Staff sizing as a mechanism of efficiency : an application of a non-parametric method

The concept of staff sizing aims to estimate or determine the ideal or optimal number of people needed to perform some organizational activities, which can be considered as a trend. So, models for staff sizing constitute a fundamental part of accurately identifying staff allocation. The objective of this paper is to propose a framework for decision-making based on Data Envelopment Analysis–DEA, to estimate the staff sizing in a Brazilian entity responsible for promoting and supporting the competitiveness and sustainable development of micro and small enterprises. Data collection was carried out in the headquarters of the entity, located in Brasilia. Firstly, interviews were carried with managers in order to assess qualitatively the needs of staff for each service unit. Secondly, the documental analysis of reports from 21 units was analyzed quantitatively in order to determine their efficiency in terms of staff sizing. The results found through DEA show that only three service units can be considered efficient in terms of staff sizing. Thus, there is a need to reduce the number of workers in most of the organization. In this context, the contributions for the entity lie in the discussion on the creation of quantitative indicators and the adoption of an efficiency analysis, which can be used to better estimate or determine the optimal quantity of staff. This paper innovates by proposing a quantitative and systematized approach to estimate the staff sizing, which is the DEA

Enterprise, project and workforce selection models for industry 4.0.

Abstract Enterprise, project, and workforce selection models for Industry 4.0. Rupinder Kaur The German federal government first coined industry 4.0 in 2011. Industry 4.0 involves the use of advanced technologies such as cyber-physical system, internet of things, cloud computing, and cognitive computing with the aim to revolutionize the current manufacturing practices. Automation and exchange of big data and key characteristics of Industry 4.0. Due to its numerous benefits, industries are readily investing in Industry 4.0, but this implementation is an uphill struggle. In this thesis, we address three key problems related to Industry 4.0 implementation namely Enterprise selection, Project selection and Workforce selection. The first problem involves identification of enterprises suitable for Industry 4.0 implementation. The second problem involves prioritization and selection of Industry 4.0 projects for the chosen digital enterprises. The third and last problem involves workforce selection and assignment for execution of the identified Industry 4.0 projects. Multicriteria solution approaches based on TOPSIS and Genetic Algorithms are proposed to address these problems. Industry experts are involved to prioritize the criteria used for enterprise, project and workforce selection. Numerical applications are provided. The proposed work is innovative and can be useful to manufacturing and service organizations interested in implementing Industry 4.0 projects for performance improvement

Simulation-based workforce assignment considering position in a social network

Globally distributed software enhancement necessitates joint efforts of workforces across various organizations, which constitutes a multifaceted social network. Here, we propose a novel modeling framework to optimally assign the workforce to software development projects considering both short and long-term benefits of the organization. The proposed framework is composed of the evaluation module, an agent-based simulation model representing the considered social network; and the assignment module, a multi-objective optimization model. The Decision Evolution Procedure of the evaluation module first calculates the position values between each pair of available workforce. Using these position values, the Extended Regular Equivalence Evaluation algorithm of the evaluation module then computes the regular and structural equivalence values between each pair of workforce. Finally, the assignment module selects the optimal workforce mix maximizing both the short (productivity) and long-term performance (robustness) of the organization. The proposed framework is demonstrated with the software enhancement process in Kuali organizational network

Simulation-based workforce assignment considering position in a social network

The goal of this paper is to propose a novel modeling framework to help project managers devise optimal workforce assignments that consider both short- and long-term aspects of projects that must be completed through a multi-organizational social network. The proposed framework is comprised of an evaluation module and an assignment module. Each time a workforce assignment is performed, the Decision Evolution Procedure of the evaluation module first calculates the position value between each pair of currently available workforce members based on various social networking parameters such as trustworthiness, influence, reputation, and proximity. Second, by using these position values, the Extended Regular Equivalence Evaluation algorithm from the evaluation module computes the regular and structural equivalence values between each pair of workforce members. Finally, the assignment module selects an optimal workforce mix that maximizes both the short-term performance (productivity) as well as the long-term performance (workforce training, and robustness) of the project organizations. Agent-based simulation and multi-objective optimization techniques are leveraged for the evaluation module and the assignment module, respectively. The proposed framework is illustrated and successfully demonstrated using the software enhancement request process in Kuali, a multi-organizational alliance-based software development project involving 12 universities. </jats:p