1. Understand context, functions, processes in a business and industrial environment and the impact of those factors on business performance
2. Identify future trends, technologies and key methodologies in a specific domain (specialization streams)
3. Design solutions applying a scientific and engineering approach (Analysis, Learning, Reasoning, and Modeling capability deriving from a solid and rigorous multidisciplinary background) to face problems and opportunities in a business and industrial environment

• Support students to combine their managerial learnings from previous courses and to face market, organization and technology challenges through real case studies.
• Guide students along cross-enterprise transformation projects, as example of consulting role in the real world.
• Familiarize with typical pillars of consulting support: M&A, post merger integration, enterprise organization, HR management, operations management, ICT governance, data management.
• Explain the main topics of consulting industry: methodologies, tools, process, economics, and management
• Improve problem solving, holistic analysis and lateral thinking
• Blend theory with real cases studies

• History of Management Consulting
• Consulting business model, players, key success factors, trend, economics and KPI, financials
• Consulting carrier, performance evaluation, incentive schemes
• M&A: goals and different models, categorization, process steps from scouting to closing, main actors (PE, Industrial, Advisors, lawyers), management incentives for deals
• Business planning for different strategic options, M&A, internationalization, new business, start up.
• Post merger integration: day-one integration topics, synergies identification and implementation, new business models enabled by M&A, organization post deal, cultural integration
• Organizational models from traditional to agile, competencies evolution, employees journeys, digital workplace
• Incentives plans from blue collars to managers
• Employer branding
• Process mapping and BPM: artificial Intelligence, workflow platforms, RPA and cognitive computing
• Predictive management
• Digital twins for process optimization
• Workforce Management
• Change Management new way: Early Adoption
• Enterprise Technology Architecture and functionalities: from Industry specific to new trends and evolution
• Data Center Management (physical, virtual, cloud, private cloud, public cloud, Cloud strategy)
• ICT Governance: Organization Model, Contract management, Investment management and monitoring, System Development Life Cycle
• Data-driven culture, decision making and productivity
• Data management, value chain
• Data monetization
• Case studies on each of the subjects

Following competences are suggested:

• P&L and Balance Sheet and basics of finance
• Investment analysis
• Operating Research and Optimization models
• Information Systems
• Cost management
• Applied Statistics

• Team project & fieldwork Evaluation (60%)
  • Milestone 1 – Framing – engagement letter
  • Milestone 2 – Scanning Results
  • Milestone 3 – Interim report
  • Milestone 4 – Final client report, deliverables and engagement close-out
  • Weekly client communication & meetings
• Individual work (20%) – class participation, assignments during courses
• Final written examination (multiple and open questions) (20%)
• Orals only if written exams have not a clear evaluation

USEFUL BIBLIOGRAPHY:

• Managing The Professional Service Firm by David H. Maister
• The End of Solution Sales, by Brent Adamson , Matthew Dixon and Nicholas Toman
• The New M&A Playbook (Christensen, Alton, Rising, Waldeck, HBR March 2011)
• "International Human Resource Management" by Peter Dowling
• "The essential HR Handbook" by S. Armstrong
• "Cases in Human Resource Management" by D. Kimball
• "Viral Change" by Leandro Herrero; vedere anche sul suo sito "Daily Thoughts"
• Agile People: A Radical Approach for HR & Managers by Pia-Maria Thoren
• Drive: The Surprising Truth About What Motivates Us by Daniel H. Pink
• Recruiting sucks, but it doesn't have to", by S. Lowisz
• Grieves e J. Vickers, «Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems (Excerpt),» ResearchGate, 2016.
• Negri, L. Fumagalli e M. Macchi, «A Review of the Roles of Digital Twin in CPS-based Production Systems,» Procedia Manufacturing, 2017.
• Stark, S. Kind e S. Neumeyer, «Innovations in digital modelling for next generation manufacturing system design,» CIRP Annals, 2017.
• Competing on Analytics: The New Science of Winning”, 2017, by Thomas H. Davenport, Jeanne G. Harris, HBR Press
• Data Strategy: How to Profit from a World of Big Data, Analytics and the Internet of Things, by Bern and Marr, Kogan Page
• The Chief Data Officer Handbook for Data Governance, by Sunil Soares, Mc Press