Journal of Scientific & Industrial Research Vol 74, December 2015, pp. 665-669
Prioritizing the Factors Affecting Innovation Capability of Steel Manufacturing SMEs Using Fuzzy Logic K R Kiron* and K Kannan PSG College of Technology, Coimbatore, Tamil Nadu, India Received 3 September 2014; revised 22 April 2015; accepted 16 August 2015 The measurement of innovation capability of an organisation is a difficult task. Various methods for the measurement of innovation have been developed. But a method involving fuzzy logic for the measurement of innovation in MSMEs has yet not been developed. In this study, eighteen factors affecting innovation capability of steel manufacturing MSMEs were obtained from literature. Experts from industry and academics were approached for prioritizing these factors. A survey was conducted based on the questionnaire prepared. These factors are prioritized using Chen’s method of ranking fuzzy numbers. This study is beneficial to the entrepreneurs and practicing managers for focusing strategies to make their firm innovative. Keywords: Innovation capability, MSME, Fuzzy logic, Prioritising factors.
Introduction With the increase in population growth, countries like India depend on Micro, Small and Medium Enterprises (MSMEs) for the creation of new employments and economic development. This makes MSMEs a vital sector in the developing countries. Indian MSMEs have developed from producing traditional goods to the manufacturing of value added products. The contribution of Indian MSMEs to the Indian GDP is growing. Indian Institute of Foreign Trade (IIFT) and Federation of Indian Chambers of Commerce and Industry (FICCI)1 pointed that, the contribution of Indian MSMEs to the total industrial enterprises in India is 80 percent with a growth rate of 10.8 percent. But, the entrepreneurs are not happy with the current situation. Raj2 identified that MSMEs in India are facing many problems. He pointed that the investment opportunity is very low in this sector. The availability of financial resources and human resources are also very low. Moreover, the technical efficiency is very low in the informal manufacturing sector in India. This affects the returns for investment for the entrepreneurs. As a result of the economic reforms of 1990s in India, the competition in the market has increased. Indian companies are facing good competition from the local and the global players. Bandyopadhyay3 studied the performance of Indian SMEs after the reforms. He pointed that manufacturing sector in India showed _____________ Author for correspondence E-mail:
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positive attitude towards these reforms. But the sector is facing many problems like liquidity crunch. The sub optimal scale of operation of the MSMEs makes them inferior while competing with the transnational organisations. Maiti and Sen4 pointed that the small scale sector in India should give importance to the complexity and heterogeneity of production and labour relations that exist in the sector. The manufacturing strategies are changing at high pace, but MSMEs lack proper funding to adapt to this situation. It resulted in obsolesce of technology, which is another problem faced by the MSMEs. The market situation is uncertain and MSMEs have to struggle with their supply chain inefficiencies. To survive in this situation, MSMEs need to adopt innovative approaches. It is vital for the companies to maintain continuous innovation in order to maintain their competiveness. Research shows the benefits of innovation5-14. But, because of its intangible nature, the measurement of innovation capability is very difficult15. For planning a strategy for innovation, the different factors affecting innovation are to be identified and sufficient leverage should be given to them in appropriate manner. This has motivated us to study and prioritize these factors. This study will help the managers to focus their strategies in proper direction. Objective & Scope of the study The objective behind this work is to prioritize the factors affecting innovation capability of steel manufacturing MSMEs by fuzzy logic method.
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This study focuses on the micro, small and medium enterprise sector in the Palakkad district of Kerala state in India. Raj2 pointed that the industrial population of the Kerala state is dominated by small enterprises. Also the contribution of the sector in total manufacturing employment is around 80 per cent. He also mentioned that the industrial economy of Kerala is dominated by small enterprises. The slow growth of the sector after 1990s had resulted in the bad performance of the manufacturing sector in Kerala. Methodology The study is empirical in nature, as statistical and other primary data had been collected through field survey. The study was conducted in a step by step procedure. A seven step procedure has been adopted for the study which is narrated herewith. STEP 1- Identification of the factors
Eighteen factors were selected which have impact on the innovation capability of a manufacturing SME. These criteria which point to innovation capability in SMEs were obtained from a detailed literature survey16-22. STEP 2- Identification of the experts
The experts are selected from industry and academic institutions. Eight experts are selected, four each from industry and academics. The experts evaluate each factors based on their knowledge and experience. The top management, the senior executives, or owner managers’ perceptions of the study variables were measured. An effort was made to contact the most senior managers in the organization presuming that they possess substantial knowledge of the firms’ operations and strategies. When the target executives were absent or unavailable for the interviews, the availability of the person next in seniority was asked. For selection of experts from accademic institutions, the persons who were actively involved in MSME sector research were selected.
weighting is to decide on the relative importance between the elements. The simple weighing methods using a 1-10 scale or 1-5 scale cannot be used here since it is difficult to quantify or predict the weight of each element by experts. Fuzzy logic method is used to allocate the weights to each element. The linguistic expressions of the experts about these factors were highly important, important, neutral, less important and not important. The linguistic terms were converted into a numerical approximate system by fuzzy logic. The corresponding values for highly important, important, neutral, less important and unimportant are [8,10,12], [5.5,7,8.5], [3.5,5,6.5], [1.5,3,4.5] and [-2,0,2] respectively. STEP 5- Calculation of left and right utility factors
Ranking the factors is based on the theory proposed by Chen24. Here the membership function used is triangular membership function. Researchers have done different studies using fuzzy logic for prioritizing factors. But no study is found in literature for prioritizing the factors affecting innovation capability of an organisation. Banarjee25 used fuzzy logic in Analytic Network Process (ANP) for finding the priority weights of different elements for assessing the visibility of research organisations. Chen method involved in calculating the right and left utility factor as shown in figure 1. The value of right utility UMi with respect to the ith factor is calculated by UM(i) = wwi (di - xmin)/(wi (xmax - xmin) – w (ai - di))
... (1)
The value of left utility UGi with respect to the ith factor is calculated by UG(i) = wwi (xmax-ci)/(wi (xmax - xmin) – w (ai-ci)) ... (2) Where w is the value for the range R= [0,w] where the fuzzy numbers fall.24 Here w is taken as 1 and so the priority factors will fall in [0,1]. wi is the weights
STEP 3- Preparation of questionnaire and data collection
A questionnaire was prepared after a detailed literature study and a brainstorming discussion with the experts in the field.The data collection from experts are by personal interview.23 STEP 4- Development of fuzzy functions.
During the survey, the experts are requested to give weightage to each of the eighteen factors based on their experience and knowledge. The purpose of
Fig 1- Left and right utility factors of various triangular functions.
KIRON & KANNAN: PRIORITIZING THE FACTORS AFFECTING STEEL MANUFACTURING
given to each respontants, ai,ci,di are the ordinate points of each triangle, xmax , xmin are the maximum and minimum values of these ordinates. Since the researchers did not want to be biased on the responses given by the experts, the weightage factor is taken as ‘1‘ in all cases. In order to have a better understanding, consider the case of the factor ‘new product development‘ as an example. The responses given by the eight experts were neutral, neutral, less important, highly important, highly important, important, important and highly important respectively. Each linguistic term is converted into numerical values by the terms mentioned above. Taking the average of the a,c and d values of the eight responses, ai = 5.438, ci = 7.125, di = 8.813 . Similarly ai,ci,di values of all factors are calculated and the maximum and minimum value is obtained as 11.530 and 1.188, which are the xmax and xmin values. Substituting these values in equation 1 and equation 2, the values of left utility factor and right utility factor are obtained. The calculated values for different factors are given in the table 1 and table 2. STEP 6- Calculation of total utility factors
The value of total utility or ordering value of each fuzzy number UTi with respect to the ith factor is calculated from the left utility factor and the right utility factor as UT(i) = (UM(i) + 1 - UG(i))/2
... (3)
STEP 7- Ranking of the factors
The eighteen factors selected for the study is arranged in the descending order of total utility. This shows the relative importance of each factors in the innovation capability of the steel manufacturing MSMEs. The result of the study is given in the table 3. Results and discussion The study shows that the improvement in product quality has the highest impact on innovation capability of a steel manufacturing SME followed by the factor ‘Reduction in product cost‘. Today customers are demanding high quality products with reduced cost and this may be the reason for these selections. Table 3 shows that the number of patents has got the least importance. It is due to the reason that SMEs patent filing, in view of low R&D expenses, is still away. The other factors that got the least weightages are number of R & D personals working in an organisation and the R & D expenses
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Table 1- Right utility factor of the factors New Product Development Output Value of new product Improvement in product quality R & D Expenses No. of R & D Personnel No. of Patents Reduction in Product Cost Extension of Product Range Learn about new technology Increase in market share Reduction in Labour Cost Production Flexibility Open up of new market Reduction in energy consumption IT enabled business soln. Improvement in working conditions Fulfillment of regulations Link with Educational Institutions
0.632 0.599 0.843 0.349 0.349 0.280 0.811 0.674 0.715 0.727 0.747 0.653 0.727 0.747 0.482 0.628 0.649 0.628
Table 2- Left utility factor of the factors New Product Development Output Value of new product Improvement in product quality R & D Expenses No. of R & D Personnel No. of Patents Reduction in Product Cost Extension of Product Range Learn about new technology Increase in market share Reduction in Labour Cost Production Flexibility Open up of new market Reduction in energy consumption IT enabled business soln. Improvement in working conditions Fulfillment of regulations Link with Educational Institutions
0.508 0.536 0.315 0.786 0.786 0.86 0.342 0.466 0.425 0.418 0.397 0.487 0.418 0.397 0.649 0.503 0.482 0.503
Table 3- Total fuzzy score of different factors Attribute Improvement in product quality Reduction in Product Cost Reduction in Labour Cost Reduction in energy consumption Increase in market share Open up of new market Learn about new technology Extension of Product Range Fulfillment of regulations Production Flexibility Link with Educational Institutions Improvement in working conditions New Product Development Output Value of new product IT enabled business soln. R & D Expenses No. of R & D Personnel No. of Patents
Score 0.764 0.735 0.675 0.675 0.655 0.655 0.645 0.604 0.584 0.583 0.563 0.563 0.562 0.531 0.416 0.281 0.281 0.210
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enormous scope for strategic decision and policy formulation in the sector considered under the study. In future studies this method can be extended to the case of other industries with appropriate modifications. References
Fig 2- Ranking of the factors.
by the firm. This points to the fact that most of the small and medium organisations are not having R & D department and they are getting technology from the giants in the industry. The reduction of product and labour costs have more priority. Increase in market share and open up of new market reaveal that innovative marketing strategies are needed by the SMEs. The linkage with educational and research organisations is given comparatively low priority. This points to the fact that educational and research organisations should focus more on the needs of MSMEs. The ranking of the factors are given in figure 2. Conclusion & Scope of future work The existence of MSMEs is vital for economic development. But the returns for investment is very low in the MSME sector. A number of strategies have been developed to address the concern in the past. The most important strategy is to develop a culture of innovation in MSMEs. The entrepreneurs realize that without continuous innovation, the survival of the organisations is very difficult in the era of globalisation. But the measurement of innovativeness or innovation capability of an organisation is tedious job. Fuzzy assessment has been used to weigh the elements of innovation and prioritize them, so that an organisation aiming to achieve its innovativeness can focus on these factors. The result of the study has
1 Innovation readiness of Indian SMEs – issues and challenges, Proc FICCI summit 2012, 1-4. 2 Raj R, Technical efficiency in the informal manufacturing sector: Firm-level evidence from an Indian state, J South Asian Dev, 6 (2011) 213-232. 3 Bandyopadhyay S, Performance of the selected clusters of small and medium industries in India in the post- reform period – An overview, J Sci Ind Res, 60 (2001) 220-231. 4 Maiti D & Sen K, The informal sector in India: A means of exploitation or accumulation?, J South Asian Dev, 5 (2010) 1-13. 5 Becheikh N, Landry R & Amara N, Lessons from innovation emperical studies in the manufacturing sector: A systematic review of the literature from 1993-2003, Technovation, 26 (2006) 644-664. 6 Damanpour F, Organisational innovation: A meta analysis of effects of determinants and moderators, Acad Manage J, 32 (1991) 555-590. 7 Park Y, Jang J & Yun M, Technological innovation survey of manufacturing firms: The case of Korea, J Sci Ind Res, 60 (2001) 186 – 196. 8 Francois JP, Favre F & Negassi S, Competence and organisation : Two drivers of innovation, Econ Innov New Technol, 11 (2002) 249-270. 9 Biswas PK, A classificatory scheme of technological innovations in rural industries, J Sci Ind Res, 60 (2001) 232 – 242. 10 Krishna VV, Patra SK & Bhattacharya S, Internationalisation of R&D and global nature of innovation: Emerging trends in India, Sci Technol Soc,17 (2012) 165-199. 11 Clancy J, Barriers to innovation in small scale industries: Case study from the briquetting industry in India, Sci Technol Soc, 6 (2001) 329- 357. 12 Kathuria V & Natarajan RR, Is manufacturing an engine of growth in India in the post-nineties? , J South Asian Dev, 8 (2013) 385-408. 13 Kharbanda VP, Strategic technology management and international competition in developing countries – the need for a dynamic approach, J Sci Ind Res, 60, (2001) 291 – 297. 14 Mukherjee PM & Roy D, Global competitiveness in the steel industry, Asia Pac Bus Rev, 6 (2010) 12-17. 15 Romijn H & Albaladejo M, Determinants of innovation capability in small electronics and software firms in southeast England, Res Policy, 31 (2002) 1053-1067. 16 Jamal T, Patterns of technological change – An innovation process to achieve indigenous technological capability in Indian paper industry , J Sci Ind Res, 60 (2001) 629 – 639. 17 Balasubrahmanya MH & Sudhirkumar R, Technological innovations and energy intensity of machine tool SMEs in Bangalore: Do process innovations contribute to energy Efficiency?, Int J Energ Technol Policy, 7 (2011) 519-536. 18 Sikka P, Analysis of in-house R&D centres of innovative firms in India , Res Policy, 27 (1998) 429-433.
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19 Madanmohan TR & Krishnan RT, Characteristics of Indian academic research underlying industrial innovations: An empirical analysis, J Sci Ind Res, 59 (2000) 294 – 299. 20 Mohan SR & Rao AR, Early identification of innovatiove and market accetable technologies – A model for improving technology transfer capabilities of public research institutes, J Sci Ind Res, 62 (2003) 865- 875. 21 Sudhirkumar R & Balasubrahmanya MH, Influence of subcontracting on innovation and economic performance of SMEs in Indian automobile industry, Technovation, 30 (2010) 558-569.
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22 Qi-Hua L, Hong-Zhang Y & Yi-Rong G, MNC’s technology correlation effect and industry agglomeration, J Sci Ind Res, 65 (2006) 787- 793. 23 Malhorta NK & Birks DF, Marketing research: an applied approach. (Prentice Hall, New Delhi) 2007,57-89. 24 Chen S, Ranking fuzzy numbers with maximizing set and minimizing set , Fuzzy Sets Syst, 17 (1985) 113-129. 25 Banerjee G, Assessing visibility of research organisation: A fuzzy analytic network process approach ” J Sci Ind Res, 73 (2014) 283- 289.
