A complete list of hard and soft constraints.
Production planning
AI production planning: improve schedules around real operating constraints
Production planning is a constraint problem before it is an AI problem. Machines, labor, changeovers, materials, priorities and maintenance windows all shape a useful schedule. The provider should make those constraints explicit and prove improvement against the current planning process.
Independent selection
A provider's payment does not determine whether it is included.
Traceable data
Profiles distinguish public sources, provider-supplied information and AIPartnerLens analysis.
Fit before volume
The shortlist focuses on a few comparable providers, not a wall of logos.
Business problem
Start with the operating problem, not the AI label
Planners spend significant time rebuilding schedules after disruptions.
Constraints are spread across ERP, MES, spreadsheets and local knowledge.
The company wants more reliable sequencing, capacity use or delivery performance.
Scheduling constraints can be represented explicitly.
ERP/MES data is sufficiently reliable for the planning horizon.
Planners can evaluate proposed schedules and edge cases.
What should be true before you request proposals?
Historical schedules and outcomes.
Reliable orders, routings, capacity and material data.
A process for planner overrides and disruption handling.
A practical sequence from discovery to operations
1. Define the decision
Document the business problem, current process, volumes, exceptions, owners and the outcome the project must improve.
2. Check data and systems
Confirm which data, documents, applications and permissions are available before selecting a model or tool.
3. Test on representative cases
Use real or representative examples, explicit acceptance criteria and a baseline against the current process.
4. Prepare production and operations
Define security, monitoring, failure handling, human escalation, ownership, documentation and maintenance before go-live.
Evidence to request
Ask for evidence that matches this use case
- Comparison against historical or current scheduling performance.
- Behavior under disruptions and infeasible constraints.
- Explanation of objective functions and trade-offs.
- ERP/MES integration and planner interface evidence.
Measures
Decide how value will be measured before the pilot
Schedule adherenceOn-time deliveryChangeover timeCapacity utilizationPlanning timeOverride rate
Providers with public signals related to this problem
This is not a ranking. Profiles appear when their public projection contains related use cases, services or technology signals. Verify fit against your exact constraints before contacting a provider.
Consulting data science, measurement marketing & AI
Ekimetrics
Ekimetrics presents itself as a provider of data science and business AI solutions focused on sales and marketing decision-making, marketing effectiveness and scaling.
Review fit on the profile
Digital engineering, data & AI
Akkodis
Akkodis combines engineering, data, analytics and AI for the design, deployment and operation of solutions at scale, including agentic systems and data platforms.
Review fit on the profile
Data, AI & agentic AI consulting
AVISIA
AVISIA presents itself as a consultancy specializing in Data, AI and agentic AI, from strategy through production.
Review fit on the profile
AI consulting & integration
CGI
CGI presents an end-to-end AI offering spanning roadmap and experimentation through integration, production deployment and operations.
Review fit on the profile
Consulting data, digital & AI
Equancy
Equancy combines digital-transformation consulting, data science, AI, governance and data engineering, with Generative AI applications already presented publicly.
Review fit on the profile
AI engineering & data
ILLUIN Technology
ILLUIN Technology designs custom systems in AI, GenAI, data science and software engineering, complemented by a product suite and its nAIxt studio.
Review fit on the profile
Data, cloud & AI
JEMS
JEMS covers the Data, Cloud & AI value chain and emphasizes production deployment of AI systems integrated into business processes and supported by a governed data foundation.
Review fit on the profileK
AI strategy, engineering & production deployment
Kayro
Kayro presents itself as an applied-AI consultancy connecting strategy, use-case selection, technical build and production deployment through delivery teams embedded with business functions.
Review fit on the profileRisks
What can make this project fail?
- Missing hidden constraints known only by experienced planners.
- Optimizing a mathematical objective that does not match business priorities.
- Schedules that cannot be executed on the shop floor.
- No practical replanning method after disruptions.
Questions for providers
Questions worth asking before a proposal
- 1.How are hard and soft constraints represented?
- 2.What happens when no feasible schedule exists?
- 3.Can planners understand trade-offs and override decisions?
- 4.How fast can the system replan after a disruption?
- 5.Which ERP or MES systems have you integrated?
Questions about production planning
Does production planning always require machine learning?
No. Constraint programming, optimization and heuristics may be more important than predictive models. The right method depends on the planning problem and available data.
What is a credible production-planning pilot?
Use real constraints and historical schedules, compare against current performance and test disruption scenarios rather than demonstrating a simplified toy schedule.
How important is planner adoption?
Critical. The schedule must fit real operating constraints and allow planners to understand or override recommendations when local information is not represented in the data.