Authors: Mohammadreza Salehi; Hadi Shirouyehzad; Reza Dabestani
Addresses: Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, P.O. Box 517, Isfahan, Iran. ' Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, P.O. Box 517, Isfahan, Iran. ' Department of Management and Economics, Tarbiat Modares University, Jalal Ale Ahmad Ave., P.O. Box 14115-337, Tehran, Iran
Abstract: In today's competitive world, productivity is a fundamental concept in assessing economic performance of organisations. Due to the fierce competition and customer requirement variation, organisations should produce various types of products. This type of production requires a sophisticated productivity measurement system and organisations still confront with the challenges of lacking an appropriate system. Labour productivity is one of the most important indices among partial productivity indicators and plays a key role in the productions and services as outcome. In this paper, labour productivity issue is examined by nearest neighbour algorithm (NNA) in order to classify products. In the following, considering the required workforce for standard parts in each category and also their production processes, multiple regression method is applied to calculate the value of products and to standardise outputs. A case study is also presented to examine the validity of proposed method. Some advantages of this method include; increasing labour productivity, improving production system, a more precise planning and responding to market fluctuation.
Keywords: product classification; product standardisation; labour productivity; multiple regression; productivity measurement; economic performance; competition; customer requirements; productivity indicators; nearest neighbour algorithm; workforce; standard parts; production processes; product value; standardised outputs; planning; market fluctuations; Pars Noor Electric Company; lighting towers ;traffic poles; Iran; productivity management; quality management.
International Journal of Productivity and Quality Management, 2013 Vol.11 No.1, pp.57 - 72
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 27 Nov 2012 *