Scholarly article on topic 'Fuzzy AHP based Plant Sustainability Evaluation Method'

Fuzzy AHP based Plant Sustainability Evaluation Method Academic research paper on "Agriculture, forestry, and fisheries"

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Abstract of research paper on Agriculture, forestry, and fisheries, author of scientific article — H.M.M.M. Jayawickrama, A.K. Kulatunga, S. Mathavan

Abstract Plant Sustainability has gained much prominence in the field of manufacturing in the recent past which has led to develop generic methodologies for plant sustainability evaluation. Wide focus has been given to environmental and economic aspects than the social domain in majority of them. It is rare to find that, the generic methodologies are capable of detail analysis especially when taken into account quantitative and qualitative factors which is commonly available among social domain. This paper presents a generic model to evaluate manufacturing plant sustainability using Fuzzy Analytic Hieratical Process (AHP). Since, social aspects frequently lead to qualitative evaluations of the experts, fuzzy logic has been used to convert the qualitative judgments into evaluable numbers, allowing Fuzzy AHP to perform an all-inclusive analysis. Moreover, the paper proposes a tool that can perform analysis at variable resolutions of available plant data. This variable resolution tool will allow evaluation to be carried out on feasibility studies on existing plants.

Academic research paper on topic "Fuzzy AHP based Plant Sustainability Evaluation Method"

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Procedia Manufacturing 8 (2017) 571 - 578

14th Global Conference on Sustainable Manufacturing, GCSM 3-5 October 2016, Stellenbosch,

South Africa

Fuzzy AHP based Plant Sustainability Evaluation Method

H.M.M.M Jayawickramaa, A.K. Kulatungaa*, S. Mathavanb

aDepartment of Production Engineering, Faculty of Engineering, University of Peradeniya, Sri Lanka bSchool of Architecture, Design and the Built Environment, Nottingham Trent University, Burton Street, Nottingham NG1 4BU, UK

Abstract

Plant Sustainability has gained much prominence in the field of manufacturing in the recent past which has led to develop generic methodologies for plant sustainability evaluation. Wide focus has been given to environmental and economic aspects than the social domain in majority of them. It is rare to find that, the generic methodologies are capable of detail analysis especially when taken into account quantitative and qualitative factors which is commonly available among social domain. This paper presents a generic model to evaluate manufacturing plant sustainability using Fuzzy Analytic Hieratical Process (AHP). Since, social aspects frequently lead to qualitative evaluations of the experts, fuzzy logic has been used to convert the qualitative judgments into evaluable numbers, allowing Fuzzy AHP to perform an all-inclusive analysis. Moreover, the paper proposes a tool that can perform analysis at variable resolutions of available plant data. This variable resolution tool will allow evaluation to be carried out on feasibility studies on existing plants.

© 2017 The Authors. Published by ElsevierB.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.Org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of the organizing committee of the 14th Global Conference on Sustainable Manufacturing Keywords: Manufacturing Plant sustainability, AHP, Fuzzy logic, Triple bottom line, Social impacts, life cycle assesment

1. Introduction

With the rapid industrial development taken place within last century has led to many environmental and social implications around the world. The scale of these implications has gradually increased over the last couple of decades and presently, many developing and developed nations are in vulnerable states with different scales. In order to find a

* Corresponding author. Tel.: +94777611401; fax: +94812393655. E-mail address: aselakk@pdn.ac.lk

2351-9789 © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.Org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of the organizing committee of the 14th Global Conference on Sustainable Manufacturing doi: 10.1016/j.promfg.2017.02.073

solution to unsustainable rapid development and its inherent implications, in 1987 a committee was appointed under the leadership of Brundtland (a politician from Norway later became director general of WHO). The committee came up with a concept of Sustainable Development, which proposed to decouple economic development with excessive consumption of natural resources. The main concept behind sustainable development philosophy is to look at development through triple bottom line (TBL) where Environmental and social aspects were considered equally along with Economical aspects in the development even though previously it focused only on economic benefits.

When development is the main concern, manufacturing sector globally plays a significant role, in the means of which industry that fulfils societal materialistic needs. Thus, it has been exponentially increased over the years and has created considerable environmental and social impacts. A holistic view spanning on product, processes and entire supply chain is requires to achieve sustainability in manufacturing (Jayal et.al [1]). Subsequently many tools and methods have been developed in last three decades which tend to drive towards sustainable development concepts in product, process and supply chain perspective. At the product level design concept has moved beyond the traditional 3R (Reduce, Reuse, Recycle) concept to 6R concept (Reduce, Reuse, Recovery, Redesign, Remanufacture, Recycle) [1]. At the process level, planning and technical development has been focused on minimizing resource consumption, GHG emissions and wastages. For further illustration, supply chain can be classified by the phases of Product life cycle as pre-manufacturing, manufacturing, use and post-use stages. However for many products, manufacturing is considered to be the critical stage among all, as it comprises enormous number of resources such as machinery, human involvements, value addition, etc. Therefore, evaluating manufacturing plant's sustainability is a paramount requirements to promote Sustainable practices. Essentially, the tool that will be used to evaluate the sustainability of a plant should look into TBL with equal importance.

There are wide spectrum of tools and methodologies available in the literature over past couple of years. These include sustainability evaluation tools based on Product [1,2,3,4,5], Process or Plant [1,2,6,7,8,9,10,11,12,13,14] besides life cycle or based on supply chain [15]. Initial studies are focused on proposing evaluating indicators. The Global Reporting Initiative (GRI) [17,18] has published a framework based on TBL in 1999 to assess the sustainability of a company [7]. In 2011 this has further elaborated to a set of 81 indicators which are expressed in various measuring units which need high amount of collected data. Importantly, these indicators can be used to evaluate any type of industry. However, this framework does not contain a way to aggregate the results of the assessment which limits the possibility of cross comparison. UN commission on sustainable development has published about 140 indicators at 2001 [7] to evaluate the governmental progress on sustainable development. These indicators have developed based on TBL and has included another bottom line as an institutional aspects. In 2002, Institute of Chemical Engineers (IChemE) [7,19] also introduced a model equally addressing TBL which consists of 50 indicators relevant to process industry. One of the common gap highlighted from these frameworks is the limited space for the cross comparison among the various industries due to the absence of an aggregating method to assess results. But these indicators can be used as a guideline to develop a tool to assess the plant sustainability.

In addition, many tools have been developed in recent years [6,7]. Major problem identified in those tools and methods is the complexity of the evaluation method due to the variety of indicators having different types of measuring units with different level of importance. T. Lu et.al [2] presents a metrics to evaluate the sustainability of a manufacturing process. Though it is not presented as an analytical method to generate an aggregated value to enable the cross comparison within different type of industries, it has suggested to use Analytic hierarchy Process(AHP) to determine the relative importance of different influencing factors and discuss the possibility to use Fuzzy logic to get measurements of non-deterministic elements. However some studies have been limited only to deterministic elements to avoid any complexities in assessment process [9,10,14]. But most of the recent studies have moved on further as using Fuzzy inference method [11,12] and Fuzzy TOPSIS [15] to analyse both deterministic and non-deterministic elements in a common bed. Especially Singh et.al [11] has developed a generic plant sustainability evaluation model based on 3BL and fuzzy inference system to assess the sustainability of manufacturing SMEs. They have focused on development of common indicators for sustainability assessment from SMEs. Abdul et.al [12] suggests similar type of approach to evaluate product and process sustainability. Other than those Danfang et.al [8] developed an application which consists with three types of questions which answered by a rating scale or a binary scale or a percentage scale. This also has the capability to assess qualitative and quantitative factors. Even this article discuss about getting an

aggregated numerical value for the plant sustainability concerning the level of importance of factors, unable to presents such methodology for the calculations.

Commonly none of above research work has considered plant built environment and plant design aspects into account when developing respective tools though those aspects play a vital role in overall plant sustainability. This has been addressed by Lokuliyana et.al [16] and Jayawickrama et.al [13] to some extended however, those work also have not suggested a proper analytical method as such other than point scale rating method. Further none of the researches mentioned above compared existing plant sustainability aspects with some benchmark or industry good practices etc. Additionally it can be conclude that socials aspect has not been considered in to an adequate depth.

Therefore, in this research, a generic framework to evaluate manufacturing plant sustainability will be developed base on 3BLs where each BL will be considered into detail extension. Rest of the paper has been arranged as follows; Section II presents the methodology followed by the sample calculation done in section III. Conclusion has been given in the section IV.

2. Methodology

As a preamble, five main phases were identified in order to develop the assessment model and for validation. However at this stage the study is not corroborated with a complete case study. But a sample calculation has been produced with this for a selected set of assessment indicators. Moreover, a descriptive account on each phase is given below.

2.1. Goal & scope definition

The ultimate goal of the study is to develop a model to evaluate the sustainability of a manufacturing plant based on TBL. Hence this model develops an index, which derives a cumulative numerical value for plant sustainability by considering both qualitative and quantitative factors related to plant built environment, manufacturing process and management and administrative contribution. However, at this stage the proposed model can be used to evaluate the sustainability of manufacturing plants in same industrial category (Ex: Ceramic, Apparel, Tyre manufacturing etc.).

2.2. Identifying the assessing indicators

In this stage, indicators pertaining to plant sustainability fall into triple bottom lines are determined. Previous studies and methodologies (GRI reporting framework [17,18], IChemE sustainability metrics [19] and T. Lu et.al [2]) have been referred to when selecting those indicators. For the consideration of the built environment guide lines from GREENSL® [20] as well as theory behind process planning, layout planning and facilities design [21] have been considered. Moreover, due to the lack of consideration in social aspects in previous studies, UNEP guide lines for social LCA [22] have been considered in this work when selecting indicators under social aspects. Additional details are gathered from Jayawickrama et.al [23] which represents a social LCA based on UNEP guidelines. Further, the importance of level of indicators vary and will depend on the selected Industry. To overcome this it is propose to use Fuzzy AHP [24,25] solving methodology to calculate weighted value for each indicator.

2.3. Development of decisions hierarchy model for Relative Plant Sustainability (RPS)

In order to analyse the assessment results with Fuzzy AHP methodology, a hierarchical model is developed in this stage. This model consists of four tiers (Fig. 1). Previously identified indicators are assigned to the fourth tier. Initially plant sustainability is divided along the TBL whereas model can calculate separately economic sustainability, environmental sustainability and social sustainability of selected manufacturing plants in the same industrial category (ceramic, tire, cement). Then in the second tier each of the TBL has been divided into three main sections: plant design, plant operations and plant administration and management. Under plant design, the built environment is considered. Plant dynamics is studied under Plant operations. In addition, there are additional indicators which are more related to

administrative work. These indicators, which are not directly a line to the manufacturing process, are: human resource management, CSR, investments, planning, marketing, however they are relevant to the sustainability. The foregoing list of factors is considered under plant administration and management. Under the third tier sub sections are assigned for each section of the 2nd tier. For instance, as shown in the Fig. 1, under the economic bottom-line, plant design section has been divided into six different subsections. These subsections are assigned in a way such that relevant assessment indicators come under the subsection in the fourth tier. As an example under plant design section one sub section is location selection and under it five assessment indicators such as Raw material availability, Transportation network, Infrastructure availability, Tax benefits and Cost for initial land preparation were assigned. Likewise, all the sub sections under each section were detailed out the level where possible measurements or comparisons could be obtained. Table 1 presents the detailed expansions up to each sub sections under triple bottom-lines. The 4th tier which comprises the assessing indicators is not presented due to the limited space. However, the expansion of one subsection is represented under the sample calculation.

Fig. 1: Hieratical Representation of each tiers of Plant Sustainability

2.4. Development of Pairwise Comparison Matrixes (PCMs) for the model

After developing the decisions hierarchy model, Pairwise Comparison Matrices (PCMs) are proposed for all tiers. Then Fuzzy AHP solving methodology can apply to calculate weighted values for each of the TBL components, sections, sub sections and assessment indicators. Calculation method adapted in this analysis is explained bellow. In the pairwise comparison, two components from a matrix are evaluated at a time in terms of their relative importance.

Triangular fuzzy numbers (TFN) are used to do the comparison which are three real numbers(Z,m,u), where I < m<u, for describing a fuzzy event. TFN values are assigned based on Table 2 which was proposed by Buyukozkan et.al [25]. These fuzzy numbers are converted to a crisp value and weight of each component is calculated by employing the de-fuzzification process. Then after the assessment, scores of each indicator are aggregated through following steps.

Wi = Weight of ith component in Tier 1.

Wj = Weight of jth component of Tier 2 under ith component under Tier 1.

Wijk = Weight of kth component of Tier 3 under jh component of Tier 2 under ith component under Tier 1.

Wjki = Weight of Ith component of Tier 4 under kth component of Tier 3 under jh component of Tier 2 under ith component under Tier 1.

Sjki = Assessment score for the Ith component of Tier 4 under kth component of Tier 3 under jh component of Tier 2 under ith component under Tier 1.

{i, j, k, l = 1:n/ n is the number of components of the relevant matrix}

Total aggregated score can be given by,

RPS = IW jSW

i=1 I j=1

XWijk I XWijklSijj

k=1 V l=1

Table 1: Detailed expansion of TBL dimensions for evaluating of a manufacturing plant sustainability

Tier О

Tier l

Tier 2

Tier 3

Manufacturing plant Economic sustainability Plant Design Product design_

sustainability Process design_

Material handling design

Location selection_

Layout preparation_

_Facilities design_

Plant operation Value addition and revenue

Efficiency_

Preventive maintenance Product quality_

Production planning

Plant management and administration Investment Planning

Environmental Plant design Similar subsections to Economic

sustainability Plant operation sustainability Reduce consumption Reuse Recycle Waste Streams

Plant management and administration ISO standardizations Green awards Green promotion

Social sustainability Plant design Similar subsections to Economic sustainability

Plant operation Employee health and safety

Employee satisfaction_

Employee equity_

_Flexibility_

Plant management and administration Employee protection_

Career development_

Employee remunerations Adherence to labour law _Commitment towards society

Table 2: Linguistic and fuzzy scale transformation chart [24,25]

Linguistic scales for importance TFN scale TFN reciprocal scale

Just equal (1,1,1) (1,1,1)

Equal importance (1/2,1,3/2) (2/3,1,2)

Weakly more importance (1,3/2,2) (1/2,2/3,1)

Strongly more importance (3/2,2,5/2) (2/5,1/2,2/3)

Very strong more importance (2,5/2,3) (1/3,2/5,1/2)

Absolutely more importance (5/2,3,7/2) (2/7,1/3,2/5)

3. Sample calculation

This sample calculation has been explained for the path shown in Fig. 1. This has done for four selected manufacturing plants (Plant A, Plant B, Plant C, Plant D) of ceramic industry. Initially TFN values were assigned for PCM of each tier considering the importance of each element for ceramic industry. Assigned TFN values and calculated weight values of the TBL dimensions are presented in Table 3. PCMs were solved according to the method proposed by Buyukozkan et.al [25]. Calculated weight values for TBL, Sections, Subsections and Indicators of the path shown in Fig. 1, are presented in the Table 4.

Table 3: PCM for TBL dimensions: Tier 3

i = TBL Economical Environmental Social Weight (W)

1 Economical (1, 1, 1) (1, 3/2, 2) (1, 3/2, 2) 0.417

2 Environmental 1 (1/2,2/3, 1) (1, 1, 1) (1, 3/2,2) 0.327

3 Social (1/2,2/3, 1) (1/2,2/3, 1) (1, 1, 1) 0.256

Table 4: Calculated weight values of TBL dimensions, Sections, Subsections and Indicators

Tier 1 Tier 2

i= TBL Weight (W) j = Economic (i=1) Weight (Wij)

1 Economical 0.417 1 Design 0.343

2 Environmental 0.327 2 Operation 0.474

3 Social 0.256 3 Management & Administration 0.183

Tier 3 Tier 4

k = Design (j=l) Weight (Wm) l = Location Selection (k=4) Weight (Wii4i)

1 Product Design 0.128 1 Raw material availability 0.314

2 Process Design 0.272 2 Transport network 0.255

3 MHS Design 0.137 3 Infrastructure availability 0.186

4 Location Selection 0.201 4 Tax benefits 0.130

5 Layout Design 0.164 5 Cost for land preparation 0.115

6 Facility Design 0.097

Then the assessment was done for each indicator of the sub section named "Location selection" (Fig. 1) for the selected plants by applying FAHP. PCM for the Transport network indicator is shown in Table 5. Assessment scores for each indicator relevant to location selection are given in the Table 6.

Table 5: PCM for transport network of assessing plants

Transport network (l=2) Plant A PlantB Plant C Plant D Score (S114l)

Plant A (1, 1, 1) (1, 3/2, 2) (3/2, 2, 5/2) (3/2, 2, 5/2) 0.364

Plant B (1/2, 2/3, 1) (1, 1, 1) (1, 3/2, 2) (3/2, 2, 5/2) 0.281

Plant C (2/5, 1/2, 2/3) (1/2, 2/3, 1) (1, 1, 1) (1, 3/2, 2) 0.200

Plant D (2/5, 1/2, 2/3) (2/5, 1/2, 2/3) (1/2, 2/3, 1) (1, 1, 1) 0.155

Table 6: Assessment scores for indicators under Location selection

l = Indicator Plant A Plant B Plant C Plant D

1 Raw material availability 0.324 0.236 0.161 0.273

2 Transport network 0.364 0.281 0.200 0.155

3 Infrastructure availability 0.382 0.278 0.177 0.163

4 Tax benefits 0.237 0.342 0.264 0.157

5 Cost for land preparation 0.258 0.187 0.303 0.252

Then the scores multiplied by the weighted values assigned for indictors to get weighted scores. Table 7 presents the weighted scores of assessing plants related to the location selection. Relatively Plant A has the highest score for location selection. Accordingly, the assessment process can be continued for all indicators and results can be aggregated based on equation 1 to get a final relative value for the sustainability for assessing plants. For instance, when, i=1 (economic), j=1 (design), k=4 (location selection), final weighted score for location selection for Plant A = W1 x W11 x W114 x X W1141S1141 = 0.417 x 0.343 x 0.201x 0.327 = 0.0094. Summation of the weighted scores for each subsection will deliver a RPS value between 0 and 1 for each plant where the highest score is the most sustainable.

Table 7: Weighted scores for each plant under location selection indicators

l = Indicator Plant A Plant B Plant C Plant D

1 Raw material availability 0.102 0.074 0.051 0.086

2 Transport network 0.093 0.072 0.051 0.040

3 Infrastructure availability 0.071 0.052 0.033 0.030

4 Tax benefits 0.031 0.044 0.034 0.020

5 Cost for land preparation 0.030 0.022 0.035 0.029

X W1141S1141 0.327 0.264 0.204 0.205

4. Conclusion

Since different types indicators are involved in evaluating the sustainability of a manufacturing plant, a complex analysis must be carried out for a comprehensive study. Further, it becomes more intricate with the inclusion of deterministic and non-deterministic division among factors. Moreover, the level of importance among factors depends on the manufacturing industry. In this work, a novel model is proposed based on AHP and fuzzy logic to address the above mention issues as suggested by T. Lu [2]. Concurrently, there are number of indicators relevant to the built environment of the plant which has been rarely addressed before. Furthermore, this model has taken into account number of social aspects which affect more in labour incentive manufacturing plants such as apparel manufacturing. In order to apply this tool, the user must have a fair knowledge on TBLs and sustainable manufacturing. However, in future, detail guideline will be prepared for each indicator at 4th tier which will improve the fairness of evaluation.

Presently, it is difficult to find benchmarks for given industry (Ex: Ceramic, Apparel, tyre manufacturing etc.), in future this gap will be filled with the assistance of existing local (Sustainable Energy Authority of Sri Lanka, factory ordinance of Sri Lanka etc.) and global standards for key indicators identified in the 4th tier. In addition to use this tool to compare two manufacturing plants of same category (ceramic, tyre, apparel etc.) it will be possible to generate comprehensive plant sustainability index for individual plants which is currently rare to find. Further, this tool will be developed as a web based application where any plant can evaluate their current status and improve the relevant underperforming areas.

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