A holonic workforce allocation model for labour-intensive manufacturing

This paper presents a new model for workforce allocation in labourintensive industries. In such industries where production processes mostly include manual assembly operations, performance is highly influenced by the availability of skilled workers. Sudden unavailability of skilled labour has significant adverse effects on production. Furthermore, as competition intensifies, production becomes more sensitive to changing market demands. Such disturbances can be attenuated by introducing flexibility in the production planning process. Workforce allocation plays a significant role in the planning process. Thus, this paper focuses on workforce allocation, and a support system is developed from the concepts of holonic manufacturing systems and PROSA reference architecture. The system was designed in unified modelling language and was tested using an object-oriented software developed in C++. The use of the holonic methodology to develop the system has helped to identify the shortfalls of the conventional method adopted in industry and develop algorithms to improve the workforce allocation process. The proposed system was simulated using production data from a computer manufacturer case study. The paper then presents a comparison of the factory’s conventional method of workforce allocation with the proposed holonic workforce allocation system. The results suggest an improved manufacturing throughput performance. *Corresponding author: Salman Saeidlou, Department of Mechanical Engineering, School of Engineering, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK E-mail: [email protected] Reviewing editor: Wenjun Xu, Wuhan University of Technology, China Additional information is available at the end of the article ABOUT THE AUTHOR Mozafar Saadat received the BSc (Hons.) degree in Mechanical Engineering from the University of Surrey, Surrey, UK, and the PhD degree in industrial automation from the University of Durham, Durham, UK. He has received various research funding in aerospace, automation and manufacturing industries, and published a wide range of peer-reviewed technical papers and editorial articles. He is currently with the Department of Mechanical Engineering, School of Engineering, University of Birmingham, Birmingham, UK, where he leads the Automation and Intelligent Manufacturing (AIM) Research Group. PUBLIC INTEREST STATEMENT A flexible and reliable allocation of workforce within a factory scheduling system is essential for the enterprise responsiveness to market volatility. Skilled human operators are considered as the core of the organisational structure in labourintensive manufacturing. Furthermore, there are several factors affecting the allocation task such as employee’s skills, their availability, flexible working hours and service demand. Therefore, the presence of a flexible and adaptable workforce allocation system is of great importance in all tiers of manufacturing supply chain. In the light of the above, we are addressing this crucial problem by introducing a novel framework based on the concept of intelligent manufacturing which offers operational advantages compared to conventional systems. Received: 17 February 2017 Accepted: 26 April 2017 © 2017 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.