Ilie Mihai Taucean

Bio: Ilie Mihai Taucean is an academic researcher from Politehnica University of Timișoara. The author has contributed to research in topics: Entrepreneurship & Lean manufacturing. The author has an hindex of 5, co-authored 14 publications receiving 57 citations.

Integrating Sustainability and Lean: SLIM Method and Enterprise Game Proposed

Abstract: Sustainability and leanness are organizational approach concepts for more efficient activities and increased competitiveness. This paper presents a study and an application of the concepts of sustainability and lean, with the purpose to capitalize on the benefits of the two concepts’ tools when used together in an industry and education activity. A literature review was carried out to evaluate qualitatively and empirically the concepts of sustainability, lean, and enterprise games, and the possibility to integrate the first two concepts into a new tool applied into an enterprise game. An online survey was done to identify which tools are used within companies in the region, how and what training methods they used, and what the reported benefits are. The survey results were used to design a new tool integrated in a new enterprise game (SLIM) developed by the authors. The game was tested and validated in educational laboratory with students and actual employees from companies. The game follows the frame of an enterprise game, considering the simulation of enterprise classical functions. The game’s purpose is to improve the activity in successive rounds. A scorecard is used to fill in and compute the key performance indicators (KPIs), and a new indicator is proposed (SLIMx). Applications of the instrument/game include: students’ training in an educational laboratory; lifelong learning; professional training in companies; and professional perfection/reconversion of potential employees and the unemployed. The SLIM game was simulated in a team of 15 players over three rounds, with teachers playing the role of the supervisor. A number of possible improvements have been identified. The next step is testing it in enterprises with various fields of activity. SLIM has proven to be an effective solution to improve organizational efficiency and motivate players to gain new knowledge.

Corporate Financial and Investment Policies When Future Financing Is Not Frictionless

Abstract: We study a model in which future financing constraints lead firms to have a preference for investments with shorter payback periods, investments with less risk, and investments that utilize more pledgeable assets. The model also shows how investment distortions towards more liquid, safer assets vary with the marginal cost of external financing and with firm internal cash flows. Our theory helps reconcile and interpret a number of patterns reported in the empirical literature, in areas such as risk-taking behavior, capital structure choices, hedging strategies, and cash management policies. For example, contrary to Jensen and Meckling (1976), we show that firms may reduce rather than increase risk when leverage increases exogenously. Furthermore, firms in economies with less developed financial markets will not only take different quantities of investment, but will also take different kinds of investment (safer, short-term projects that are potentially less profitable). We also point out to several predictions that have not been empirically examined. For example, our model predicts that investment safety and liquidity are complementary: constrained firms are specially likely to decrease the risk of their most liquid investments.

Usage of Machine Learning for Strategic Decision Making at Higher Educational Institutions

Abstract: Decisions made at the strategic level of Higher Educational Institutions (HEIs) affect policies, strategies, and actions that the institutions make as a whole. Decision’s structures at HEIs are depicted in this paper and their effectiveness in supporting the institutions’ governance. The disengagement of the stakeholders and the lack of using efficient computational algorithms lead to 1) the decision process takes longer; 2) the “whole picture” is not involved along with all data necessary; and 3) small academic impact is produced by the decision, among others. Machine learning is an emerging field of artificial intelligence that using various algorithms analyzes information and provides a richer understanding of the data contained in a specific context. Based on the author’s previous works, we focus on supporting decision-making at a strategic level, being deans’ concerns the preeminent mission to bolster. In this paper, three supervised classification algorithms are deployed to predict graduation rates from real data about undergraduate engineering students in South America. The analysis of receiver operating characteristic (ROC) curve and accuracy are executed as measures of effectiveness to compare and evaluate decision tree, logistic regression, and random forest, where this last one demonstrates the best outcomes.

Tax structure and entrepreneurship

Abstract: Using macro-level panel data, we examine the effects of taxation and tax progressivity on entrepreneurship in a large group of European countries. We address two main objectives. First, we try to explore whether tax increases discourage entrepreneurial activity, distinguishing between the effects on existing self-employment and new self-employment (nascent entrepreneurship). Second, we investigate the impact of tax progressivity on entrepreneurship, focusing in particular on the impact on new self-employment. We find that tax progressivity at higher-than-average incomes has a robust negative effect on nascent entrepreneurship. We discuss on the policy implications of our results.