Appraisements of material handling system in context of fiscal and environment extent. A comparative grey statistical analysis

• Pages: 2-28
• Published date: 13 February 2017
• DOI: https://doi.org/10.1108/IJLM-09-2015-0163
• Date: 13 February 2017
• Author: Anoop Kumar Sahu,Atul Kumar Sahu,Nitin Kumar Sahu
• Subject Matter: Management science & operations,Logistics

Appraisements of material

handling system in context of

fiscal and environment extent

A comparative grey statistical analysis

Anoop Kumar Sahu

Department of Mechanical Engineering, J.K.I.E., Bilaspur, India, and

Atul Kumar Sahu and Nitin Kumar Sahu

Department of Industrial and Production Engineering,

Guru Ghasidas Central University, Bilaspur, India

Abstract

Purpose –In present research, the authors conducted the massive literature review and collected the

information, in regards to material handling system (MHS) to build a multi criteria MHS hierarchical module

consists of ecological cum fiscal criteria. Moreover, similar literature review assisted the authors to resolve

and eventually construct the effectual and robust approach. The purpose of this paper is to facilitate the

managers for benchmarking the MHS alternatives operating under similar module via robust decision

support system (DSS).

Design/methodology/approach –In present research, the proposed module dealt with ecological

(subjective) and fiscal (objective) criteria, where subjective criteria associated with incompleteness, vagueness,

imprecision, as well as inconsistency, solicited the discrete information in terms of Grey set via linguistic scale

from experts panel. The objective information (capital) has been assigned by expert’s panel in terms of

Grey set. To robustly evaluate and select the admirable MHS, three approaches named: degree of possibility,

technique for order preference similar to ideal solution as well as Grey relational analysis fruitfully applied to

connect and unite discrete information.

Findings –The performance evaluation of MHSs has been carried out under concert of individual

fiscal criteria excluding ecological criteria in past researches. Moreover the previous developed DSS tackled

sole approach under individual fiscal criteria. The authors found the broad applications of fuzzy sets except

Grey set theory in the same context for measuring the performance of MHS alternatives. Aforesaid research

gaps have been transformed into research objectives by incorporating the module for both fiscal cum

ecological criteria. This research embraces a robust DSS, which has been explored to select the admirable

MHS alternative.

Originality/value –An empirical case study has been carried out in order to demonstrate the legitimacy of

holistic Grey-MCDM method, implemented over multi criteria MHS hierarchical module. Proposed DSS seems

to be the best for organisations, which believe to appraise and select the MHS including fiscal as well as

ecological criteria excluding individual fiscal criteria. Moreover, subjective cum objective or individual

subjective or objective criteria can be extended with respect to varieties of MHSs.

Keywords Ecological criteria, Grey set theory, Material handling system (MHS),

Multi criteria decision making (MCDM)

Paper type Research paper

1. Introduction

Various manufacturing firms have stimulated towards adopting the ecological friendly

asset/apparatus/equipments due to more rationales, i.e. explicit instructions of government

towards firm to manage the greenness in the context of supply chain management,

World Health Organization policies, Environmental Care Organization principle guidelines,

green community, etc. (Kutz, 2009; Kay, 2012; Sahu et al., 2012, 2013a, 2014a, b; Furmans,

2015). In last decade, the quantity of firms have augmented, which re-pressurised the

firm in order to preserve the ecological performance for eliminating the virus, offensive

ills and unwanted diseases, etc. (Styles et al., 2009; Sahu et al., 2012, 2013a, 2014a, b).

The International Journal of

Logistics Management

Vol. 28 No. 1, 2017

pp. 2-28

© Emerald PublishingLimited

0957-4093

DOI 10.1108/IJLM-09-2015-0163

Received 19 September 2015

Revised 25 December 2015

14 April 2016

30 July 2016

Accepted 4 August 2016

The current issue and full text archive of this journal is available on Emerald Insight at:

www.emeraldinsight.com/0957-4093.htm

2

IJLM

28,1

In continuation to this, it has also been investigated that equipment performance is

accountable to hike or decline the ecological performance (Ballantine, 2007; Kutz, 2009;

Deshmukh and Sunnapwar, 2013).

More willingly than aforesaid points, in manufacturing firms, material handling systems

(MHSs) are explored in purpose to carry out the bulk/materials from point of origin towards

its destination point. In today’s era, ecological researchers concluded that equipments are

liable for producing about 25-40 per cent of total air pollution. Furthermore the gases, which

are produced by equipments can neither be eliminate nor control absolutely but can be

diminished via measuring the performance of equipments in the context of ecological cum

fiscal criteria by an exploration of decision-making hierarchical structure/platform/module

(Sahu et al., 2014a, e, 2015a, b,c). In present research, the authors proposed a novel decision

support systems (DSSs), which aid the firm to benchmark the valuable MHSs operating

under ecological cum fiscal criteria in purpose to diminish the aforesaid issues. The few

definitions in regards to MHSs are elaborated below.

It is defined as a machine, which covers up short-distance within the plant, warehouse,

transportation agency, etc. for transferring the stuff from one location to another location.

It can be exercised to create “time and place utility”through the handling, storage, and

control of material, as distinct from manufacturing, i.e., fabrication and assembly

operations, which creates “form utility”by changing the shape, form, and makeup of

material. It is defined as the movement of materials (raw materials, scrap, semi-finished and

finished products) to, through, and from productive processes; in warehouses and storage;

and in receiving and shipping areas (Ataeepour and Baafi, 1999; Castillo and Peters, 2002;

Furmans, 2015). It is considered as an integral part of the supply chain of any business

associated with production, consumption, transformation of one or more goods (Atmani and

Dutta, 1996; Ataeepour and Baafi, 1999).

In last decade, many researchers found three issues in regards to MHS. The first issue is

to adapt the module/index, which could tackle the ecological and fiscal measures excluding

individual fiscal measures (Castillo and Peters, 2002) is indeed necessary. Second issue is to

assess the performance of MHSs in the context of multi criteria decision making (MCDM)

under discrete data “including incomplete information against criteria”(Chen, 1985; Chen

et al., 2003; Chen and Chen, 2006, 2009; Long and Jun, 2007; Chien, 2014; Dey and Cheffi,

2013; Sahu et al., 2012, 2013a, b, 2014a, b, c, d, e, 2015a, b, c). Third issue is to develop

sophisticated module accompanied with miscellaneous methods of MCDM, which possesses

the competency to practically handle the incomplete information for selecting the admirable

MHS (Ataeepour and Baafi, 1999; Castillo and Peters, 2002; Chakraborty and Banik, 2006;

Onut et al., 2009; Amer and Abdulaziz, 2011; Mesa-Frias et al., 2013). Consequently, the

authors incorporated entire aforesaid issues and eventually viewed these issues as

momentous research gaps and transformed these research gaps into research objectives, i.e.

evaluation and selection of the admirable MHS amongst preferred alternatives under

incomplete information adhering ecological cum fiscal criteria. In present research, the

evaluation multi criteria MHS appraisement module (associated with principal ecological

as well as fiscal criteria) has been constructed. In this context, unclear, incompleteness,

vagueness, imprecision, as well as inconsistency associated with linguistic cum

reasonable evaluation information, has been tackled with exploration of Grey set theory.

Finally, an empirical case research has been conducted to assess the legitimacy of

proposed DSS. The proposed DSS induced criteria/measures into module, which can be

extended with respect to set of MHSs, so that manager could elect the MHS and control

environmental exploitation and others issues resulted by pollution (Amirkhanian and

Baker, 1992; Atmani and Dutta, 1996; Ataeepour and Baafi, 1999; Castillo and Peters,

2002; Chakraborty and Banik, 2006; Onut et al., 2009; Amer and Abdulaziz, 2011; Sahu

et al., 2012, 2013a, b; Furmans, 2015).

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Grey statistical

analysis