Information-dependent processes in incubated and graduated technology-based companies: a comparative case study

Nowadays, competitiveness introduces new behaviors and leads companies to a discomforting situation and often to non adaptation to environmental requirements. A growing number of challenges associated with control of information in organizations with engineering activities can be seen, particularly, the growing amount of information subject to continuous changes. The innovative performance of an organization is directly proportional to its ability to manage information. Thus, the importance of information management is recognized by the search for more competent ways to face current demands. The purpose of this article was to analyze informationdependent processes in technology-based companies, through the four major stages of information management. The comparative method of cases and qualitative research were used. The research was conducted in nine technology-based companies which were incubated or recently went through the incubating process at the Technological Park of Sao Carlos, in the state of Sao Paulo. Among the main results, it was found that in graduated companies information management and its procedures were identified as more conscious and structured in contrast to those of the incubated companies.

PERFORMANCE EVALUATION OF RESOURCE-AWARE BUSINESS PROCESSES USING STOCHASTIC AUTOMATA NETWORKS

In this work, we study the performance evaluation of resource-aware business process models. We define a new framework that allows the generation of analytical models for performance evaluation from business process models annotated with resource management information. This framework is composed of a new notation that allows the specification of resource management constraints and a method to convert a business process specification and its resource constraints into Stochastic Automata Networks (SANs). We show that the analysis of the generated SAN model provides several performance indices, such as average throughput of the system, average waiting time, average queues size, and utilization rate of resources. Using the BP2SAN tool - our implementation of the proposed framework - and a SAN solver (such as the PEPS tool) we show through a simple use-case how a business specialist with no skills in stochastic modeling can easily obtain performance indices that, in turn, can help to identify bottlenecks on the model, to perform workload characterization, to define the provisioning of resources, and to study other performance related aspects of the business process.