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The objective of this article is to conduct a systemic analysis relating investment in knowledge for a production line to financial losses and returns. A systemic map was created based on the experience of a multidisciplinary group and a review of the literature about knowledge management, people management, and quality management systems. From estimates and initial data of a food production line, a dynamic model was created to simulate scenarios for five years. Concerning the studied production line, in the best-case scenario, within two years and five months after investing the capital, the investor will recover, due to loss reduction, all the investment in knowledge. In five years, the estimated return on investment was approximately twenty times the amount initially invested. [ABSTRACT FROM AUTHOR]

This paper discusses an analytic-based decision analysis tool called Employee-Job Assignment System (EJAS) for solving the employee-job assignment problem (EJAP). The EJAS is intended to determine an employee-job assignment (EJA) solution that optimizes the employees' work efficiency and job satisfaction. Two person-job (P-J) fit criteria are considered: (1) competency-based P-J fit, and (2) preference-based P-J fit. Depending on the decision-maker's selection, the EJAS determines an EJA solution that maximizes the chosen P-J fit. Additionally, it is capable of generating several near-optimal solutions to assist the decision-maker in selecting a suitable EJA solution. Significance: Effective management of the human resource is an important issue for every business organization. Assigning the right employees to the right jobs can help the organization to increase its productivity and/or enhance its service efficiency. The proposed decision analysis tool enables responsible decision-makers to develop the employee-job assignment solution that considers both competency and job preference; thus, achieving increased productivity and better job satisfaction. [ABSTRACT FROM AUTHOR]