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See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/270903118 Market Dynamics in Technology-Based Industries: Pioneering Advantage, Customer Experience and Adaptive Chapter · January 2013 DOI: 10.1007/978-81-322-0746-7 CITATIONS READS 1,618 1 author: Gurumurthy Kalyanaram Massachusetts Institute of Technology 37 PUBLICATIONS 1,815 CITATIONS SEE PROFILE All content following this page was uploaded by Gurumurthy Kalyanaram on 16 January 2015 The user has requested enhancement of the downloaded file Department of Management Studies, Indian Institute of Science, Bangalore Driving the Economy through Innovation and Entrepreneurship Emerging Agenda for Technology Management Driving the Economy through Innovation and Entrepreneurship Department of Management Studies, Indian Institute of Science, Bangalore Editors Driving the Economy through Innovation and Entrepreneurship Emerging Agenda for Technology Management Department of Management Studies Indian Institute of Science Bangalore Editors ISBN 978-81-322-0745-0 ISBN 978-81-322-0746-7 (eBook) DOI 10.1007/978-81-322-0746-7 Springer New Delhi Heidelberg New York Dordrecht London Library of Congress Control Number: 2013933346 # Springer India 2013 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer Permissions for use may be obtained through RightsLink at the Copyright Clearance Center Violations are liable to prosecution under the respective Copyright Law The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made The views expressed by the authors are their own and are not necessarily shared by the editors or the publisher or the organizations that the authors represent The publisher makes no warranty, express or implied, with respect to the material contained herein Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) Preface Technological advancement, contributing as the key driving force to the development and prosperity of advanced countries, is a part of history In the 1980s and 1990s, technology development contributed to the rapid advancement and transformation of newly industrializing economies (NIEs) In the New Millennium, BRICS (Brazil, Russia, India, China, and South Africa) economies have been experiencing relatively high rate of economic growth, thereby influencing and shaping global economic growth, particularly by the means of industrialization through knowledge intensive industries and entrepreneurship This shift of focus from industrialized to NIEs and further to BRICS economies has been pushing technology development and management to the forefront repeatedly as an agenda for global economic thinking The way BRICS economies in association with NIEs and industrialized countries contribute to the emergence of new technologies, in addition to the innovation and management of existing technologies, would determine and contribute to the emergence of a new global economy, in the future It is in this backdrop that the Department of Management Studies, Indian Institute of Science, Bangalore, which is one of the oldest management schools in the country, decided to organize an International Conference on Technology Management between18 and 20 July 2012 Considering that economic growth in the BRICS and other developing economies in the new century is driven not solely by traditional factor inputs, but more importantly by technology-driven innovation and new generation of entrepreneurship, we have accordingly identified the theme of the International Conference: Driving the Economy Through Innovation and Entrepreneurship: Emerging Agenda for Technology Management We invited theoretical as well as empirical research papers including industry case studies relating to the theme from across the world We received impressive response in the form of more than 240 paper abstracts from 23 different countries by 31 October 2011 We short-listed 167 paper abstracts and communicated to the authors accordingly, requesting them to submit full papers by 31 January 2012 Overall, we got 128 full papers, and based on a double-blind review process, we short-listed a total of 74 papers (comprising 145 authors across 20 countries) for the final presentation v vi Preface This volume comprising three parts is a compendium of 72 papers presented in the International Conference on Technology Management conducted during 18–20 July 2012 at the Indian Institute of Science, Bangalore The three parts of the volume cover papers under three different streams, namely, (1) Technology Development, Sustainability and Markets; (2) Development of Human Resources for Innovation and Technology Management; and (3) ICT Applications, EGovernance and New Product Development Under each stream, papers focusing on diverse sectors such as agriculture, industry and services as well as regions ranging from emerging markets like India to Latin America are presented Further, these papers varied in terms of their methodologies ranging from model building and testing to the development of theoretical propositions and empirical data analysis, apart from case studies Overall, these papers provide a description and analysis of contemporary technology management issues covering diverse economies in the world The organizers of the conference are greatly indebted to all the authors (from academia, R&D institutes and laboratories, government organizations and industry) who have come from different parts of the world, presented their papers and contributed to their intensive and fruitful discussions We express our sincere gratitude to all the referees who had reviewed the paper in every stage, starting from short-listing of the abstracts, to the final form as it appears in this book We are thankful to all the session chairs who conducted the proceedings meaningfully and impressively We are equally grateful to all of our sponsors, particularly the Government of Karnataka, Defense Research and Development Organization (DRDO), Government of India, Bharath Electronics Limited (BEL), Bharath Earth Movers Limited (BEML) and Karnataka Knowledge Commission (KKC), Bangalore, in addition to our institute authorities without whose support this event could not have been organized in this grand manner Finally, we strongly believe that this volume will contribute significantly to the understanding of emerging issues in technology management, in the era of globalization, and facilitate further research in industry as well as academia Editors About Us The Indian Institute of Science (IISc), Bangalore, founded by Jamsetji Nusserwanji Tata, came into existence on 27 May 1909 Over the period, IISc has emerged as an institution of higher learning pursuing excellence in research and education in diverse fields of science and engineering It is probably the oldest and the finest institution of its kind in India; it also has a very high international rating in the academic world IISc provides facilities for post-graduate research and advanced instruction in traditional as well as in many important emerging areas of science and engineering and collaborates with industry and other research institutions in solving challenging problems in science and technology Today, IISc faculty members carry out research and consultancy projects in six different divisions comprising 40 departments/centers/cells/laboratories The Department of Management Studies at the Indian Institute of Science (IISc), established in 1948, is one of the oldest departments of management in India It has been running postgraduate programmes and the doctoral programme since the mid1950s Based in the premier research institute of higher learning, it positions itself to train students in futuristic areas like technology management, business analytics and policy analysis The origin of the Department can be traced back to 1947 when the section of economics and social sciences was set up This pioneering step was largely a result of the long-term vision of J N Tata, who had sown the seeds of management education and research in the country This vision was given substance by the resolution of the Indian Institute of Science to establish a “ .Philosophical and Educational Department, including methods of education, ethics and psychology, Indian history and archaeology, statistics and economics, and comparative philology” In the eventful almost six and half decades of its existence, the Department has made pioneering contributions in management education and research in India Today, the Department comprising 11 faculty and staff members offers the masters of management programme leading to a specialization in business analytics or technology management and research programme leading to the degree of Ph.D vii 820 S.R Venugopalan et al Table Central tendency of maturity of each dimension Dimensions ! Knowledge and Central tendency # Median Weighted avg Technology 2.44 2.343 Human 3.12 2.982 information 3.35 3.34 Organization 3.00 3.05 Mean of medians 2.98 2.93 Fig Maturity of each dimension and all 35 components Table Central tendency reported by PLMSSP and PLMSU Knowledge and Central tendency Technology Human information Median Overall 2.44 3.12 3.35 PLMSU 2.33 2.66 3.08 PLMSSP 2.70 3.00 3.91 Organization 3.00 3.00 3.38 Overall mean 2.98 2.77 3.25 more detailed level than previous studies, which have addressed the differences in overall maturity or the differences associated with a single level of decomposition, at the component level In this section, the 37 responses collected from PLMSSP (19 different organizations) and 19 responses from PLMSU (10 different organizations) are analyzed and discussed The results are examined in the light of Obj 2: The central tendency of the PLMS deployment maturity levels reported by the respondents is used to compare the overall performance across each dimension (presented in Table 2), and the graphical representation of the same is presented in Fig The central tendency of the PLMS deployment maturity levels reported by the respondents is used to compare the overall performance across each dimension Central tendency: The median maturity of PLMSU is 2.77, and that of PLMSSP is 3.25 This clearly shows that the PLMS deployment maturity of PLMSSP is higher This result is to be expected because PLMSSP are motivated, by design, to promote their products and services, and this is a good reason for this difference PLMSSP Perspectives of Users and Service Providers on Deployment Maturity 821 Fig Maturity of each dimension and components claim, naturally, that their products and services are superior The results are also graphically presented in Figs and Table These results show that the maturity levels of PLMSSP and PLMSU are comparable 5.2 Differences Between PLMS Deployment Maturity of PLMSSP and PLMSU The differences between the PLMS deployment maturity levels of PLMSSP and PLMSU are identified and analyzed in this section using the following nonparametric tests: (a) Mann–Whitney U test – to test the differences between the two groups of organizations (b) Wilcoxon signed-rank test – to test the differences between the two related groups by combining (pairing) the samples 5.2.1 Differences Between PLMSU and PLMSSP – Mann–Whitney U Test The results of the test are given in Table and discussed below The two groups of organizations are found to differ statistically in terms of the 11 components identified from Table above and summarized in Appendix A at the end In most cases, the level of maturity in PLMSSP is at least one level higher than in PLMSU This can be seen in Fig 2, presented earlier in this chapter PLMSSP are motivated, by design, to promote their products and services, and this is a good reason for this difference PLMSSP claim, naturally, that their products and services are superior in terms of the following: (a) Functional capabilities of the technology resources made available (b) Enabling quicker learning and skill development, in terms of human resources 822 S.R Venugopalan et al Table Differences PLMSSP and PLMSU Index TR1 Components IT – systems/infrastructure/ technology TR2 Deployment architecture of info system TR3 Enterprise application integration TR4 Product data (PD) and its integration TR5 Information security TR6 Computer-aided design TR7 Computer-aided engineering TR8 Computer-aided testing/ troubleshooting TR9 Computer-aided manufacturing HR1 Organizational teams/people their focus HR2 Organization culture HR3 PLM concept understanding HR4 Learning curve of the users and support HR5 Employees’ satisfaction and level of involvement HR6 Organization structure and design KIR1 Product data/information management KIR2 Collaborative workspace KIR3 PLM tools KIR4 Adaptation to regulatory changes KIR5 Accessibility of the documents KIR6 Best practices KIR7 Legacy data migration and cleanup for PLM KIR8 Manage training requirements KIR9 Design standardization/compliance KIR10 Classification and release KIR11 PLM process and application trained employees KIR12 Knowledge management OR1 Organizational processes to support PLM OR2 PLM vision OR3 Program management and leadership OR4 PLM process maturity OR5 PLM strategy OR6 Enterprise PLM roadmap OR7 Innovation OR8 PLM implementation Mann–Whitney Wilcoxon Asymp sig U W Z (2-tailed) 274.500 977.500 À1.418 0.156 232.500 935.500 À2.129 0.033 294.000 221.500 276.000 245.500 236.500 339.000 997.000 924.500 979.000 948.500 939.500 1042.000 À1.051 À2.349 À1.397 À1.903 À2.136 À0.225 324.500 259.000 1027.500 À0.499 0.618 962.000 À1.655 0.098 246.000 320.000 202.000 949.000 À1.877 0.061 1023.000 À0.566 0.572 905.000 À2.767 0.006 253.500 956.500 À1.761 0.078 257.500 282.500 960.500 985.500 À1.736 0.083 À1.226 0.220 255.500 197.500 229.000 200.000 157.500 249.000 958.500 900.500 932.000 903.000 860.500 952.000 À1.755 À2.763 À2.234 À2.770 À3.519 À1.840 0.079 0.006 0.026 0.006 0.000 0.066 256.500 250.500 293.500 222.500 959.500 953.500 996.500 925.500 À1.709 À1.841 À1.048 À2.291 0.087 0.066 0.295 0.022 249.500 275.500 952.500 978.500 À1.815 0.070 À1.377 0.168 281.500 305.000 182.500 254.000 198.500 284.000 253.000 984.500 1008.000 885.500 957.000 901.500 987.000 956.000 À1.256 À0.830 À3.052 À1.744 À2.735 À1.198 À1.842 0.293 0.019 0.162 0.057 0.033 0.822 0.209 0.407 0.002 0.081 0.006 0.231 0.065 Perspectives of Users and Service Providers on Deployment Maturity 823 (c) Incorporation of industry’s “best practices” in their PLM systems, thus enabling better utilization of knowledge and information resources (d) Being consistent with the higher maturity of the PLM-implemented industries’ processes and roadmap This, however, is apparently only their unfounded belief rather than being a well-researched conclusion One of the most important observations in this research is that PLMS deployment maturity is viewed very consistently by both PLMSSP and PLMSU, with very few exceptions Also, the above differences between their perceptions are only of the order of a single level of maturity There is no statistically significant difference between PLMSSP and PLMSU in their views on PLMS deployment maturity levels defined across the remaining 24 of the 35 component areas Keeping the above observations in mind, there is no strong argument to reject the null hypothesis that there is no difference between the maturity levels reported by PLMSSP and PLMSU This should serve to encourage the two to understand each other’s expectations, requirements, and capabilities to take PLM systems to more productive and sophisticated levels of application 5.2.2 Differences Between PLMSSP and PLMSU – Wilcoxon Signed-Rank Test Following the overall comparison presented above, the PLMS deployment maturity assessments made by the two groups of organizations are specifically analyzed For this, the differences, if any, between the paired responses from the two groups are determined, analyzed, and explained The responses from each PLMSSP are paired with those from the respective PLMSU that received the service The paired data is analyzed using the Wilcoxon signed-rank test In this test case, H0: There is no difference between the PLMS deployment maturity levels of PLMSSP and PLMSU The Wilcoxon signed-rank test statistics for the matched pairs are given in Table There are 22 components in which a statistically significant difference is found These are summarized in Appendix A at the end There is no statistically significant difference between PLMSSP and PLMSU in their views on PLMS deployment maturity levels defined across the remaining 13 of the 35 components Here also, the level of maturity reported by the respondents from PLMSSP is at least one level higher than that reported by those in PLMSU The 22 components for which there are statistically significant differences include the 11 components reported in the overall comparison (Mann–Whitney U test) made in the previous subsection In the technology resource dimension, most of the components in IT and in the engineering design domain and tools show statistically significant differences between the two groups of organizations In the human resource dimension, all the components except the component “PLM understanding” show differences In the knowledge and information, and organizational 824 S.R Venugopalan et al Table Differences between PLMSSP and PLMSU – Wilcoxon matched pairs signed-rank test grouping variable – organization type Components – technology Z Asymp sig (2-tailed) 0.009 TR1 À2.594a 0.000 TR2 À3.518a TR3 À1.423a 0.155 0.001 TR4 À3.473a 0.061 TR5 À1.877a TR6 À3.181a 0.001 0.000 TR7 À4.617a 0.202 TR8 À1.276a TR9 À0.845a 0.398 Components – human Z Asymp sig (2-tailed) 0.021 HR1 À2.311a HR2 À2.432a 0.015 0.463 HR3 À0.734a 0.000 HR4 À4.740a 0.011 HR5 À2.534a HR6 À2.544a 0.011 Components – knowledge and information Z Asymp sig (2-tailed) 0.454 KIR1 À0.750a 0.082 KIR2 À1.740a KIR3 À4.016a 0.000 0.001 KIR4 À3.427a KIR5 À5.123a 0.000 0.000 KIR6 À5.744a 0.060 KIR7 À1.883a KIR8 À1.520a 0.128 0.003 KIR9 À3.001a 0.038 KIR10 À2.079a KIR11 À3.916a 0.000 0.002 KIR12 À3.061a Components – organizational Z Asymp sig (2-tailed) 0.036 OR1 À2.096a 0.543 OR2 À0.608a 0.872 OR3 À0.162b 0.000 OR4 À5.227a 0.070 OR5 À1.811a OR6 À4.072a 0.000 0.223 OR7 À1.220a OR8 À3.965a 0.000 a Based on positive ranks resource dimensions also, there are significant differences in many of the critical components including “adaptation to regulatory changes,” “design standardization/ compliance,” “classification and release,” “PLM process,” “organizational processes and PLM implementation.” Keeping the above observations in mind, there are sufficient reasons to reject the null Hypothesis that there is no difference between the maturity levels reported by PLMSSP and PLMSU Perspectives of Users and Service Providers on Deployment Maturity 825 The significant differences can be attributed to the following reasons: (a) The number of responses from PLMSU is more than that from PLMSSP (b) Significant differences between the two groups exist when one of the groups exhibits larger dispersion of responses within the group (c) The number of junior-level respondents in PLMSSP is more than that in PLMSU The above observations point to the need for further analysis on the samples, and the same is addressed in the next subsection using senior-level respondents 5.2.3 Differences Between Senior Management in PLMSSP and PLMSU – Wilcoxon Signed-Rank Test The responses from senior-level respondents from PLMSSP are paired with those from the respective PLMSU that received the services Ten responses from PLMSSP are paired with ten from the respective PLMSU and compared for differences using the Wilcoxon signed-rank test The Wilcoxon test statistics are tabulated in Table There are only two components for which statistically significant differences exist These two belong to knowledge and information resources The most significant difference identified in this study between the two groups occurs with respect to the two components, “best practices” and “PLM process and application trained employee’s component area.” The median maturity reported by respondents in PLMSSP for both components is 4, and the median reported by those in PLMSU is The views of PLMSSP that the incorporation of industry “best practices” and PLM processes in their PLM systems, thus enabling better utilization of knowledge and information resources by PLMSU, are reflected in their higher rating of PLMS deployment maturity It is reiterated here that PLMS deployment maturity is viewed very consistently by both PLMSSP and PLMSU, with very few exceptions An analysis of the remaining 33 of the 35 components reveals that there is no statistically significant difference in PLMS deployment maturity levels rated by the two groups of organizations Based on the above observations, there is no reason for rejecting the null hypothesis that there is no difference between the maturity levels reported by PLMSSP and PLMSU The views about PLMS deployment maturity held by respondents of PLMSSP are consistently higher by at least one level Major Research Contributions and Conclusions This research contributes to the theoretical and empirical analysis of PLMS deployment maturity assessment This research provides a detailed, component-level snapshot of the current levels of PLMS deployment maturity among the industries The research helps organizations to evaluate PLMS deployment maturity with 826 S.R Venugopalan et al Table Differences between PLMSSP and PLMSU – Wilcoxon matched pairs signed-rank test (senior management) – grouping variable – organization type Components – technology Z Asymp sig (2-tailed) 0.123 TR1 À1.543a 0.121 TR2 À1.552a TR3 À0.730a 0.465 0.380 TR4 À0.877a 0.566 TR5 À0.574a TR6 À1.066a 0.286 0.102 TR7 À1.634a 0.314 TR8 À1.006a TR9 À0.425a 0.671 Components – human Z Asymp sig (2-tailed) 0.403 HR1 À0.837a HR2 À0.960a 0.337 0.952 HR3 À0.061b 0.132 HR4 À1.508a 0.250 HR5 À1.150a HR6 À0.552a 0.581 Components – knowledge and information Z Asymp sig (2-tailed) 0.857 KIR1 À0.180a 0.791 KIR2 À0.265a KIR3 À0.997a 0.319 0.135 KIR4 À1.496a KIR5 À0.604a 0.546 0.008 KIR6 À2.636a 0.086 KIR7 À1.715a KIR8 À1.200a 0.230 0.135 KIR9 À1.496a 0.399 KIR10 À0.844a KIR11 À2.309a 0.021 0.088 KIR12 À1.706a Components – organizational Z Asymp sig (2-tailed) 0.140 OR1 À1.474a 0.389 OR2 À0.862a 0.167 OR3 À1.382a 0.083 OR4 À1.732a 0.103 OR5 À1.631a OR6 À1.869a 0.062 0.237 OR7 À1.184a OR8 À0.948a 0.343 a Based on negative ranks respect to other organizations The questionnaire developed in this research can be used by industries to evaluate the PLMS maturity, which can in turn be used to compare the PLMS maturity across industries Scales were developed and tested, and the framework was operationalized in the form of a questionnaire This research provides a detailed, component-level snapshot of the current levels of PLMS deployment maturity with the perspective of service providers and users Perspectives of Users and Service Providers on Deployment Maturity 6.1 827 Limitations and Scope for Future Research The total number of responses used to find the final outcomes is not sufficient to generalize the results To make an improved validation, many more organizations need to be assessed The measurement of outcome of PLM systems implementation is not in the scope of this work The mutual effects of enterprise application such as ERP and PLM systems have also been kept out of scope This research work can be extended to other industries which are adopting PLM systems Further analysis with larger sample sizes will enable the study of industry-specific effects With sufficient data, the framework presented and discussed here can be extended to other enterprise systems such as ERP, SCM, and CRM systems 828 S.R Venugopalan et al Appendix A Differences Between PLMSSP and PLMSU Components that show significant statistical differences S no Statistical tests Dimension Mann–Whitney Technology U test Human Knowledge and information Organization Wilcoxon signed-rank test Technology Human Knowledge and information Organization Wilcoxon signed-rank test Technology Human Knowledge and information Organization Components Deployment architecture, product data and its integration across its lifecycle, and CAE Learning curve PLM tools, adaptation to regulatory changes, accessibility of documents, best practices, and PLM process PLM process maturity and enterprise PLM roadmap IT – systems and infrastructure, deployment architecture, product data and its integration across its lifecycle, CAD, and CAE Organizational team, organizational culture, learning curve, employees’ support to PLM, and organizational structure and design PLM tools, adaptation to regulatory changes, accessibility of documents, best practices, design standardization/ compliance, classification and release, PLM process, and knowledge mgmt Organizational process, PLM process maturity, enterprise PLM roadmap, and PLM implementation Nil Nil Best practices and PLM process and application trained employees Nil Remarks Accept the null hypothesis since there are no sufficient reasons to reject the null hypothesis Reject the null hypothesis since there are significant differences in most of the critical components across the dimensions Accept the null hypothesis Perspectives of Users and Service Providers on Deployment Maturity 829 References Balasusbramaniam K, Mahalingam G, Saji Joseph K, Shivananand MI (2003) Data migration for PLM implementation – some challenges In: Proceedings of PLM symposium, Bangalore, India, 16–18 July 2003 Batenburg R, Versendaal J, Helmes RW (2005) The maturity of product lifecycle management in Dutch organizations: a strategic alignment perspective In: Proceedings of the International Conference on product lifecycle management: emerging solutions and challenges for global networked enterprise, Lyon, France Inderscience, Geneva, pp 436–450 Christopher J (2003) Virtual product development In: HP Asia VPD conference http://h20427 www2.hp.com/event/kr/ko/mcae2003/pdf/Track1_1.pdf Malhotra NK (2004) Marketing research, 4th edn Pearson Education India, New Delhi Ramanathan K (1988) Technometric model – measurement of technology at the firm level International Journal of Science and Public Policy 15(4):230–249 Saaksvuori A, Immoneon A (2004) Product lifecycle management, 2nd edn Springer, New York Sharma A (2005) Collaborative product innovation: integrating elements of CPI via PLM framework Comput Aided Des 37(13):1425–1434 Stark J (2005) Product lifecycle management – 21st century paradigm for product realization Springer, London Venugopalan SR, Ramakrishnan G, Ganesh LS, Prakash Sai L (2009) A framework for assessing the maturity of product lifecycle management practices In: Proceedings of PDMA India IV annual international conference NPDC 2009: new product development – challenges in meltdown times, IITM, Chennai, India, December 17–19 2009, pp 159–171 Walvekar R, Subbanarasaiah A (2004) Maturity assessment of PLM components: a positive step towards an effective PLM implementation In: Second National Conference on IT enabled product development strategies, PSG Tech, Coimbatore, India, December 2004, pp 1–4 Part 3.5 New Product and Services Development ICT in New Product Development: Revulsion to Revolution Nityesh Bhatt and Abhinav Ved Introduction In 2011, Mahindra group entered the bike segment after success in scooter segment Honda has launched 42 bikes in different categories like sports, adventure, motor scooter, etc., all over the world (Kanan 2010) In 2010, 60 bikes were launched in India, and 30 were about to get launched by companies like Suzuki, TVS, BMW, Bajaj, Honda, Yamaha, etc (New Bikes in India 2010; Shravan 2009) In addition to rising customer demands and increased competition, information and communication technology (ICT) revolution can also be attributed for this scenario ICT plays an important role in ensuring speed and quality at every stage of new product development (NPD) In today’s fast-paced, competitive world, NPD with flexibility and innovation is essential for success New products can be seen in two ways In conventional way, new product means risk, huge investment, failure or prestige issue But in modern era, new product means opportunity, innovation, improvement, profits and success For example, M has realised 25% of its sales from products developed in the last years (Takeuchi and Nonaka 1986) With software packages like ERP and PLM, ICT not only helps in integration and information exchange but also helps in meeting deadlines and making projects profitable Literature Review IT for NPD is necessary condition but not sufficient for potential benefits (Durmusoglu et al 2006) New products provide increased sales, profits and competitive strength for most organisations (Sivadas and Dwyer 2000) NPD involves successive steps from N Bhatt (*) • A Ved Institute of Management, Nirma University, Ahmedabad, India e-mail: nityesh@imnu.ac.in; abhinav_ved@yahoo.co.in Department of Management Studies, Indian Institute of Science, Bangalore, Driving the Economy through Innovation and Entrepreneurship, DOI 10.1007/978-81-322-0746-7_68, # Springer India 2013 833 834 N Bhatt and A Ved idea generation to final launch of product to the market The process of NPD includes the steps like idea generation, idea screening, concept development and testing, business analysis, beta testing and market testing, technical implementation and commercialization (Venture Navigator 2007) Internet and related technologies can add significant value during each stage of NPD (Howe et al 2000) NPD has long been recognised as one of the corporate core functions (Huang et al 2004) During the past 25 years, NPD has increasingly been recognised as a critical factor in ensuring continued existence of firms (Biemans 2003) A study of more than 700 of the Fortune 1,000 companies indicates that new products provide approximately one-third of their profits (Booz, Allen and Hamilton 1982) The rate of market and technological changes has accelerated in the past years, and this turbulent environment requires new methods and techniques to bring successful new products to the marketplace (Goodwin 2009) Particularly for companies with short product life cycles, it is important to quickly and safely develop new products and new product platforms that fulfil reasonable demands on quality, performance and cost (Ottosson 2004) This global commitment to innovation can be seen from the growth of worldwide R&D spending from an estimated $525 billion in 1996 to approximately $1.1 trillion in 2007 (National Science 2007) Microsoft’s Chief Information Officer, Stuart Scott, notes that his firm now spends 45% of its IT budget on supporting NPD efforts, a significant increase from the 30% in the past (Murphy 2007) Durmusoglu (2009) argues that IT infrastructure is rare, imperfectly imitable and imperfectly mobile and thereby constitutes a capability of the firm that can lead to efficiencies in business processes Three moderating factors, intensity of competition, technological turbulence and market turbulence, were posited to affect the strength of the relationship between a firm’s IT infrastructure capability and the efficiency of its NPD process (Durmusoglu 2009) Higher use of IT tools in NPD was found in those companies where the importance of product or project as well as project risk was more to the firm (Barczak et al 2007) Haverila and Ashill (2011) describe that IT helps in NPD process by gathering and disseminating product and market information (Haverila and Ashill 2011) With the examples of Xerox and Toyota’s NPD process, Jusco (2010) describes the use of information technology to bridge the knowledge gap among product development team members and utilise knowledge in a way to develop products faster (Jusco 2010) Based on literature studied, authors have created a framework shown in Fig to discuss the role of ICT in NPD It has three broad components First component covers the drivers for deployment of ICT tools in NPD Second component deals with various ICT tools, while the last component highlights its impact Drivers for IT deployment in NPD are easy to understand With higher income and education, aspiration level of people is touching new heights There is a cut-throat competition in every market due to privatisation and globalisation Widespread proliferation of various kinds of media has made the customers informed about various products and services All these factors necessitate effective utilisation of information technology resources in the most efficient and innovative way ICT in New Product Development: Revulsion to Revolution 835 Fig Drivers of ICT in NPD and its impact Effective Usage of Information Technology All above-mentioned advantages for NPD can be achieved through effective application of ICT tools like simulation design CAD/CAM, 3D modelling, virtual team, knowledge management, data mining/warehousing, artificial intelligence and software tools like product life cycle management (PLM) These applications independently and/or in combination boost NPD process 3.1 Simulation Design and Modelling Simulation means creating virtual environment and testing product virtually in the environment required It helps in product testing without actually building the design, thus results in cost saving Another advantage is level of details one can get from simulation Delphi automotive is working on noise comfort for passengers in cars and pedestrians around the vehicles with the help of simulation Simulation reduces development time and more efficient design of car (Delphi 2011) Software available with Siemens for plant simulation provides benefits like 3–6% savings in initial investment, increases existing system productivity by 15–20%, reduces new system costs by 5–20%, optimises resource consumption and re-use and reduces inventories by 20–60% and throughput time by 20–60% (Plant Simulation 2011) Automation and CAD software have helped in a great way to manufacturing and construction industry Vonderembse et al (1997) conducted in-depth, on-site interviews with executives from four companies to understand how these organisations cope with automation, integration and manufacturing system performance The earthmoving company found substantial improvement in efficiency, low cost and shorter delivery time For electric generator company, ICT not only enhanced efficiency but also helped in reduction of order delivery time because part drawings 836 N Bhatt and A Ved could be easily and quickly retrieved; design changes could be incorporated quickly with CAD and transferred to shop floor (Vonderembse et al 1997) Information technology is a platform for concurrent engineering, well known for the construction industry (Salomone 1996) It improves management of engineering process through better control of data, engineering activities, changes and product configurations IT has enabled multinational firms to work 24 Â and for 365 days, operating from different time horizons It is estimated that concurrent engineering can save around 30–40% reduction in project time and 60–80% reduction in design changes after release (Stark 1992, 1998) Engineering department at L&T is using the Intergraph 3D plant design system (PDS) software for developing a 3-dimensional model of the project encompassing all related aspects including the layout, piping, mechanical, electrical, instrumentation and civil engineering disciplines Some of the direct benefits derived from this system include auto-extraction of piping isometrics from the 3D CAD models, auto-extraction of structural fabrication drawings through softwares such as dX-Steel, and checking of interference between various elements of piping, equipment, structural, instrumentation, etc., through the simulated ‘walk-through’ It has helped in not just reducing errors but increased prestige of the company too (Larsen and Toubro 2010) 3.2 Knowledge Management (KM) Knowledge management is the process of generating, documenting and disseminating knowledge among its employees As the tacit knowledge is converted into explicit knowledge in KM, employee attrition does not affect the organisations too much Employee learns from the past experiences occurred from similar projects; thus, KM helps in increasing productivity of employees in the organisation (Bharadwaj 2000) Motorola capitalised on its portable pager business to develop portable cellular telephones Corning used its expertise in glass technology to develop optical fibres (Ozer 2000) Dow Chemical—by focusing on the active management of its patent portfolio—has generated over $125 million in revenues from licensing and other ways of exploiting their intangible assets (Knowledge Management 2010) BP business managers attributed around $260 million of added value by using this approach A practical example of this has been in the cost reduction in the construction of European retail sites At the beginning of 1998, a challenge was set of reducing the build costs of retail sites in Europe by 10% A joint venture between BP and Bovis was responsible for the management of these activities in Europe The Alliance invited the BP KM Team to help them achieve this outcome Finally, company realised savings of $74 million in 1998 giving them competitive advantage in the mature European marketplace This knowledge is now also being leveraged on a global scale by project engineers in Venezuela, China, Poland and Japan (KM and British Petroleum 2011) ... Nemmadi: The Bytes and Bites of E-Governance in Karnataka, India 633 Madhuchhanda Das Aundhe and Ramesh Narasimhan An Empirical Investigation into the Extent... interrelationship amongst the various factors and the relative importance of each of these factors and their contribution to the final innovation capability and performance of the firm The questionnaire... as an input for the second stage of the study The second stage of the study included the quantitative analysis using an online questionnaire (Simsek et al 2005; Slater and Atuahene-Gima 2004) The