Operations & Supply Chain Optimization

What We Do

We turn operational planning from an art into an engineering discipline. Most organizations plan with spreadsheets, rules of thumb, and experienced guesswork. That works — until demand shifts, capacity changes, or complexity exceeds what intuition can handle.

Production planning and scheduling — mathematical optimization of production sequences, batch sizing, resource allocation, and changeover scheduling. Our MoTo platform generates executable production plans that respect real constraints
Supply chain network optimization — inventory positioning, reorder policies, supplier allocation, and distribution planning. Multi-echelon optimization that balances service levels against working capital
Demand forecasting — statistical and ML-based demand prediction, including intermittent demand, seasonal patterns, and new product introductions. Forecast accuracy measurement and continuous improvement
Capacity planning — medium-to-long-term capacity analysis: when to add shifts, invest in equipment, or outsource. Scenario-based planning with quantified trade-offs
Digital twin for operations — building living models of your production and supply chain that mirror reality and enable continuous what-if analysis. Using our Aipokit platform for process twins and MoTo for planning twins
Simulation and scenario analysis — testing operational changes (new products, demand spikes, supplier disruptions) in a simulated environment before committing resources

Our Approach

Operations optimization is inherently quantitative — but the hard part isn’t the math, it’s getting the constraints right. We spend significant time understanding how your operations actually work: the unwritten rules, the workarounds, the constraints that aren’t in the ERP master data.

We build models iteratively. The first version is deliberately simple — capturing the main constraints and objectives, producing results your planners can validate against their experience. Then we add complexity where it matters, always checking that additional model sophistication actually improves decisions.

Our MoTo platform provides the scheduling and optimization engine. Aipokit handles the process layer. Together, they create an operational digital twin that planners use as a daily tool, not a one-time analysis.

Typical Engagements

Planning maturity assessment — evaluating your current planning processes, tools, and data quality. Identifying where optimization has the highest impact and what groundwork is needed
Scheduling optimization — building and deploying a production scheduling system for a specific plant or production area. Typically 8-12 weeks from kickoff to first production use
Inventory optimization — redesigning reorder policies and safety stock calculations across your product portfolio. Data-driven, replacing spreadsheet-based rules with optimization models
Operational digital twin — building a simulation model of your production or supply chain for ongoing scenario analysis and continuous improvement