Research paper Supply chain management: some sectoral dissimilarities in the Indian manufacturing industry Sanjay Jharkharia Department of Mechanical Engineering, Bundelkhand Institute of Engineering and Technology Jhansi, Jhansi, India, and Ravi Shankar Department of Management Studies, Indian Institute of Technology Delhi, New Delhi, India Abstract Purpose To explore the dissimilarities, if any, in supply chain practices among different sectors of Indian manufacturing industry through development and testing of some hypotheses. Design/methodology/approach A questionnaire-based survey was conducted on four sectors of the Indian manufacturing industry. These are Auto, Engineering, Process and FMCG (Fast Moving Consumer Goods) sector. Statistical tests such as t-tests and regression analysis were conducted to test the hypotheses. Findings It is observed from this study that the companies in the auto sector significantly differ from those in the other sectors in the adoption of SCM practices. Engineering and auto sectors have some similarities in certain aspects of SCM. Research limitations/implications Though the survey covers many companies, which together constitute many supply chains, it would be interesting to consider a few supply chains in their totality in further research on the topic. Practical implications The analysis reveals that there are few fundamental differences in the operation and working of different sectors and these might be the cause of the observed dissimilarities in their supply chain practices. Originality/value This paper fulfills the gap of identifying and testing the dissimilarities among some sectors in their SCM practices. The analysis would be useful for the practicing managers in developing strategies for their supply chains. Keywords Manufacturing industries, India Paper type Conceptual paper Introduction For more than a decade, SCM has received increased attention in the industry and the academia as it reportedly helps the companies in achieving competitive advantage. The literature on SCM discusses in detail its implementation by the industries but little is known about the dissimilarities among various sectors of the industries. In India, few surveys on SCM have been reported (e.g. Sahay et al., 2003; Jharkharia and Shankar, 2004) but no insight has emerged about the dissimilarities, if any, in SCM practices of different sectors. These observations have motivated the authors to explore the dissimilarities, if any, among various sectors in The current issue and full text archive of this journal is available at www.emeraldinsight.com/1359-8546.htm 11/4 (2006) 345 352 q Emerald Group Publishing Limited [ISSN 1359-8546] [DOI 10.1108/13598540610671798] their supply chain practices. In this article, six hypotheses have been proposed. These hypotheses have been tested through a questionnaire-based survey on four sectors of Indian manufacturing industry. The main objective of this paper is to explore the dissimilarities among sectors of manufacturing industry in terms of their supply chain practices and formulate the relevant hypotheses. Later, these hypotheses are to be tested for the sectors of Indian manufacturing industry. The next section of this paper deals with the literature review and hypotheses development. This is followed by the description of research methodology, hypotheses testing, and discussion of the results. We conclude with the limitations of this research and directions for further research. The authors would like to put on record their appreciation to the two anonymous referees for their valuable suggestions, which have enhanced the quality of the paper over its earlier version. Received: 30 May 2003 Accepted: 17 July 2004 345
Literature review and hypotheses development In this section, sector-specific hypotheses are formulated on the basis of available literature. Attitude of the major stakeholder of the supply chain In most cases, one partner in a supply chain (normally major stakeholder or original equipment manufacturer, OEM) is so dominant over the others that it may unilaterally dictate its own terms and conditions over the smaller partners of the value chain. In this regard, Munson et al. (2000) have observed that the major stakeholder in a supply chain may take some of the decisions of common interest at its own without considering the interests and constraints of the smaller partners (Munson et al., 2000). Similar observations have been made by Moberg et al. (2002), who noted that the larger supply chain partners often cause inconvenience to other supply chain members. The larger partners of the supply chains often force the other partners to comply with these decisions. Non-compliance may sometimes lead to loss of business for the smaller partners. Such attitude of the major stakeholders of the supply chains may be termed as the dictatorial attitude. Munson et al. (2000) have further elaborated on the dictatorial attitude of the major stakeholder and observed that common examples of such attitude include asking the partners, irrespective of their constraints, to use advanced IT-tools for the automation of the supply chain, supply just-in-time etc. Kehoe and Boughton (2001) have observed that the power within the auto sector lies very much within the brand owners rather than with the dealers or the first or second tier suppliers. Therefore, the OEM may dictate their terms on the smaller partners. In this regard, Rassameethes et al. (2000) have reported that the US automakers have directed their first tier suppliers to use electronic data interchange (EDI) irrespective of their constraints. These observations lead to the formulation of the hypothesis: H1. Dictatorial attitude of the major stakeholder of the supply chain is more common in the auto sector. Incentives to the supply chain partners It is suggested in the literature (Munson et al., 2000; Ballou et al., 2000) that some rewards or incentives should be provided to the small partners in the supply chain for collaborative information sharing. Ballou et al. (2000) have discussed these rewards and noted that a large partner can support the smaller partners in the following ways:. by providing status of the preferred partner in the supply chain;. by providing training, information or problem solving assistance to the suppliers; and. through another form of incentive, such as the use of referent power. In referent power, the larger partners may allow the smaller partners to use their brand name (e.g. Intel Inside ) for their benefits. In terms of financial incentives, some large companies have set example by subsidizing the EDI start-up costs of their small trading partners (Munson et al., 2000). Due to a relatively complex bill of materials and high-level of outsourcing in the engineering and auto sectors, the companies in these sectors are more interactive with their suppliers (Kehoe and Boughton, 2001). Some of these suppliers are vital to the success of the company. By developing strategic alliances with the small supply chain partners the large companies are benefited in a variety of ways such as quality, reduced inventory levels, low product development time, responsiveness etc. Therefore, the companies in these sectors may provide incentives to the small partners for forging strategic alliances and information sharing. These observations lead to the formulation of the hypothesis: H2. Auto and engineering sectors believe more in providing incentives to the supply chain partners. Activities related to internal business The increased competition and globalization motivate the business managers to pay more attention towards internal business activities. The improvement in these activities such as inventory turnover, assets utilization, operating cost, manufacturing lead-time, just-in-time environment etc. results in lean and responsive supply chains. Auto supply chains are the examples of such lean and agile supply chains (Kehoe and Boughton, 2001). Due to highly competitive environment in the auto sector, managers are motivated to adopt the latest tools and techniques of industrial engineering. The automotive sector thus defines the industry standards in any country (Bhateja and Banwet, 1999) and its study enables one to study the emerging trends in the developing countries (Dangayach and Deshmukh, 2001). Under such situations it may be assumed that the auto sector pays relatively more attention to internal business activities. Hence the hypothesis is formulated as: H3. Compared to other sectors, the auto sector pays relatively more attention to improve the internalbusiness activities. Product-related information sharing with suppliers Due to relatively more complex bill of materials, outsourcing is a common practice in the auto and engineering sectors. Outsourcing demands active collaboration between the manufacturers and the suppliers in product development. To ensure the success of these collaborations, it is essential that the manufacturers in these sectors frequently share information with their suppliers on a routine basis. The observations of the Arthur D. Little survey (1999) indicate that the auto sector is a leading user of SCM software. The implementation of SCM software is an indicator that the companies in the auto sector share information with suppliers and other supply chain members on a regular basis. It is reported that the companies in the auto and engineering sectors involve suppliers in the forecasting and product development activities. It is further reported that in sharing product design, strategic use of IT has become a global practice (Saxena and Sahay, 2000). These observations lead to the formulation of the following hypotheses: H4. Auto and engineering sectors more frequently share product related information with the suppliers. H5. Compared to other sectors, auto and engineering sectors make relatively more use of IT in sharing of design data. Investments towards the IT-enablement of supply chain The IT-enablement of a supply chain not only improves the responsiveness but also brings accuracy in communication. 346
Therefore, use of IT in supply chains has now become a necessity for the survival of the companies (Sahay et al., 2003). It is observed from the KPMG supply chain survey (Freeman, 1998) that the auto sector has integrated its IT systems more than any other sector. Similar trends have been reported by Fodor (2000). These observations lead to the formulation of the following hypothesis. H6. Compared to others, auto sector has made more investments towards the IT-enablement of its supply chain. Research methodology A questionnaire-based survey was conducted to test the validity of proposed hypotheses. Four sectors from Indian manufacturing industry were selected for the administration of the questionnaire. These are: (1) Auto; (2) Engineering; (3) Fast Moving Consumer Goods (FMCG); and (4) Process. Among these four sectors, auto sector is seen as a flagship bearer and is frequently regarded as a barometer measuring the current wealth of a nation s economy (Childerhouse et al., 2003). The extreme complexities and large bill of materials makes it an ideal case for the study of SCM. The companies selected for the survey in this sector includes both; the OEM as well as the component suppliers. The FMCG sector is characterized by the intense competition and low level of participation by suppliers (Sahay et al., 2003). The companies selected for the survey in this sector include toiletries manufacturer, food products, and OTC (over the counter) products manufacturers. The engineering sector is recognized for long lead-time in product development and manufacturing (Dangayach, 2001; Jharkharia and Shankar, 2004). The companies selected for survey in this sector include light and heavy engineering industries, white goods manufacturers, castings makers etc. The companies selected for the survey in process sector include fertilizer, cement, paints, steel and other such process companies. These four sectors from the manufacturing industry are highly diversified in nature and it may be assumed that these are the representative sectors of the entire manufacturing industry. Though no specific supply chains were targeted in this study, the sample companies together constituted many diversified supply chains. Therefore, a study of the perceptions and practices of these surveyed companies on SCM related issues might provide a fair assessment of the supply chains in the Indian manufacturing industry. Data analysis and testing of hypotheses This section deals with the data analysis and testing of the proposed hypotheses. In data analysis, we first present the respondents profile. This is followed by the results related to the reliability and validity of the questionnaire, and nonresponse bias. Finally, testing of the proposed hypotheses has also been reported in this section. Survey response and respondents profile Of the five hundred questionnaires sent, 112 filled-up questionnaires were returned. Four of these were incompletely filled and were therefore discarded from the analysis. This gives a response rate of 21.6 per cent, which is satisfactory for such surveys (Malhotra and Grover, 1998). Of the 108 usable responses, auto and engineering sectors comprised 31.5 per cent each, FMCG 14.8 per cent and process sector 22.2 per cent (Figure 1). In terms of turnover, 10.2 per cent of the respondents had annual turnover of less than five million dollars; 14.8 per cent with a turnover in the range of 5-20 million dollars, 38 per cent in the range of 20-100 million dollars and 37 per cent of the respondent companies had a turnover of more than 100 million of dollars (Figure 2). Reliability and validity of the questionnaire Cronbach s coefficient (a) was calculated to test the reliability and internal consistency of the responses. For the questions reported in this study, the values of a have been found to be more than 0.5 with an average value of 0.73, which indicate a high degree of internal consistency in the responses. Two main types of validity, content and construct validity, were Figure 1 Percent of respondents of the survey in different sectors Figure 2 Annual turnovers of the respondent companies in millions of dollars Questionnaire development The questionnaire was designed on a five-point Likert scale. It contains many supply chain issues including a few reported in this study. The postal survey method was used for its administration. Sample was taken form the Directory of ISO 9000 companies in India and India s 500 largest wealth creator companies (Gandhok et al., 2002). In sampling, it was tried to ensure that the sample companies fulfill two minimum criteria: firstly, the annual turnover is more than one million of dollars, and secondly, the employee strength is more than 100. 347
tested respectively by: firstly, review of the literature and pilot survey, and secondly, factor analysis. Factors were extracted using varimax rotation. As suggested by Kim and Mueller (1978) and Hair et al. (1995), an item is considered to load on a given factor if the factor loading from the rotated factor pattern is 0.40 or more for that factor. The factor loading for the questionnaire item used in H1 is 0.719. The loading for the questionnaire item used in the H2 is 0.585. The factor loadings of the questionnaire items used in H3 are shown in Table I. In H4, the factor loadings for the two items used in this study are 0.653 and 0.575 respectively. In hypothesis 5, the factor loading for the relevant item is 0.631. In the hypothesis 6, initially 10 items were used in the relevant question. Later, 4 items were dropped due to low factor loadings. The items used in the question and their factor loadings are shown Table II. Table III t-tests for the assessment of non-response bias Items for comparison Test results Mean value Provision of incentives to partners in the 20.354 a 2.09 d supply chain for information sharing 105 b 2.46 e (16c) 0.201 c Level of product development-related 20.655 a 3.238 d information sharing with the suppliers 103 b 3.381 e (10d) 0.514 c Level of agreement with the policy 0.386 a 2.952 d of immediately communicating the 103 b 2.857 e changes in product design and organizational policies to the supply chain partners (16d) 0.700 c Notes: a t value; b degree of freedom; c 2-tailed significance; d mean of early respondents; e mean of late respondents Non-response bias Non-response bias, if any, may be tested by comparing the answers of early and late respondents (Lambert and Harrington, 1990). Therefore, it was assessed by comparing the responses, which were received late after sending two or more reminders (45 in this case) with the early respondents, which were received without a reminder or with a single reminder (63 in present case). The results from the t-tests on some key variables of this study suggest that the early respondents do not significantly differ from the late responses (see Table III). Therefore, non-response bias is ruled out in this study. Table I Factor loadings of the items used in H3 Items Factor loadings Inventory turnover 0.631 Assets utilization 0.526 Throughput time 0.511 Purchase lead-time 0.462 Manufacturing lead-time 0.675 Operating costs 0.751 Just-in-time environment 0.574 Stabilized master schedule 0.670 Forecasting accuracy 0.593 Table II Factor loadings of the items used in H6 Items Factor loadings Bar-coding 0.659 Extranet 0.805 EDI 0.794 AS/RS 0.736 Supply chain software 0.656 ERP software 0.615 Computer hardware 0.260 Local area network 0.312 Office automation 0.143 Employee training 0.193 Testing of hypotheses Descriptive statistics and t-tests have been used to test the hypotheses on the SPSS (version 10.00) software. Hypothesis 1 In the question related to this hypothesis, the respondents were asked to assign weightage in addressing the dictatorial attitude of major stakeholders of their supply chain on the five-point Likert scale. On this scale, 1 and 5 correspond to very low and very high weightage respectively. An independent sample t-test is conducted to test this hypothesis (Table IV). In this test, auto sector is compared against rest of the surveyed sectors. The results of this test indicate that the auto sector significantly differ (p ¼ 0:007) in the weightage required for the management of dictatorial attitude of major stakeholder of the supply chain. The results of the t-tests in Table IV are sufficient to accept the proposed hypothesis. Hypothesis 2 To test this hypothesis the respondents were asked about the provision of incentives to partners in their supply chain for information sharing. They were asked to give the opinion of their organization on the five-point Likert scale. An independent sample t-test is conducted to test this hypothesis (Table V). The results of the t-test indicate that auto and engineering sectors significantly differ from the rest of the sectors in providing incentives in various forms to their suppliers. This result is valid at a significance level of 0.001 therefore the hypothesis is accepted. Table IV Independent sample t-test for attitude of major stakeholder (auto versus rest) Items for comparison Test results Mean value Weightage required in the management 2.769 a 3.1875 d of dictatorial attitude of major 100 b 2.5286 e stakeholder of the supply chain 0.007 c sector; e mean of rest of the sectors 348
Table V Independent sample t-test for incentives to partners (auto and engineering versus rest) Item for comparison Test results Mean value Incentives are provided to partners for 3.519 a 1.9219 d information sharing 101.969 b 1.3250 e 0.001 c and engineering sector; e mean of rest of the sectors Hypothesis 3 To test this hypothesis the respondents were asked about the importance assigned by their organization to some internal business activities for the purpose of supply chain performance measurement. They were requested to respond on the five-point Likert scale. To test this hypothesis, the importance assigned by the respondents to internal business measures in measuring the performance of a supply chain is compared on independent sample t-test (Table VI). The results of t-tests indicate that for nine items, which belong to internal business measures, the mean values of importance assigned by auto sector is more than the rest of Table VI Independent sample t-test for internal business measures (auto sector versus rest of the sectors) Items for comparison Test results Mean value Weightage assigned to following issues as internal business measures Inventory turnover 2.327 a 4.6129 d 80.049 b 4.2286 e 0.022 c Assets utilization 2.435 a 4.2903 d 71.866 b 3.7536 e 0.017 c Throughput time 2.994 a 4.4516 d 97 b 3.9118 e 0.003 c Purchase lead time 2.303 a 4.3333 d 98 b 3.9143 e 0.023 c Manufacturing lead time 2.481 a 4.1935 d 99 b 3.7429 e 0.015 c Operating cost 1.207 a 4.1613 d 99 b 3.9429 e 0.269 c Just-in-time environment 3.985 a 4.6129 d 95.008 b 3.9143 e 0.000 c Stabilized master schedule 3.985 a 4.1000 d 95.008 b 3.8406 e 0.000 c Forecasting accuracy 1.341 a 4.2581 d 97 b 3.7536 e 0.183 c sector; e mean of rest of the companies the sectors. Further, in seven of these nine items considered for internal business the difference in the importance assigned between the auto and other industries is significant (at a p value of 0.05 or less) therefore the hypothesis is accepted. Hypothesis 4 To test this hypothesis, respondents were asked about the levels of adoption of the following practices in their organization: firstly, product related information sharing with suppliers, and secondly, prompt communication of changes in product design and organizational policies to supply chain partners. An independent sample t-test is conducted to test this hypothesis (Table VII). The sectors are compared on these two items as stated above. The results of the t-tests indicate that the mean values are more for the auto and engineering sectors. The differences in these mean values are also significant at a level of 0.05 or below. Therefore, the hypothesis is accepted. Hypothesis 5 To test this hypothesis the respondents were asked to indicate the level of use of IT in design data sharing with their supply chain partners. An independent sample t-test is conducted to test the hypothesis (Table VIII). The results of the t-test verify that auto and engineering sectors are ahead of others in using IT for design data sharing in the supply chain. This result is significant at a level of p ¼ 0:01, therefore the hypothesis is accepted. Hypothesis 6 Regarding investment in automation of supply chains, ten IT tools, which are commonly used for the IT-enablement of a supply chain, were included in the questionnaire. Respondents were asked to indicate the investments made by their organization on these tools in reference to their Table VII Independent sample t-test for product-related information sharing with suppliers (auto and engineering sector versus rest of the sectors) Items for comparison Test results Mean value Level of information sharing related to 2.413 a 3.4923 d product development with suppliers 103 b 2.9750 e 0.018 c Changes in product design and 2.254 a 3.1231 d organizational policies are immediately 103 b 2.5750 e communicated to the supply chain 0.026 c partners and engineering sector; e mean of rest of the sectors Table VIII Independent sample t-test for use of IT in design data sharing (auto and engineering sector versus rest of the sectors) Item for comparison Test results Mean value Use of IT in design data sharing 2.643 a 3.3906 d 102 b 2.7250 e 0.010 c and engineering sector; e mean of rest of the sectors 349
annual turnover. The answers were obtained on the five-point Likert scale. Four items, namely computer hardware, local area network, office automation, and employee training were excluded from the analysis due to low factor loadings. The items finally used for comparison were: investments in bar-coding, extranet, EDI, automated storage and retrieval system (AS/RS), SCM software, and Enterprise Resource Planning (ERP) software. The descriptive statistics for the use of these items in the surveyed sectors is shown in Table IX. To test this hypothesis, auto sector is compared with rest of the sectors covered in the questionnaire on independent sample t-test. The comparison is made for the degree of investment, in reference to their annual turnover, made by these sectors on advanced IT tools for supply chain automation. The results of the t-test are shown in Table X. These results do not indicate a significant difference between auto and other sectors. Therefore, descriptive statistics (Table IX) were referred to observe the trends of investments by the different sectors. From the descriptive statistics it is observed that the engineering sector has made least investment in each of the IT tools under consideration. Therefore, H6 is now revised as follows: H6. (Revised) Compared to others, engineering sector has made lesser investment for the IT-enablement of its supply chain. To test this hypothesis, a t-test is conducted for engineering versus rest of the sectors. The results of this t-test are shown in Table XI. Table IX Descriptive statistics for H6 Investment towards following IT tools, in reference to the annual turnover Sectors n Mean Standard Deviation Bar-coding Auto 32 2.4688 0.6672 Engineering 31 2.0000 1.0954 FMCG 15 2.1333 1.3020 Process 23 2.3043 1.2590 Extranet Auto 29 2.6207 1.3736 Engineering 31 2.4516 1.4338 FMCG 15 3.0000 0.8452 Process 22 3.6364 1.3290 EDI Auto 29 2.6897 1.2278 Engineering 32 2.5625 1.5645 FMCG 15 2.4000 1.2421 Process 23 3.1304 1.5755 AS/RS Auto 30 2.8333 1.4641 Engineering 31 2.4516 1.1500 FMCG 14 2.2857 1.3260 Process 22 3.4091 1.1406 Supply chain software Auto 32 3.0000 1.3678 Engineering 32 2.2813 1.3966 FMCG 15 3.2000 0.8619 Process 23 3.1739 1.4350 ERP Auto 31 4.0000 1.2910 Engineering 33 3.5152 1.7342 FMCG 15 4.0667 1.0328 Process 22 4.4091 1.0538 Table X Independent sample t-test for investments towards IT tools (auto sector versus rest of the sectors) Items Test results Mean value Degree of investment in bar-coding 1.313 a 2.4688 d 99 b 2.1304 e 0.192 c Degree of investment in Extranet 21.009 a 2.6207 d 95 b 2.9559 e 0.275 c Degree of investment in EDI 20.078 a 2.6897 d 97 b 2.7143 e 0.938 c Degree of investment in AS/RS 0.350 a 2.8333 d 95 b 2.7313 e 0.727 c Degree of investment in supply 0.780 a 3.0000 d chain software 100 b 2.7714 e 0.437 c Degree of investment in ERP 0.296 a 4.0000 d software 99 b 3.9143 e 0.778 c sector firms; e mean of rest of the sectors Table XI Independent sample t-test for investments towards IT-tools (engineering sector versus rest of the sectors) Items Test results Mean value Degree of investment in bar-coding 1.319 a 2.0000 d 99 b 2.3429 e 0.190 c Degree of investment in Extranet 2.012 a 2.4516 d 95 b 3.0455 e 0.047 c Degree of investment in EDI 0.693 a 2.5625 d 97 b 2.7761 e 0.490 c Degree of investment in AS/RS 1.603 a 2.4516 d 95 b 2.9117 e 0.112 c Degree of investment in supply 2.902 a 2.2813 d chain software 100 b 3.1000 e 0.005 c Degree of investment in ERP 1.897 a 3.5152 d 46.421 b 4.1471 e 0.064 c Notes: a t value; b degree of freedom; c 2-tailed significance; d mean of engineering sector; e mean of rest of the sectors From these results it is observed that the difference between the engineering and the rest of the sectors is significant for investment in extranet ( p ¼ 0.047) and supply chain software ( p ¼ 0.005). It is marginally significant ( p ¼ 0.064) in case of investment in ERP. For other automation tools such as barcoding, EDI, and AS/RS the difference is not statistically significant but in all of these cases the mean values of 350
investment are the least for the engineering sector. Further, extranet and supply chain software are two important supply chain automation tools and for both of these tools engineering sector has made significantly less investment. Therefore, the hypothesis may be accepted. Discussion and implications for managers In this study four sectors from the Indian manufacturing industry have been compared for their dissimilarities, if any, in their supply chain practices. It is observed from the study that on many issues auto sector has some similarities with the engineering sector. It might be due to the similarities in the pattern of manufacturing in these two sectors, e.g. these sectors normally have a complex bill of materials. Many of the raw materials and components are outsourced from a large number of suppliers. In most cases there are many suppliers for a single component and raw material. As against auto and engineering sectors, in the process and FMCG sectors relatively fewer raw materials and finished components are used in the final product. The major stakeholders in the auto sector can exercise some power or influence over small suppliers and other entities in the supply chain. This power is attributed to the key position that they have in their supply chains. Therefore, the top management of auto sector should utilize this opportunity for more information sharing and up gradation of the IT facilities in their supply chains to make these more efficient and integrated. The small companies in the auto sector may not be having enough funds, and technical and managerial expertise in the IT-enablement of their supply chains. Therefore, the large companies in this sector should support the smaller companies by providing necessary assistance in the process of building up an effective IT-enabled supply chain. In the H3, it is observed that the auto sector is more conscious in improving the internal business measures. Here it should also be noted that the auto sector has long been a leader in implementing the latest practices of industrial engineering be it TQM, JIT, BPR etc. It is also known for its lean and agile supply chain. Therefore, following the policies of the auto sector, the other sectors should also place high priority to improve the internal business activities. In the H4 and H5, it is validated that there is relatively more IT-based product related information sharing in the auto and engineering sectors. This might be because of the reason that in these two sectors the product design is not confined to the premises of the final manufacturer but it also takes place at the sites of the suppliers. In such situations, a close coordination between the manufacturers and the components suppliers is essential for the concurrent new product development. As IT is capable of reducing the delays in information sharing it has to be extensively used by the auto and engineering sector for the sharing of data related to product design. In the H6 (revised), it is observed that of the commonly used IT tools for supply chain automation, engineering sector has made lesser investment compared to the other sectors. The engineering sector is known for longer lead-time in new product development and many of the products in the engineering sector have a long manufacturing lead-time. Therefore, it is suggested that by investing in these automation tools, various lead-times may be shortened. It may also result in concurrent new product development, responsiveness and better customer service. Further, in today s competitive environment, the use of IT is a necessity for the survival of the companies (Sahay et al., 2003). Fortunately, these days the cost of technology has come down and it is coming further down which may encourage the companies to go for adopting IT-tools towards improving the effectiveness of their supply chain. The hypothesized findings indicate that the SCM has its own importance but different sectors are adopting it as per their own constraints and working environments. From a practical perspective, the analysis reveals that there is some fundamental dissimilarity in the operations and working of some sectors and this might be the cause of the observed dissimilarities in their supply chain practices. Finally, though the hypotheses have been tested on the data derived from the Indian companies these have been developed on the basis of the literature and the empirical studies across the globe. In that sense these hypotheses validate the global pattern of the sectors under study. Limitations of work and directions for further research As with all other research, this study too has some limitations. In this section we identify these limitations and offer some suggestions for future research. A significant limitation of the research is the low sample size. Another limitation of this research is that though it covers many companies, which together constitute many supply chains, it would be interesting to consider a few supply chains in their totality, for example, the supply chain of an automobile manufacturer right from the end supplier to the final customer. Due to some constraints we could not target such study but the future research may be focused on such supply chains. Another limitation of this study is the absence of other issues such as organizational culture, risk-taking propensity etc. on which the sectors may differ. These issues should be considered in future studies. References Arthur D. Little (Global Management Consulting) (1999), An Arthur D. 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