Anticipation of Problems in Innovative Projects Based on OTSM-TRIZ: Operative Algorithm to Assess Resources and Solutions in Project Development – Innovative Projects Based on OTSM-TRIZ

Anticipation of Problems in Innovative Projects Based on OTSM-TRIZ: Operative Algorithm to Assess Resources and Solutions in Project Development – Innovative Projects Based on OTSM-TRIZ

Christopher Nikulin (Universidad Técnica Federico Santa María, Chile), Constanza Céspedes Domínguez (Universidad Técnica Federico Santa María, Chile), Raul Stegmaier (Universidad Técnica Federico Santa María, Chile), Sabrina Estefania Nino (Universidad Técnica Federico Santa María, Chile), Pablo Viveros (Universidad Técnica Federico Santa María, Chile) and Niccolò Becattini (Politecnico di Milano, Italy)
DOI: 10.4018/978-1-5225-7152-0.ch010


In this chapter, an integrated proposal is described to guide analysts and developers in identifying and selecting optimal alternative solutions in innovative projects. The integration is inspired by the theory of inventive problem solving, and specifically the recent evolution of the OTSM-TRIZ with a body of knowledge of risk analysis assessment. The authors propose a solution assessment indicator based on TRIZ-resources to anticipate a lack of resources when solutions are proposed. The solution assessment considers both risk assessment logic for evaluation and TRIZ resources for parameter classification and categorization. Finally, the solution assessment indicator aims to anticipate potential uncertainty by considering both qualitative and quantitative teamwork approaches. Moreover, this chapter presents a case study that involves a group of young designers and engineers working on a gripper design project, where the designers must develop a new product for a university laboratory.
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The quest for efficiency and productivity in the current globalized environment, with fierce competition among companies, has motivated the development of methodologies to support the decision-making process, particularly in scenarios involving multiple variables or criteria for selection (Berumen, 2007). All industrial projects require a clear definition regarding the expected requirements and goals to achieve successful project results. Indeed, industries in the next decade will need to address new challenges based on different constraints, owing to the highly dynamic environments characterizing the current global market (McKinsey Global Institute, 2012). The drivers that motivate industries to change are firstly derived from internal company reasons, encouraged by the need for product and process improvements (Ziv-Av and Reich, 2005). Secondly, they are caused by external “events” (such as changes in environmental or social policies), which directly affect the growth and internal processes of organizations (Jaffe et al., 2002). These issues are non-trivial tasks in anticipating emerging problems caused by changes (Nikulin et al., 2018). However, changes based on problems and constraints cannot be considered only as a negative aspect, and in this scenario, particular attention is dedicated to constraints (technical, economic, and organizational), owing to their potential role as triggers for generating creative solutions in different knowledge fields.

Despite the difficulties in dealing with company problems (in terms of quantity and the manner in which they are managed), it is worth mentioning that these problems also play a positive role for companies. They indirectly determine the performance of any system, as their existence represents an opportunity for improvement (Rahman, 1998), particularly for innovative companies that make use of creativity techniques to meet new requirements continuously. Given this premise, plenty of models are available in the literature to aid decision makers in predicting managerial risks and recognizing obstacles for strategic decisions, so as to prevent or at least mitigate their impact (Viveros et al., 2012; Barbera, 2012; Viveros et al., 2014). Nevertheless, these models fall short in properly understanding the consequences or potential limitations of technical solutions in practice. Moreover, their applicability to specific industrial contexts is not always feasible or effective. They do not consider distinctive features of firms, and for this reason, many different models exist in the literature (Braunscheidel and Suresh, 2009), which are fundamental for correctly identifying critical integration areas required to guarantee project effectiveness.

In general, when a project is initiated independently from a specific industry, the analysts or developers must define directions for the formal development of project tasks. The research purpose must be established; that is, what the project objectives are. Indeed, during the initial stages, project solutions focused on addressing a specific problem have to be described, as well as the complex scenario and the manner in which the investigation will aid in solving it. It is important to note that objectives must be clearly defined in order to avoid deviations in the research process. The objectives are the solution guide and must be congruent with the current situation during the entire process (Hernández, 2006).

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