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Customer stories | Food manufacturers using Titan

Stories
How food manufacturers turn data into action

1 use case
1 model
Success

Real stories across planning, production, finance and Sales. See what teams shipped and how they scaled.

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Weeks One model ISO 27001

Results achieved with Titan

Customer results achieved with Titan

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How we helped our customers with their Data and AI ambitions



Featured

Jan Zandbergen Group

Planners ask Ask Titan in Microsoft Teams and get direct answers on orders, stock and capacity.

  • Faster decisions in the daily rhythm
  • Governed model in Titan
  • Explainable answers and checks
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Case study

Meyer QSL

Better visibility on stock and flows across multiple hubs, with faster response to demand swings.

  • Central view of inbound and outbound stock
  • Faster planning decisions for key lanes
  • Less manual reporting and spreadsheets
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Case study

Mola BV

More stable production planning and less waste by connecting factory data and sales orders in one model.

  • Better alignment between sales and production
  • Clear overview of stock age and slow movers
  • Support for daily planning meetings
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Case study

PG Kaas

End to end insight from intake to packed product, with views on yield, waste and margin per product.

  • Live yield overview per product and shift
  • Earlier detection of process losses
  • Support for investment and improvement decisions
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Case study

Luiten Food

Better control of stock, ageing and margin in a complex meat portfolio, with clear signals on risk and waste.

  • Stock visibility across locations and categories
  • Earlier detection of stock at risk of expiry
  • Insights for pricing and customer mix
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Case study

Goodlife Foods

More reliable planning and stock insight across multiple sites and customer channels.

  • Unified view on demand and production
  • Clear KPIs for planners and operations
  • Foundation for later AI use cases
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Case study

Clean Solutions Group

Insight into service performance, routes and consumption, with a shared view across sales and operations.

  • Service performance per customer and region
  • Early signals on issues in stock and routes
  • Data foundation for commercial and operations talks
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Looking for a similar outcome? Discuss a use case .

Frequently asked questions

How comparable are these cases to our situation?

The best comparison is the decision flow, the systems involved, and the plant complexity. If you share those three, we can point you to the closest case and explain the typical starting scope.

Are the impact numbers guaranteed?

No. Outcomes depend on scope, data quality, adoption, and the starting situation. We validate a focused use case first before scaling.

How were results measured in these cases?

Where possible, results are based on operational measurements and validation sessions with the customer team. If a metric is indicative, it should be presented as observed impact during rollout, not a guaranteed outcome.

What was the typical first use case in these rollouts?

Most cases start with one daily decision: stock risk, production planning, downtime, service risk, yield loss, or margin visibility. We start small, validate definitions, and deliver something usable quickly.

How fast did teams see the first live output?

Typically within weeks. After kickoff we confirm access, connect the first source, and deliver a first live output with validated numbers.

When does Ask Titan make sense in the rollout?

Often after the first governed Titan model and dashboards are stable. Then Ask Titan can answer questions on the same definitions inside Microsoft Teams.

Want a similar outcome?

In a short call we map your plants and systems, pick one daily decision to improve, and define the first step.

Discuss your use case