Innovation is a key strategy for competitiveness in the global market by setting a stage for economic progress. The practice of innovation is fragmented and centered on specific cases. This presentation contributes to better understanding of the process of innovation which is considered from a data-driven perspective. The proposed approach extends the practice of integration of users and stakeholders into product, manufacturing, and service development activities. The fact that the product and process requirements are elicited from multiple sources and analyzed with the modern analytical tools is likely to lead to business success. Selected concepts of creativity, inventiveness, innovation, and innovation facilitators such as leadership, entrepreneurship, and idea incubation are introduced. Business rules and best practices enhancing innovation are discussed. The data stored in data warehouses is a valuable source of process improvement and innovation. Methodologies and tools supporting innovation are presented, for example, data mining, process modeling, dependency analysis, and social networks. Process modeling is a backbone for defining the best innovation practices. Many of the classical analysis tools when combined with data and text mining tools offer a viable innovation toolkit. Increasing customer base is of paramount importance in the global economy. Companies compete in various ways, including the design of large product portfolios aimed at meeting expectations of an individual customer. Meeting these individual customer expectations could significantly increase complexity of products and manufacturing. Various approaches have been considered to manage the product and manufacturing complexity. Some of these strategies such as modularity, mass customization, assemble-to-order, and supply chain management and the underlying modeling approaches are considered in the presentation. Though the task of product complexity reduction does not appear to have a direct-- link to innovation, the research demonstrated in the paper shows that the relationship between the two is meaningful. Many of the design and complexity management approaches are based on data mining. Data mining-algorithms determine products sought by the customers that can be produced in large quantities. Various principles of mass customization are discussed in the context of innovation and product complexity management. The impact of the innovation and mass customization on products, manufacturing, and service is illustrated with examples. The ideas outlined in the presentation are illustrated with industrial examples.
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