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Enablers and Barriers of Sustainable Manufacturing: Results from a Survey of Researchers and Industry Professionals
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ScienceDirect Procedia CIRP 29 (2015) 562 – 567
The 22nd CIRP conference on Life Cycle Engineering
Enablers and Barriers of Sustainable Manufacturing: Results from a Survey of Researchers and Industry Professionals Neeraj Bhanota*, P. Venkateswara Raoa, S.G. Deshmukha a
Department of Mechanical Engineering, Indian Institute of Technology Delhi, New Delhi, 110116, India
* Corresponding author. Tel.: +91-9873279084; E-mail address: [email protected]
Abstract Sustainable Manufacturing (SM) has gained significant importance in today’s competitive environment as many organizations still depend on natural resources and at the same time generate wastes and environmental pollution. However, the adoption of SM is a huge challenge for organizations since most of them are not aware on how to utilize the enablers and mitigate the effect of barriers of SM. This paper tends to present the opinions of various researchers around the globe and industry professionals on the important enablers and barriers and analyze them using statistical techniques to highlight the differences in opinions for strategic implementation of SM. © 2015 2015 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V.This is an open access article under the CC BY-NC-ND license © Peer-review under responsibility of the International Scientific Committee of the Conference “22nd CIRP conference on Life Cycle (http://creativecommons.org/licenses/by-nc-nd/4.0/). Engineering.under responsibility of the scientific committee of The 22nd CIRP conference on Life Cycle Engineering Peer-review Keywords:Sustainable Manufacturing; Enablers and Barriers; Comparison; Researchers and Industry Professionals.
1. Introduction Sustainability is all about the ability to sustain. However, the importance of economic, environmental and social dimensions varies from time to time and according to different criteria. Various activities such as “product design, manufacturing by-products, by-products produced during product use, etc.” have also been included in the supply chain core activities [1]. The success of an industry depends on its manufacturing performance where competitive environment is followed by superior performance. Hence, organizations need to evaluate their performance at regular intervals to ensure high-level of performance in order to stay competitive in global competition [2]. U.S. Department of Commerce [3] defined sustainable manufacturing (SM) as “creation of manufactured products that use processes that are nonpolluting, conserve energy and natural resources, and are economically sound and safe for employees, communities and consumers” which clearly implies fostering of domestic and international conditions for doing business in addition to fulfilling basic dimensions of sustainability. For a developing nation like India, manufacturing industry plays an indispensable part in its economy. It is evident from the fact
that the percentage share of manufacturing in Indian GDP varies from 13-16% [4]. However, it is important to note that, this share has been continually decreasing since January, 2007. India is ranked amongst the largest economies in the world and it is expected to continue its growth rapidly over the next two decades. But this growth is subject to various challenges accompanying it. With growing economy, India is bound to experience dramatic increase in demand for materials and energy, hence putting serious constraints on natural resources such as land, water, minerals, and fossil fuels, and driving up energy and commodity prices. Moreover, increasing activity will lead to increase in level of waste and pollution, particularly in the form of higher GHG emissions, which can ultimately restrict the India’s ability to grow, rendering its momentum unsustainable. Due to the ongoing trend, it has thus become a need to develop and pursue manufacturing activities, which helps in maximizing economic and social benefits along with minimizing environmental impact [5]. In today’s globalised scenario, the collaboration of academia and industry professionals is imperative when it comes to identifying the solutions for the sustainability issues. There have been similar studies on investigating the driving factors and barriers of implementing SM initiatives in
2212-8271 © 2015 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 scientific committee of The 22nd CIRP conference on Life Cycle Engineering doi:10.1016/j.procir.2015.01.036
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Malaysian automotive companies [2]; investigation of current SM situation of manufacturing sector among Sri Lankan [6] and Spanish companies [7], and an assessment of Indian and German industry for sustainability initiatives [8]. However, no paper has studied the existing practical differences of opinions and perceptions between academia and industry professionals. In this paper, the enablers and barriers to SM in the manufacturing sector industry are analyzed using statistical analysis to assess the differences between the opinions of various researchers around the globe and industry professionals focusing small, medium and large scale industries of Ludhiana (a city in the Indian State of Punjab, which is also known as "Manchester of the East" as it has an established manufacturing base for engineering products). The statistical analysis is, however, carried out by applying ‘independent t-test’ statistical method to compare the significance of differences. The statistical analysis will provide the significance of the difference between enablers and barriers of researchers and industry professionals group. In addition to this, the magnitude of the difference in these factors is also assessed, if any difference exists. Hence, this study will help the industry professionals; government and other policy makers to understand the challenges faced in implementing SM issues in manufacturing organizations. 2. Research Methodology Table 1 and Table 2 present a list of 10 enablers and 10 barriers identified as an outcome of review of research articles relating to topics like implementing sustainable manufacturing concepts [6], sustainable development issues [7], sustainable manufacturing initiatives [2], green manufacturing concepts [8], Indian strategy for manufacturing [9], which are almost synonyms of SM, and after discussing with experts and academicians working in the field of SM. Table 1. Description of Enablers for Sustainable Manufacturing. No.
Enablers
Description
E1
Pressure from market
Trade and Commercial Practices, Competitors, Customer Satisfaction.
E2
Government promotions and regulations
Law Enforcement and Judicial Regulations, Private-Public Participation and Accountability.
E3
Economic Benefits
Recurring & Long-Term Financial Yields.
E4
Investment in Innovation & Technology
Advance Technological Initiatives for Performance Enhancement.
E5
Lowering Manufacturing Cost
Efficient Process Management with Minimum Waste outputs.
E6
Improving Quality
Innovative Process, Product Quality, Enhanced Production.
E7
Education and Training System
Inducting periodical deployment of workers training and upgraded technological education.
E8
Attracting Foreign Direct Investment
Liberalization of Universal Economic Ties.
E9
Infrastructure facilities in Transportation sector
Infrastructure development for viable Air, Rail and Road Connectivity.
E10
Development in E-Economy
Deployment of E-Technology in Manufacturing Sector.
Table 2. Description of Barriers for Sustainable Manufacturing. No.
Barriers
Description
B1
Lack of awareness of sustainability concepts
No or limited access to sustainability literature.
B2
Lack of awareness programs conducted locally
No awareness of sustainability trends.
B3
Lack of awareness of local customers in green products
Not enough publicity about green products.
B4
Negative attitudes towards sustainability concepts
Insignificant knowledge of sustainability concepts.
B5
Lack of funds for green projects
Neglected approach for judicious funds distribution.
B6
Lack of standardized metrics or performance benchmarks
Absence of practicable guidelines and parameters.
B7
Lack of support from senior leaders
Total neglect by concerned top brass.
B8
Cost too high
Initial high costs for sustainable technology implementation.
B9
Power Shortage
Need for improving present power production and distribution.
B10
Low Availability of Credit
Need based allocation of funds at low interest rates by banking and financial institutes.
Researchers from different countries have been approached for the survey keeping in mind that the perceptions on sustainability issues tend to be the same since theories concerning sustainable manufacturing are fundamentally the same regardless of any regions. On the other hand, in case of the industry professionals, the busy schedule of industry professionals everywhere did not help us to obtain online responses. Hence, we decided to zero in on Ludhiana city (Punjab, India) due to its geographic importance in manufacturing and since it was also located at a distance of approximately 300 km from the authors’ base (IIT Delhi). The survey was also constrained to one city because the sociopolitical and economic factors vary with regions. Moreover, Ludhiana is a hub of manufacturing firms in North India; there are a lot of small-scale manufacturing firms, which have sustainability issues, providing valuable insights potentially for the study. A questionnaire was developed to obtain opinions of researchers working in the field of SM and industry professionals (from Ludhiana city). The participants were asked to rate the importance of enablers and barriers on a scale of 1 to 5, where 1 means very low influence, 2 means low influence, 3 stands for medium influence, 4 means high influence and 5 means very high influence. The required participation time ranges from 10 to 15 minutes. Before the release of the questionnaire, academic and industrial experts with knowledge in the field of SM reviewed it for clarity and understandability. As a pilot study, the questionnaire was
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filled by 3 researchers and 4 industrial experts and was revised in terms of language to increase understandability. An online survey was then started using the www.qualtrics.com website. Initially, researchers working in the field of SM were searched using Scopus database and were sent the survey requests at the mentioned email id’s and were also requested to provide references whom they also consider suitable for the survey. These researchers were spread across all over the world ranging from U.S.A, India, Germany, Australia, Italy, etc. Altogether 314 researchers have been contacted using Scopus Database and referrals and 110 researchers provided the responses making response rate to be 35%. However, 4 responses were found to be invalid and were discarded. Hence, 106 useful and reasonable responses have been received from researchers all around the world. In case of industry survey, email requests have been sent to the members (according to the id’s available in online database) of three main organizations/associations of Ludhiana (a town in North India): Federation of Auto Parts Manufacturers, Micro, Small & Medium Enterprises Development Institute and Chamber of Industrial and Commercial Undertakings. Altogether, 229 online survey requests had been sent, out of which 49 responses were obtained online turning the response rate to be 21%. However, 3 responses were found to be invalid and were discarded. Hence, 46 useful and reasonable responses have been received from industry professionals in online mode. Compared with number of responses obtained from researchers, efforts were further made to get close to response rate of researchers. Hence, personal visits were made to some of the reputed industries and their referrals, and senior managers were explained the survey and responses were collected through interview mode. In this way, 53 valid responses were obtained in offline mode giving a total of 99 useful and reasonable responses from industry professionals. The total time duration for the survey has been 2 months approximately being August and September, 2014. Further, reminders were sent to the respondents after approximately 710 days from date of sending the survey request and in total 3 reminders were sent to the respondents.
3. Results and Discussions Initially, Cronbach’s alpha value is used to assess the reliability of the data collected through the questionnaire, which tells the suitability of data for further analysis and valuable interpretation. Then, the importance of enablers and barriers are calculated through their mean values. Very low mean values of any enabler/barrier suggest that the particular enabler/barrier is not important and should be eliminated from the study. Later on, central tendency of the data is measured by calculating the standard deviation values since the mean value is not always sufficient. Lastly, significance of the difference is assessed by 'independent t-test'. The measures of central tendency and results of the tests conducted on the obtained data are presented as follows: 3.3. Descriptive Statistics Table 3 presents the group statistics of enablers and barriers for sustainable manufacturing on the basis of responses provided by both groups. The mean value of all the factors is considered to check their importance between both groups. The internal consistency analysis is carried out using the software SPSS 21.0 for MacBook Pro, to measure the reliability of each factor in terms of the Cronbach’s alpha. An alpha value of 0.7 is often considered the criteria for establishing internal consistency on a scale of 0 to 1, where '0' means that the data is not reliable and '1' means that the data is fully reliable. However, a value of 0.6 is also considered sufficient for newly collected data. If necessary (alpha < 0.6), items are eliminated in order to achieve an increase of the Cronbach’s alpha value [8]. In this study, during the initial analysis, none of the factors was eliminated to improve the reliability. The Cronbach’s alpha value of 0.959 for the enablers and 0.867 for the barriers is achieved on the combined data of both groups being researchers and industry professionals, which is considered good, and hence it can be concluded that the data is highly reliable.
Table 3. Group Statistics of Enablers and Barriers for Sustainable Manufacturing. Job Profile: 1 – Researchers; 2 – Ind. Professionals 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2
Enablers E1 E2 E3 E4 E5 E6 E7 E8 E9 E10
Mean (On a Scale of 1-5)
Std. Deviation
3.58 3.52 3.81 2.54 3.92 3.23 3.75 3.52 3.76 3.82 3.82 4.10 3.31 3.63 3.08 2.70 3.12 3.12 3.16 3.04
1.04 1.00 0.96 1.19 1.05 1.07 0.97 1.03 1.11 0.81 0.94 0.83 0.98 1.02 1.18 1.08 1.06 1.14 1.11 1.11
Barriers B1 B2 B3 B4 B5 B6 B7 B8 B9 B10
Mean (On a Scale of 1-5)
Std. Deviation
3.92 3.88 3.64 3.70 3.75 3.46 3.24 3.48 3.74 3.52 3.81 3.51 3.72 3.53 3.97 3.84 3.40 3.58 3.41 3.54
0.98 1.07 0.89 1.04 0.98 0.90 1.24 1.15 1.08 1.19 0.87 0.92 0.96 1.21 0.93 0.93 1.13 1.00 0.97 1.03
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The 2nd, 3rd and 4th Columns of Table 3 present the group statistics of enablers wherein, the mean value of all enablers is more than 2.54 on a scale of 1-5, which highlights that all the enablers are important in both groups. The examination of the mean values suggests that the 'economic benefits' is the most important in researchers group (mean of 3.92) and 'improving quality' in industry professionals group (mean of 4.10). 'Attracting foreign direct investment' is least important in researchers group (mean of 3.08) and 'government promotions and regulations' in industry professionals group (mean of 2.54). In addition to this, the standard deviation measure is used to measure confidence in statistical conclusions. The standard deviation of data from both groups varies from a minimum value of 0.81 for 'lowering manufacturing cost' to maximum value of 1.19 for 'government promotions and regulations’ from industry professionals group only. The last three Columns of Table 3 present the group statistics of barriers wherein, the mean value of all barriers is more than 3.24 on a scale of 1-5, which highlights that all the barriers are considerable and really important in both groups. The examination of the mean values of all the barriers suggests that the 'cost too high' is the most important in researchers group (mean of 3.97) and 'lack of awareness of sustainability concepts' in industry professionals group (mean of 3.88) whereas 'negative attitudes towards sustainability concepts' is least important in researchers group (mean of 3.24) and 'lack of awareness of local customers in green products' is least important in industry professionals group (mean of 3.46). In addition to this, the standard deviation measure is used to measure confidence in statistical conclusions. The standard deviation of data from both groups varies from a minimum value of 0.87 for 'lack of standardized metrics or performance benchmarks' to maximum value of 1.24 for 'negative attitudes towards sustainability concepts’ from researchers group only.
3.2. Independent t-test for Means Comparison Two entirely different and independent samples of respondents from researchers and industry professionals group are considered to conduct an independent t-test (twotailed) to assess the impact of enablers and barriers in both groups. The following hypotheses are set for the independent t-test: The Null Hypothesis (H0) assumed is H0: μResearchers= μIndustry Professionals and the alternate hypothesis (H1) is H1: μResearchers ≠ μIndustry Professionals A value of 0.05 is used for alpha and the actual 't' value is calculated as follows:
(1)
where: = Sample Mean = Pooled Variance n = Sample Size Conducting Levene’s test for Equality of Variances in case of enablers, it can be observed that the p-value of enablers 1, 3, 4, 6, 7, 8, 9 and 10 is more than 0.05 and hence, it can be concluded that variances are equal and 'EVA' row has to be selected whereas the p-value for enablers 2 and 5 is less than 0.05 and hence, it can be concluded that variances are not equal and 'EVNA' row has to be selected. Table 4 assesses the significance in the difference of impact for enablers in between both groups using t-test for equality of means. Similarly, in case of barriers, the p-value of barriers 1, 2, 3, 4, 5, 6, 8, 9 and 10 is more than 0.05 and hence, 'EVA' row has to be selected whereas p-value of barrier 7 is less than 0.05 and hence, 'EVNA' row has to be selected. Table 5 again assesses the significance in the difference of impact for barriers in both groups.
Table 4. Comparing Enablers for Researchers and Industry Professionals by Independent t-test. Enablers
Levene's Test for Equality of Variances F
EVA .107 EVNA EVA 8.221 E2 EVNA EVA .344 E3 EVNA EVA .655 E4 EVNA EVA 13.853 E5 EVNA EVA 3.776 E6 EVNA EVA .000 E7 EVNA EVA .123 E8 EVNA EVA .597 E9 EVNA EVA .123 E10 EVNA *S.D. = Significantly Different E1
t-test for Equality of Means
Sig.
t
Sig. (2-tailed)
Mean Difference
Std. Error Difference
.744
.422 .422 8.488 8.426 4.611 4.608 1.708 1.705 -.396 -.400 -2.254 -2.265 -2.259 -2.257 2.391 2.398 .009 .009 .773 .774
.674 .673 .000 .000 .000 .000 .089 .090 .693 .690 .025 .025 .025 .025 .018 .017 .993 .993 .440 .440
.06032 .06032 1.27597 1.27597 .68277 .68277 .23957 .23957 -.05403 -.05403 -.28026 -.28026 -.31494 -.31494 .37850 .37850 .00143 .00143 .11997 .11997
.14304 .14286 .15033 .15144 .14809 .14816 .14023 .14052 .13657 .13517 .12431 .12375 .13939 .13956 .15827 .15782 .15322 .15360 .15514 .15510
.005 .558 .419 .000 .053 .984 .726 .441 .727
Cohen’s d (To assess Effect Size)
Remarks
0.0592
-------
0.1228 (0.0613) 0.6472 (0.3079)
S.D. (Small) S.D. (Medium)
0.2397
-------
-0.0576
-------
-0.3164 (0.1562) -0.3171 (0.1565) 0.3356 (0.1655)
S.D. (Small) S.D. (Small) S.D. (Small)
0.0013
-------
0.1085
-------
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Table 5. Comparing Barriers for Researchers and Industry Professionals by Independent t-test. Barriers
Levene's Test for Equality of Variances F
EVA .787 EVNA EVA 1.651 B2 EVNA EVA .086 B3 EVNA EVA .799 B4 EVNA EVA 1.446 B5 EVNA EVA 1.396 B6 EVNA EVA 10.063 B7 EVNA EVA .071 B8 EVNA EVA 1.286 B9 EVNA EVA .968 B10 EVNA *S.D. = Significantly Different B1
t-test for Equality of Means
Sig.
t
Sig. (2-tailed)
Mean Difference
Std. Error Difference
.376
.319 .318 -.411 -.409 2.140 2.146 -1.486 -1.489 1.392 1.387 2.446 2.441 1.261 1.252 1.023 1.023 -1.203 -1.208 -.925 -.923
.750 .751 .682 .683 .034 .033 .139 .138 .166 .167 .015 .015 .209 .212 .307 .307 .230 .229 .356 .357
.04574 .04574 -.05546 -.05546 .28064 .28064 -.24900 -.24900 .22070 .22070 .30627 .30627 .19173 .19173 .13331 .13331 -.17953 -.17953 -.12969 -.12969
.14348 .14391 .13496 .13572 .13115 .13077 .16758 .16718 .15858 .15911 .12523 .12545 .15203 .15319 .13026 .13028 .14927 .14867 .14017 .14046
.200 .770 .373 .231 .239 .002 .790 .258 .326
3.3. Effect Size to assess Mean Differences Cohen’s d is considered one of the most important attributes to measure the 'effect size' [10], which assesses the magnitude of mean differences. It highlights the differences in the 'means' of two samples in three categories being; small, medium and large. Cohen’s d value is calculated only in case null hypothesis has been rejected while conducting statistical test. However, it holds very little or no-relevance in case null hypothesis has not been rejected.
(2)
(3)
If the values of 'effect size' are until 0.2 then it is considered small effect, values until 0.5 means medium effect and values until 0.8 denotes large effect [10]. Tables 4 and 5 also highlights the values of Cohen’s d on the basis of which Effect Size are calculated for each of the enablers and barriers after deciding the row to be considered based on Levene’s test for equality of variances [8].
Cohen’s d (To assess Effect Size)
Remarks
0.0448
-------
-0.0577
-------
0.3004 (0.1485)
S.D. (Small)
-0.2086
-------
0.1954
-------
0.3434 (0.1692)
S.D. (Small)
0.1829
-------
0.1436
-------
-0.1689
-------
-0.1298
-------
These findings highlight the statistical significance being different or equal for each enabler and barrier in both groups along with the amount of difference, e.g. impact of enabler 2 (government promotions and regulations) is different amongst researchers and industry professionals by small means. Similar interpretation holds for other enablers and barriers too. 4. Discussion and Insights Gained 4.1 Enablers & Barriers with No Significant Difference This is interesting to know that for 5 enablers and 8 barriers; there is common consensus between both groups and hence no significant difference has been found in them. It has also been observed that 'lowering manufacturing cost’; 'investment in innovation & technology' and 'pressure from market' emerge as top 3 priority enablers along with 'lack of awareness of sustainability concepts' and 'cost too high' as top 2 barriers in both groups according to their mean values. Table 6 presents the enablers and barriers with no significant difference between both the groups.
Table 6. Discussion on Enablers and Barriers with No Significant Difference. Name 1. Lowering Manufacturing Cost
2. Investment in innovation & Technology 3. Pressure from Market
Enablers Remarks Most challenging task as it involves managing the processes efficiently with minimum wastes generation.
Necessary to enhance the performance of processes by implementing advanced technological initiatives. Enables industries to satisfy the customer requirements considering competitive scenario.
Name 1.Lack of awareness of sustainability concepts
2. Cost too high
Barriers Remarks Mitigation Measures No or limited access to • Suitable training workshops can be conducted for sustainable literature restricts practitioners from time to time wherein problems the awareness of various faced by industry can be discussed and guidance concepts and techniques by can be provided for implementing sustainability. which practitioners can • Government can issue guidelines as part of implement sustainability. industrial training. High initial costs of • Incentives by government in the form of tax rebate, implementing the sustainable financial and technical assistance to industries in technology constrain the implementing sustainable technologies etc. industry practitioners from • Industries should also make efforts such as reducing investing in it and waiting for cost of overwastes produced, judicious utilization of longer duration to gain resources such as energy, water etc. economic benefits make it • Researchers can devise cost effective sustainable further tough for them. technologies for industries.
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4.2 Enablers & Barriers with Significant Difference In case of enablers, it can be observed that almost half the enablers statistically have significant difference between both groups. The impact of 'government promotions and regulations', 'economic benefits', 'attracting foreign direct investment' is higher in researchers group as compared to industry professionals whereas the impact of 'improving
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quality' and 'education and training system' has been observed more in case of industry professionals. Similarly, in case of barriers the impact of 'lack of awareness of local customers in green products' and 'lack of standardized metrics or performance benchmarks' is higher in researchers group as compared to industry professionals. Table 7 presents the enablers and barriers with significant difference between both the groups.
Table 7. Discussion on Enablers and Barriers with Significant Difference Name 1. Government promotions and regulations 2. Economic benefits 3. Attracting foreign direct investment Enablers 4. Improving Quality 5. Education and Training system
Barriers
1. Lack of awareness of local customers in green products 2. Lack of standardized metrics or benchmarks
Researchers Consider important for effective implementation of policies and rules. Consensus that implementing sustainable technology in manufacturing helps gain 'economic benefits'. Believe that it will help the industry in getting access to funds, which could be used for expansion, updating technology, improving processes and infrastructure. Achieving high quality with lower costs is feasible provided industry is open to avenues such as 'foreign direct investment', 'government promotions' etc. May not have considered the current scenario of quality and availability in education and hence ranked it as little less important enabler. Consensus that if customers will not be aware of green products, then there will be no point for industries to pursue the same goal. Developed a lot of sustainability frameworks to evaluate the sustainability of organizations.
5. Concluding Remarks In this paper, the responses on important enablers and barriers have been collected through online questionnaire survey and presented after validating the data by statistical analysis for strategic implementation of SM. i) Based on the survey there seems to be huge difference in line of operation of researchers and industry professionals with regard to SM. However, both groups need to collaborate in order to work together to strengthen the enablers and mitigate barriers. ii) Although it is not desirable to pay equal attention to all the enablers and barriers at the same time, it will be useful to identify causal relationships in each of the enablers and barriers respectively as it will help in identifying and focusing on critical enablers and barriers. iii) There is a need to come up with suitable models in the form of case studies implementing sustainability as to how the problems emerged in due course of time and how they were suitably tackled. 6. Limitations of Survey i) The survey responses are limited to the extent to which the respondents themselves are subjected to their own perceptions and beliefs based on their observations, intuition and experience. ii) The coverage of the industry professionals had been a limitation for the study since there does not exist any online database of operational employees like the one in case of researchers. Hence, many of the industry professionals had to be approached in person.
Industry Professionals Feels burdened with current rules and regulations and not of any help to improve their performance. Find it expensive to implement sustainable technology currently considering economic benefits in longer run. Averse to it as there is a sense of belief that it could lead to loss of market share to bigger foreign players. Currently facing the challenges of improving quality of products and processes at low cost considering tough competition and increasing prices of utilities. Feel the lack of quality education system due to absence of adequate amount of professional institutes with the help of which they can upgrade and adapt new technology. State that they already have high demanding pressure from market as far as green products and green processes are considered. State the absence of guiding frameworks to implement sustainability in organizations as per different criteria.
iii) Even though the industry respondents were limited to a city of Punjab, it is fairly representative of the industry. References [1] Linton J, Klassen R, Jayaraman V. Sustainable supply chains: An introduction. Journal of Operations Management 2007;25(6):1075–82. [2] Amrina E, Yusof SM. Drivers and Barriers to Sustainable Manufacturing Initiatives in Malaysian Automotive Companies. In: Kachitvichyanukul V, Luong HT, Pitakaso R, editors. Proceedings of the Asia Pacific Industrial Engineering & Management Systems Conference 2012, pp. 629-634. [3] U.S. Department of Commerce. How does commerce define sustainable manufacturing? 2010. Available from: http://www.trade.gov/competitiveness/sustainablemanufacturing/how_do c_defines_SM.asp [4] World Development Indicators. Manufacturing, value added (% of gdp). 2013. Available from: http://data.worldbank.org/products/wdi [5] Joung CB, Carrell J, Sarkar P, Feng SC. Categorization of indicators for sustainable manufacturing. Ecological Indicators 2013;24:148–57. [6] Kulatunga AK, Jayatilaka PR, Jayawickrama M. Drivers and Barriers to Implement Sustainable Manufacturing Concepts in Sri Lankan Manufacturing Sector. In: Seliger G, editor. Proceedings of the 11th Global Conference on Sustainable Manufacturing - Innovative Solutions 2013, pp. 172-177. [7] Koho M, Torvinen S, Romiguer AT. Objectives, enablers and challenges of sustainable development and sustainable manufacturing: Views and opinions of Spanish companies. In International Symposium on Assembly and Manufacturing (ISAM), IEEE 2011, pp. 1-6. [8] Mittal VK, Sangwan KS, Herrmann C, Egede P. Comparison of Drivers and Barriers to Green Manufacturing: A Case of India and Germany. In: Nee, Song and Ong, editors. Re-engineering Manufacturing for Sustainability 2013, In proceedings of the 20th CIRP International Conference on Life Cycle Engineering (LCE 2013), Singapore, pp. 723728. [9] National Manufacturing Competitiveness Council. The National Strategy for Manufacturing. 2006. Available from: http://nmcc.nic.in/pdf/strategy_paper_0306.pdf [10] Wilcox RR, Tian TS. Measuring effect size: a robust heteroscedastic approach for two or more groups. Journal of Applied Statistics 2011;38(7):1359-68.
ScienceDirect Procedia CIRP 29 (2015) 562 – 567
The 22nd CIRP conference on Life Cycle Engineering
Enablers and Barriers of Sustainable Manufacturing: Results from a Survey of Researchers and Industry Professionals Neeraj Bhanota*, P. Venkateswara Raoa, S.G. Deshmukha a
Department of Mechanical Engineering, Indian Institute of Technology Delhi, New Delhi, 110116, India
* Corresponding author. Tel.: +91-9873279084; E-mail address: [email protected]
Abstract Sustainable Manufacturing (SM) has gained significant importance in today’s competitive environment as many organizations still depend on natural resources and at the same time generate wastes and environmental pollution. However, the adoption of SM is a huge challenge for organizations since most of them are not aware on how to utilize the enablers and mitigate the effect of barriers of SM. This paper tends to present the opinions of various researchers around the globe and industry professionals on the important enablers and barriers and analyze them using statistical techniques to highlight the differences in opinions for strategic implementation of SM. © 2015 2015 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V.This is an open access article under the CC BY-NC-ND license © Peer-review under responsibility of the International Scientific Committee of the Conference “22nd CIRP conference on Life Cycle (http://creativecommons.org/licenses/by-nc-nd/4.0/). Engineering.under responsibility of the scientific committee of The 22nd CIRP conference on Life Cycle Engineering Peer-review Keywords:Sustainable Manufacturing; Enablers and Barriers; Comparison; Researchers and Industry Professionals.
1. Introduction Sustainability is all about the ability to sustain. However, the importance of economic, environmental and social dimensions varies from time to time and according to different criteria. Various activities such as “product design, manufacturing by-products, by-products produced during product use, etc.” have also been included in the supply chain core activities [1]. The success of an industry depends on its manufacturing performance where competitive environment is followed by superior performance. Hence, organizations need to evaluate their performance at regular intervals to ensure high-level of performance in order to stay competitive in global competition [2]. U.S. Department of Commerce [3] defined sustainable manufacturing (SM) as “creation of manufactured products that use processes that are nonpolluting, conserve energy and natural resources, and are economically sound and safe for employees, communities and consumers” which clearly implies fostering of domestic and international conditions for doing business in addition to fulfilling basic dimensions of sustainability. For a developing nation like India, manufacturing industry plays an indispensable part in its economy. It is evident from the fact
that the percentage share of manufacturing in Indian GDP varies from 13-16% [4]. However, it is important to note that, this share has been continually decreasing since January, 2007. India is ranked amongst the largest economies in the world and it is expected to continue its growth rapidly over the next two decades. But this growth is subject to various challenges accompanying it. With growing economy, India is bound to experience dramatic increase in demand for materials and energy, hence putting serious constraints on natural resources such as land, water, minerals, and fossil fuels, and driving up energy and commodity prices. Moreover, increasing activity will lead to increase in level of waste and pollution, particularly in the form of higher GHG emissions, which can ultimately restrict the India’s ability to grow, rendering its momentum unsustainable. Due to the ongoing trend, it has thus become a need to develop and pursue manufacturing activities, which helps in maximizing economic and social benefits along with minimizing environmental impact [5]. In today’s globalised scenario, the collaboration of academia and industry professionals is imperative when it comes to identifying the solutions for the sustainability issues. There have been similar studies on investigating the driving factors and barriers of implementing SM initiatives in
2212-8271 © 2015 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 scientific committee of The 22nd CIRP conference on Life Cycle Engineering doi:10.1016/j.procir.2015.01.036
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Malaysian automotive companies [2]; investigation of current SM situation of manufacturing sector among Sri Lankan [6] and Spanish companies [7], and an assessment of Indian and German industry for sustainability initiatives [8]. However, no paper has studied the existing practical differences of opinions and perceptions between academia and industry professionals. In this paper, the enablers and barriers to SM in the manufacturing sector industry are analyzed using statistical analysis to assess the differences between the opinions of various researchers around the globe and industry professionals focusing small, medium and large scale industries of Ludhiana (a city in the Indian State of Punjab, which is also known as "Manchester of the East" as it has an established manufacturing base for engineering products). The statistical analysis is, however, carried out by applying ‘independent t-test’ statistical method to compare the significance of differences. The statistical analysis will provide the significance of the difference between enablers and barriers of researchers and industry professionals group. In addition to this, the magnitude of the difference in these factors is also assessed, if any difference exists. Hence, this study will help the industry professionals; government and other policy makers to understand the challenges faced in implementing SM issues in manufacturing organizations. 2. Research Methodology Table 1 and Table 2 present a list of 10 enablers and 10 barriers identified as an outcome of review of research articles relating to topics like implementing sustainable manufacturing concepts [6], sustainable development issues [7], sustainable manufacturing initiatives [2], green manufacturing concepts [8], Indian strategy for manufacturing [9], which are almost synonyms of SM, and after discussing with experts and academicians working in the field of SM. Table 1. Description of Enablers for Sustainable Manufacturing. No.
Enablers
Description
E1
Pressure from market
Trade and Commercial Practices, Competitors, Customer Satisfaction.
E2
Government promotions and regulations
Law Enforcement and Judicial Regulations, Private-Public Participation and Accountability.
E3
Economic Benefits
Recurring & Long-Term Financial Yields.
E4
Investment in Innovation & Technology
Advance Technological Initiatives for Performance Enhancement.
E5
Lowering Manufacturing Cost
Efficient Process Management with Minimum Waste outputs.
E6
Improving Quality
Innovative Process, Product Quality, Enhanced Production.
E7
Education and Training System
Inducting periodical deployment of workers training and upgraded technological education.
E8
Attracting Foreign Direct Investment
Liberalization of Universal Economic Ties.
E9
Infrastructure facilities in Transportation sector
Infrastructure development for viable Air, Rail and Road Connectivity.
E10
Development in E-Economy
Deployment of E-Technology in Manufacturing Sector.
Table 2. Description of Barriers for Sustainable Manufacturing. No.
Barriers
Description
B1
Lack of awareness of sustainability concepts
No or limited access to sustainability literature.
B2
Lack of awareness programs conducted locally
No awareness of sustainability trends.
B3
Lack of awareness of local customers in green products
Not enough publicity about green products.
B4
Negative attitudes towards sustainability concepts
Insignificant knowledge of sustainability concepts.
B5
Lack of funds for green projects
Neglected approach for judicious funds distribution.
B6
Lack of standardized metrics or performance benchmarks
Absence of practicable guidelines and parameters.
B7
Lack of support from senior leaders
Total neglect by concerned top brass.
B8
Cost too high
Initial high costs for sustainable technology implementation.
B9
Power Shortage
Need for improving present power production and distribution.
B10
Low Availability of Credit
Need based allocation of funds at low interest rates by banking and financial institutes.
Researchers from different countries have been approached for the survey keeping in mind that the perceptions on sustainability issues tend to be the same since theories concerning sustainable manufacturing are fundamentally the same regardless of any regions. On the other hand, in case of the industry professionals, the busy schedule of industry professionals everywhere did not help us to obtain online responses. Hence, we decided to zero in on Ludhiana city (Punjab, India) due to its geographic importance in manufacturing and since it was also located at a distance of approximately 300 km from the authors’ base (IIT Delhi). The survey was also constrained to one city because the sociopolitical and economic factors vary with regions. Moreover, Ludhiana is a hub of manufacturing firms in North India; there are a lot of small-scale manufacturing firms, which have sustainability issues, providing valuable insights potentially for the study. A questionnaire was developed to obtain opinions of researchers working in the field of SM and industry professionals (from Ludhiana city). The participants were asked to rate the importance of enablers and barriers on a scale of 1 to 5, where 1 means very low influence, 2 means low influence, 3 stands for medium influence, 4 means high influence and 5 means very high influence. The required participation time ranges from 10 to 15 minutes. Before the release of the questionnaire, academic and industrial experts with knowledge in the field of SM reviewed it for clarity and understandability. As a pilot study, the questionnaire was
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filled by 3 researchers and 4 industrial experts and was revised in terms of language to increase understandability. An online survey was then started using the www.qualtrics.com website. Initially, researchers working in the field of SM were searched using Scopus database and were sent the survey requests at the mentioned email id’s and were also requested to provide references whom they also consider suitable for the survey. These researchers were spread across all over the world ranging from U.S.A, India, Germany, Australia, Italy, etc. Altogether 314 researchers have been contacted using Scopus Database and referrals and 110 researchers provided the responses making response rate to be 35%. However, 4 responses were found to be invalid and were discarded. Hence, 106 useful and reasonable responses have been received from researchers all around the world. In case of industry survey, email requests have been sent to the members (according to the id’s available in online database) of three main organizations/associations of Ludhiana (a town in North India): Federation of Auto Parts Manufacturers, Micro, Small & Medium Enterprises Development Institute and Chamber of Industrial and Commercial Undertakings. Altogether, 229 online survey requests had been sent, out of which 49 responses were obtained online turning the response rate to be 21%. However, 3 responses were found to be invalid and were discarded. Hence, 46 useful and reasonable responses have been received from industry professionals in online mode. Compared with number of responses obtained from researchers, efforts were further made to get close to response rate of researchers. Hence, personal visits were made to some of the reputed industries and their referrals, and senior managers were explained the survey and responses were collected through interview mode. In this way, 53 valid responses were obtained in offline mode giving a total of 99 useful and reasonable responses from industry professionals. The total time duration for the survey has been 2 months approximately being August and September, 2014. Further, reminders were sent to the respondents after approximately 710 days from date of sending the survey request and in total 3 reminders were sent to the respondents.
3. Results and Discussions Initially, Cronbach’s alpha value is used to assess the reliability of the data collected through the questionnaire, which tells the suitability of data for further analysis and valuable interpretation. Then, the importance of enablers and barriers are calculated through their mean values. Very low mean values of any enabler/barrier suggest that the particular enabler/barrier is not important and should be eliminated from the study. Later on, central tendency of the data is measured by calculating the standard deviation values since the mean value is not always sufficient. Lastly, significance of the difference is assessed by 'independent t-test'. The measures of central tendency and results of the tests conducted on the obtained data are presented as follows: 3.3. Descriptive Statistics Table 3 presents the group statistics of enablers and barriers for sustainable manufacturing on the basis of responses provided by both groups. The mean value of all the factors is considered to check their importance between both groups. The internal consistency analysis is carried out using the software SPSS 21.0 for MacBook Pro, to measure the reliability of each factor in terms of the Cronbach’s alpha. An alpha value of 0.7 is often considered the criteria for establishing internal consistency on a scale of 0 to 1, where '0' means that the data is not reliable and '1' means that the data is fully reliable. However, a value of 0.6 is also considered sufficient for newly collected data. If necessary (alpha < 0.6), items are eliminated in order to achieve an increase of the Cronbach’s alpha value [8]. In this study, during the initial analysis, none of the factors was eliminated to improve the reliability. The Cronbach’s alpha value of 0.959 for the enablers and 0.867 for the barriers is achieved on the combined data of both groups being researchers and industry professionals, which is considered good, and hence it can be concluded that the data is highly reliable.
Table 3. Group Statistics of Enablers and Barriers for Sustainable Manufacturing. Job Profile: 1 – Researchers; 2 – Ind. Professionals 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2
Enablers E1 E2 E3 E4 E5 E6 E7 E8 E9 E10
Mean (On a Scale of 1-5)
Std. Deviation
3.58 3.52 3.81 2.54 3.92 3.23 3.75 3.52 3.76 3.82 3.82 4.10 3.31 3.63 3.08 2.70 3.12 3.12 3.16 3.04
1.04 1.00 0.96 1.19 1.05 1.07 0.97 1.03 1.11 0.81 0.94 0.83 0.98 1.02 1.18 1.08 1.06 1.14 1.11 1.11
Barriers B1 B2 B3 B4 B5 B6 B7 B8 B9 B10
Mean (On a Scale of 1-5)
Std. Deviation
3.92 3.88 3.64 3.70 3.75 3.46 3.24 3.48 3.74 3.52 3.81 3.51 3.72 3.53 3.97 3.84 3.40 3.58 3.41 3.54
0.98 1.07 0.89 1.04 0.98 0.90 1.24 1.15 1.08 1.19 0.87 0.92 0.96 1.21 0.93 0.93 1.13 1.00 0.97 1.03
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The 2nd, 3rd and 4th Columns of Table 3 present the group statistics of enablers wherein, the mean value of all enablers is more than 2.54 on a scale of 1-5, which highlights that all the enablers are important in both groups. The examination of the mean values suggests that the 'economic benefits' is the most important in researchers group (mean of 3.92) and 'improving quality' in industry professionals group (mean of 4.10). 'Attracting foreign direct investment' is least important in researchers group (mean of 3.08) and 'government promotions and regulations' in industry professionals group (mean of 2.54). In addition to this, the standard deviation measure is used to measure confidence in statistical conclusions. The standard deviation of data from both groups varies from a minimum value of 0.81 for 'lowering manufacturing cost' to maximum value of 1.19 for 'government promotions and regulations’ from industry professionals group only. The last three Columns of Table 3 present the group statistics of barriers wherein, the mean value of all barriers is more than 3.24 on a scale of 1-5, which highlights that all the barriers are considerable and really important in both groups. The examination of the mean values of all the barriers suggests that the 'cost too high' is the most important in researchers group (mean of 3.97) and 'lack of awareness of sustainability concepts' in industry professionals group (mean of 3.88) whereas 'negative attitudes towards sustainability concepts' is least important in researchers group (mean of 3.24) and 'lack of awareness of local customers in green products' is least important in industry professionals group (mean of 3.46). In addition to this, the standard deviation measure is used to measure confidence in statistical conclusions. The standard deviation of data from both groups varies from a minimum value of 0.87 for 'lack of standardized metrics or performance benchmarks' to maximum value of 1.24 for 'negative attitudes towards sustainability concepts’ from researchers group only.
3.2. Independent t-test for Means Comparison Two entirely different and independent samples of respondents from researchers and industry professionals group are considered to conduct an independent t-test (twotailed) to assess the impact of enablers and barriers in both groups. The following hypotheses are set for the independent t-test: The Null Hypothesis (H0) assumed is H0: μResearchers= μIndustry Professionals and the alternate hypothesis (H1) is H1: μResearchers ≠ μIndustry Professionals A value of 0.05 is used for alpha and the actual 't' value is calculated as follows:
(1)
where: = Sample Mean = Pooled Variance n = Sample Size Conducting Levene’s test for Equality of Variances in case of enablers, it can be observed that the p-value of enablers 1, 3, 4, 6, 7, 8, 9 and 10 is more than 0.05 and hence, it can be concluded that variances are equal and 'EVA' row has to be selected whereas the p-value for enablers 2 and 5 is less than 0.05 and hence, it can be concluded that variances are not equal and 'EVNA' row has to be selected. Table 4 assesses the significance in the difference of impact for enablers in between both groups using t-test for equality of means. Similarly, in case of barriers, the p-value of barriers 1, 2, 3, 4, 5, 6, 8, 9 and 10 is more than 0.05 and hence, 'EVA' row has to be selected whereas p-value of barrier 7 is less than 0.05 and hence, 'EVNA' row has to be selected. Table 5 again assesses the significance in the difference of impact for barriers in both groups.
Table 4. Comparing Enablers for Researchers and Industry Professionals by Independent t-test. Enablers
Levene's Test for Equality of Variances F
EVA .107 EVNA EVA 8.221 E2 EVNA EVA .344 E3 EVNA EVA .655 E4 EVNA EVA 13.853 E5 EVNA EVA 3.776 E6 EVNA EVA .000 E7 EVNA EVA .123 E8 EVNA EVA .597 E9 EVNA EVA .123 E10 EVNA *S.D. = Significantly Different E1
t-test for Equality of Means
Sig.
t
Sig. (2-tailed)
Mean Difference
Std. Error Difference
.744
.422 .422 8.488 8.426 4.611 4.608 1.708 1.705 -.396 -.400 -2.254 -2.265 -2.259 -2.257 2.391 2.398 .009 .009 .773 .774
.674 .673 .000 .000 .000 .000 .089 .090 .693 .690 .025 .025 .025 .025 .018 .017 .993 .993 .440 .440
.06032 .06032 1.27597 1.27597 .68277 .68277 .23957 .23957 -.05403 -.05403 -.28026 -.28026 -.31494 -.31494 .37850 .37850 .00143 .00143 .11997 .11997
.14304 .14286 .15033 .15144 .14809 .14816 .14023 .14052 .13657 .13517 .12431 .12375 .13939 .13956 .15827 .15782 .15322 .15360 .15514 .15510
.005 .558 .419 .000 .053 .984 .726 .441 .727
Cohen’s d (To assess Effect Size)
Remarks
0.0592
-------
0.1228 (0.0613) 0.6472 (0.3079)
S.D. (Small) S.D. (Medium)
0.2397
-------
-0.0576
-------
-0.3164 (0.1562) -0.3171 (0.1565) 0.3356 (0.1655)
S.D. (Small) S.D. (Small) S.D. (Small)
0.0013
-------
0.1085
-------
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Table 5. Comparing Barriers for Researchers and Industry Professionals by Independent t-test. Barriers
Levene's Test for Equality of Variances F
EVA .787 EVNA EVA 1.651 B2 EVNA EVA .086 B3 EVNA EVA .799 B4 EVNA EVA 1.446 B5 EVNA EVA 1.396 B6 EVNA EVA 10.063 B7 EVNA EVA .071 B8 EVNA EVA 1.286 B9 EVNA EVA .968 B10 EVNA *S.D. = Significantly Different B1
t-test for Equality of Means
Sig.
t
Sig. (2-tailed)
Mean Difference
Std. Error Difference
.376
.319 .318 -.411 -.409 2.140 2.146 -1.486 -1.489 1.392 1.387 2.446 2.441 1.261 1.252 1.023 1.023 -1.203 -1.208 -.925 -.923
.750 .751 .682 .683 .034 .033 .139 .138 .166 .167 .015 .015 .209 .212 .307 .307 .230 .229 .356 .357
.04574 .04574 -.05546 -.05546 .28064 .28064 -.24900 -.24900 .22070 .22070 .30627 .30627 .19173 .19173 .13331 .13331 -.17953 -.17953 -.12969 -.12969
.14348 .14391 .13496 .13572 .13115 .13077 .16758 .16718 .15858 .15911 .12523 .12545 .15203 .15319 .13026 .13028 .14927 .14867 .14017 .14046
.200 .770 .373 .231 .239 .002 .790 .258 .326
3.3. Effect Size to assess Mean Differences Cohen’s d is considered one of the most important attributes to measure the 'effect size' [10], which assesses the magnitude of mean differences. It highlights the differences in the 'means' of two samples in three categories being; small, medium and large. Cohen’s d value is calculated only in case null hypothesis has been rejected while conducting statistical test. However, it holds very little or no-relevance in case null hypothesis has not been rejected.
(2)
(3)
If the values of 'effect size' are until 0.2 then it is considered small effect, values until 0.5 means medium effect and values until 0.8 denotes large effect [10]. Tables 4 and 5 also highlights the values of Cohen’s d on the basis of which Effect Size are calculated for each of the enablers and barriers after deciding the row to be considered based on Levene’s test for equality of variances [8].
Cohen’s d (To assess Effect Size)
Remarks
0.0448
-------
-0.0577
-------
0.3004 (0.1485)
S.D. (Small)
-0.2086
-------
0.1954
-------
0.3434 (0.1692)
S.D. (Small)
0.1829
-------
0.1436
-------
-0.1689
-------
-0.1298
-------
These findings highlight the statistical significance being different or equal for each enabler and barrier in both groups along with the amount of difference, e.g. impact of enabler 2 (government promotions and regulations) is different amongst researchers and industry professionals by small means. Similar interpretation holds for other enablers and barriers too. 4. Discussion and Insights Gained 4.1 Enablers & Barriers with No Significant Difference This is interesting to know that for 5 enablers and 8 barriers; there is common consensus between both groups and hence no significant difference has been found in them. It has also been observed that 'lowering manufacturing cost’; 'investment in innovation & technology' and 'pressure from market' emerge as top 3 priority enablers along with 'lack of awareness of sustainability concepts' and 'cost too high' as top 2 barriers in both groups according to their mean values. Table 6 presents the enablers and barriers with no significant difference between both the groups.
Table 6. Discussion on Enablers and Barriers with No Significant Difference. Name 1. Lowering Manufacturing Cost
2. Investment in innovation & Technology 3. Pressure from Market
Enablers Remarks Most challenging task as it involves managing the processes efficiently with minimum wastes generation.
Necessary to enhance the performance of processes by implementing advanced technological initiatives. Enables industries to satisfy the customer requirements considering competitive scenario.
Name 1.Lack of awareness of sustainability concepts
2. Cost too high
Barriers Remarks Mitigation Measures No or limited access to • Suitable training workshops can be conducted for sustainable literature restricts practitioners from time to time wherein problems the awareness of various faced by industry can be discussed and guidance concepts and techniques by can be provided for implementing sustainability. which practitioners can • Government can issue guidelines as part of implement sustainability. industrial training. High initial costs of • Incentives by government in the form of tax rebate, implementing the sustainable financial and technical assistance to industries in technology constrain the implementing sustainable technologies etc. industry practitioners from • Industries should also make efforts such as reducing investing in it and waiting for cost of overwastes produced, judicious utilization of longer duration to gain resources such as energy, water etc. economic benefits make it • Researchers can devise cost effective sustainable further tough for them. technologies for industries.
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4.2 Enablers & Barriers with Significant Difference In case of enablers, it can be observed that almost half the enablers statistically have significant difference between both groups. The impact of 'government promotions and regulations', 'economic benefits', 'attracting foreign direct investment' is higher in researchers group as compared to industry professionals whereas the impact of 'improving
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quality' and 'education and training system' has been observed more in case of industry professionals. Similarly, in case of barriers the impact of 'lack of awareness of local customers in green products' and 'lack of standardized metrics or performance benchmarks' is higher in researchers group as compared to industry professionals. Table 7 presents the enablers and barriers with significant difference between both the groups.
Table 7. Discussion on Enablers and Barriers with Significant Difference Name 1. Government promotions and regulations 2. Economic benefits 3. Attracting foreign direct investment Enablers 4. Improving Quality 5. Education and Training system
Barriers
1. Lack of awareness of local customers in green products 2. Lack of standardized metrics or benchmarks
Researchers Consider important for effective implementation of policies and rules. Consensus that implementing sustainable technology in manufacturing helps gain 'economic benefits'. Believe that it will help the industry in getting access to funds, which could be used for expansion, updating technology, improving processes and infrastructure. Achieving high quality with lower costs is feasible provided industry is open to avenues such as 'foreign direct investment', 'government promotions' etc. May not have considered the current scenario of quality and availability in education and hence ranked it as little less important enabler. Consensus that if customers will not be aware of green products, then there will be no point for industries to pursue the same goal. Developed a lot of sustainability frameworks to evaluate the sustainability of organizations.
5. Concluding Remarks In this paper, the responses on important enablers and barriers have been collected through online questionnaire survey and presented after validating the data by statistical analysis for strategic implementation of SM. i) Based on the survey there seems to be huge difference in line of operation of researchers and industry professionals with regard to SM. However, both groups need to collaborate in order to work together to strengthen the enablers and mitigate barriers. ii) Although it is not desirable to pay equal attention to all the enablers and barriers at the same time, it will be useful to identify causal relationships in each of the enablers and barriers respectively as it will help in identifying and focusing on critical enablers and barriers. iii) There is a need to come up with suitable models in the form of case studies implementing sustainability as to how the problems emerged in due course of time and how they were suitably tackled. 6. Limitations of Survey i) The survey responses are limited to the extent to which the respondents themselves are subjected to their own perceptions and beliefs based on their observations, intuition and experience. ii) The coverage of the industry professionals had been a limitation for the study since there does not exist any online database of operational employees like the one in case of researchers. Hence, many of the industry professionals had to be approached in person.
Industry Professionals Feels burdened with current rules and regulations and not of any help to improve their performance. Find it expensive to implement sustainable technology currently considering economic benefits in longer run. Averse to it as there is a sense of belief that it could lead to loss of market share to bigger foreign players. Currently facing the challenges of improving quality of products and processes at low cost considering tough competition and increasing prices of utilities. Feel the lack of quality education system due to absence of adequate amount of professional institutes with the help of which they can upgrade and adapt new technology. State that they already have high demanding pressure from market as far as green products and green processes are considered. State the absence of guiding frameworks to implement sustainability in organizations as per different criteria.
iii) Even though the industry respondents were limited to a city of Punjab, it is fairly representative of the industry. References [1] Linton J, Klassen R, Jayaraman V. Sustainable supply chains: An introduction. Journal of Operations Management 2007;25(6):1075–82. [2] Amrina E, Yusof SM. Drivers and Barriers to Sustainable Manufacturing Initiatives in Malaysian Automotive Companies. In: Kachitvichyanukul V, Luong HT, Pitakaso R, editors. Proceedings of the Asia Pacific Industrial Engineering & Management Systems Conference 2012, pp. 629-634. [3] U.S. Department of Commerce. How does commerce define sustainable manufacturing? 2010. Available from: http://www.trade.gov/competitiveness/sustainablemanufacturing/how_do c_defines_SM.asp [4] World Development Indicators. Manufacturing, value added (% of gdp). 2013. Available from: http://data.worldbank.org/products/wdi [5] Joung CB, Carrell J, Sarkar P, Feng SC. Categorization of indicators for sustainable manufacturing. Ecological Indicators 2013;24:148–57. [6] Kulatunga AK, Jayatilaka PR, Jayawickrama M. Drivers and Barriers to Implement Sustainable Manufacturing Concepts in Sri Lankan Manufacturing Sector. In: Seliger G, editor. Proceedings of the 11th Global Conference on Sustainable Manufacturing - Innovative Solutions 2013, pp. 172-177. [7] Koho M, Torvinen S, Romiguer AT. Objectives, enablers and challenges of sustainable development and sustainable manufacturing: Views and opinions of Spanish companies. In International Symposium on Assembly and Manufacturing (ISAM), IEEE 2011, pp. 1-6. [8] Mittal VK, Sangwan KS, Herrmann C, Egede P. Comparison of Drivers and Barriers to Green Manufacturing: A Case of India and Germany. In: Nee, Song and Ong, editors. Re-engineering Manufacturing for Sustainability 2013, In proceedings of the 20th CIRP International Conference on Life Cycle Engineering (LCE 2013), Singapore, pp. 723728. [9] National Manufacturing Competitiveness Council. The National Strategy for Manufacturing. 2006. Available from: http://nmcc.nic.in/pdf/strategy_paper_0306.pdf [10] Wilcox RR, Tian TS. Measuring effect size: a robust heteroscedastic approach for two or more groups. Journal of Applied Statistics 2011;38(7):1359-68.