Before NPLAN, Fey's planning had structural limitations that restricted both the speed and quality of decision-making:

• The planning cycle took up to 3 days to execute
• S&OP and production scheduling were disconnected, with no synchronized model between them
• Scenario simulation was not feasible in practice
• Inventory health was compromised, with excess and stockouts coexisting in the same portfolio
• WIP levels were high, signaling a misalignment between plan and execution

The result was a slow review cycle that left the operation exposed to demand fluctuations and production changes that it could neither anticipate nor respond to quickly.

The implementation of NPLAN structured an integrated planning layer connecting S&OP, MPS, materials planning, and production capacity, fully integrated with SAP S/4HANA. The integration covered materials, resources, routings, bills of materials, production orders, sales orders, and sales plans.

The project ran over four months, in iterative cycles with frequent validation between Fey's Logistics, Planning, Production, and Technology teams and the NPLAN implementation team. Decisions were made collaboratively through operational analysis, indicators, and comparative simulations.

Key technical and methodological elements:

• Integration with SAP S/4HANA covering all relevant planning objects, with no manual data bridges or parallel spreadsheets.
• Parameterization of inventory policies with prioritization criteria by channel and product family, replacing ad hoc decisions with a structured logic.
• Structuring of scenarios allowing for multivariable simulations that simultaneously consider demand, production capacity, inventory policies, and operational parameters.
• Iterative testing rounds (unit, advanced, and integrated) with continuous review of parameters, operational sign-off, and KPI tracking throughout the entire project.

What changed in practice

The planning cycle is no longer the bottleneck

A process that used to take three days now runs in three hours. The team has shifted from spending most of their time executing the plan to analyzing and making decisions.

S&OP and production scheduling are now connected

Previously, the tactical plan and shop floor scheduling lived in different realities. After NPLAN, the production plan became more realistic and directly connected to sequencing, reducing the gap between what was planned and what was executed.

Scenario simulation has become viable

The ability to simulate multivariable scenarios regarding demand, capacity, inventory policy, and operational parameters has changed how Fey's planning team makes decisions. The team now compares alternatives and chooses the best path instead of reacting to events.

Inventory health has improved significantly

With structured policies and a connected planning model, Fey has eliminated the previous coexistence of excess and stockouts. WIP inventory fell by 35%.

The 2026 evolution: AI agents

With the planning platform stabilized, Fey and NPLAN moved to the next phase and introduced AI agents into the planning routine.

The architecture operates in three layers. At the base, SAP S/4HANA remains the transactional system and data source. In the middle layer, the NPLAN calculation engine performs optimization, MRP explosion, and plan validation. At the top, language models interpret orders, explain results, and orchestrate interactions between agents.

The configuration includes specialist agents, such as a dedicated inventory agent, and an orchestrator agent that coordinates interactions between them to compile analyses and respond to operational demands. A concrete use case is multi-scenario comparison, where the orchestrator interacts with the inventory specialist agent to present options, explain trade-offs, and support decision-making.

Security by design

Fey's complete operational dataset is not sent to the AI models. Interactions use only data samples, preserving the confidentiality of the operational database.

Concrete results

• -35% in WIP inventory
• Planning cycle compressed from 3 days to 3 hours
• Significant expansion of simulation capacity for decision support
• More team time available for analysis and identifying opportunities
• Evolution of the teams' analytical and decision-making maturity
• More realistic production plan connected to sequencing
• Greater integration between planning and execution
• Higher reliability of planning data
• Improved Supply Chain governance

Fey's transformation stemmed from the integration of technology, processes, and cross-departmental collaboration. Once this foundation was solid, AI agents were layered on top to enhance analytical capacity and response speed, keeping the planning engine at the heart of every calculation.

The sequence was the key to success: first a viable, connected plan, then analytical intelligence built upon it.