15%
nos custos de setup e otimização da alocação de recursos
SHAiPE: o framework da LTPlabs
Defina as decisões
Definir o objetivo do escalonamento
nível de serviço, throughput, utilização, custo
Clarificar o nível de decisão
turno, linha, fábrica, equipa, rota
Definir o âmbito do escalonamento
trabalhos, tarefas, ordens, pessoas, máquinas
Identifique o que realmente importa
Alinhar os objetivos operacionais
nível de serviço, lead time, utilização, produtividade, cumprimento do plano
Mapear o processo atual de escalonamento
regras, intervenções manuais, estrangulamentos
Definir as regras de negócio e as restrições operacionais
capacidade dos recursos, tempos de setup, datas de entrega, dependências de produção
Potencie com IA
Combinar dados de procura, capacidade e operação para otimizar o escalonamento considerando restrições do mundo real
- DadosProcura e carga de trabalhoCapacidade e recursosRestrições operacionaisHistórico de execuçãoModelo de IAResultadosEscalonamento otimizadoNível de serviço das ordensAlocação de recursos / OEE
Gerar planos de escalonamento que assegurem o melhor equilíbrio entre nível de serviço, eficiência, utilização e viabilidade
Prototipe a solução
Implementar um motor de escalonamento para gerar e comparar diferentes planos de escalonamento
Realizar um piloto em ambiente operacional real para validar os planos gerados
Implementar um processo de reescalonamento rápido, incorporando desvios operacionais intradiários (falhas de fornecedores, avarias de máquinas, entre outros)
Escale com confiança
Integrar a solução com o ERP e o MES
Formar as equipas de planeamento para maximizar a adoção e as equipas de produção para maximizar o cumprimento dos planos
Estabelecer mecanismos de governação e rotinas de revisão à medida que a procura e as restrições evoluem
Em síntese
O impacto no seu negócio
Melhoria do nível de serviço e do cumprimento dos prazos através de planos de escalonamento alinhados com a procura e as restrições reais
Melhor utilização dos recursos entre equipas, ativos e capacidade
Redução de atrasos, tempos de inatividade e estrangulamentos através de um melhor sequenciamento e alocação
Decisões operacionais mais rápidas, adaptadas às alterações diárias e às disrupções
RelacionadosConteúdo
Conteúdo
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Resultados de projetos da LTPlabs em empresas do setor vitivinícola
From two weeks to hours: optimizing production planning at industrial scale LTPlabs helped a large industrial manufacturer reduce production planning lead time from up to two weeks to hours by implementing a Mixed Integer Linear Programming (MILP) optimization solution. Today, 95% of sales orders are planned directly from model recommendations, with each optimization run taking approximately one hour. 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.
A LTPlabs ajudou um grande fabricante industrial a reduzir o tempo de planeamento da produção de até duas semanas para apenas algumas horas, através da implementação de uma solução de otimização baseada em Programação Linear Inteira Mista (MILP).
Matheurísticas vs. otimização clássica: o que um problema real de escalonamento da produção nos ensinou
Um exemplo real na indústria do papel
De dados fragmentados e processos desconectados a uma base escalável de gestão de dados mestre
Uma empresa do setor alimentar centralizou os dados mestre, digitalizou e automatizou dados anteriormente introduzidos de forma manual e criou, em seis semanas, uma base reutilizável para a governação de dados.
Como irá a IA redefinir o papel dos planeadores industriais?
O planeador do futuro não desapareceu. Está a fazer algo diferente.
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