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This article presents a fuzzy cognitive map for the evaluation of innovation in organizations.

The purpose of this paper is to develop a model to evaluate the innovative capacity of organizations based on fuzzy cognitive maps (FCM), particularly for micro, small and medium-sized enterprises (MSMEs). The specification of the innovation evaluation model based on FCM was carried out with the “Intelligent Decision Support System” methodology. It is a six-step methodology: selection of experts, definition of concepts and relationships, model design, inference, interpretation and decision.

Our approach yielded good results in three case studies, effectively determining the level of innovation in an organization. The fuzzy cognitive maps demonstrated a high level of accuracy, with an accuracy of 82% in the Colombian case studies and 92% in the global case studies. These results highlight the effectiveness of the model for quantitatively assessing levels of innovation within organizations. Furthermore, the study revealed the most influential and essential innovative activities/variables within organizations, contributing significantly to the improvement of their operations and competitiveness.

It is important to automate the definition of the relationships between the concepts of the context and of our FCM. It is also possible to improve the behavior of the FCM by analyzing the variables with a greater impact on the level of innovation and very dynamic in the context since they are the variables to be observed in real-time to follow the evolution of the innovative behavior of an organization.

The study found that innovative activities emerged as an influential factor in organizations, essential to improving their operations and competitiveness. Our model can help in identifying areas that require improvement to impact positively organizations. By improving innovation assessment through the FCM model, organizations can anticipate higher profitability because innovations are often closely tied to revenue generation and cost savings. The tool can determine the necessity of new products or services, improve operational processes or enter new markets.

The previous results in the literature show that although there are relevant advances on this topic, there is not enough knowledge to provide clear guidelines for evaluating innovation and improving performance in an organization using intelligent systems. Also, previous works have not defined a framework for evaluating innovation in MSMEs based on FCMs. They also do not use the data of an organization to assess the key characteristics related to innovation. This work applies FCM to automate the evaluation of the process and the capacity for innovation in an organization. These are the main differences between our approach and previous studies.

The authors propose the concept of “Autonomic Cycle for innovation processes,” which defines a set of tasks of data analysis, whose objective is to improve the innovation process in micro-, small and medium-sized enterprises (MSMEs).

The authors design autonomic cycles where each data analysis task interacts with each other and has different roles: some of them must observe the innovation process, others must analyze and interpret what happens in it, and finally, others make decisions in order to improve the innovation process.

In this article, the authors identify three innovation sub-processes which can be applied to autonomic cycles, which allow interoperating the actors of innovation processes (data, people, things and services). These autonomic cycles define an innovation problem, specify innovation requirements, and finally, evaluate the results of the innovation process, respectively. Finally, the authors instance/apply the autonomic cycle of data analysis tasks to determine the innovation problem in the textile industry.

It is necessary to implement all autonomous cycles of data analysis tasks (ACODATs) in a real scenario to verify their functionalities. Also, it is important to determine the most important knowledge models required in the ACODAT for the definition of the innovation problem. Once determined this, it is necessary to define the relevant everything mining techniques required for their implementations, such as service and process mining tasks.

ACODAT for the definition of the innovation problem is essential in a process innovation because it allows the organization to identify opportunities for improvement.

The main contributions of this work are: For an innovation process is specified its ACODATs in order to manage it. A multidimensional data model for the management of an innovation process is defined, which stores the required information of the organization and of the context. The ACODAT for the definition of the innovation problem is detailed and instanced in the textile industry. The Artificial Intelligence (AI) techniques required for the ACODAT for the innovation problem definition are specified, in order to obtain the knowledge models (prediction and diagnosis) for the management of the innovation process for MSMEs of the textile industry.