This service evaluates and characterises the performance, quality, and accuracy of advanced AI algorithms applied to industrial operational processes and demand forecasting. It enables companies to identify the most suitable solutions for improving efficiency, decision-making, and forecasting capabilities.
The service assesses different advanced algorithms for specific applications in manufacturing, warehouse management, logistics optimisation, and demand forecasting. Evaluations are performed using synthetic data generated through digital twins based on the operational processes under study, ensuring realistic and controlled testing environments.
In addition, the service evaluates algorithm performance for demand forecasting in manufacturing and distribution companies, focusing on improving forecast accuracy, reducing uncertainty, and supporting better planning decisions.
As an alternative to digital twins or dedicated datasets, two additional approaches are offered:
– Use of curated operational data: previously available customer data is analysed to evaluate AI-based process optimisation solutions. This includes an initial assessment of data quality and process characteristics using advanced data processing and machine learning techniques.
– Use of real-time data acquisition systems: pilot deployments based on sensor networks, communication technologies, and contactless identification systems (e.g. asset or material tracking) enable the generation of real-time datasets for algorithm validation in operational environments.
The service follows a structured process that includes:
– Definition of use case and key performance indicators (KPIs)
– Data assessment and preparation (synthetic or real data)
– Selection and benchmarking of advanced algorithms
– Validation under realistic operational conditions
– Analysis of results and recommendations for implementation
Performance is evaluated using measurable indicators such as forecast accuracy, computational efficiency, scalability, robustness, and impact on operational KPIs (e.g. service level, lead time, resource utilisation).
The main outcome is a comprehensive technical report that benchmarks algorithm performance against predefined indicators and provides clear recommendations to support decision-making and solution deployment.
Typical applications include production planning optimisation, warehouse resource allocation, logistics routing, and demand forecasting improvement. For example, manufacturing companies can improve production scheduling accuracy, while logistics operators can optimise inventory levels and distribution flows.
This service is designed for:
– Industrial and logistics companies seeking to improve operational efficiency, reduce costs, and enhance forecasting capabilities
– AI solution developers and providers (e.g. ERP or decision-support tools) aiming to validate and benchmark their algorithms in realistic industrial scenarios
Customers should have access to operational data or defined use cases, although support is provided to generate or capture the required datasets when needed. ITA provides the necessary expertise, infrastructure (including digital twins and pilot environments), and advanced analytics capabilities to ensure reliable and actionable results.
To start a project, companies can contact ITA to define their use case, assess data availability, and establish the evaluation scope. This initial step leads to a tailored proposal, followed by execution, validation, and delivery of results.
