Building an annual inspection calendar under strict regulations is a complex and time consuming task. By applying AI-driven optimization techniques, this Italian certification company automated a highly-constrained planning process, reducing manual effort while improving accuracy, consistency, and scalability.

The client is an Italian certification company operating across multiple regions and regulatory domains. Its core activities require coordinating inspections performed by qualified inspectors, each subject to specific geographic, regulatory, and operational constraints.

Every year, the company had to rebuild its inspection calendar entirely from scratch. The process was highly complex and relied heavily on manual work and expert knowledge.

Key challenges included:

  • Inspectors qualified only for specific domains and regions
  • Strict regulatory and internal constraints governing inspections
  • A large amount of tacit knowledge distributed across multiple experts
  • High risk of errors, inefficiencies, and non compliance

The manual planning process was time intensive, difficult to scale, and increasingly unsustainable.

The solution leverages operations research, an advanced AI like discipline designed to solve complex optimization problems under many constraints.

By combining constraint modeling, optimization algorithms, and structured data, the system is able to generate feasible and optimized inspection calendars automatically, even in highly complex scenarios.

The project began with an in-depth domain analysis to capture and formalize all regulatory rules, internal policies, and operational constraints.

This knowledge was transformed into:

  • A unified, structured model of all constraints
  • A centralized source of truth previously scattered across experts
  • An automated system capable of generating an optimized yearly calendar

The result is a repeatable and scalable process that can be executed each year with minimal manual intervention.

The automated optimization system delivered measurable benefits:

  • Significantly faster planning cycles
  • Reduced manual workload and dependency on individual experts
  • Higher accuracy and consistency in planning outcomes
  • Improved visibility into workload imbalances and data issues

Importantly, the system can also detect unsolvable scenarios in advance, allowing the organization to address data errors or constraint conflicts before planning begins.