Four Technical Resources: What Their Evidence Can Establish
Four newly published technical notes connect executable tests, evaluation protocols, and reference architectures to specific business decisions without overstating their evidence.
Machine Learning and Optimization in Italian Industry: Six Testable Business Cases
Six technically grounded ML and optimization pilots for Italian manufacturing and distribution firms, with required data, credible baselines, decision metrics and failure conditions.
Entropy, Incomplete Information, and Machine Learning: What Margins Cannot Tell Us
Why maximum entropy, donor-based IPF, and machine learning can imply different distributions from the same incomplete data—and how to evaluate their decisions.
Quantum Inventory Optimization: Connecting Machine Learning, QUBO, and Operational Outcomes
An evidence-based explanation of QUBO and QAOA for inventory decisions, including the role of machine-learning demand estimates, classical baselines, and downstream simulation.
Learning Demand and Testing Distribution Policies with Agent-Based Simulation
How learned demand estimates and agent-based simulation can be combined to compare inventory, allocation, and distribution policies under operational uncertainty.
Machine Learning for Inventory Decisions: Why Forecast Accuracy Is Not Enough
A forecast is useful only when it improves replenishment decisions. Here is a practical method for evaluating machine-learning demand models by inventory cost, service and robustness—not accuracy alone.
Industrial Computer Vision Evaluation Protocol
Technical Resources / Industrial Computer Vision A model that identifies visible defects in a benchmark is not necessarily suitable for a specific production line. The practical question is whether it misses unacceptable defects, generates a manageable volume of false alarms, and behaves consistently across batches and acquisition conditions. This article sets out a bounded evaluation…
Robotics and Edge AI Reference Architecture
Technical Resources / Robotics and Edge Intelligence Installing an inference model at the edge does not, on its own, demonstrate that a robotic application is reliable. The engineering question is whether a specified perception workload can be executed within a bounded time, produce interpretable outputs, and pass results through a separately supervised decision interface. This…
Agentic AI Evaluation Protocol
Technical Resources | Agentic AI and Workflow Reliability An AI agent can produce a convincing answer without having performed the correct operation. A request to retrieve an invoice may be answered fluently even when the wrong record was queried. An automated refund may be described as authorized despite an unverified approval signal. In enterprise applications,…
MCP Invoice-Agent Source-Level Case Study
This case study examines a publicly archived, synthetic invoice-workflow training artifact and identifies what its released source-level tests establish, what remains untested, and how a rigorous evaluation can proceed. It is a methodological demonstration, not an account of an operational financial system.
