Retail chains

In the distribution network, “order peaks (?)” arise when orders and deliveries accumulate at the same reception and dispatch times. Added to this are the daily ills — absenteeism and lack of personnel, delays of carriers, blockages caused by weather changes, sudden jumps or drops in demand after campaigns or movements of competitors. Operational flexibility is necessary, but it cannot be costly. Nadii is designed for such “non-ideal” conditions: it combines demand forecasts (seasonality, promotions, events) with supply forecasts (probabilistic delivery times and supplier service levels — On Time In Full), as well as with limitations on warehouse capacity, loading/unloading zones and couriers.

Based on this, it estimates the load on the nodes in advance and actively shapes the flows: it moves batches to earlier hours, consolidates them into full logistics units, regulates the frequency and size of routes, balances shipments between warehouses and, if necessary, slows down or accelerates marketing activities so as not to “flood” operations. Cost-probabilistic optimization is at its core: in one coherent decision (“when, how much, where and where to move the goods”), Nadii simultaneously smoothes the work profile, maintains availability “on time” and minimizes the total cost - especially in disordered, real operating conditions.

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What you gain:

stable throughput and less overtime at peak times

Lower transport costs due to better consolidation and frequency planning

fewer delays and bottlenecks, meeting deadlines and delivery standards (SLAs),

greater availability without increasing the safety margin,

Consistency of decisions from demand planning to shipping schedules — in a single decision model.

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Surpluses in one place and shortages in another are a double cost: here you lose sales, there you freeze capital. In the omnichannel there is also a conflict of priorities - the web store, marketplace, retail and B2B have different SLAs, commissions and influence on the image. Nadii looks at the end-to-end network and treats inventory as a shared pool, not silos. As a result, it first balances inventory with relocations (rather than adding purchases), and at the same time conducts smart bookings per channel so that online sales do not limit availability in stores — and vice versa. The bottom line is consistent forecasts and costs: we forecast demand per channel and location (with promotions, seasonality, events) and supply (delivery times and service level of suppliers), after which we compare profitability: whether to move goods faster and cheaper from A to B or buy from a supplier. When temporary gaps occur in less strategic positions, Nadii automatically estimates the potential of lost sales and the cost of each path (impact on margin and image in a given channel, fees/penalties, cost of transport and campaign postponements) and adjusts buffers and bookings to minimize the total cost and consequences. Already at the stage of creating an order proposal, the system considers an equalization scenario - therefore, you buy less, sell more, and capital is not wasted in the warehouse.

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What you gain:

• Fewer purchases, more sales: First, you use what you have — lower operating inventory and lower storage costs.
• Free working capital: surpluses do not linger in inactive locations/channels; cash returns to circulation instead of “standing on the shelf”.
• Higher SLAs per channel: Ecommerce and retail networks get goods when they need them — without cannibalizing between channels.
• Less markdowns and “series ends”: relocations instead of fire-extinguishing sales.
• Shorter lead time and more stable cash flow: fewer emergency, express orders with suppliers (expedite), more planned, predictable movements in the network.

One Nadii decision combines demand and supply forecasts, costs, channel SLAs, and the potential for offsets. Therefore, the benefits do not appear “separately” - in the same movement you improve availability, limit new purchases and unlock working capital.

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Promotions can completely change demand: short-term sales jumps, shift in demand from weeks after the action (customers buy earlier), planogram requirements and mandatory presentation stock. Classic planning often “smooths out” the story and loses the effect of the campaign — hence the empty shelves in the middle of the action and the “tips” after it.

Nadii approaches the subject holistically. We build daily forecasts for the product in a specific store and channel, with the isolated impact of promotion, seasonality and substitution. Forecasts are combined with the campaign calendar, exposure parameters (presentation stock, planogram) and supply realities: delivery times, supplier service level and minimum order batches. Based on this, Nadii plans to stock up just before the start of the action, and in the process — adjusts the complementary deliveries according to the sales pace, while protecting the required stock on the shelf.

The system automatically determines the last day of the order, predicts the decrease in stock after the campaign and, when justified, recommends transfers between stores instead of new purchases. It also supports stock reservations for individual channels (shop, e-commerce, B2B) in order to avoid mutual limitation of availability; surpluses in one location in the first place propose to move, rather than add new orders.

Each recommendation from Nadia weighs the risk of lack against the cost of excess, taking into account logistical constraints and campaign objectives. In the reports, we show the real cost of maintaining the presentation inventory and signal when it is disproportionate to the potential of the store. The result is full shelves during the promotion, no excess product at the end of the promotion, less frozen capital, higher margin — and smooth operations without nervous deliveries. It all comes down to a single, multidimensional decision.

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What you gain:

• Full shelves in the promotion, without “ballast” after the action: proper overtaking, hit runs, controlled descent of stock.
• Less frozen capital: first you use what you have; surpluses do not “lag” in bad locations.
• Higher level of service and margin: fewer shortages at peak demand, fewer markdowns to extinguish fires.
• Exposure cost transparency: You see the real cost of presentation inventory and can consciously calibrate it to the potential of the store/category.
• Operational peace: no peaks or nervous “dispatches” — the campaign runs smoothly from start to finish.

In short: Nadii coordinates demand and supply forecasts, exposure parameters, marketing calendar and costs in a single operational decision. The effect is promotions that earn, not fall behind.

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With tens of thousands of SKUs and hundreds of locations, manually adjusting replenishment policies just doesn't scale. It is harder for policymakers to be sure that policies are implemented as planned, and for operations to clearly explain the trade-offs between availability, transport costs, minimum volumes and deadlines. The natural temptation is then to simplify the methods “so that they count quickly” — at the expense of precision and money.

Nadii It was created in response to the need for action on such a scale. The core is cost-probabilistic algorithms developed by ML specialists with the title Kaggle Grandmaster. The system works automatically by default: it self-adjusts replenishment policies, cleans anomalies in the data, creates order proposals and passes them to the ERP; and when EDI is not there, it generates orders in supplier format (email/print). From the vending machine, it takes into account MOQ, logistics packaging, multicurrency, tariffs and supplier discounts, consolidates items to full transport units.

Nadia's engine manages computing power sensibly: uses a flexible server pool and adjusts the “weight” of models to the weight of the product and node. Critical items we count more often and more accurately, less important — less often, but with the same, cost-probabilistic goal. Thanks to this, you have scale without resorting to simple rules and full transparency: with each recommendation, you can see why was created and What compromises accepted.

Nadia's algorithms take into account full range of factors, which change the sales signal and the ability to execute: seasonality, campaigns, events, prices and availability of competitors, lead time and service levels of suppliers, storage and transport restrictions, market place rules, exposure minimums — and much more. Thanks to this microformats in demand, supply or cost They quickly translate into micro-decisions: allocation and reservations, shipping schedules, relocations and order proposals.

The conversion frequency is parameterisable: daily, several times a day, or every few days — depending on the category, channel and your guidelines. The user only intervenes where it is of great value (news, exceptions, strategic decisions); the rest happens in the background, consistently and with a footprint in decisions.

What you gain:

• Scale without excessive staff: more SKUs and locations without increasing team load.
• Fewer errors and delays: Repetitive decisions made automatically and on time.
• Shorter “forecast → order → delivery” cycle: fewer operating bottlenecks.
• Full range coverage: also novelties (with quick introduction of starting quantities).
• Consistency with ERP/WMS: one decision stream, zero data copying.

Nadii organizes the assortment “from general to detail”. For each SKU and each location, it manages statuses (e.g. active, sold out, on order, retired) and, if necessary, synchronizes these statuses with the ERP or maintains them independently. Thanks to this, you can plan sales and inventory taking into account the real state of the situation, and reports show the results and movement of goods by statuses, which facilitates decisions on including/excluding items from the offer.

In parallel, the system conducts the life cycle of the product: novelties (ramp-up), season (maintenance of availability), extinction (ramp-down). In the case of shelf-life sensitive goods, Nadia signals the risk in advance, selects the moment of the last order and, when justified, proposes transfers to sell on time rather than dispose of.

If you sell sets/sets, Nadii will combine the demand for the kit with the demand for the components: it forecasts, controls the states and automatically proposes purchases under both levels so as not to block sales by the lack of a single element. It also supports simple picking so that the purchasing plan keeps up with the sales plan of the kits.

What you gain:

• Less “dead” inventory and overdue — earlier signals and controlled extinguishing instead of extinguishing fires.
• ‍Better use of shelf and capital — statuses per location + exposure cost.
• ‍Consistent implementation of novelties and seasons — clear ramp-up/ramp-down rules and the right moment of the last order.
• ‍Clog-free kits — forecasts and purchases linking kits with components, fewer shortages “by one part”.
• ‍Less manual work, more predictability — statuses and life cycles are systemic, and decisions are supported by reports and alerts.

In short: Nadii combines statuses, life cycle, sets and exposure rules into a single decision logic. The result is a clear assortment, stable availability and working capital where it really sells.

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Central warehouse, region, store or online channel — Nadii delivers Probabilistic Forecasts Adequate to any level. Models combine sales history with information about promotions, prices, seasonality and special events. In parallel, we forecast supply: delivery times and service levels at suppliers, so that decisions take into account real risks on the supply side.
In the short term, the system uses confirmed orders and reservations, in the long term — models learning on a continuous basis, Nadii constantly monitors the error of forecasts and automatically switches methodswhen the demand regime changes (for example, after the start of the campaign or at the beginning of the season). Decisions are not based on “one number”, but on the distribution of probability and cost: risk of lack vs. cost of excess.

What you gain:

• Stable availability with lower inventory: decisions weighted by risk and cost.
• Fewer surprises: early signals of peak demand or threat on the supplier side.
• Consistency of planning from DC to store: aggregations “up” and “down” without detour.
• Better budgeting and cash flow: Forecasts translate into a purchasing and inventory plan.
• Confidence in promotions and seasons: models recognize uplift, extinction and substitution effects.

Sales, purchasing, and logistics often work on different KPIs. Nadii delivers common desktop with the genesis of the result: prediction error, conscious purchase under discount (smart buying), supplier delay, limitation of warehouse or transport capacity. Each recommendation has cost rationale (lost margin, excess cost, transportation cost, cash impact) and audit trail — know who changed the decision and why.
Data and decisions can be automatically sent to BI/Data Lake tools. Roles and permissions ensure that management sees the macro picture and operations see concrete actions to take, with comments and alarms only where intervention makes sense.

What you gain:

• ‍One data source: no more parallel “versions” of indicators.
• Faster decisions and fewer disputes: everyone sees the same facts and costs.
• Better alignment of objectives between departments: common KPIs and common genesis of the result.
• Predictable impact on P&L: operating decisions related to margin and inventory.
• Compliance and control: full access to change tracking and automatic reports defined at the beginning of the collaboration — with views for each management level (C-level, managers, operations)