15%
cost reduction in setups and optimized resource allocation
SHAiPE: the LTPlabs framework
Set the Decision
Define the scheduling objective
service, throughput, utilization, cost
Clarify the decision level
shift, line, plant, team, route
Set the scheduling scope
jobs, tasks, orders, people, machines
Highlight what matters
Align on operational targets
service level, leadtime, utilization, productivity, adherence
Map the current scheduling process
rules, manual interventions, bottlenecks
Define business rules and operational constraints
resource capacity, setup times, due dates, production dependencies
Augment with AI
Combine demand, capacity, and operational data to optimize schedules under real-world constraints
- DataDemand & workloadCapacity and resourcesOperational constraintsExecution historyAI ModelResultsOptimized schedulingOrder service levelResource allocation/OEEResource allocation/OEE
Prescribe the schedule that delivers the best trade-off across service, efficiency, utilization, and feasibility
Prototype your solution
Deliver a scheduling engine to generate and compare scheduling decisions
Run a practical pilot to validate schedules in a real operating setting
Deploy faster way of rescheduling to incorporate intra-day operational deviations (supplier non-delivery, machine failure, etc.)
Expand to scale
Integrate the solution into ERP and MES
Train planning teams to maximize adoption and production teams to maximize plan adherence
Establish governance and review routines as demand and constraints evolve
Nutshell
What this means for your business
Higher service and on-time execution through schedules aligned with real demand and constraints
Better resource utilization across teams, assets, and capacity
Lower delays, idle time, and bottlenecks through smarter sequencing and allocation
Faster operational decisions that adapt to daily changes and disruptions
InsightsConteúdo
Conteúdo
Relacionado
Aproveitando a programação de produção da fábrica de impressão
O esforço dos funcionários da litografia no agendamento foi reduzido
Proven business results of AI in the wine industry
What LTPlabs has measured in real winery projects
When matheuristics beat classic optimization: lessons from a real production scheduling problem
A real example in the paper industry
Renovando o planejamento de operações de uma fábrica inteira
Melhore uma unidade de impressão de uma empresa líder em manufatura
The most common mistakes in GenAI adoption
Why organizations that start by asking "how do we automate this?" often end up frustrated and what to ask instead.
Gestão do Conhecimento e Geração de Insights com Apoio de IA
Um aplicativo web centralizado que utiliza IA Generativa para oferecer aos usuários as análises de que precisam.
Get started today
Beyond the hype: Exploring Generative AI
The ultimate guide to get started with GenAI today. In this guide, we explore practical applications of this transformative technology and the value it can bring to your organization, beyond the hype.
I want this e-Book!
Value Chain & OperationsMore