exceptions, and closing the loop with receiving and invoicing. It answers "when, how much, and how effectively are those orders actually placed?"

The two functions depend on each other. Procurement defines the framework; order management makes sure every transaction respects that framework. Modern Supply Chain platforms integrate both so that procurement strategies — preferred suppliers, contracted volumes, commercial terms — are automatically reflected in every order decision, without manual handoffs.

Supplier order management software is a system that automates and optimizes the creation, validation, and tracking of purchase orders sent to suppliers. It covers the full order lifecycle — from calculating replenishment needs based on demand and inventory, to generating and dispatching purchase orders, to monitoring delivery performance and managing exceptions.

Basic tools treat this as a transactional flow: convert a requirement into a PO, send it, track it. Advanced solutions go much further by connecting every order to demand forecasts, inventory policies, and broader Supply Chain planning, so that each PO reflects actual future need rather than a static reorder point set months ago.

This end-to-end alignment is what separates modern AI-driven order management from legacy purchasing systems that still operate largely in isolation from planning.

No — demand sensing strengthens S&OP, it does not replace it. S&OP is a cross-functional process that aligns commercial, operational, and financial plans over a medium-to-long-term horizon. Demand sensing operates on a much shorter horizon and feeds into that process with more accurate short-term signals.

In practice, demand sensing improves the quality of the demand input that enters the S&OP cycle. When the short-term picture is more accurate, S&OP discussions can focus on strategic decisions and exceptions rather than debating whether the numbers are right. Flowlity is designed with this integration in mind — demand sensing feeds directly into the S&OP workflow, shifting the conversation from "is the forecast accurate?" to "what do we do about the deviations we've detected?".

Demand sensing and demand forecasting serve different but complementary roles in Supply Chain planning.

Demand forecasting typically operates over a longer horizon — weeks to months — and relies heavily on historical sales patterns, seasonality, and trend analysis to build a baseline plan. It answers the question: what do we expect demand to be over the coming period?

Demand sensing operates over a much shorter horizon — days to two weeks — and focuses on detecting deviations from that plan using real-time signals. It answers a different question: what is actually happening right now, and how should we adjust?

Think of it this way: forecasting sets the course, and demand sensing makes the real-time course corrections. Flowlity combines both in a single platform — the probabilistic engine builds the baseline forecast and continuously adjusts it with real-time sensing, so planners work from one unified view rather than reconciling two separate outputs.

Demand sensing is a short-term Supply Chain capability that uses AI, machine learning, and real-time data signals to detect and respond to changes in demand as they happen. Unlike traditional planning approaches that update monthly or weekly based on historical averages, demand sensing continuously analyzes market signals — point-of-sale data, order patterns, inventory positions, and external factors — to adjust near-term forecasts at a granular level (typically SKU × location × day).

The goal is not to predict demand months out, but to sharpen the next 1–14 days of the plan so that replenishment, production, and allocation decisions reflect what is actually happening in the market. This makes it a powerful complement to the broader demand forecasting process. Flowlity's probabilistic engine is built for exactly this: it continuously recalibrates short-term forecasts at the SKU-location level, giving planners an always-current demand picture without manual rework.

Store replenishment and inventory optimization are closely related, but they don't operate at the same level.

Store replenishment focuses on execution decisions. It answers questions like: when should a store be restocked, and in what quantity? It operates at a local level, ensuring that each store has the products it needs to meet demand.

Inventory optimization, on the other hand, works at a broader level. It determines how much inventory should exist across the entire Supply Chain, and how it should be distributed between warehouses, distribution centers, and stores.

In practice, replenishment is about moving stock, while inventory optimization is about positioning it correctly in the first place.

The two are deeply connected. Without proper inventory optimization across the Supply Chain, replenishment decisions are made on a weak foundation. Conversely, even the best inventory strategy fails if replenishment execution is not aligned.

This is why modern planning platforms combine both capabilities. By integrating store replenishment with Inventory Optimization, companies can ensure that every restocking decision contributes to overall Supply Chain performance, not just local efficiency.

A store replenishment software helps retailers and distributors determine when and how much inventory should be restocked across every point of sale in their network. It combines demand forecasts, current inventory levels, supplier lead times, and operational constraints to generate optimized replenishment decisions at the SKU-store level.

The role of the software is to automate the thousands of small decisions that planners otherwise make manually every week: when to trigger a replenishment, how many units to send, whether to prioritize one store over another when supply is tight.

By working at this granularity, modern replenishment software reduces both stockouts — which directly impact revenue and customer experience — and excess inventory that ties up working capital and drives markdowns at end of season.

Promotion Management Software helps companies plan, simulate, and optimize promotions — but the real difference lies in how closely it is connected to Supply Chain reality.

Traditional Trade Promotion Management (TPM) tools focus on managing budgets, discounts, and commercial agreements between manufacturers and retailers. They answer the question: "how much should we invest in promotions?"

But they often miss a more critical one: "can we execute this promotion without creating stock issues or margin loss?"

Modern Promotion Management platforms — like Flowlity — go further by integrating promotions directly with Demand Forecasting and Inventory Management.

This means every promotion is evaluated not just for its financial potential, but for its operational feasibility.

The result: fewer stockouts, less excess inventory, and promotions that actually deliver profitable growth.

A Supply Chain dashboard focuses on monitoring operational KPIs, while a Supply Chain control tower provides a broader and more advanced operational management environment.

Control towers combine several capabilities, including real-time visibility, predictive analytics, collaboration tools, and decision support mechanisms. Their goal is not only to display information but also to orchestrate Supply Chain operations across multiple stakeholders.

While dashboards provide an overview of key metrics, control towers analyze relationships between demand, supply, and inventory data in order to anticipate disruptions and coordinate responses.

In modern Supply Chain platforms, dashboards often serve as the entry point to control tower capabilities, providing the visibility required to guide operational decisions.

Strategic simulations play an important role in Sales and Operations Planning (S&OP) by helping organizations align operational plans with business objectives.

During S&OP cycles, planners typically need to evaluate whether the current plan can meet expected demand, service level targets, and financial objectives. Strategic simulations allow teams to test different assumptions and compare their impact before finalizing the plan.

For example, companies can simulate scenarios such as: increased demand for a product family, changes in supplier reliability, capacity adjustments in production or logistics.

Because these simulations operate at an aggregated level, they provide a clear view of overall Supply Chain performance rather than focusing on individual SKUs. This makes them particularly useful for executive reviews, where decision-makers need to understand how operational choices affect broader business goals.

By enabling scenario comparison and strategic alignment, simulations help transform S&OP discussions into data-driven decision processes.

Flowlity enables companies to run strategic Supply Chain simulations directly within their planning environment, allowing teams to test decisions before implementing them in operations.

Rather than working with isolated models, simulations are based on real planning data such as forecasts, inventory targets, supplier lead times, and capacity constraints. This ensures that the scenarios reflect actual operational conditions.

Teams can then run what-if simulations to evaluate situations such as: demand surges or unexpected market changes, supplier delays or disruptions, adjustments to production or supply capacity, new service level targets.

Each scenario can be compared side by side, allowing planners and executives to understand the impact on key metrics such as service levels, inventory levels, and supply reliability.

This approach transforms simulation into a practical decision-support tool, enabling organizations to evaluate strategic options before committing to them.

Most organizations implement Supply Chain simulation by integrating it into their existing planning processes rather than running it as a separate analytical exercise. Building complex, standalone simulation models from scratch tends to produce insights that are hard to act on because they live outside the operational workflow.

Instead, modern simulation tools are connected directly to planning data — demand forecasts, inventory levels across echelons, supplier lead times, production capacity — so scenarios are built on the same numbers that drive day-to-day decisions. This makes simulation outputs immediately actionable.

From there, teams typically start with one or two high-stakes questions (service-level targets, inventory policy changes, risk of a supplier disruption), then expand the use of simulation as part of the S&OP or strategic review cycle once the first results prove their value.

A Supply Chain dashboard focuses primarily on monitoring performance, while Supply Chain analytics helps analyze data and support decisions.

Dashboards typically display key metrics such as service level, forecast accuracy, stock coverage, or inventory turnover. Their role is to provide a quick overview of operational performance and help teams detect anomalies.

Supply Chain analytics goes further by enabling deeper investigation into the causes of operational issues. Advanced analytics tools can identify patterns in demand variability, highlight data quality issues, or simulate the impact of different planning scenarios.

Modern Supply Chain platforms combine both capabilities. Dashboards provide high-level visibility, while analytics modules allow planners to explore data in detail and identify the actions that will improve performance.

A Supply Chain dashboard is a centralized interface that aggregates operational data and presents key performance indicators in a clear and actionable way. It allows Supply Chain teams to monitor critical metrics such as inventory levels, demand trends, stock coverage, and service levels from a single environment.

Instead of navigating across multiple tools such as ERP reports, spreadsheets, and analytics platforms, planners can quickly understand the current state of operations and detect potential risks. Modern Supply Chain dashboards are designed not only to display KPIs but also to help teams prioritize actions and identify emerging disruptions.

When integrated with planning platforms, dashboards become a daily decision tool that supports better coordination across forecasting, inventory management, and supply planning processes.

Forecasting and strategic simulation answer two different questions. Forecasting aims to predict future demand as accurately as possible — it is about narrowing uncertainty to a single best estimate, typically at the SKU or category level. That is a necessary input for operational planning.

Strategic simulation goes one step further by evaluating how the Supply Chain would behave under a range of possible outcomes, not just the most likely one. Instead of asking "What will happen?" it asks "What could happen, and how should we prepare for it?"

This approach surfaces the vulnerabilities of a plan before they materialize and helps planners design strategies that remain effective even when conditions change — whether that's a demand spike, a supplier failure, or a change in lead times.

Supply Chain simulation software is used to support strategic decision-making across the full range of planning processes, from inventory policy design to network strategy. Its core value is enabling leaders to test decisions in a digital environment before committing real budget or operational changes.

Typical applications include evaluating inventory policies (safety stock levels, service-level targets), preparing for disruptions such as supplier failures or transport shocks, comparing alternative supply strategies (single-sourcing vs. dual-sourcing, near-shoring decisions), and stress-testing the S&OP plan.

Rather than relying solely on forecasts, which describe one likely future, simulation allows planners to explore multiple possible futures and understand the explicit trade-offs between cost, service level, and risk. This is particularly valuable when the cost of a wrong decision — a stockout on a key SKU, a failed product launch — is high.

Supply Chain simulation software creates a digital representation of your Supply Chain, allowing planners to test strategies and analyze potential outcomes before executing them in the real world.

Instead of making decisions based on a single forecast or static assumption, simulation explores a range of possible scenarios. It models how the Supply Chain behaves when variables change — such as demand fluctuations, supplier delays, or shifts in service level targets.

This approach enables organizations to answer critical questions such as: What happens if demand grows faster than expected? How will supplier lead-time variability impact inventory levels? What inventory policy ensures the right balance between availability and cost?

By running multiple scenarios, planners gain a deeper understanding of the trade-offs between service level, inventory, cost, and operational risk. Simulation therefore acts as a strategic decision-support layer on top of planning processes such as S&OP and Supply Planning.

Supply Chain simulation is the process of modeling the behavior of a Supply Chain in order to evaluate different strategies before implementing them in reality. It is essentially a safe environment in which to rehearse decisions that would be costly or risky to experiment with live.

By creating a digital representation of the Supply Chain — its nodes, flows, lead times, capacity, and demand patterns — planners can test scenarios such as sharp demand variability, supplier disruptions, changes in inventory policy, or the addition of new production sites. The simulation shows how the end-to-end system would respond, not just individual nodes.

Simulation therefore helps companies move from reactive decision-making, where trade-offs are made under pressure, to proactive strategic planning where risks and opportunities have been modeled, compared, and debated before commitment.

MRP (Material Requirements Planning) focuses specifically on planning what materials are needed, when they should be ordered, and in what quantities to support production or distribution.

ERP (Enterprise Resource Planning) is a broader system that covers multiple business functions — including finance, procurement, manufacturing, HR, and sometimes Supply Chain planning.

Most ERP systems include a basic MRP module. However, these built-in MRP modules often rely on simplified logic (fixed safety stocks, static lead times) and lack the advanced forecasting and optimization capabilities offered by dedicated MRP software.

Modern MRP solutions, like Flowlity, are designed to complement ERP systems — not replace them. They integrate with existing ERPs to enhance material planning with AI-driven forecasting, probabilistic inventory models, and real-time visibility.

MRP software (Material Requirements Planning) is a Supply Chain planning tool used to determine which materials are needed, when they should be ordered, and in what quantities to meet production or distribution requirements.

Traditional MRP modules are often embedded within ERP systems and use deterministic logic — fixed safety stocks, static lead times, and manual forecast inputs — to generate material plans.

Modern MRP software goes further by integrating AI-driven demand forecasting, probabilistic inventory optimization, and real-time data to create more accurate and adaptive replenishment plans.

These advanced capabilities help Supply Chain teams move beyond reactive planning and build material strategies that anticipate demand changes, supplier variability, and inventory risks.

Production planning and production scheduling work at two different horizons. Production planning determines what should be produced, when production should occur, and in what quantities — based on demand forecasts, available capacity, material availability, and other Supply Chain constraints. It typically operates over weeks to months.

Production scheduling focuses on the operational execution of those plans on the shop floor. It organizes the detailed sequence of manufacturing tasks, machine assignments, changeover sequences, and production timelines, usually at a daily or even hourly granularity.

Manufacturing production planning software supports strategic planning decisions at the Supply Chain level — deciding which SKUs to run when, on which plant — while production scheduling tools focus on operational manufacturing execution. In practice, the two layers feed each other: planning sets the targets, scheduling makes them real.

A few recurring patterns signal that traditional, location-by-location inventory planning has reached its limit. If your teams are constantly tweaking safety stock levels by hand, chasing stock imbalances between warehouses or stores, or struggling to maintain service levels despite sitting on high inventory, those are clear signs.

Other indicators are frequent inter-site transfers to plug stockouts, a long tail of slow-moving SKUs trapped at the wrong location, and planning discussions that revolve around "who needs stock from whom" rather than "how much should we order overall."

MEIO becomes essential as soon as your Supply Chain behaves as a network rather than a set of isolated locations — typically from the moment you operate two or more echelons or run distribution across multiple regions.

Traditional inventory planning optimizes each location independently, often using static safety stock rules and average demand forecasts. Every warehouse or store is treated as a silo: its inventory target is set in isolation, with no visibility on what sits upstream or downstream.

Multi-echelon inventory optimization (MEIO) takes a network-wide approach. It explicitly models how inventory decisions at one location impact the rest of the Supply Chain, and dynamically adjusts inventory levels across all nodes together. Instead of each site carrying its own protective buffer, the network holds just enough stock at the right echelons to absorb variability.

The result is fewer duplicated safety stocks across the network and the ability to achieve better service levels with significantly less total inventory.

MEIO stands for Multi-echelon inventory optimization. It is a Supply Chain planning method that determines optimal inventory levels across multiple locations simultaneously, rather than optimizing each node independently.

It considers the entire network — including suppliers, production sites, warehouses, and distribution centers — to position inventory where it delivers the highest service level with the lowest total stock.

By accounting for demand variability and lead times across the network, MEIO enables companies to reduce inventory while improving service.

Absolutely — and in many cases, mid-size companies benefit the most. The reason is that Supply Chain complexity tends to grow faster than headcount: a new warehouse, a few added product lines, or international expansion quickly pushes spreadsheet-based planning past its limits. Yet these same companies rarely have the budget or IT bandwidth for a heavy enterprise planning suite.

Modern DRP tools solve that gap. Cloud-native platforms like Flowlity connect to existing ERP systems in a few weeks, require no dedicated data science team, and deliver AI-driven replenishment recommendations out of the box.

This allows mid-size companies to structure their Supply Chain with enterprise-grade logic — dynamic safety stock, multi-echelon visibility, exception-based workflows — without adding the operational complexity that comes with legacy platforms. It's planning maturity without the implementation overhead.

DRP software is used by Supply Chain professionals responsible for managing inventory and distribution networks. Typical users include Supply Chain directors, demand planners, inventory managers, and distribution planners — anyone whose day-to-day decisions shape where stock sits and how it moves.

The industries that most commonly deploy DRP solutions are retail, wholesale distribution, manufacturing, and spare-parts networks. What these organizations have in common is a multi-echelon footprint — several warehouses, regional depots, or stores — where local decisions compound quickly into network-wide imbalances.

They rely on DRP to maintain high service levels while controlling inventory costs, especially when product ranges are large and demand patterns differ between locations. For mid-market companies, modern DRP tools make this possible without the headcount or IT budget traditionally needed for this kind of planning.

Inventory management focuses on tracking and controlling stock levels: what's on hand, what's in transit, what's reserved, and when items are received or issued. It is essentially an operational and accounting view of stock at a given moment.

DRP goes further. It plans how inventory should flow across the network over time — deciding when to push or pull stock between nodes, what quantities to replenish, and how to sequence orders so that every site remains in balance as demand evolves.

In practice, DRP works on top of inventory management: the ERP or WMS keeps the record of what you have, while DRP decides what you should do next to match that stock to future demand.

DRP and MRP are often confused because both deal with planning, but they operate on different halves of the Supply Chain and answer different questions.

DRP and MRP address different parts of the Supply Chain.

MRP (Material Requirements Planning) focuses on production planning. It determines which raw materials and components are needed to manufacture products.

DRP, on the other hand, focuses on distribution. It determines how finished products should be allocated across warehouses and distribution centers.

In many organizations, DRP works alongside:

• Demand Planning
• Inventory Optimization
• Supply Planning

Together these processes ensure that products are produced and distributed efficiently.

In practice, the distinction matters because the two systems optimize different trade-offs: MRP minimizes production disruption and component shortages, while DRP minimizes distribution imbalances and stockouts at the point of sale. Modern platforms like Flowlity integrate both views so that production and distribution stay synchronized as demand evolves across the network.

DRP stands for Distribution Requirements Planning. It is a Supply Chain planning method used to determine how much inventory should be distributed, when products should be replenished, and where stock should be located across a distribution network of warehouses, regional hubs, and stores.

The underlying logic is to translate expected customer demand into a coherent plan of inter-site transfers and replenishment orders, so that stock sits as close as possible to the point of sale without creating excess at any single node.

DRP ensures that inventory flows between warehouses, distribution centers, and stores are synchronized with demand — avoiding the classic pattern where one regional hub is stocked out while another is overstocked with the same SKU.

Supply chain scalability is the ability to support business growth without increasing costs, risks or complexity at the same pace. It ensures service levels and efficiency remain stable as the business expands.

In supply chain management, CPFR refers to a collaborative approach where retailers and suppliers coordinate forecasts, promotions, and replenishment plans. The objective is to better align supply and demand across the supply chain and reduce inefficiencies such as stockouts and overstocks.

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CPFR stands for Collaborative Planning, Forecasting and Replenishment. It is a supply chain collaboration model designed to help business partners jointly plan demand forecasts and replenishment activities by sharing selected planning information.

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.

A Bill of Materials (BOM) is a detailed list of all components, parts, and raw materials required to manufacture a finished product. It specifies the quantities needed for each item and sometimes the assembly sequence.

In manufacturing and supply chain management, the BOM is essential for planning procurement and production, ensuring that all necessary components are available to produce the final product.

Safety stock is a buffer quantity kept on hand to absorb unexpected demand spikes or supplier delays. It is typically calculated using the desired service level, demand variability, lead time variability, and average consumption. The goal is to maintain product availability without holding excessive inventory, striking a balance between service and cost.

Classic formulas assume demand follows a normal distribution and lead times stay constant — assumptions that rarely hold in volatile markets. Dynamic safety stock models use probabilistic forecasting to recalculate buffers item-by-item as demand patterns and supplier performance shift, preventing stockouts during peaks while avoiding excess stock during quieter periods.

A lead time is the amount of time between placing an order (or launching production) and the moment the goods are delivered or available for use. It may include order processing time, manufacturing time, and transportation time.

In supply chain management, understanding and controlling lead times is crucial for proper planning: long or highly variable lead times require more safety stock and greater anticipation to avoid stockouts.

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An SKU (Stock Keeping Unit) is a unique identifier assigned to a product for inventory tracking purposes. It typically corresponds to a distinct item reference, including specific product characteristics when relevant (such as size, color, or model).

SKUs enable precise inventory management: each SKU represents a separately managed stock unit, making it easier to track stock levels, sales, and replenishment for every product variant.

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A MOQ, or Minimum Order Quantity, is the minimum number of units a supplier agrees to sell in a single order (or that a buyer commits to purchasing). For example, if a supplier sets an MOQ of 100 units, every order must include at least 100 units of that product.

This concept is important in supply chain management because it affects purchasing or production batch sizes, inventory levels, and unit costs (larger orders often enable better pricing).

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Inventory optimization consists of determining and maintaining the right stock levels to meet customer demand while minimizing tied-up working capital and storage costs. It is important because excess inventory wastes resources, while insufficient stock leads to stockouts and lost sales. Effective optimization balances service levels with cost efficiency across the entire product portfolio.

Modern inventory optimization software moves beyond static reorder points and spreadsheet rules by combining probabilistic forecasting, dynamic safety stocks, and multi-echelon logic. It continuously adapts inventory policies to real demand signals, lead-time variability, and service objectives — turning inventory management from a reactive exercise into a strategic lever for margin and cash flow.

The bullwhip effect refers to the phenomenon where small variations in customer demand become increasingly amplified as they move upstream in the supply chain. For example, a slight increase in demand at the retail level can lead distributors—and then manufacturers—to place much larger orders, causing excessive stock fluctuations.

This effect is often driven by poor communication and poorly synchronized forecasts between partners. Understanding and controlling it (through better collaboration and real-time data sharing) helps prevent inconsistent inventory levels and operational inefficiencies.

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ABC analysis is an inventory segmentation method based on the Pareto principle. It classifies items into three categories: A items (high value, ~20% of SKUs representing ~80% of value), B items (moderate value), and C items (low value but high volume). This classification helps prioritize management efforts — applying tighter controls and better forecasting to A items while using simpler methods for C items.

Modern planning teams often combine ABC with an XYZ classification based on demand variability (X = stable, Y = seasonal, Z = erratic). The resulting ABC/XYZ matrix drives different replenishment strategies for each segment — from automated reordering for stable A items to closer human oversight for volatile, high-value SKUs.

Integrated Business Planning is what S&OP looks like when it is fully wired into financial and strategic planning, rather than sitting beside them. The distinction matters: IBP treats revenue, margin, cash, and strategic KPIs as first-class inputs to every monthly decision, not as a separate conversation.

IBP, or Integrated Business Planning, is the evolution of the S&OP process that aligns supply chain planning with financial and strategic objectives. It connects demand, supply, inventory, and financial plans into a single integrated framework, enabling cross-functional decision-making that links operational execution to business strategy.

In an IBP cycle, the executive meeting discusses more than volumes and service levels. It reviews the P&L and cash consequences of the chosen plan, tests scenarios against strategic objectives, and aligns resource allocation accordingly. The result is cross-functional decision-making that links operational execution directly to business outcomes, turning the planning process into a genuine driver of company performance.

Supply Planning ensures that raw materials, components, and purchased products are available in the right quantities and at the right time to meet the demand defined by the forecasting and inventory plans. It is the discipline that turns "what we will need" into "what we need to order, from whom, and when".

In practice, Supply Planning covers supplier management, purchase order scheduling, allocation across plants and warehouses, and lead time optimization. The core trade-off it manages is between service levels and cost efficiency: too little supply and you face stockouts; too much and you tie up working capital.

AI-driven Supply Planning tools like Flowlity add probabilistic inventory targets and dynamic lead time modeling, so supply decisions adapt automatically to variability instead of relying on static reorder rules.

Production planning is the process of organizing and scheduling manufacturing activities by balancing demand forecasts, plant capacity, material availability, and delivery deadlines. It sits between demand planning and shop-floor execution: it translates what customers are expected to need into a feasible plan of what to produce, when, where, and in what quantities.

The goal is to run production efficiently — meeting customer orders on time, optimizing the use of machines and labor, and minimizing scrap, changeovers, and inventory buildup.

Modern Production Planning software adds an AI layer on top of traditional MRP logic, taking into account demand variability and capacity constraints simultaneously, so plans remain feasible even when the market is volatile. This helps manufacturers avoid the repeated replanning cycles that plague spreadsheet-based approaches.

DDMRP (Demand Driven Material Requirements Planning) is a planning approach that combines traditional MRP with strategic buffer stocks positioned at key points in the Supply Chain. Instead of relying solely on forecasts, DDMRP uses actual demand signals to drive replenishment, absorbing variability and reducing the bullwhip effect. It helps maintain optimal inventory levels while improving service rates.

In practice, DDMRP positions buffers where variability matters most — typically at decoupling points between suppliers, production, and distribution. Each buffer is dynamically sized based on lead time, average usage, and variability, and monitored through a color-coded zone system (green, yellow, red) that gives planners clear priorities without overreacting to forecast noise.

The FIFO method (“First In, First Out”) is a stock management rule that ensures the oldest items in inventory are used or sold first. In other words, the first product that enters is the first to leave.

This approach is especially important for perishable goods or items prone to obsolescence, as it prevents products from staying in storage too long and deteriorating. By applying FIFO, companies maintain healthy inventory rotation, reduce waste, and minimize value loss.

FIFO vs LIFO: what's the difference?

LIFO (Last In, First Out) is the opposite approach: the most recently received stock is shipped first. FIFO is preferred for perishable goods, pharmaceuticals, and food & beverage, where shelf life matters. LIFO can be advantageous in specific accounting contexts but often leads to obsolete stock sitting in warehouses.

When should you use FIFO?

FIFO is essential in industries where products expire or lose value over time: food & beverage, cosmetics, pharmaceuticals, and electronics. It's also the default standard for most retail and e-commerce operations, where customer satisfaction depends on delivering fresh, up-to-date products.

How does FIFO impact inventory costs?

By ensuring older stock moves first, FIFO reduces write-offs from expired or obsolete products. It also provides a more accurate picture of inventory valuation, since the remaining stock reflects the most recent (and often higher) purchase costs. For companies managing thousands of SKUs, this directly translates into better inventory optimization and lower carrying costs.

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Supply Chain collaboration refers to the close cooperation between the different actors in a Supply Chain — manufacturers, distributors, suppliers, and retailers — based on shared information and aligned objectives. Rather than each party optimizing its own slice of the flow, collaboration treats the Supply Chain as a single end-to-end system.

It matters because the cost of information silos is measured directly in working capital and missed sales. When partners don't share forecasts or inventory positions, each one inflates safety stock, reacts late to demand shifts, and amplifies variability upstream.

Collaboration reduces those silos, improves joint forecast accuracy, shortens lead times, and — critically — helps every partner respond faster to demand changes or disruptions. In volatile markets, that responsiveness is what protects service levels without inflating inventory across the network.

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?"

A stockout occurs when an item is no longer available at the moment a customer—or a production line—needs it. This leads to missed sales and can harm customer satisfaction. To prevent stockouts, it’s important to rely on accurate demand forecasts, maintain adequate safety stock, and monitor inventory levels in real time. Collaboration with suppliers (to reduce lead times or secure faster replenishments) and the use of alerting tools can also help avoid these situations.

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.

Supply Chain planning is the discipline that turns future customer needs into an executable operational plan while keeping cost, service, and working capital under control. Its goal is straightforward to state and hard to execute: the right products, in the right quantities, at the right place, at the right time.

Supply chain planning involves coordinating demand, supply, and production activities to meet customer needs efficiently while minimizing costs. It encompasses demand forecasting, inventory management, production scheduling, and logistics optimization to ensure the right products are available at the right time and place.

When these disciplines are integrated rather than run as silos, planning becomes a competitive advantage rather than a cost center. Modern platforms add an AI layer that ties forecasting, inventory optimization, and supply planning together in a single decision loop, so trade-offs between service level, inventory, and cost are made explicitly — not absorbed implicitly at each functional boundary.