Automarket use cases

Several operational scenarios and more. For each: what problem we solve, what we measure and what the team gets

Dark stores

Order picking in a dark store

A picker walks dozens of routes per shift. Automarket shows exactly where an order loses time: in the aisles, at the fridges, in the kitchen or at the handover buffer

  • Order picking time
  • Path length
  • Pauses and waiting
  • Zone dwell time
  • Deviation from optimum

PilotingCase: picker routes at Yandex Lavka

  1. The problem

    Extra walking, product search, waiting at the kitchen and buffer, bottlenecks, picking SLA

  2. What we measure

    Order route, path length, pauses, zone dwell, pick points, deviation from the optimum

  3. What you get

    Faster picking and processes tuned on real trajectories

Stores

The store sales floor

Tags on baskets and staff show how customers really move around the sales floor, where they linger and where they meet sales assistants. You can see how long a customer–employee contact lasts and in which zones customers lack help

  • Staff routes
  • Customer path
  • Customer–employee contacts
  • Sales floor heatmaps
  • Load peaks
  • Impact of layout changes

Pilot in 2 storesPilot: Magnit Kosmetik sales floor

  1. The problem

    Uneven staff load, shopper routes, peaks, layout and promo zones

  2. What we measure

    Customer path across the floor, time at shelves and in zones, customer–employee contacts, staff routes, heatmaps and traffic density

  3. What you get

    A clear customer journey, staff where customers need them and a layout validated by data

DCs and warehouses

Distribution centres and warehouses

In a warehouse or sort centre it matters not only where people are, but also where pallets, carts and vehicles are. The system records placement and movement and shows bottlenecks in real time

  • Pallet placement
  • Loading into the right truck
  • People and vehicle movement
  • Congestion and idle time
  • Zone load

PilotingCase: pallet placement at a Yandex Market sort centre

  1. The problem

    Asset search, long routes, idle time, narrow aisles, zone load peaks

  2. What we measure

    Routes of people, trolleys and objects, waiting, zone dwell, heatmaps

  3. What you get

    Visibility into internal movement and bottleneck detection

Industry

Manufacturing sites

In manufacturing, Automarket helps you see material flows and the movement of staff and equipment between stages. Monitoring presence in hazardous zones is a separate scenario configured for each site

  • Material flows
  • Equipment movement
  • Waiting between stages
  • Presence in zones
  • Events and integrations

Discuss an industry pilot

  1. The problem

    Material flows, staff and equipment movement, waiting between stages

  2. What we measure

    Historical trajectories, zones, idle time, events and integrations

  3. What you get

    Process analytics. We define specific effects only after an industry pilot

Don't see your process?

Describe your site and task — we'll suggest which metrics a pilot can capture and what result can realistically be tested

Get a pilot plan
  • A pilot on one site — from two weeks to 2 months
  • KPIs agreed before the start
  • Report with a before/after comparison

Yandex Lavka

Piloting

Yandex Lavka is a fast grocery delivery service. Orders are picked by staff in the service's own dark stores, so picking speed directly determines how quickly a customer gets their order

Dark store plan with picker tracks between shelving, fridges and packing tables
Picker tracks on a dark store plan
Task
Optimised routes for pickers
Goal
Reduce order picking time
What the system does
  • Tracks pickers' movement around the dark store
  • Builds optimised picking routes
  • Shows how shelf layout affects picking speed
  • Finds idle time and waiting
Impact in the case
Up to 20% faster picking

Yandex Market

Piloting

Yandex Market is one of Russia's largest marketplaces. Orders pass through sort centres, where parcels are grouped on pallets by destination and loaded into trucks. A pallet in the wrong row or the wrong truck means a delivery delay and an extra trip

Diagram: pallet rows in the loading zone and trucks by destinationR22EKBR23EKBR24SPBEKBSPBSMR
In the right rowPlaced unevenlyIn the wrong rowRoute to the truck for its destination
Control scheme: pallet rows in the loading zone and trucks by destination
Task
Monitoring pallet placement in storage and loading zones
Goal
Fewer manual direction checks and mis-sent pallets
What the system does
  • Checks whether a pallet is in the right row and placed straight
  • Matches pallets to the truck for their destination and logs loading errors
  • Gives management a live view of zone occupancy, deviations and operation times
Impact in the case
Up to ₽1M per month per sort centre

Magnit Kosmetik

Pilot in 2 stores

Magnit Kosmetik is a chain of cosmetics and household goods stores within the Magnit retail group. On the sales floor it matters where staff spend their time and how customers move around the store

Visualiser screen with a heatmap of site zones
This is what a heatmap looks like in Visualiser. Pilot data is not published
Task
Optimising sales floor operations
Goal
Staff efficiency and service quality
What we track
  • Customer path across the floor: route, zones of interest and time at shelves
  • Customer–employee contacts: where, when and how long a consultation lasts
  • Zones where customers wait for help but no employee is nearby
  • Staff load by zone and time of day
Stage
The system has been agreed with the partner and is being deployed on site
Results
Published once approved by the partner

Partners

Companies we develop the technology and projects with

  • MagnitMagnit
  • VkusVillVkusVill
  • Yandex LavkaYandex Lavka
  • X5 GroupX5 Group
  • Yandex MarketYandex Market

Get in touch

How can we reach you
Tell us more about the taskOptional — helps us prepare the pilot
Site type
Custom developmentOptional — a case similar to ours or a new product
What you need

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