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Optimization · Business Intelligence · AI

We solve coupled decisions together

The production plan affects procurement, the markdown decision affects stock distribution, line capacity affects the delivery date. We build those links into one model and make the decisions together, from data infrastructure to optimization.

Let's talk about your problem →Our work

Çimsa: a 15-site production, logistics and sales network in one optimization model

Clients we work with

Results

Measured impact from real projects

Projects delivered
65
Enterprise clients
19
Dashboards built
200+

Industries

Which decisions are yours?

Pick your industry to see what we've done there and the case studies behind it.

Retail

We turn store and product data into stock, price and allocation decisions.

What we've done

  • Initial allocation of new products to stores, automatic replenishment from the warehouse and store-to-store transfers
  • Markdown decisions based on a 14-day sales forecast for every repriced product
  • Weekly measurement of lost sales, broken size runs and non-selling stock
  • Data warehouse, Power BI reporting and store staffing norms

Product

  • Opalgo — Stock allocation decisions: plans initial allocation, replenishment and store-to-store transfers in one system, taking store capacity and sales potential into account.

What we do in Retail →

Manufacturing

From steel to cement, building materials to ceramics — we bring line, kiln and supply capacity together with demand forecasting in a single plan.

What we've done

  • Planning the production, logistics and sales network in one optimization model, down to the fuel recipe and purchasing decision
  • Matching open orders to raw material while respecting line capacity and every stage of the bill of materials
  • A demand forecast retrained every month and a forecasting app the planners run themselves
  • A demand forecasting PoC that missed its target: where we stopped and why

What we do in Manufacturing →

Finance

We turn customer behaviour and process data into analytical decisions.

What we've done

  • End-to-end data science training for bankers
  • Master–apprentice mentoring for analytics teams: the team builds its own projects
  • Churn prediction, target audience management and product recommendation models
  • HR analytics: hiring assessments against performance, employee attrition prediction

What we do in Finance →

Sports & federations

We turn referee, evaluator and fixture assignments into optimization that respects rules and fairness.

What we've done

  • Referee and assessor assignment with an optimization model that respects the rules and fairness; the board makes the final call
  • Database design for the management system and migrating data from the old system

What we do in Sports & federations →

Gaming & digital media

We turn event data streaming from millions of users into an analytics warehouse that answers subscription and content questions in seconds.

What we've done

  • Game event design and marketing dashboards
  • Combining PostgreSQL and S3 data in a single ClickHouse analytics warehouse
  • A discovery and audit of a BigQuery analytics stack across seven areas

What we do in Gaming & digital media →

Products

Ready-built answers to recurring problems

We turned the decisions we kept meeting across projects into products.

All products →

Retail planning

One season, dozens of coupled decisions

A map of fashion retail planning decisions from pre-season to in-season. Open a group to see the algorithms inside.

38 algorithms

Pre-season

Merchandise Financial Planning 1
  • Long-range demand forecast (macro)
Range Plan 1
  • Product lifecycle curve forecasting
Product strategy 1
  • Consumer trend tracking
Collection development 3
  • AI product design recommendation
  • Product performance forecasting
  • SKU rationalization
Assortment Planning 3
  • Initial price setting
  • Mid-range demand forecast
  • Size analysis (PrePack optimization)
Store clustering 1
  • AI store grouping
Sourcing 2
  • Supplier performance and risk tracking
  • Supplier selection recommendation

In-season

Allocation — initial shipment 2
  • Automated initial allocation
  • Allocation strategy recommendation
Replenishment 1
  • Forecast-driven automatic replenishment
RPT 2
  • RPT requirement forecasting
  • RPT quantity calculation
Inter-store transfer 3
  • Inter-store product optimization
  • Block, single-unit and broken-size decisions
  • Store opening/closing impact
Markdown, promotion and campaign 2
  • Price elasticity model
  • Markdown optimization
Business intelligence 3
  • Stock-out and lost sales calculation
  • Broken-size and idle stock analysis
  • Algorithm performance measurement
Warehouse and logistics 5
  • Network design optimization
  • Warehouse layout optimization
  • Picking optimization
  • Route optimization
  • Return packing optimization
CRM and customer analytics 8
  • Customer segmentation
  • Churn prediction
  • Lifetime value prediction
  • Recommendation systems
  • Dynamic pricing
  • Product ranking algorithm
  • Review classification
  • Campaign audience building

See the full map →

How we work

We start small

  1. 1

    Discovery

    We understand your problem and your data together and pick the decision where a measurable difference is possible.

  2. 2

    Pilot

    We build a first working model on a small scope with your own data and measure the result with you.

  3. 3

    Scale

    We make what works permanent: data infrastructure, reporting and decision models.

    Areas we work in at this stage

Team

You work with the people who build it

Meet the team →

570 hours of corporate training delivered through the Academy so far. Academy →

Contact

Tell us the problem, we'll look at it together

Fill in the form or write to [email protected] directly; we will get back to you shortly.

[email protected]LinkedIn