Smart Supply Networks allows for recording the quantitative changes of stocks of products in distribution points. It also adaptively schedules delivery of new products, based on statistics of consumption and demand in real time. It comes as a special module that connects directly to the customer’s ERP system.


Implementation Results

Using Smart Supply Networks will enable businesses to gain significant effect:

  • increasing the level of service (by up to 10%);
  • increasing profits (25% increase);
  • improving the quality of schedules (finding more profitable decisions);
  • reducing complexity and labor intensity in the work of dispatchers;
  • reducing dependence on the human factor;
  • mitigation of risks of order nonperformance;
  • global assessment of the situation (taking into account the details and internal interactions in the network);
  • transparency and availability of information.


Smart Supply Networks can be used in distribution companies, dealer and retail networks, large logistics centers, on enterprises with advanced supply chains, as well as for major online stores with more than 10 sales outlets.


Supply chain management involves coordinated interaction between factories and suppliers for production planning, warehouses and inter-regional transport companies for supply planning and delivering goods to regional stores, maintaining warehouse stock balance, as well as sales forecasting for stores.


When companies try to solve such problems, they are faced with the following challenges:

  • complex network structure, a lot of options of order execution;
  • different importance of orders;
  • distributed production capacities;
  • dynamic, time-varying limitations on production and transportation;
  • various transportation costs, depending on the direction, type of transport and volume of cargo;
  • necessity to take into account current warehouse stock balance and the state of planned operations;
  • low accuracy of sales forecasts.


Smart Supply Networks makes it possible to schedule and flexibly reschedule the supply plan by selecting the most suitable suppliers, based on the dynamically changing schedule of products sales and release.

The system supports the optimal insurance stocks of materials and components needed for production, taking into account time and possible disruptions of supplies.


Smart Supply Networks can dynamically plan production volumes, redistribute production capacities and offer optimal stocks of finished products to meet the forecast demand in the sales network.


The system makes it possible to manage the supply network, taking into account the following limitations:

  • availability of warehouse;
  • quarantine storage of finished products;
  • specific mode of production;
  • calendar (working days and hours);
  • fixed production;
  • fixed transportation;
  • time and cost of transportation and production;
  • customer preferences;
  • location of customer and order execution time;
  • production limitations;
  • transportation limitations.



Entering and recording data on the current situation in the network

  • Full and partial data loading from accounting systems
  • Data communication via csv, json and api system files
  • Displaying and editing orders, approved production and transportation plans, stock balance
  • Displaying the network on a map and work schedules of nodes.

Adaptive rescheduling

  • Forming a more profitable work plan
  • Ensuring the high level of service
  • Determining feasibility and optimal order delivery time
  • Transportation planning (what and when should be transported in the network)
  • Production planning (what, where and when should be produced)
  • Checking feasibility of the previously approved plans in the current conditions
  • Automatic consolidation of orders
  • Formation of situational flexible delivery schedule for each node in the network
  • Formation of procurement plans for materials, components, etc.
  • Choosing the best supplier in each situation
  • Situational route choice in the network (through which nodes to be transported)
  • Situational choice of production processes (according to materials stock balance and cost)
  • Taking into account temporary and permanent limitations on delivery canals and production nodes.


Application and analysis of planning results

  • Possibility of manual adjustments of the planned operations with automatic adaptation and optimization of the rest of the plan
  • Forming a list of production and transportation deviations from the approved plans
  • Analyzing microeconomics of each order execution
  • Reasons for deviations of orders from the preferred settings (time, supplier)
  • Planned movements within the warehouse at each node
  • Necessity for additional operations (not previously approved) for each order
  • Tracking changes as a result of proactive improvements or new events.


Design and optimization of supply network

  • Visual network editing (creating nodes, canals on the map)
  • Setting demand patterns in network nodes for products
  • Assignment of transportation and production limitations in the network
  • Creation and comparison of several alternative network configurations
  • Dynamic recalculation of plans in response to changing network structure
  • Analysis of changes in plan indicators taking into account changes in configuration or limitations (production capacity, timetables, etc.) in the network
  • Analysis of feasibility of different order volumes at the current or proposed network configuration.



  • End-to-end planning in distributed heterogeneous networks
  • Always current plans (as opposed to calculated on demand)
  • Automatic re-planning depending on events
  • Human involvement in the planning process
  • Possibility to adjust individual sections of the plan
  • Possibility of custom settings and criteria for different workflow participants

Implementation Experience

The system is in operation at Coca-Cola in Germany and is used daily by national order management team for planning and analysis of orders coming from distribution centers of retail chains in Germany.

Currently, Coca-Cola Germany’s network consists of more than 8 plants for production of beverages and more than 300 distribution centers.

Main results:

  • Maximization of retail sales KPIs, increased operating and net profit. Increasing scheduling quality (finding more profitable solutions);
  • Reducing complexity and labor intensity in the work of dispatchers, reducing dependence on the human factor;
  • Mitigation of risks of order nonperformance;
  • Global assessment of the situation (taking into account the details and internal interactions in the network);
  • Transparency and availability of information.

Implementation of the system has made it possible to increase the order execution coefficient by 7% for peak days, and receive savings on transportation costs by up to 20% on some orders.

Moreover, the system prototype has been tested in the LEGO company on real retail network data for the USA. The results of experiments show that the system implementation can increase profits by 18-25% due to transition to the real-time economy.

 System Demo Video

Multi-agent supply chain management system for Coca-Cola in Germany: 5 factories, 300 storage warehouses and approximately 1000 stores (in Russian)

Multi-agent system for managing supply of building materials for Gazprom Neft in Yamal (in Russian)

Our publications on Smart Supply Networks (in English)

Our publications on Smart Supply Networks (in Russian)

Learn more about Smart Supply Networks