Identify, evaluate and develop Artificial Intelligence applications in the Manufacturing and Supply Chain processes
The Open Program “AI and Operations” in hybrid mode, developed in collaboration with Bologna Business School – BBS, It provides a concrete method for transforming business needs into AI projects that are feasible, measurable, and adaptable within production, logistics, and supply chain processes.
The course combines online training on data, security, contractual aspects, and the main families of AI solutions with three days of in-person sessions organized as a lab, dedicated to building business cases. Participants work in groups with the support of the faculty on real problems and develop a Project Work with validation criteria and an adoption roadmap.
You learn to transform a business need into an analytical problem, with objectives, KPIs, and verifiable requirements.
Learn the criteria for evaluating data quality and choosing the AI approach that is most consistent with the use case.
Develop a complete business case, from defining the problem to the adoption roadmap.
Identifies AI applications and measurable benefits in production, logistics, and the supply chain.
Strengthens competitiveness, agility and the ability to innovate operational models.
Build a shared foundation between Operations, IT, Digital, and Innovation to drive the adoption of AI.
The program is designed for managers and professionals of Operations, Production, Quality, Maintenance, Logistics and Supply Chain. It is also addressed to managers IT, Digital and Innovation that want to develop concrete AI use cases in their own company
The course is characterized by a practical and applied approach, with a balanced mix of classroom lectures, case study discussions, testimonies from industry experts, team work, individual activities, and company visits. The faculty consists of academics, consultants, and professionals with direct experience in the topics of digital transformation and operations.
The teaching materials and exercises are available on the Bologna Business School e-learning platform for study and review between sessions.
Alberto Regattieri — Professor of Operations and Manufacturing Systems, University of Bologna; Educational Director of the program.
Massimiliano Ghini — Director, European Center for Team Emotional Intelligence.
Michele Bonfiglioli — CEO, Bonfiglioli Consulting; Executive Director of the program.
Umberto Mirani — Business Unit Director, Italy, Bonfiglioli Consulting.
Marco Brandalesi — Chief Supply Chain, Manufacturing & Operational Excellence, Bonfiglioli Consulting.
Stefano Alpi — Chief Transformation & Marketing Officer Advisory, Innovation, Marketing, ICONSULTING.
Duration: 7 days | Format: Hybrid Online and On-Campus Learning | Language: English
At the end of the course a certificate is issued. Diploma of participation, After reaching 80% of classroom hours.
At the end of the Open Program, the participants will know:
Understanding where AI can generate measurable benefits in production, logistics, and supply chain processes.
Transform a business need into an analytical problem with verifiable objectives, indicators, and requirements.
Prepare an adequate database, recognizing its limitations, risks, and usage constraints.
Compare different AI approaches and choose the one that is most consistent with the use case and operational context.
Develop and present a complete business case, with validation criteria and an adoption plan.
Introduction to artificial intelligence in manufacturing
Building a common framework on the role of AI in Operations and identifying use cases with concrete business potential.
Main contents:
Essential concepts of AI, machine learning and generative AI.
From the digitalization of processes to the use of AI in production systems.
Digital AI vs Physical AI.
Use cases in production, quality, maintenance, logistics, planning, and supply chain.
Expected benefits, limitations, risks, and organizational conditions for adoption.
First selection of business problems to be explored in the project work.
Structuring and preparing the database
Tools to move from the business problem to a database usable for the analysis and development of an AI solution.
Main contents:
Typical sources: ERP, MES, WMS, PLM, quality and maintenance systems, sensors and IIoT.
Analysis unit, variables, labels, temporal granularity, and connection keys.
Data quality: completeness, consistency, accuracy, timeliness, duplicates, and anomalous values.
Cleaning, normalization, integration, and documentation of the dataset.
Data separation for development and verification; error prevention and data leakage.
Construction of a data dictionary and a data readiness assessment sheet.
Data security and contractual issues
Definition of the technical, organizational, and contractual conditions for the controlled use of AI data and tools.
Main contents:
Data classification and principles of minimization, segregation and access control.
Anonymization or pseudonymization, transfer, storage and deletion of data.
Risks related to cloud platforms, external models, APIs, and generative AI tools.
Confidentiality, ownership of data, intellectual property, rights over outputs, and limitations on reuse.
Roles and responsibilities between the company, suppliers, and project participants.
Essential clauses and checks for confidentiality agreements, licenses, AI services, and experiments.
AI approaches and selection criteria
Comparison between the main family of approaches and selection of the solution that is most consistent with objectives, available data and operational constraints.
Main contents:
Descriptive, predictive and prescriptive analysis.
Supervised and unsupervised machine learning; forecasting and anomaly detection.
Computer vision, language processing, and generative AI.
Optimization, simulation, and hybrid solutions integrated with corporate systems.
Criteria for selection: expected value, availability of data, required accuracy, explainability, time, costs, scalability, and maintenance.
Definition of baseline, evaluation metrics and acceptance criteria.
Definition of cases and verification of data
Precise definition of the problem, the measure of the result and the technical scope of the project work.
Main contents:
Presentation and selection of business cases.
Mapping of the process, the users and the decisions to be supported.
Definition of objectives, baseline, KPIs and success criteria.
Data exploration and quality verification, coverage and representativeness.
Identification of information gaps and realistic corrective actions.
Group work plan and choice of the approach to be tested.
Results of the session: Shared problem statement, evaluated dataset, and case development plan.
Development and validation of solutions
Construction of a first solution and evaluation of the results against the baseline and against the needs of the process.
Main contents:
Final data preparation and construction of relevant variables.
Development of the model, the prompt or the workflow consistent with the case.
Setting up of tests and separation between development, validation and testing.
Analysis of results, errors and critical cases.
Comparison with process experts and revision of hypotheses.
Iteration on the solution and documentation of the choices.
Results of the session: prototype or design of a solution with initial results, limitations and validation criteria.
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