Production & Resource Planner
A linear-programming model for the business's production-planning problem — what to make, how much, in what mix, allocated to which demand. Run weekly or per planning cycle, with documented improvement vs. the prior method.
- Engagement
- 10–16 week build · ongoing operation
- Built for
- Production directors · Supply chain leads · Plant managers
Production decisions at most manufacturers — what to produce, in what quantities, against what input mix, allocated to which downstream demand — are made through ERP-driven heuristics plus production-manager judgment. The result is workable but rarely optimal against the actual constraint set.
What this is
A production-planning engagement for manufacturers where the planning decisions are constraint-optimization problems. Three layers:
- Problem formulation. The production decision expressed as a linear program with the business's constraints (input availability, production capacity, recipe rules, downstream demand commitments, quality bounds).
- Solver. Gurobi, CPLEX, or open-source equivalents depending on license and scale. Hybrid heuristic-plus-exact methods for the larger instances.
- Operational integration. Per-planning-cycle execution, planner UI for review and scenario exploration, integration with the business's ERP for execution.
How it's built
Python optimization stack (pyomo or pulp for the formulation layer, Gurobi or CBC for the solver). Per-engagement, the formulation captures the business's actual constraint structure rather than fitting the problem to a generic template.
What you get
- The formulated LP model with documented constraints.
- The solver deployment.
- Per-cycle planning pipeline.
- Planner UI for review and what-if analysis.
- Baseline-and-improvement documentation.
Engagement is shape, not list.
Length and price are functions of the data and the destination. The shape below is the typical engagement.
- Length
- 10–16 week build · ongoing operation
- Lead
- Bogdan
- Cadence
- Async, weekly
- Bar
- Production
Scoped during the discovery call against the actual data and the operation it integrates with.
Principal engineer. Architecture and most code ships through one keyboard.
Written updates between, calls when the decision needs the room.
Async correctness, capacity under burst, observability at every boundary.
Products this composes with.
Same suite, or vertical-specialized versions in another.
- Same suite · Operations Algorithms Suite
Logistics & Routing Optimizer
A routing-and-distribution optimization engine — solves vehicle routing problem (VRP) variants for the business's fleet and constraints, produces daily route assignments with documented cost reduction vs. the prior method.
- Same suite · Operations Algorithms Suite
Workforce Scheduling Engine
A workforce-scheduling optimizer that produces shift assignments against the business's constraints (skills, availability, labor law, demand forecasts) — runs weekly or daily depending on the cadence, with manager-side UI for review and override.
- Same suite · Operations Algorithms Suite
Demand Forecasting & Inventory Engine
A forecasting layer at SKU grain — combines time-series baselines with external signal (macro indicators, social-and-search signal, weather, consumer-behavior priors) — feeds inventory and replenishment decisions with documented uncertainty.
What buyers ask about this one.
Why LP instead of an APS (Advanced Planning Solution) like SAP IBP?
Enterprise APS is excellent for businesses that fit its data model. For mid-market manufacturers and for enterprises with non-standard constraints (custom blending formulas, multi-recipe production lines, recipe-cost-and-availability tradeoffs), custom LP captures the constraint set faithfully. Many engagements end up running alongside the business's existing APS, not replacing it.
What kinds of problems do you model?
Production planning (what to make, when), blending (what input mix for what output mix), multi-echelon supply allocation (from production to distribution centers to channels), recipe-and-substitution optimization (where input substitution is allowed within quality bounds). Per-engagement, the problem formulation matches the business's actual decision structure.
How does it handle uncertainty in demand and input cost?
Two paths. For modest uncertainty, scenario-based LP (run the LP under representative scenarios, evaluate solution stability). For larger uncertainty, stochastic programming or robust optimization where the engagement scopes the methodology. We're upfront when the problem fits each approach.
What's the typical impact?
Per-engagement varies. Margin uplift from input-mix optimization is typically in the 2–8% range; production-throughput gains from better sequencing typically 5–15%. The engagement scopes baseline measurement so the impact is documented, not asserted.
Pricing?
Scoped to problem complexity and integration depth. Discovery call covers both.
If the deliverable matches the gap, the next step is one call.
We'll scope length and price against your data and the operation it integrates with. No retainer, no fishing.
Bogdan and team · async-first · OP—2026