University of Commerce Luigi Bocconi

The course focuses on the new product development process as a complex inter-functional management topic, which requires strategic initiative, aligned organizational solutions and appropriate supporting methodologies. Collaborative innovation is presented in order to discuss the potentialities of the involvement of external players in the innovation process, also thanks to the opportunities offered by internet-based technologies. Despite the increasing relevance of innovation strategies and new product development, few companies seem to have mastered their ability to identify, create, and exploit opportunities for innovation on a systematic basis. Crafting and delivering a new product is not an easy and intuitive process, but the result of a set of structured and organized practices. This course explores these practices and exploits the tools and techniques that can be used to this purpose. The New Product Development and Open Innovation course is organized in two main parts. The first provides a set of integrated frameworks and tools to effectively design and manage the strategies, processes, and techniques for innovation. It provides the conceptual tools to understand the nature and characteristics of different types of innovation, as well as practical insights on how to design and manage a new product development process. The second part of the course is focused on how digital environments can help companies to open their boundaries and pursue processes of open and collaborative innovation, involving several external partners in their new product development activities. Special attention is paid to the role of users in enhancing innovation and to ad-hoc mechanisms supporting their active involvement, among which user communities, virtual knowledge brokers, and Open Source Systems.

Over the last decades, public decision making has developed from a traditional hierarchical model to managerialist approaches (New Public Management) and more recently to the public governance paradigm. This latter approach emphasizes the involvement and engagement of stakeholders different in nature and interests in order to create shared value and to reach sustainable goals. This paradigm aims to generate better-informed and long-lasting solutions through inclusive and dynamic decision-making processes. Being able to design, implement, and manage public policies consistent with this paradigm is increasingly relevant for all involved stakeholders, including public, private, and non-profit organizations. This course offers the understanding of the complexity of decision-making processes in the public sector, with a focus on implications due to different governance models, multiple stakeholders, and public-private relations. The course provides tools, competences, and skills in order to understand, critically discuss, and to design public (as well as collaborative) governance models and decision-making processes to support strategic choices of public interest and/or relevance. The course uses formal lectures and a mix of class discussions (involving practitioners who share concrete public governance examples with the class), case studies, incidents, and simulations. 

This course provides an overview of data management architectures and analytics procedures aimed at organizing, describing, and modeling big data, both structured and unstructured. The course discusses both technical aspects of data management/analytics and topics related to analysis managerial evaluation including how to translate the outputs into meaningful business insights. The course examines topics including relational databases such as OLTP, Data warehouse, and SQL language; big data and NoSQL databases, distributed file system, Hadoop, Spark, and Data Lake concept; data understanding and data preparation; models and statistical techniques applied to Big Data; regression and classification trees; ensemble methods (random forest and boosted trees); logistic regression; supervised artificial neural networks; models' performance evaluation; big data ingestion and management; data preparation and cleaning; machine learning algorithms application; and machine learning model evaluation. The course requires students have a basic understanding of descriptive and inferential statistics and basic computer skills as a prerequisite.

This course seeks to understand the concepts and the techniques required to conduct marketing research and to know how to apply them in real world marketing research problems in order to make better business decisions. In this course students are introduced to different stages of the marketing research process. The course examines different types on research designs, how to collect and scrutinize data, and quantitative research methodologies and their applications to various data sets which can be used to solve real-world business problems. The contents of this course comprise theory, concepts, and frameworks relevant to marketing, and empirical methodology and their applications to real-world datasets. The topics include but are not limited to: exploratory/descriptive/causal research: research design and data collection; experimental design; sampling; A/B testing; consumer segmentation: cluster analysis; perceptual maps: factor analysis; market response modeling; field experiments; and conjoint analysis. The course recommends students be have a basic knowledge of linear regressions and t-tests as a prerequisite.

This course illustrates how Operations and Supply Chain Management can be managed to properly deal with health, social, and environmental issues and how to transform such a challenge into a source of competitive advantage. The course focuses on specific topics related to the Triple Bottom Line and to the Circular Economy paradigms, by linking sustainability concepts with the product life cycle, from its design, manufacturing, distribution, and possible end-of-life recovery options. The teaching style of this course is consistent with its learning goals and is based on case discussions, group work, real examples, and on the interactions with guest speakers from companies that are coping with these issues. During the course, topics are analyzed moving from real-life case-histories, so as to make the students aware not only of the technicalities related to sustainability in Operations and Supply Chain Management, but also of the most valuable experiences of companies and of industries that are leading the process toward a more sustainable operating system. Topics covered include: mega trends and competitiveness; synergies between profits and sustainable practices in Operations Management; design for environment; sustainability and vendor selection; sustainability and production; lean management and six-sigma; sustainable logistics, transportation, and packaging; reverse logistics and closed-loop supply chains; sustainability and performance measurement.

The course presents the structure of European and US financial markets and discusses the rules and principles that govern trading and price formation in the most advanced electronic trading platforms and auction markets. The course discusses how to trade securities on electronic order book markets like the London Stock Exchange, Borsa Italiana, Nyse-Euronext, NASDAQ, NYSE, or alternative trading systems (lit and dark pools). During the course, students participate in a trading simulation game prepared to practice real-time trading in the market. The course covers: market microstructure and research objectives, trading process, continuous vs batch auction, orders and order properties, market participants and the role of market makers, market structure, trading sessions (call and continuous auction markets), execution systems (order-driven, quote-driven, and hybrid markets), trading rules for order driven markets, price formation, matching rules, guidelines for price monitoring, price discovery, circuit breakers and market crashes, pricing and trading fees (make-take vs symmetric pricing structure), algorithmic trading, and high frequency trading (HFT), regulatory debate (U.S. and Europe) on dark liquidity, tick size, trading fees, and closing auction volume.

Family firms - firms that are owned, managed and controlled by a family, or a limited number of individuals – represent the vast majority of all firms, and major contributors to a country’s employment, GDP, wealth, and business knowledge. This course aims at developing students’ skills in analyzing the specific features of family firms, assessing their key problems and opportunities, and creatively proposing strategic and organizational solutions. The course is targeted to the next generation of controlling-family members, to students who may be willing to start their career in a family or private firm, and to those who plan to consult or provide professional services to family-controlled companies. Understanding the unique features of these firms is essential to develop a successful leadership career in such organizational settings or, more broadly, to understand the strategic logic of family-controlled competitors, suppliers, and customers. Participants are challenged to improve their personal skills in the areas of communication, conflict resolution, diagnostic assessment, solutions finding, and writing academic papers or case-based materials. This highly interactive course includes active simulations, role plays, videos, guest speakers, and real-case discussion.