Integration of Missing Parameters into a DES Tool

Qian Wang,Reda Nujoom,Maji Abubakar

doi:10.1088/1757-899x/436/1/012016

Abstract

Products are usually made by accomplishing a series of manufacturing processes in a sequential flow line that is also known as a manufacturing system. Today, lean methods are widely adopted by many manufacturing plants as a popular model in designing, implementing, operating or managing a manufacturing system. It has been proved as a cost-effective approach to boost system efficiency and productivity by consistently seeking and removing any non-value added activities (i.e., wastes) during a production with a small or without any additional investment. Nevertheless, identification of these wastes using the traditional lean methods does not include such wastes as amounts of energy consumption and CO2 emissions. For human centered assembly lines, for instance, it is reported that applying highly skilled, flexible and dynamic workers into production lines is also a good practice for implementing a lean manufacturing system in which each worker performs multiple tasks amongst stations. On the other hand, most studies on manufacturing systems using the modelling simulation methods failed to consider parameters of energy consumption, CO2 emissions and human factors that may also impact the overall system performance. The simultaneous prediction, which relates to amounts of energy consumption and CO2 emissions and effects of human factors (or human performance) for a manufacturing system evaluation, is often overlooked by researchers or system designers partially due to a lack of existing DES (discrete event simulation) tools that enable incorporating these parameters into an established DES model. This paper presents a study by addressing these issues aiming to incorporate these missing parameters of energy consumption, CO2 emissions and human factors (age and experience) into a DES model.

Highlights

The concept of lean production or manufacturing emphasizes the importance of eliminating “any nonvalue-added wastes” in every aspect of manufacturing-related activities thereby increasing manufacturing efficiency and productivity at a workplace, reducing time required for manufacturing a product and improving quality of final products
The simultaneous prediction, which relates to amounts of energy consumption and CO2 emissions and effects of human factors for a manufacturing system evaluation, is often overlooked by researchers or system designers partially due to a lack of existing DES tools that enable incorporating these parameters into an established DES model
This paper reports a latest development aimed at addressing the above issues by attempting to create a user-friendly method incorporating some parameters of energy consumption, CO2 emissions and a couple of human factors or attributes into an integrated DES model

Summary

Introduction

The concept of lean production or manufacturing emphasizes the importance of eliminating “any nonvalue-added wastes” in every aspect of manufacturing-related activities thereby increasing manufacturing efficiency and productivity at a workplace, reducing time required for manufacturing a product and improving quality of final products. Published under licence by IOP Publishing Ltd towards a reduction of the seven wastes (identified by the traditional lean methods), which are the waste of overproduction, the waste of waiting for parts to arrive, the waste of conveyance or transport system, the waste in processing or operations, the waste of inventory, the waste of motion and the waste of rework These wastes often do not comprise the environmental wastes in terms of such as energy consumption and CO2 emissions relating to manufacturing activities; and these environmental wastes add no value to manufactured products. These tools do not provide facilities that allow system designers to combine parameters of such as energy consumption and CO2 emissions, and human attributes (or human performance) within an investigation of the overall system performance This is because, for example, in a DES model, the workers are defined and treated as the same as parts, conveyors and processing machines and so on.

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