Invest or divest? On the relative improvement potential in outsourcing manufacturing [ ]

Transcription

1 Invest or divest? On the relative improvement potential in outsourcing manufacturing [ ] Mandar Dabhilkar* and Lars Bengtsson Department of Industrial Engineering and Management, University of Gävle Gävle, Sweden *Corresponding author: , Version Abstract This article clarifies the comparative effect of outsourcing in relation to alternative manufacturing practices. The analysis focuses on plant performance and contrasts the effect of outsourcing manufacturing with initiatives such as investments in advanced manufacturing technology, changes towards lean manufacturing, internal cooperation between design and manufacturing, as well as intensified supplier and customer cooperation. Our sample of 267 Swedish manufacturing plants was subjected to multiple regression analysis (response rate 47%). We found that in comparison to outsourcing manufacturing, the other measured manufacturing development initiatives had a much stronger ability to predict improvements in plant performance. Furthermore, when outsourcing had positive effects on plant performance, these effects were more likely to be realized when concurrent initiatives were taken to develop certain manufacturing capabilities. The overall conclusion that follows from these findings is that there is a greater performance improvement potential in investing in, rather than divesting, the manufacturing function. Outsourcing should mainly be used to set free resources in order to enable a development of certain manufacturing capabilities. Thus, manufacturing still matters, and this paper further elucidates how the manufacturing function can be developed to drive competitive advantage in an age of outsourcing. Keywords: Empirical research, outsourcing manufacturing, plant performance, Sweden 1

2 Introduction Outsourcing manufacturing has become a major industrial trend. This development is an understandable reaction to the turbulent environment and the many opportunities that globalization provides. However, our position is that there are alternatives to outsourcing that also have to be considered, such as investments in advanced manufacturing technology, changes towards lean manufacturing, increased internal cooperation between design and manufacturing, as well as intensified supplier and customer cooperation. In the discourse on the future of manufacturing, it is our view that these alternatives have been overshadowed by the many promises of outsourcing. Therefore, we have devoted this article to presenting findings from a study that clarifies the effect of outsourcing manufacturing in relation to these other initiatives. Previous large-scale survey studies on the performance impact of outsourcing manufacturing at the plant level are few and show mixed results. While the work of Gilley and Rasheed (2000) as well as Mol et al. (2005) indicate no direct effects, the work of Görg and Hanley (2005) indicates some positive effects. The fact that there are few previous studies and these show mixed results is most certainly a motive to undertake further empirical analyses on outsourcing manufacturing and its effects on plant performance. In addition we suggest the following two reasons. Firstly, the previous studies just mentioned investigate the effects of outsourcing in isolation. But, as we have argued, it has become increasingly important to take a more holistic approach. This has also been underlined in the work of Voss (2005) as well as Laugen et al. (2005) who make clear that that the decision whether and what to outsource has become an increasingly important strategic choice in the portfolio of alternative manufacturing practices that engineering industry companies have at hand today. Secondly, we are in need of studies that measure outsourcing manufacturing in a different way than previously has been done. To begin with, we need to know if manufacturing has been outsourced during the studied period of time at the plant (simply, yes or no?) and if so, we need to know the change in cost for purchased materials as a share of total manufacturing cost due to this decision. Our study meets these criteria, while previous studies do not, see e.g., Gilley and Rasheed (2000), Görg and Hanley (2005), and Mol et al. (2005). These only measure cost for purchased materials as a share of the total manufacturing cost at a given point in time. However, in our view it is not the level of purchase per se that is of interest. Rather, it is the change in purchase, given that a company actually has outsourced manufacturing. Conceptual model and hypotheses Figure 1 depicts the factors under investigation in our study. Our approach suggests direct positive effects of the manufacturing development initiatives on plant performance, and direct as well as indirect, positive effects of outsourcing manufacturing on plant performance. We were inspired by the recommendations in the work of Voss (2005) when designing our approach. Therefore, the decision whether to outsource was treated as a distinct strategic choice, in relation to the alternative manufacturing development initiatives. Insert Figure 1 here Direct effects of manufacturing development initiatives 2

3 Our survey focuses on five different kinds of manufacturing development initiatives: Investments in advanced manufacturing technology, changes towards lean manufacturing, internal cooperation between design and manufacturing, cooperation with suppliers and cooperation with customers. On a general level our choice of manufacturing development initiatives resemble the action programs measured in the International Manufacturing Strategy Surveys, see e.g. Laugen et al. (2005). As depicted in Figure 1, our approach assumes a direct positive impact of these practices on plant performance. It is not controversial today to assume that these manufacturing practices have a positive impact on plant performance and therefore we do not expand this section in greater detail. Instead we make reference to key studies in each area. See for example Jonsson (2000) on advanced manufacturing technology, Shah and Ward (2003) on lean manufacturing, Swink (1999) on design-manufacturing cooperation, Ragatz et al. (1997) on supplier integration and Droge et al. (2004) on supplier and customer integration. Our first set of hypotheses was formulated as follows: Hypothesis 1a: Investments in advanced manufacturing technology have a positive effect on plant performance. Hypothesis 1b: Changes towards lean manufacturing have a positive effect on plant performance. Hypothesis 1c: Increased internal cooperation has a positive effect on plant performance. Hypothesis 1d: Increased supplier cooperation has a positive effect on plant performance. Hypothesis 1e: Increased customer cooperation has a positive effect on plant performance. Direct and indirect effects of outsourcing manufacturing In view of a manufacturing strategy perspective the decision whether to outsource is in our study treated as a distinct strategic choice. We perceive it as a traditional make-or-buy decision. We define outsourcing manufacturing as having parts or a family of parts that formerly were manufactured within the focal plant manufactured by an external supplier. We find support for our approach to study outsourcing manufacturing in the work of Venkatesan (1992), Welch and Nayak (1992), Quinn and Hilmer (1994), Cánez et al. (2000), McIvor (2000) and Momme (2002). In order to clarify the effect of outsourcing in relation to alternative manufacturing practices we had to develop an additional three sets of hypotheses (H2, H3a-H3e, and H4a-H4e). The first was formulated as follows: Hypothesis 2: Outsourcing manufacturing has a positive effect on plant performance. It simply assumes that companies which decide to rely on suppliers for parts that formerly were manufactured in-house now performs better as a consequence of this decision, ceteris paribus. Companies with small changes in purchase may expect small improvements and companies with larger changes in purchase may expect larger improvements in overall plant performance. It is fundamental to expect improved performance as a consequence of an outsourcing strategy. There are several reasons why. First of all, there is the cost motive. Reduced costs 3

4 can be achieved if the focal firm turns to suppliers that operate in countries with lower wages. This can also be achieved if the supplier provides goods or services to many customers, often called external economies of scale. Secondly, there is the focus motive. Better performance at the focal firm can be achieved as a result of an increased focus on core activities and outsourcing of non core activities to external suppliers. Thirdly, there is the capacity motive. The focal firm can improve its responsiveness to variability in demand by outsourcing peak demand to suppliers. Finally, there is the innovation motive. By taking advantage of the supplier s higher innovation capability the focal firm can improve time to market for its end product. Hypothesis 2 does not differentiate between these motives/outsourcing strategies and therefore we formulated Hypothesis 3a-3e as follows: Hypothesis 3a: Outsourcing manufacturing to low-wage countries has a positive effect on plant performance. Hypothesis 3a: Outsourcing manufacturing to low-wage countries has a positive effect on plant performance. Hypothesis 3b: Outsourcing manufacturing for greater external economy of scale has a positive effect on plant performance. Hypothesis 3c: Outsourcing manufacturing for increased focus on core business has a positive effect on plant performance. Hypothesis 3d: Outsourcing manufacturing for increased responsiveness to variability in demand has a direct positive effect on plant performance. Hypothesis 3e: Outsourcing manufacturing for increased access to innovation capability has a positive effect on plant performance. Finally, there is the interaction effect between outsourcing manufacturing and concurrent manufacturing development initiatives. One of the main reasons for companies to face the make-or-buy dilemma is a finite amount of resources. It is not possible to have world class manufacturing capabilities in all areas. Therefore companies need to prioritize, which can be done by allocating resources to core activities and at the same time outsourcing non core activities. The already formulated hypotheses do not deal with this issue in detail and therefore the following five were developed: Hypothesis 4a: Positive effects of outsourcing manufacturing on plant performance are more likely to be realized by concurrent investments in advanced manufacturing technology. Hypothesis 4b: Positive effects of outsourcing manufacturing on plant performance are more likely to be realized by concurrent changes towards lean manufacturing. Hypothesis 4c: Positive effects of outsourcing manufacturing on plant performance are more likely to be realized by concurrent efforts to increase internal cooperation. Hypothesis 4d: Positive effects of outsourcing manufacturing on plant performance are more likely to be realized by concurrent efforts to increase supplier cooperation. 4

5 Hypothesis 4e: Positive effects of outsourcing manufacturing on plant performance are more likely to be realized by concurrent efforts to increase customer cooperation. Methodology Data collection and sample A postal survey was distributed to a disproportionately stratified random sample of 563 manufacturing plants of Swedish engineering industry companies (see Table 1). This sampling technique is suggested when the population consists of subgroups with different numbers of plants (Forza, 2002). After two reminders, 267 of the targeted plants returned the instrument to us, which yielded an overall response rate of 47%. Insert Table 1 here Statistics Sweden s Business Register 2003 of manufacturing plants with more than 50 employees within ISIC codes was used as the sample frame. Data were collected during early spring 2004; whenever we use the phrase the studied three-year period in the article we mean the calendar years 2001, 2002 and The unit of analysis was manufacturing plants of engineering industry companies and the survey was addressed to the production manager. In order to detect response bias, we tried to come in contact with a random sample of 98 plants that after two reminders declined to fill in the form and return it to us. These plants were contacted by telephone. 34 plants agreed to answer five key questions from the original instrument in the following three topic areas: the strategic role of the manufacturing function, the degree of outsourcing manufacturing and plant performance. This enabled a comparison between those that participated in the survey and those that did not. No significant response bias was detected. Those that declined to participate in the telephone interview as well gave the following reasons for not participating in our study: Three units were not proper manufacturing plants and 23 plants were not interested due lack of time. At the remaining 38 plants it was not possible to contact the production manager during the time of our study. As a consequence of choosing a disproportionately stratified random sample, we had to add weights to our sample. The reason is that we wanted our sample to represent the population of Swedish engineering industry to the greatest possible extent. Smaller plants were weighted higher and larger plants were weighted lower. Details are given in Table 1. Stepwise regression analysis We subjected the data to stepwise regression analysis in order to test our initially stated research hypotheses. Independent variables entered into the analysis are described in Table 2, and dependent variables used in the analysis are described in Table 3. Influential observations as well as outliers were searched for. Outliers outside two standard deviations were found and discarded and therefore the number of cases in each regression model varies (see Table 5). We also checked for multicollinearity by using the Variance Inflation Factor (VIF) provided by SPSS. Hair et al. (1998) suggest that a VIF value above 10 is a reason to worry. In our regression models this value never exceeded Insert Table 2 here Insert Table 3 here 5

6 Measures (H1a H1e) The basic idea in our approach to measure manufacturing development initiatives was to balance changes done during the studied three-year period on the one hand, and the capability level of the manufacturing plant in that particular aspect on the other hand. This is why we multiply changes done during the last three years in one aspect (e.g., investments in advanced manufacturing technology) with the capability level of the plant in a corresponding aspect (e.g., the level describing the strategic role of the manufacturing function). Both sides, i.e., changes done and capability levels, were transformed to 5-point scales in order to avoid having one side weighted higher than the other. The reason for not measuring each side in isolation and instead opting for a more dynamic approach is that we have tried to respond to the recommendation in the work of Boyer and Pagell (2000). The advanced manufacturing technology (NEWTECH) construct was based on the work of Chen and Small (1996) and operationalized as follows. On the one hand, we asked for the change in usage of advanced manufacturing technology during the last three years. On the other, we asked for the strategic role of the manufacturing function within the company. The latter was a summated scale of three items (summed up and then divided by the number of included items) that measured: (1) to what extent the company s ability to compete on the market was based on effective manufacturing processes and if it was senior management s view that it was important these processes surpassed the capability of main competitors, (2) how investments done in manufacturing processes were linked to the overall company strategy and if the ability of these manufacturing processes were matched to the demands of the targeted market segments, and (3) whether the development in manufacturing technology (relevant to the company) was continuously monitored and if the manufacturing function participated in decisions concerning the adoption and implementation of this technology. The construct used to assess changes done towards lean manufacturing (LEANMFG) was mainly based on the work of Karlsson and Åhlström (1996). We asked in a composite measure for changes done towards lean manufacturing during the last three years in the following three aspects: (1) degree of flow orientation, (2) changes in manufacturing processes to improve flexibility and (3) usage of multifunctional teams on the shop floor. Then we assessed the work organizational infrastructure in a composite measure on three parameters: (1) actual percentage of shop floor workers in multifunctional teams, (2) to what extent different white-collar work tasks close to production was performed by shop floor teams, and (3) the actual amount of time per week that a shop floor worker spends on whitecollar work close to production, such as planning, industrial engineering, continuous improvements, and external contacts with suppliers and customers. The construct to measure internal cooperation (INTERNALCO) was based on the work of Swink (1999). On the one hand we asked if the manufacturing function played an important role in the development of the product that was manufactured at the plant. On the other hand, we assessed the capability to cooperate internally, i.e., between design and manufacturing, on a summated scale. The respondent was asked to indicate to what extent: (1) the manufacturing function informs product development personnel about the possibilities and abilities that the manufacturing function has to offer, (2) the manufacturing function suggests design changes that increase manufacturability, (3) manufacturing and design processes are concurrently engineered, (4) the manufacturing function participates early in product development projects, and (5) personnel of the manufacturing and design functions cooperate intensively. 6

7 The construct to measure supplier cooperation (SUPPLIERCO) was based on the work of Krause (1998), Primo and Amundson (2002) and Ragatz et al. (2002). Firstly, we measured the change in supplier cooperation during the last three years. Secondly, we assessed the supplier cooperation capability level on a summated scale. The respondent was asked to indicate to what extent: (1) the most important suppliers are involved early in the development of new products, (2) common cost reduction projects are carried out with the most important suppliers, (3) production plans and systems are accessible for the most important suppliers, (4) the most important suppliers contribute to improvements of the product, and (5) active cooperation takes place to adapt to each other s manufacturing processes. The construct to measure customer cooperation (CUSTOMERCO) is just the analogue extension of the construct to measure supplier cooperation. This issue has previously been discussed in the work of Droge et al. (2004). The last item was omitted though, because it was not appropriate to use in a customer cooperation context. We asked for the change in customer cooperation during the last three years and then assessed the customer cooperation capability level on a summated scale. The respondent was asked to indicate to what extent: (1) the most important customers are involved early in the development of new products, (2) common cost reduction projects are carried out with the most important customers, (3) production plans and systems are accessible to the most important customers and (4 the most important customers contribute to improvements of the product. Measures (H2) This construct was inspired by the work of Corswant and Fredriksson (2002). The intensity in outsourcing manufacturing during the studied three-year period (OUTINT) was measured as follows. On the one hand, we asked for the change in degree of cost for purchased materials as a share of the total manufacturing cost for the main product line during the studied threeyear period. This variable was subsequently multiplied by a dummy variable (No = 0, Yes = 1) that questioned whether or not any manufacturing tasks had been outsourced during the studied three-year period. The reason for choosing a three-year period was that we perceived outsourcing as a change process that we wanted to evaluate the impact of. Moreover, the other initiatives in the instrument (e.g., investments in advanced manufacturing technology) that we measured were also assessed on a three-year basis in order to have commensurable variables. 130 out of 267 plants responded that they had outsourced manufacturing during the studied three-year period (49%). 31 out of these 130 also checked that they had insourced manufacturing. For these plants our outsourcing intensity measure (OUTINT) was not valid anymore (no change in purchase). Therefore, we used annual turnover for outsourced manufacturing ( ) as share of total turnover (2003) as a proxy in these 31 cases. Measures (H3a H3e) In attempting to grasp each outsourcing strategy to the highest possible extent our idea was to try to find a balance between the motives for outsourcing manufacturing (why?) with the characteristics of the outsourcing process (how?). This is why these two dimensions were multiplied. Motives and characteristics were measured on five-point scales in the instrument. As in the previous constructs we avoided weighting one side higher than the other by calculating an average from 1 5 for included items on motives and characteristics, respectively. The reason for z-transforming the outsourcing strategy variables was to avoid a higher weight for them in comparison to the outsourcing intensity variable when they were multiplied together. 7

8 Outsourcing manufacturing to low-wage countries (LWC) was measured by multiplying the cost motive variable by the degree of outsourcing to low-wage countries. The following items were included in the cost motive variable: (1) reduce cost for outsourced component, (2) increase cost control, and (3) reduce share of fixed costs. Outsourcing manufacturing for greater external economies of scale (EES) was measured by multiplying the cost motive variable by the mean value of the following two variables: (1) purchase responsibility for the outsourced component as well as the manufacturing of it and (2) the new supplier having higher volumes of the outsourced component. Outsourcing manufacturing for increased focus on core competencies (FOCUS) was measured by multiplying the focus motive variable by the variable measuring to what extent components of low customer value were outsourced. The following items were included in the focus motive variable: to increase focus and to refine operations for clearer flows. Outsourcing manufacturing for increased responsiveness to variability in demand (RESPONSIVENESS) was measured by multiplying the capacity motive variable by the variable measuring to what extent components of low customer value were outsourced. The following items were included in the capacity motive variable: (1) lack of capacity, (2) to set free resources for other purposes, and (3) to increase volume flexibility. Outsourcing manufacturing for increased access to innovation capability (INNOVATION) was measured by multiplying the innovation motive variable by the variable that measured to what extent components of high customer value was outsourced. The following items were included in the innovation motive variable: (1) get access to competence, (2) increase quality, (3) take advantage of the supplier s higher innovation capability, (4) faster introduction of new products, and (5) lower development costs. Measures (H4a H4e) The interaction of outsourcing intensity and each of the manufacturing development initiatives were calculated by multiplication. The reason for the z-transformation was to avoid weighting the development initiatives higher in the multiplication. Measures (Plant performance) As mentioned earlier, the dependent variables are described in Table 3. All items were simultaneously subjected to factor analysis (Varimax rotation). Factor scores were saved and used as dependent variables. The items that loaded on the first three factors (EFFICIENCY, FLEXIBILITY and Time To Market) were measured in one construct and the other items that loaded on the New Product Development factor were measured in another construct. There is an important difference between the two. The former emphasised changes during the studied three-year period at the plant. The latter emphasised the new product development capability of the company in relation to main competitors and the time of the study. Results Zero-order correlations are presented in Table 4 and results of the regression analyses are presented in Table 5. 8

9 Insert Table 4 here Insert Table 5 here Interpretation of regression models In comparison to outsourcing, initiatives to develop the different manufacturing capabilities of a company have a much stronger impact on improving plant performance. And in most cases when outsourcing has an effect on plant performance, these effects are more likely to be realized when concurrent initiatives are taken to develop certain manufacturing capabilities. If improvements in overall plant efficiency are taken as an example, which in our study consist of speed, dependability, quality and cost, we find that investments in advanced manufacturing technology, changes towards lean manufacturing and intensified customer cooperation have a particularly strong impact. And any effects of outsourcing manufacturing are more likely to be realized by concurrent initiatives to intensify customer cooperation. Prior to discussing results and drawing conclusions it is important to also study the correlation matrix in Table 4. In stepwise regression analysis the decision on which predictors to be included in the model is out of the hands of the researcher and made on purely mathematical criteria. The independent variable that has the highest correlation with the dependent variable is first entered into the model. The next independent variable to be entered is the one with the highest semi-partial correlation with the dependant variable, and so forth. As a consequence, slight differences in semi-partial correlation may be a reason for a variable not to appear in the final regression model. To give an example, one cannot draw the conclusion that intensified supplier cooperation is not of importance to improved plant efficiency only because the variable does not show up in the final regression model. Since supplier cooperation is so strongly correlated to efficiency, a more reasonable interpretation is that the variation in efficiency which is possible to explain by intensified supplier cooperation already has been accounted for by other variables in the model, such as intensified customer cooperation, for example. Moreover, most literature in supply chain management that the authors have come across so far emphasises supplier integration. Therefore, we were surprised that customer cooperation had such a strong influence in our regression models. However, deeper analyses show that most plants in our sample that have emphasised customer cooperation are first or second tier suppliers. Thus, customer cooperation in our study is in many cases actually a form of reverse supplier cooperation. Our second regression model predicts improvements in flexibility, which in our study includes both product mix flexibility and volume flexibility. Again, initiatives to develop certain capabilities of the manufacturing function more strongly predict improvements in our dependent variable. Advanced manufacturing technology and supplier cooperation are the strongest predictors of variation in the flexibility improvement indicator. Outsourcing has an effect on flexibility as well, both in interaction with investments in advanced manufacturing technology as well as in trying to get access to the supplier s higher innovation capability. Initially, we expected hypothesis H3d, i.e., increased responsiveness to variability in demand by outsourcing components of low customer value, to have a particularly strong influence on flexibility. But there was not even a significant correlation. In light of the result of this regression analysis, a possible explanation is that efforts to develop certain manufacturing capabilities may improve the capacity of a company s manufacturing system. But when the same company outsources peak demand to a contractor, no changes are actually taking place within the company and therefore no improvements in flexibility can be expected. Finally, one should note the strong positive correlation for H3b, i.e., outsourcing for greater external 9

10 economies of scale. This strategy seems to be successful. However, there are other variables already in the model that account for its unique share of variation. Our third regression model predicts improvements in time to market, which in our study includes both time for developing new products and costs of introducing new products. From a manufacturing development point of view, changes towards lean manufacturing and intensified customer cooperation are the strongest predictors of this indicator. We also find that the positive effects of outsourcing manufacturing are more likely to be realized by concurrent efforts to intensify customer cooperation. There is a strong positive correlation for H3e, i.e., efforts to access innovation capability of the supplier. However, this effect is already accounted for by H4e, i.e., the interaction effect of concurrent outsourcing and customer cooperation. There are two additional factors that have a noteworthy influence on time to market. The strategy to focus on core competencies and outsource components of low customer value has a positive effect. The strategy to reduce cost by outsourcing components to low-wage countries has a negative effect. Our fourth and final regression model predicts new product development capability in relation to competitors. We measured the degree of change in new products as well as the number of new products introduced on the market. The analysis shows that there are two main factors that have an influence on new product development. Firstly, investments in advanced manufacturing technology and secondly, close cooperation between design and manufacturing. Summary Direct effects of manufacturing development initiatives On a general level we find support for H1a H1e. Investments in advanced manufacturing technology, changes towards lean manufacturing as well as intensified internal, supplier and customer cooperation are strong predictors of improvements in plant performance. Direct effects of outsourcing manufacturing On a general level we do not find support for H2. Table 4 shows a positive correlation between outsourcing manufacturing and efficiency, flexibility and time to market. However, in terms of explaining a unique and significant share of the variation in our dependent variables, there are other predictors that have a much stronger influence. Direct effects of different outsourcing strategies On a general level we find partial support for H3a H3e. Surprisingly and contrary to what we expected, outsourcing manufacturing to low-wage countries has a negative effect on time to market (H3a: not supported). Outsourcing manufacturing for greater external economies of scale has a strong positive correlation with three of our performance improvement indicators but does not explain a significant share of the variation in our dependent variables (H3b: not supported). Outsourcing manufacturing of low customer value in order to increase internal focus on core competence has a positive effect on improving time to market (H3c: supported). Unexpectedly, outsourcing manufacturing in order to increase responsiveness to variability in demand was not even correlated to any one of our four performance improvement indicators (H3d: not supported). Outsourcing manufacturing in order to access innovation capability is strongly correlated to flexibility and time to market and explains a unique share of the variation in the former (H3e: supported). Interaction effects of outsourcing manufacturing 10

11 On a general level we find partial support for H4a H4e. Positive effects of outsourcing manufacturing are more likely to be realized by concurrent investments in advanced manufacturing technology (H4a) or intensified customer cooperation (H4e). We did not find any support for the hypothesised interaction effects of outsourcing and lean manufacturing (H4b), outsourcing and intensified internal cooperation (H4c), or outsourcing and intensified supplier cooperation (H4d). However, in case of the latter (i.e. H4d), one should note the strong positive correlations to efficiency, flexibility and time to market. Discussion The main result of our investigation is that in comparison to outsourcing, different initiatives to develop manufacturing capability have a much stronger impact on improving plant performance. And in most cases when outsourcing has an effect on plant performance, these effects are more likely to be realized when concurrent initiatives are taken to develop certain manufacturing capabilities. We offer the following possible explanations for this. A reason for the few and weak direct effects of outsourcing manufacturing may be the degree of change in value added. Deeper analyses show that the average firm that outsources has obtained a 10% change in value added during the studied three-year period. Conversely, this means that 90% of the business in such a company is unchanged. It is likely that 10% is too small a change in order to have a significant impact on overall plant performance. Further research should clarify this. An explanation for the strong positive effects of developing manufacturing capability in comparison to outsourcing manufacturing is that the former entails changed organizational behaviour patterns, whereas the latter only implies that less desired parts of the business are cut off. Previous studies show that it is rather demanding to change organizational behaviours in order to develop certain abilities for increased manufacturing capability, but at the same time equally rewarding (see e.g., Teece et al., (1997) and Schroeder et al., (2002)). If we turn our attention to the second part of our claim, i.e., that positive effects of outsourcing are more likely to be realized by concurrent initiatives to develop manufacturing capability, there are two possible reasons for this as well. Firstly, the kind of outsourcing which is done concurrently as specific manufacturing capabilities are developed is growth oriented in nature, while outsourcing manufacturing without such intentions is defensive in nature and aims at cutting costs only. The outsourcing frameworks that we have studied propose outsourcing for the main reason of further emphasizing core manufacturing capabilities in order to leverage skills and resources for increased competitiveness and growth, see for example Venkatesan (1992), Welch and Nayak (1992), Quinn and Hilmer (1994), Cánez et al. (2000), McIvor (2000) and Momme (2002). But defensive outsourcing is often carried out by market underperformers to stop bleeding or because it has become fashionable, see for example Bettis et al. (1992). The latter is a not uncommon result of a view that manufacturing does not matter and is a commodity which should be bought at the lowest possible price on the market (Arnold, 2000; Ramaswamy & Rowthorn, 2000) Secondly, it is also likely that companies that have developed a higher level of manufacturing capability are more competent purchasers. The reason for this is a more advanced absorptive capacity, see e.g., Cohen and Levinthal, (1990). On the manufacturing plant level this issue has recently been explored in a multiple case study on supply chain learning (Bessant, 11

12 Kaplinsky, & Lamming, 2003). It is shown that supply chain learning only really occurs when the focal firm plays an active role in assisting processes of learning amongst other firms in the value chain, and proceeds further when it recognises that it also has something to learn from these firms. Conclusion During the studied three-year period, 49% of the surveyed companies had undertaken an initiative to outsource manufacturing tasks. However, the outcome of our investigation forces us to insist on a more restrictive attitude towards this trend. From a managerial point of view, manufacturing companies would be in greater favour of a repositioning of focus to initiatives that really matter and have a significant positive impact on plant performance. The study shows that there is a far greater improvement potential in investing in advanced manufacturing technology, implementing lean manufacturing practices, and cooperating internally, with suppliers, and with customers, than there is in outsourcing manufacturing. Finally, one should admit that there are some positive effects of outsourcing manufacturing as well. These are, however, more likely to be realized if concurrent initiatives are taken to develop the manufacturing capability of the company. References Arnold, U. (2000). New dimensions of outsourcing: A combination of transaction cost economics and the core competencies concept European Journal of Purchasing & Supply Management 6(1), Bessant, J., Kaplinsky, R., & Lamming, R. (2003). Putting supply chain learning into practice. International Journal of Operations and Production Management, 23(2), Bettis, R., Bradley, S., & Hamel, G. (1992). Outsourcing and industrial decline. Academy of Management Executive, 6(1), Boyer, K., & Pagell, M. (2000). Measurement issues in empirical research: Improving measures of operations strategy and advanced manufacturing technology. Journal of Operations Management, 18, Cánez, L., Platts, K., & Probert, D. (2000). Developing a framework for make-or-buy decisions. International Journal of Operations and Production Management, 20(11), Chen, I., & Small, M. (1996). Planning for advanced manufacturing technology: A research framework International Journal of Operations and Production Management, 16(5), Cohen, W., & Levinthal, D. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), Corswant, F., & Fredriksson, P. (2002). Sourcing trends in the car industry: A survey of car manufacturers' and suppliers' strategies and relations. International Journal of Operations and Production Management, 22(7), Droge, C., Jayaram, J., & Vickery, S. (2004). The effects of internal versus external integration practices on time-based performance and overall firm performance. Journal of Operations Management, 22, Forza, C. (2002). Survey research in operations management: A process-based perspective. International Journal of Operations and Production Management, 22(2), Gilley, K., & Rasheed, A. (2000). Making more by doing less: An analysis of outsourcing and its effects on firm performance. Journal of Management, 26(4), Görg, H., & Hanley, A. (2005). International outsourcing and productivity: Evidence from the Irish electronics industry. North American Journal of Economics and Finance, 16(2),

13 Hair, J. P., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis. New Jersey: Prentice Hall. Jonsson, P. (2000). An empirical taxonomy of advanced manufacturing technology. International Journal of Operations and Production Management, 20(12), Karlsson, C., & Åhlström, P. (1996). Assessing changes towards lean manufacturing. International Journal of Operations and Production Management, 16(2), Krause, D. (1998). The antecedents of buying firms' efforts to improve suppliers. Journal of Operations Management, 17, Laugen, B., Acur, N., Boer, H., & Frick, J. (2005). Best manufacturing practices: What do the best performing companies do? International Journal of Operations and Production Management, 25(2), McIvor, R. (2000). A practical framework for understanding the outsourcing process. Supply Chain Management: An International Journal, 5(1), Mol, M., van Tulder, R., & Beije, P. (2005). Antecedents and performance consequences of international outsourcing. International Business Review, 14(5), Momme, J. (2002). Framework for outsourcing manufacturing: Strategic and operational implications. Computers in industry, 49, Primo, M., & Amundson, S. (2002). An explorative study of the effects of supplier relationships on new product development outcomes. Journal of Operations Management, 20, Quinn, J., & Hilmer, F. (1994). Strategic outsourcing. Sloan Management Review, Summer, Ragatz, H., Handfield, R., & Petersen, K. (2002). Benefits associated with supplier integration into new product development under conditions of technology uncertainty. Journal of Business Research, 55, Ragatz, H., Handfield, R., & Scannell, T. (1997). Success factors for integrating suppliers into new prduct deveopment. Journal of Product Innovation Management, 14, Ramaswamy, R., & Rowthorn, R. (2000). Does manufacturing matter? Harvard Business Review, November-December, 32. Schroeder, R., Bates, K., & Junttila, M. (2002). A resource-based view of manufacturing strategy and the relationship to manufacturing performance. Strategic Management Journal, 23, Shah, R., & Ward, P. (2003). Lean manufacturing: Context, practice bundles and performance. Journal of Operations Management, 21, Swink, M. (1999). Threats to new product manufacturability and the effects of development team integration processes. Journal of Operations Management, 17, Teece, D., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), Welch, J., & Nayak, P. (1992). Strategic sourcing: a progressive approach to the make-or-buy decision. Academy of Management Executive, 6(1), Venkatesan, R. (1992). Strategic sourcing: To make or not to make. Harvard Business Review, November-December, Voss, C. (2005). Paradigms of manufacturing strategy re-visited. International Journal of Operations and Production Management, 25(12),

14 Figures and tables Manufacturing development initiatives -New technology -Lean manufacturing -Internal cooperation -Supplier cooperation -Customer cooperation (H1a-H1e) Plant performance -Efficiency -Flexibility -Time to market -New product development (H4a-H4e) (H2, H3a-H3e) Outsourcing manufacturing -Intensity -Strategy Low-wage countries External economies of scale Focus on core competence Responsiveness to variability in demand Access to innovation capability Figure 1. Conceptual model. 14

15 Table 1. Sample characteristics and response rate Stratum Population (ISIC 28-35) Target sample Answered the survey Response rate 43% 53% 45% 50% 61% 47% Weight

16 Table 2. Descriptive statistics of independent variables Variable Scale Mean St d N Manufacturing development initiatives H1a NEWTECH Change in usage of advance manufacturing technology X Strategic role of the manufacturing function 5-point scales H1b LEANMFG Change towards lean manufacturing X Organizational infrastructure 5-point scales H1c INTERNALCO Role of manufacturing in design X Design-manufacturing integration capability level 5-point scales H1d SUPPLIERCO Change in supplier cooperation X Supplier integration capability level 5-point scales H1e CUSTOMERCO Change in customer cooperation X Customer integration capability level 5-point scales Outsourcing manufacturing H2 OUTINT Change in purchase X Outsourcing manufacturing (No = 0, Yes = 1) Percentage Different outsourcing strategies H3a LWC OUTINT X [Cost motive X Low-wage countries] Z Percentage H3b EES OUTINT X [Cost motive X [Purchase responsibility for component outsourced + New supplier has higher volumes] M ] Z Percentage H3c FOCUS OUTINT X [Focus motive X Component of low customer value outsourced] Z Percentage H3d RESPONSIVENESS OUTINT X [Capacity motive X Component of low customer value outsourced] Z Percentage H3e INNOVATION OUTINT X [Innovation motive X [Design responsibility outsourced + Component of high customer value] M ] Z Percentage Interaction of outsourcing manufacturing and manufacturing development initiatives H4a OUTINT X NEWTECH OUTINT X [NEWTECH] Z Percentage H4b OUTINT X LEANMFG OUTINT X [LEANMFG] Z Percentage H4c OUTINT X INTERNALCO OUTINT X [INTERNALCO] Z Percentage

17 H4d H4e OUTINT X SUPPLIERCO OUTINT X CUSTOMERCO OUTINT X [SUPPLIERCO] Z Percentage OUTINT X [CUSTOMERCO] Z Percentage Note: Z = Z-transformed (standardized), M = Mean value calculated of included items. 17

18 Table 3. Factor analysis of dependent variables EFFICIENCY FLEXIBILITY TTM NPD 1. Speed Dependability Quality Cost Product mix flexibility Volume flexibility Time for developing new products Costs for introducing new products Degree of change in new products in relation to competitors Number of new products introduced in comparison to competitors Variance explained Cronbach's alpha Note: Items 1-8 were measured on 9-point scales (-4 = significant deterioration, 4 =significant improvement). Items 9 10 were measured on 5-point scales (1 = far less significant/far more seldom, 5 = far more significant/far more often). 18

19 Table 4. Zero-order correlations EFFICIENCY FLEX TTM NPD H1a NEWTECH 0.419** 0.322** 0.168** 0.224** H1b LEANMFG 0.294** ** 0.164** H1c INTERNALCO 0.141* 0.191** ** H1d SUPPLIERCO 0.366** 0.287** 0.279** 0.161** H1e CUSTOMERCO 0.452** 0.278** 0.405** 0.133* H2 OUTINT 0.128* 0.232** 0.181** H3a LWC * * H3b EES 0.105* 0.281** 0.116* H3c FOCUS ** H3d RESPONSIVENESS H3e INNOVATION ** 0.252** H4a OUTINT X NEWTECH 0.272** 0.291** 0.279** H4b OUTINT X LEANMFG * 0.108* H4c OUTINT X INTERNALCO * 0.134* H4d OUTINT X SUPPLIERCO 0.229** 0.243** 0.298** H4e OUTINT X CUSTOMERCO 0.279** 0.232** 0.386** Note: * Correlation is significant at the 0.05 level (two-tailed). ** Correlation is significant at the 0.01 level (two-tailed). 19

20 Table 5. Results of regression analyses EFFICIENCY FLEX TTM NPD H1a NEWTECH 0.290** 0.233** 0.232** H1b LEANMFG 0.251** 0.161** H1c INTERNALCO 0.209** H1d SUPPLIERCO 0.193** H1e CUSTOMERCO 0.302** 0.269** H2 OUTINT H3a LWC * H3b EES H3c FOCUS 0.136* H3d RESPONSIVENESS H3e INNOVATION 0.166** H4a OUTINT X NEWTECH 0.127* H4b OUTINT X LEANMFG H4c OUTINT X INTERNALCO H4d OUTINT X SUPPLIERCO H4e OUTINT X CUSTOMERCO 0.117* 0.262** R Standard error of the estimate F (full model) *** *** *** *** N Note: * Standardized B-coefficient is significant at the 0.05 level. ** Standardized B-coefficient is significant at the 0.01 level. *** Regression model is significant at the 0.01 level. 20