Six ways we turn your data into decisions.

Supply Chain Network & Inventory Optimization

The structure of your network and the placement of your stock decide your cost base before a single order ships. We build the models that get both right.

Capabilities

  • Network design: where plants, DCs, and dark stores should sit, and which lanes connect them
  • Multi-echelon inventory optimization: safety stock set jointly across the chain, not tier by tier
  • Allocation and replenishment logic that respects capacity, MOQs, and shelf-life constraints
  • Scenario planning: cost and service impact of demand shifts, new nodes, or supplier changes

Methods

  • MILP
  • network flows
  • simulation
  • MEIO

Outcomes

  • Lower holding cost: without breaking service-level targets
  • Fewer stockouts: stock positioned where demand actually lands
  • Defensible decisions: every trade-off quantified, not argued

Production Planning & Scheduling Optimization

Optimal master production schedules and shop-floor sequences that balance demand forecasts, capacity constraints, and inventory targets, minimizing cost while maximizing service levels.

Capabilities

  • Master production scheduling that balances demand, capacity, and inventory targets
  • Detailed sequencing that minimizes changeover time and optimizes machine utilization
  • Optimal lot sizing balancing setup cost, holding cost, and operational constraints
  • Material requirement timing: order the right quantity when production actually needs it
  • Capacity balancing across machines, lines, and facilities to expose real bottlenecks

Methods

  • MILP
  • constraint programming
  • metaheuristics
  • finite scheduling

Outcomes

  • Fewer changeovers: sequences built around setup families
  • Higher OTIF: delivery dates that the schedule can actually keep
  • Visible capacity: know which constraint binds before it bites

AI Demand Forecasting & Supply Sensing

Machine-learning forecasts at SKU–location granularity that read seasonality, promotions, and external signals, and feed replenishment and S&OP directly instead of dying in a spreadsheet.

Capabilities

  • SKU × location × week demand forecasts with backtested, honest accuracy metrics
  • Promotion, price, and event effects modeled explicitly, not smoothed away
  • New-product and sparse-history forecasting via attribute-based models
  • Supply sensing: early-warning signals on supplier delays and inbound risk
  • Forecast-to-plan integration: outputs land in replenishment and S&OP systems

Methods

  • gradient boosting
  • hierarchical reconciliation
  • probabilistic forecasts
  • MLOps

Outcomes

  • Higher forecast accuracy: measured against your current baseline
  • Less firefighting: exceptions surfaced before they hit the shelf
  • One number: planning, buying, and finance work off the same forecast

Predictive Maintenance & Smart Quality Analytics

Sensor and process data turned into failure predictions and quality-drift alerts: fewer unplanned stops, tighter first-pass yield, and maintenance spent where it matters.

Capabilities

  • Failure prediction models on vibration, temperature, and process-parameter streams
  • Remaining-useful-life estimation to move from calendar-based to condition-based maintenance
  • Quality-drift detection that flags process excursions before scrap accumulates
  • Root-cause analytics linking quality outcomes to upstream process variables

Methods

  • anomaly detection
  • survival models
  • SPC + ML
  • time-series

Outcomes

  • Fewer unplanned stops: failures caught in the data before the breakdown
  • Higher first-pass yield: drift corrected while product is still in spec
  • Smarter spend: maintenance hours directed by risk, not routine

Retail Analytics

Assortment, pricing, and store performance analytics for retail chains, from category insights down to store-level replenishment logic, built for merchants who have to act on it weekly.

Capabilities

  • Assortment optimization: which SKUs earn their shelf space, by store cluster
  • Price and promo analytics: elasticity, cannibalization, and markdown timing
  • Store performance benchmarking that separates execution from location effects
  • Store-level replenishment parameters tuned to local demand patterns

Methods

  • clustering
  • elasticity models
  • causal inference
  • A/B testing

Outcomes

  • Sharper assortment: space allocated to what actually sells, per cluster
  • Margin recovered: promos that pay back and markdowns timed right
  • Store-level action: insights that end as replenishment settings, not decks

LLM & GenAI Implementation

Production-grade GenAI grounded in your enterprise data: document intelligence, decision copilots, and workflow automation that survive contact with real users and real audits.

Capabilities

  • RAG systems over your contracts, SOPs, and reports with source-cited answers
  • Decision copilots that sit on top of your forecasts and optimization outputs
  • Document processing pipelines: extraction, classification, and validation at scale
  • Workflow automation with human-in-the-loop controls and full audit trails
  • Evaluation harnesses so accuracy is measured, not assumed

Methods

  • RAG
  • agentic workflows
  • fine-tuning
  • LLM evals

Outcomes

  • Hours back: routine document and reporting work automated
  • Grounded answers: every response traceable to a source
  • Production-ready: monitoring, evals, and guardrails included