From two weeks to hours: optimizing production planning at industrial scale

The challenge: fragmented production planning and unreliable delivery dates

A large industrial manufacturer operates a highly complex production environment, with 14 industrial units across three factories, approximately 200 production lines, more than 1,000 clients, and over 5,000 products, primarily produced under a make to order strategy.

With products moving across multiple industrial units, reliable production planning requires coordinating capacity, materials, workforce, inventory, and customer commitments across interconnected operations.

Production planning was fragmented across six planners responsible for different industrial units, with significant reliance on manual analysis, legacy applications, and individual expertise.

Coordinating thousands of products and orders made it difficult to systematically assess production constraints and balance delivery commitments with operational costs. As a result, confirming an order could take up to two weeks, with limited reliability in the resulting delivery dates.

The company needed a scalable way to evaluate all orders and production constraints simultaneously and generate feasible plans and more reliable delivery commitments.

The solution: production planning optimization using Mixed Integer Linear Programming

LTPlabs developed a production planning optimization solution centered on a Mixed Integer Linear Programming model, designed to generate weekly production plans that minimize delivery delays and operational costs while respecting the constraints of the industrial environment.

Each planning cycle combines commercial and operational inputs, including order backlog, sales forecasts, inventory, production costs, routes and bills of materials, work center and workforce availability, and production strategy. The model determines delivery weeks and production plans while providing visibility into raw material requirements, resource utilization, and inventory allocation.

The optimization accounts for key production constraints such as inventory flows, minimum production quantities, batch and lot sizes, setups, oven capacity, mold maintenance, and resource availability. At full scale, a 16 week planning horizon covers approximately 3,000 products and 2,000 sales orders, with more than one million decision variables and 500,000 constraints.

Hierarchical optimization balances delivery performance and operational efficiency, while slack variables preserve feasibility in exceptional situations and flag cases requiring planner review.

How the optimization model represents industrial production constraints

The production planning optimization solution was integrated into the manufacturer's existing technology infrastructure, including SAP and the Gurobi optimization solver.

Automated data pipelines connect enterprise data with the optimization model and return planning results to the operational environment.
A dedicated planner interface allows the production planning team to:

• access optimization recommendations;
• review proposed production and delivery plans;
• analyze exceptions;
• understand resource and material requirements;
• configure relevant parameters for each planning cycle.

The solution transformed production planning from a fragmented process into an automated decision support system embedded in daily operations.

Results: production planning reduced from weeks to hours

The solution transformed production planning from a fragmented, manually intensive process into an automated decision-support system embedded in daily operations.

The main results include:

• Up to two weeks → hours: planning results that could previously take up to two weeks can now be obtained within hours.
• ~1 hour optimization runtime: a full model execution takes approximately one hour.
• 95% of sales orders model-planned: 95% of sales orders are now planned directly from optimization model recommendations.
• Daily operational adoption: the planner interface has become an everyday tool for the production planning team.
• Greater focus on exceptions: planners can validate exceptional situations instead of manually constructing complete production plans.
• More systematic delivery planning: production constraints and customer commitments are evaluated through a common analytical framework.
• Faster and more reliable delivery commitments for customers: by generating a feasible production plan much faster, the manufacturer can provide customers with a delivery date sooner after an order is placed. More reliable production plans also reduce the need to subsequently revise communicated delivery dates, creating greater predictability for customers.

By combining large scale mathematical optimization, industrial constraints, enterprise data integration, and a planner focused interface, the company established a faster and more systematic planning process, reducing the risk of missed delivery dates and embedding optimization into core production planning decisions.

Most importantly, it shifts production planning from a largely manual and fragmented activity toward optimization-driven decision-making, helping the manufacturer produce more reliable delivery commitments, reduce the risk of missed delivery dates, and scale planning decisions across a highly complex industrial network.