The effect of global supply chain configuration on the relationship between supply chain improvement programs and performance

Int. J. Production Economics 143 (2013) 285–293

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Int. J. Production Economics journal homepage: www.elsevier.com/locate/ijpe

The effect of global supply chain configuration on the relationship between supply chain improvement programs and performance Federico Caniato a, Ruggero Golini b,n, Matteo Kalchschmidt b a b

Politecnico di Milano, Department of Management, Economics and Industrial Engineering, Piazza Leonardo da Vinci, 32, 20133 Milano, Italy Universita degli Studi di Bergamo, Department of Economics and Technology Management, Viale Marconi 5, 24044 Dalmine (BG), Italy

a r t i c l e i n f o

abstract

Article history: Received 15 September 2010 Accepted 16 May 2012 Available online 25 May 2012

This paper investigates the moderating effect of global supply chain (SC) configurations on the relationship between SC improvement programs and operational performance improvement, based on data collected from the fifth edition of the International Manufacturing Strategy Survey (IMSS). Configurations are defined through the level of sourcing, manufacturing and sales of a plant outside the continent where it is located. Four configurations emerge: Locals, Shoppers, Barons and Globals. Results show that global SC configurations do have a moderating effect. Some configurations yield higher returns on their investments in SC than others. Moreover, Locals can achieve further improvements by adopting distribution strategy and supplier development programs. However, Barons experience negative effects from investing in coordination with customers and suppliers. & 2012 Elsevier B.V. All rights reserved.

Keywords: Globalization Supply chain management IMSS

1. Introduction The globalization of supply chains (SC) has always been simultaneously an opportunity and an issue for manufacturing companies (e.g., Dornier et al., 2008; Taylor, 1997). Increasing competitive pressures force them to expand operations beyond national boundaries in order to source materials and components, manufacture products and sell their products. Reduced trade barriers and advances in communication technologies also make ¨ this possible (e.g., Hulsmann et al., 2008; Skjott-Larsen and Schary, 2007). Thus, SCs are more complex and difficult to control and errors in management can impact a company’s performance ¨ significantly (e.g., Hulsmann et al., 2008). For all these reasons, the literature has recently focused on global SC management (e.g., Prasad and Babbar, 2000). Companies extending their operations globally obtain potential relevant advantages such as lower sourcing costs and access to broader markets (Ferdows, 1997b; Vereecke and Van Dierdonck, 2002). However, they also face new challenges, including longer lead times, more complex networks and higher risks (Levy, 1997; Minner, 2003; Womack and Jones, 1996). For this reason, different approaches have been used in the academic literature to examine the analysis and management of SCs at a global level. Gereffi et al. (2005) proposed global value chain analysis as an approach to analyze the configuration and

n

Corresponding author. E-mail address: [email protected] (R. Golini).

0925-5273/$ - see front matter & 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.ijpe.2012.05.019

structure of the entire chain from raw materials to final customers. Chung et al. (2004) adapted the networked enterprises concept to the global case, focusing on the network of closely related firms that contribute to manufacturing products. Several other authors (Bello et al., 2004; Cohen and Mallik, 1997; MacCarthy and Atthirawong, 2003; Murray et al., 1995) provided more management oriented contributions that adopt the perspectives of single firms that need to coordinate and manage sourcing, manufacturing and distribution activities on a global scale. Within SC management literature, attention has been paid to the identification of SC management best practices. Specifically, several authors have focused on how companies invest in their SC in order to improve operational performance, examining factors such as cost, delivery, quality and flexibility (Craighead et al., ¨ 2007; Golini and Kalchschmidt, 2011; Juttner et al., 2003; Minner, 2003; Tang, 2006). In this field, attention has typically been paid to specific improvement programs, such as supply strategy redesign, coordination with suppliers, distribution strategy redesign and coordination with customers. In the context of global SCs, however, the benefits of such improvement programs may be hampered by the previously mentioned challenges. For example, coordination with customers and suppliers may become more difficult due to physical and cultural distance or the presence of intermediaries (often needed when operating on a global scale). Research has not yet clarified which programs lead to real improvements when SCs become global. Therefore, the aim of this paper is to address this specific issue, i.e., to investigate the relationship between SC improvement

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programs and operational performance in different contexts. Specifically, we adopt a configuration approach, thus analyzing the moderating effect of global SC configurations on the aforementioned relationship. We investigate this issue using a large panel of data gathered in 2009 as part of the fifth edition of the International Manufacturing Strategy Survey (IMSS 5). This paper is structured as follows: first, we present a review of the relevant literature on the topic and then, we specify our research framework and objects. Subsequently, we illustrate our methodology and data analysis and finally we discuss our results and draw conclusions.

2. Literature review SC management literature defines global SC management as the combination of three main operational processes: global sourcing, i.e., the management of supplier relationships from a global perspective (e.g., Murray et al., 1995); global manufacturing, i.e., the management of manufacturing activities distributed all over the world (e.g., MacCarthy and Atthirawong, 2003); and global distribution, i.e., how companies manage their sales and distribution channels globally (e.g., Bello et al., 2004). Even if these processes have often been analyzed separately, they are typically interrelated: in order to support global distribution, companies need to invest in new foreign plants and manage suppliers on a global scale (Buckley and Ghauri, 2004). Similarly, companies that purchase on a global scale sometimes decide to invest in foreign manufacturing facilities in order to have better control over the SC (Ferdows, 1997a). Literature provides evidence of significant correlations among global sourcing, manufacturing and distribution. For example, Bozarth et al. (1998) highlighted four stages of global sourcing maturity in which the last phase is distinguished by the development of global sourcing networks with several production facilities. Other authors also provided evidence of patterns of internationalization in manufacturing and distribution processes (Chetty and Holm, 2000; Knudsen and Servais, 2007; Shi and Gregory, 1998). For this reason, these different concepts are usually considered interrelated under the term ‘‘global supply chain management’’ (Prasad and Babbar, 2000). Even if global sourcing, manufacturing and distribution are interrelated, not all companies behave in the same way. For this reason, literature has investigated configurations in the use of global SC management. Rudberg and Olhager (2003) focused on sourcing and distribution processes and they identified four clusters of companies according to the number of sites per organization and number of organizations in the network. Similarly, Cagliano et al. (2008) found four clusters of companies characterized by global or local sourcing and distribution. Specifically, they identified companies that are still local, companies that have globalized only sourcing or distribution and companies that have gone global for both. These clusters provide evidence that some companies decide to stay local and manage local SCs. Other companies prefer to extend globally in either distribution or purchasing, leading to different configurations. In the end, some companies extend their boundaries over the world and manage true global SCs that involve both purchasing and distribution at a global level; however, the manufacturing component is not considered in this work. Some authors have focused on the impact of global SCs on company performance even in the absence of conclusive evidence. Although many authors highlight the benefits of globalization, other authors claim that globalization may have a negative impact on performance. Global SCs, by definition, cannot be as fast and seamless as local ones (Levy, 1997; Minner, 2003;

Womack and Jones, 1996); lean SCs typically require short distances to accommodate frequent deliveries and lower inventories. Longer distances may also require the use of intermediaries and may increase the number of actors in the value chain, thus making the integration of the network more complex and thereby increasing the bullwhip effect (Lee et al., 1997). Cultural distances and possible lack of trust between companies can also make the delineation of agreements more difficult and can impact the return on SC investments (Levy, 1997). In the end, extending SCs globally increases lead times and, by consequence, inventory levels (Carter and Narasimhan, 1990; Frear et al., 1992; Zeng and Rossetti, 2003). In order to improve operational performance, companies can act differently (Krause et al., 1998; Tan, 2001; Watts and Hahn, 1993). For instance, they can leverage the definition of a supply strategy and a purchasing organization (Driedonks et al., 2010). Other authors showed the positive impact of supplier development programs (Choi and Hartley, 1996; Handfield et al., 2006) and vendor rating systems (Golini and Kalchschmidt, 2011; Muralidharan et al., 2001) focusing the attention on the importance of keeping the entire network under control. Attention has been also paid to the adoption of complex distribution systems (Beamon, 1998) and coordination with customers and suppliers (Lee and Whang, 2000). Finally, risks associated with global SCs (such as strikes or political issues, fluctuating exchange rates, supply disruptions and lead time variability) can be limited by using multiple supply sources (Minner, 2003) and different distribution channels. Ensuring communication lines in crisis situations and developing joint continuity plans with customers and suppliers are other types of SC investments designed to mitigate risk in global SCs (Craighead ¨ et al., 2007; Juttner et al., 2003; Tang, 2006). Petersen et al. (2006) showed that structures, processes, business capabilities, international language capabilities and top management commitment are critical to the effectiveness of global SC management. This is in line with other authors, including Quintens et al. (2006), Zeng (2003) and Gelderman and Semeijn (2006). For all these reasons, the relationship between globalization, SC investment and operational performance is rather complex. In the current literature, however, few contributions analyze this interaction. Golini and Kalchschmidt (2011) focused on the upstream component of the SC. The authors found that SC management investments have a mediating effect on the relationship between global sourcing and materials inventory level. Specifically, companies that have been increasing global sourcing over time have also increased investments in the coordination with suppliers: in this way, inventory performances are comparable to those of companies that have decided to stay local. A similar result has also been found in the downstream element of the SC: Golini and Kalchschmidt (2010) found out that companies that have invested in global distribution have also increased the coordination with customers in order to keep lead time performance under control. However, these contributions have considered the different parts of the SC separately and have focused only on a limited set of performance data.

3. Objectives Given the limitations highlighted in the existing literature, in this work, we aim to provide a clearer understanding of the relationship between SC improvement programs, performance and globalization. Specifically, we want to extend previous findings (Golini and Kalchschmidt, 2010, 2011; Knudsen and Servais, 2007) by including different performance indicators and considering different global SC configurations.

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Fig. 1. Research framework.

A global SC configuration can be defined by examining a firm’s level of globalization in sourcing, manufacturing and distribution (Cagliano et al., 2008). Such a configuration is expected to impact the relationship between SC improvement programs and performance improvement. For example, a company that sources, manufactures and distributes primarily on a local scale can experience different effects from SC investments when compared to a company that operates globally. For dependent variables, we consider operational performance. Previous research has often focused on a limited set of performance indicators, whereas in this paper we consider improvements in cost, delivery, flexibility, lead time and quality performance. The literature broadly recognizes the impact of globalization and SC decisions on these performance dimensions (Carter and Narasimhan, 1990; Christopher, 2000; Frear et al., 1992; Frohlich and Westbrook, 2001; Minner, 2003; Pyke and Cohen, 1990). For independent variables, we consider SC improvement programs. Coherently with the literature (Frohlich and Westbrook, 2001; Golini and Kalchschmidt, 2011), these programs include:

 Upstream programs (i.e., supply strategy redesign, supplier



development, integration and coordination with suppliers) (Chan and Kumar, 2007; Krause et al., 1998; Muralidharan et al., 2001; Ogden and Carter, 2008). Downstream programs (i.e., distribution strategy redesign, integration and coordination with customers) (Devaraj et al., 2007; Gunasekaran et al., 2008; Kulp et al., 2004).

To these programs, given our focus on the globalization issue, ¨ we added risk management improvement programs (Juttner et al., 2003; Tang, 2006). We summarized our research framework in Fig. 1. In this framework, global SC configuration acts as a moderator variable (Baron and Kenny, 1986) between SC improvement programs and performance improvement. Different clusters of companies (e.g., local companies versus global companies) may experience different effects (positive, negative or null) from an SC improvement program on a particular performance. Therefore, we seek to address the following research question: RQ. Do global SC configurations have a moderating effect on the relationship between SC improvement programs and performance improvement? If this is the case, what kind of effect?

Strategy Survey (IMSS 5) collected in 2009. Originally launched by London Business School and Chalmers University of Technology, this project studies manufacturing and SC strategies within the assembly industry (ISIC 28-35 classification) through a detailed questionnaire administered simultaneously in many countries by local research groups. Responses are then gathered in a unique global database (Lindberg et al., 1998), which is available only to those who have actively participated in data collection. The basic structure of the questionnaire is as follows: the first section of the questionnaire pertains to the business unit, in order to gather general information (e.g., company size, industry, production network configuration, competitive strategy and business performance) on the context in which manufacturing takes place, whereas the other sections refer to the plant’s dominant activity, focusing on manufacturing strategies, practices and performance. Dominant activity is defined as the most important activity, which best represents the plant. The plant is chosen as the unit of analysis in order to avoid problems related to business units with multiple plants operating in different ways. In each edition, the questionnaire is partially redesigned in order to ensure alignment with the most recent research goals. To that end, a special section in the last edition was devoted to the globalization of manufacturing. Data in each country are gathered in that country’s native language and the questionnaire is translated and back-translated to check for consistency (Behling and Law, 2000). Companies are usually randomly selected from economic datasets and then the operations, production or plant manager is contacted and asked to assist in the research. If the respondent agrees, the questionnaire is sent. Where appropriate, a reminder is sent after a few weeks. Questionnaires that are sent back are controlled for missing data, typically handled on a caseby-case basis by directly contacting the company again. Every country then controls the gathered data for late respondent bias by company size and industry. The overall response rate is 18.3% of the questionnaires sent (10.6% of the contacted companies). The sample used in this study is described in Table 1. In particular, 452 companies (from the 729 in the global database) provided information for this study1 ; these companies come from 21 different countries. The sample consists primarily of small companies (52.4% of the sample), but medium and large companies are also represented. Different industrial sectors from the assembly

4. Methodology In order to investigate the research question above, we used the data from the fifth edition of the International Manufacturing

1 We deleted records not providing information on the used variables. Moreover, we deleted cases with less than 20 employees or more than 16,000 from the sample. We also deleted cases not providing the ISIC code classification.

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Table 1 Descriptive statistics in terms of (a) country, (b) size, (c) industrial sector (ISIC codes).

Table 2 SC improvement programs items, factors loadings and Cronbach’s alpha. Item name

Item description

(a) Country

N

%

Belgium Brazil Canada China Denmark Estonia Germany Hungary Ireland Italy Japan Korea Mexico Netherlands Portugal Romania Spain Switzerland Taiwan UK USA

25 27 15 31 13 18 24 50 4 33 13 14 10 31 5 23 26 25 24 9 32

5.5 6.0 3.3 6.9 2.9 4.0 5.3 11.1 .9 7.3 2.9 3.1 2.2 6.9 1.1 5.1 5.8 5.5 5.3 2.0 7.1

Total

452

100.0

Supply strategy Rethinking and restructuring supply strategy and the organization and management of supplier portfolio through e.g., tiered networks, bundled outsourcing, and supply base reduction Supplier Implementing supplier development and vendor development rating programs Coordination Increasing the level of coordination of planning w/suppliers decisions and flow of goods with suppliers including dedicated investments (e.g., information systems, dedicated capacity/tools/ equipment, dedicated workforce) Distribution Rethinking and restructuring distribution strategy strategy in order to change the level of intermediation (e.g., using direct selling, demand aggregators, multiechelon chains) Coordination Increasing the level of coordination of planning w/customers decisions and flow of goods with customers including dedicated investments (e.g., information systems, dedicated capacity/tools/ equipment, dedicated workforce) Risk Implementing SC risk management practices management including early warning system, effective contingency programs for possible SC disruptions Cronbach’s alpha

Factor loading .708

.772 .828

.769

.794

.785

.868

(b) Eigenvalue 4 1; explained variance: 60%. Sizea

N

%

Small Medium Large

237 81 134

52.4 17.9 29.6

Total

452

100.0

(c) ISICb

N

%

28 29 30 31 32 33 34 35

158 125 8 61 28 22 33 17

35.0 27.7 1.8 13.5 6.2 4.9 7.3 3.8

Total

452

100.0

a Size: small: less than 250 employees, medium: 251–500 employees, large: over 501 employees. b ISIC Code (Rev. 3.1): ISIC Code (Rev. 3.1): 28: manufacture of fabricated metal products, except machinery and equipment; 29: manufacture of machinery and equipment not classified elsewhere; 30: manufacture of office, accounting, and computing machinery; 31: manufacture of electrical machinery and apparatus not classified elsewhere; 32: manufacture of radio, television, and communication equipment and apparatus; 33: manufacture of medical, precision, and optical instruments, watches and clocks; 34: manufacture of motor vehicles, trailers, and semi-trailers; 35: manufacture of other transport equipment.

industry are considered, primarily from the manufacturing of fabricated metal products, machinery and equipment sectors. With respect to the research framework shown in Fig. 1, we defined different constructs for performance improvement, SC improvement programs and global SC configurations. For the former two, we used exploratory factor analysis (principal component with varimax rotation), whereas for the latter we used cluster analysis. We also checked for common method bias for perceptive measures, i.e., performance improvement and SC improvement programs. We applied Harman’s one-factor test by performing a factor analysis on all the perceptive items (Podsakoff et al., 2003). The one factor solution explains only 38% of the total

variance and the model suggests a solution with at least three factors (considering eigenvalues above 1). We can conclude that common method bias is not a cause of concern in this data. Given the variety of the sample, we decided to control our regression for company size (measured as the number of employees of the company) and GNI per capita (World Bank 20082 data, Atlas method) of the country where the plant is located.3 Company size is generally considered a relevant contingent variable affecting both global SC configuration and SC improvement programs (Cagliano et al., 2008; Carter and Narasimhan, 1990; Quintens et al., 2005; Scully and Fawcett; Sousa and Voss, 2008). We also controlled for GNI per capita given the international nature of the sample. Evidence suggests that companies in different countries have, on average, different degrees of SC globalization (Cagliano et al., 2008) or implementation of SC management practices (Fernie, 1995). All the measures and constructs are detailed in the following sections.

4.1. SC improvement programs In evaluating SC improvement programs, we used six items that refer to improvement programs in SC management (Table 2). As mentioned in the literature review, we included upstream programs (i.e., supply strategy, supplier development, coordination with suppliers), downstream programs (i.e., distribution strategy, coordination with customers) and risk management improvement programs. Items were measured on a 1–5 Likertlike scale, referring to the level of investment in that program in the last three years. These items are inter-correlated (see Table A1 in Appendix). By running a factor analysis, we obtained a onefactor solution, representing the overall investment in the SC, with Cronbach’s alpha of .868, which explains 60% of the total 2

For Taiwan, 2006 data have been used as 2008 was not available. Other control variables (i.e. position in the SC), were not considered as they were not significant in the analyses. 3

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Table 3 Performance improvement factors loadings and Cronbach’s alpha. Item

Factor Flexibility Quality Delivery Cost Lead time

Volume flexibility Mix flexibility

.835 .841

Manufacturing conformance Product quality

.849 .821

Delivery reliability Delivery speed

.804 .766

Procurement costs Unit manufacturing cost

.796 .827

Procurement lead time Manufacturing lead time Cronbach’s alpha

.805 .659 .814

.801

.771

.741 .702

Eigenvalue 4 .53; explained variance: 82%; loadings below .4 are not shown.

variance. Therefore, in the rest of the analysis, we considered both the SC investment factor (calculated as the average of the individual improvement programs) and the single improvement programs (considering their differential contribution when compared to a single factor). 4.2. Performance improvement In order to measure operational performance improvement, we considered 11 items measured on a 1–5 Likert-like scale that indicated the improvement in the last three years. We decided to use performance improvement for consistency with the improvement programs. However, before proceeding with the analysis, we took into consideration the performance relative to competitors. This is highly and positively correlated to the performance improvement. This means that companies that have improved their performance the most are also more likely to perform better than their competitors. The performance indicators were grouped into five constructs through exploratory factor analysis: flexibility, quality, delivery, cost and lead time (Table 3). The validity and reliability of such constructs is assessed by the total variance explained (82%), factor loadings always higher than .65 and Cronbach’s Alpha, always higher than .7 (Nunnally et al., 1967). We acknowledge that using five factors does not optimize the parsimony in terms of number of variables; in fact, the lowest eigenvalue is only .546. However, in comparison to other more parsimonious models, this model has the highest interpretability, validity and reliability of the constructs.4 Moreover, our results align with several previous studies that considered the same dimensions (Devaraj et al., 2007; Frohlich and Dixon, 2001; Laugen et al., 2005; Miller and Roth, 1994). 4.3. Global SC configurations

289

and sales are globalized. To measure this, we used the percentage of purchases, manufacturing and sales outside the continent where the plant is based. Table 4 provides descriptive statistics for the considered variables. On average, companies tend to be only partially globalized in sourcing and sales, whereas manufacturing tends to be managed locally, but standard deviation also shows a relevant variability within the sample. Based on these three variables, a two-step cluster analysis was performed. First, hierarchical cluster analysis, based on Euclidean distance and the Ward method, was used to identify the most suitable number of clusters and the cluster centroids. The hierarchical cluster analysis suggested four as the number of clusters. We tried other clustering algorithms (single linkage, centroid linkage and average linkage) and the four clusters solution appears to be stable. Next, the K-means clustering algorithm was used to iteratively assign each firm to a cluster (Ketchen and Shook, 1996). Each cluster obtained in this way represents a different global SC configuration because each is characterized by different levels of global sourcing, manufacturing and sales. Table 5 provides the description of the identified clusters. The vast majority of companies in our sample tends to stay completely local (Locals represent 64.2% of the sample) and only 4.9% of the sample show a true global SC (Globals). We also found companies – i.e., Shoppers, representing 11.5% of the sample – that have a high level of global sourcing, which manage purchases from different areas of the world, whereas their manufacturing and distribution processes are locally focused. Finally, the Barons (19.5% of the sample) tend to source and manufacture locally but distribute to different countries outside their continent. This result is in line with findings in Cagliano et al. (2008) that, using 2005 data from the same IMSS database, identified four clusters. In that paper, only global sourcing and distribution were considered, whereas we also considered manufacturing. However, in our analysis, only Globals have a significant level of global manufacturing. Therefore, we can confirm the existence of the same four clusters with more recent data, even when global manufacturing is included. In addition, we can see that Barons and Shoppers have a certain degree of global manufacturing (around 5%), even if it is limited in comparison to Globals.

5. Results For the variables identified, namely performance improvement and SC improvement programs, we assessed significant differences among clusters by means of an ANOVA analysis (Table 6). Table 4 Descriptive statistics of global SC variables. Variable

Minimum

Maximum

Mean

Std. dev.

Global sourcing Global manufacturing Global sales

0 0 0

100 90 90

13.50 5.03 16.22

19.941 13.916 21.436

To answer our research question, we needed to define configurations based on the extent to which sourcing, manufacturing Table 5 Global SC configurations: means and descriptive statistics. 4 The performance items considered are all quite correlated and the analysis of the eigenvalues would suggest to use a single factor that would be a measure of how good the company is overall. However, for the purpose of this study, we wanted to have a clearer distinction among different performance so we sought for a multiple factors solution. For instance, using two factors, the flexibility items would go together with the quality items with problems of interpretability. Moreover, using two factors, other items (e.g. cost items) cross load on both factors, with additional problems of reliability and validity. Similar issues rise with 3 and 4 factors. With 5, eventually, we found an acceptable compromise.

Variable

Locals

Barons

Shoppers

Globals

Sample average

Global sourcing Global manufacturing Global sales

4.12 1.42 5.11

13.15 4.92 48.37

48.83 6.04 8.49

55.00 50.59 52.45

13.50 5.03 16.22

Total number Percentage

290 64.2

88 19.5

52 11.5

22 4.9

452 100

290

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Table 6 Variables values for each clusters. In brackets clusters that are significantly different according to Scheffe statistic. Variable

Local (1)

Barons (2)

Shoppers (3)

Globals (4)

Sample average

ANOVA sig.

Flexibility Cost Quality Delivery Lead time SC Investment Supply strategy Supplier development

3.31 2.79 3.21 3.25 2.88 2.72 2.92 2.96 2.72 (2) 2.38 2.73 2.63 755 (1;4) 27,048 (2;4)

3.35 2.80 3.10 3.20 2.91 2.92 3.15 3.18 3.13 (1) 2.42 2.73 2.93 1135 (1) 33,394 (1)

3.60 2.98 3.30 3.34 3.00 3.05 3.25 3.38 3.00

3.18 2.66 3.20 2.98 2.73 2.98 3.23 3.45 3.05

3.35 2.81 3.20 3.24 2.89 2.81 3.02 3.08 2.85

– – – – – .041 – .021

2.75 2.92 2.98 932

2.73 2.73 2.68 3133 (1) 38,486 (1)

2.44 2.75 2.73 965

– – –

Coordination w/suppliers Distribution strategy Coordination w/customers Risk management Size GNI per capita

The results of this analysis show few differences in some SC improvement programs and no differences in performance improvement. This result is not completely unexpected because other studies did not find a straightforward relationship between business success and the level of SC globalization (Kotabe and Omura, 1989; Steinle and Schiele, 2008). However, globalization typically impairs operational performance, especially when not supported by investments in the SC (Golini and Kalchschmidt, 2011; Handfield, 1994). Consequently, our analysis suggests that companies that globalize their SC (Barons, Shoppers and Globals) do not always support this strategy with investments in the SC and this leads to dispersed performance indicators. In fact, the only differences found in SC investments pertain to Locals who have the tendency to invest less than the others. Finally, in terms of descriptive variables, Locals are smaller and located in countries with a lower GNI per capita when compared to Barons and Globals. This result is quite intuitive and aligns with the literature. Despite global growth is an increasingly viable strategy for smaller companies, it still requires investments and capital that are less readily available to them (Cagliano et al., 2008; Cavusgil, 1980; Lee and Whang, 2000; Quintens et al., 2005). Moreover, Globals are structurally larger because they have other production facilities around the world. Next, in order to answer our research question, we adopted a hierarchical linear regression model. In the first step, only company size (calculated as the natural logarithm) and GNI are entered as control variables. In the second step, we entered SC investment. Finally, through a stepwise procedure, we inserted the single SC improvement programs. These were calculated as the difference between the SC investment factor and the value of the single improvement programs. In this way, we could measure the differential effect of the single improvement program over the overall SC investment. We ran this procedure within each of the four clusters, for each of the five considered performance factors (cost, delivery, flexibility, lead time, quality). In this way, we arrived at 20 regression models. Each step of the procedure was controlled for multicollinearity by checking the variance inflation factor (VIF) of the independent variables. R-square change was also considered in order to evaluate whether or not the new model has more explanatory power than the previous: when the variables inserted are significant, the Rsquare change is always significant. VIF was always lower than 1.27 and the cut-off point is usually between 5 and 10 (Hair et al., 1998; Menard, 2002; Neter et al., 1989). Therefore, multicollinearity is not considered problematic for any model. The overall results of

34,410

29,687

.16

.000 .000

regression analyses are represented in Table 7. This table provides standard estimates for the different regression models. In considering the control variables first, we observe that size is never significant in explaining higher performance improvements. This result is quite surprising because in the literature, large focal firms are usually considered to be in a better position for profitable investment in the SC. This result can be related to the fact that we are considering performance improvement and not absolute performance. Therefore, our analysis shows that also smaller companies, if they are able to invest in SC management, can achieve good improvements in their performance. In addition, the country’s GNI has a very limited impact: it negatively affects delivery, lead time and quality for Locals. In evaluating SC improvement programs, we observe that the SC investment factor (calculated as the average of all the SC improvement programs) has a widespread and strong impact. It is significant in almost all regression analyses for Locals, Shoppers and Barons, but not for Globals (Globals, in fact, just show a statistically weak relationship between SC investment and flexibility improvement). This can be attributed to the limited number of companies in this cluster as well as the many different configurations that Globals employ in their manufacturing networks (Sweeney, 1994) and this is therefore not really comparable with the other clusters. However, for the remaining clusters (Locals, Shoppers and Barons), SC investment has a widespread effect: it is significant in every model except for flexibility for Barons. This means that Locals, Shoppers and Barons can always realize some benefits from investments in the SC. This result is quite interesting if we consider that performance indicators are measured in terms of improvement. Firms typically experience diminishing marginal returns from their investments (Mueller, 1972), but, in this case, investing in SC seems to always bring good returns. This can be attributed to the fact that SC management is still a relatively new discipline for companies and investments can bring good returns regardless of the SC configuration. By looking at the standardized coefficients in greater detail, we can see that Shoppers experience the greatest positive impact from SC investments. In particular, for flexibility and delivery, the coefficient is significantly higher than for the other groups. Moreover, for Shoppers, the R-square is also high: at .538 for Delivery and .463 for Flexibility, this indicates that almost half the variance in the sample is determined by SC investments. This result tells us that SC investment is mostly effective for managing a global supplier base, whereas the

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Table 7 Results of the regression procedure for the final models. Cluster

Independent

Cost

Delivery

Flexibility

Lead time

Quality

St. beta

Sig.

St. beta

Sig.

St. beta

Sig.

St. beta

Sig.

St. beta

Sig.

GNI Size (ln) SC Investment Distribution strategy Supplier development R-square

.055 .014 .302

.339 .809 .000

.124 .035 .292 .131

.030 .538 .000 .019

.092 .057 .288

.101 .327 .000

.123 .033 .376 .109

.027 .552 .000 .044

.126 .135n

.025

.142 .095 .159 .124 .183 .111n

.014 .104 .007 .043 .003

GNI Size (ln) SC Investment Coordination w/customers Coordination w/suppliers R-square

.178 .052 .286

.010 .008 .175

.927 .945 .139

.089 .908 .020

.125 .096 .374 .241 .310 .253n

.228 .339 .001 .019 .003

Shoppers

GNI Size (ln) SC investment R-square

.041 .031 .439 .162nn

.771 .825 .002

.104 .066 .721 .538nn

.335 .544 .000

.225 .108 .626 .463nn

.057 .357 .000

.067 .114 .560 .308nn

.612 .391 .000

.252 .025 .471 .328nn

.056 .850 .000

Globals

GNI Size (ln) SC Investment R-square

.163 .269 .331 .137

.503 .290 .163

.038 .084 .254 .062

.879 .748 .299

.083 .187 .460 .202n

.723 .442 .050

.020 .058 .261 .063

.937 .823 .287

.007 .290 .028 .088

.978 .268 .905

Locals

Barons

n

.103nn

.128n .100 .623 .011

.139n

.035 .025 .331

.749 .812 .004

.123nn

.180n .185 .012 .263

.132n

.033

R-square change sig. at the last model o .05. R-square change sig. at the last model o .01.

nn

effect on a global customer base is limited. This result is supported looking at Barons, who experience a much lower return on their SC investment. Finally, Locals receive a good return from SC investments because they can perform them on a local scale, where the relationship with suppliers and customers is more direct and often relies on mutual knowledge and reputation (Lissoni, 2001; Porter, 2000). Therefore, even if Locals invest less, they can achieve performance comparable to the others (see Table 6). Looking at the contribution of the individual SC improvement programs, different considerations emerge. First, significant marginal contributions can be identified. Therefore, even if SC investments have a positive effect on performance, the area in which these investments are made is important. Thus, companies need to pay attention to where investments are made and must specifically address the global SC configuration they are adopting. Results change from one cluster to another. Distribution strategy (on delivery, lead time and quality) and supplier development (on flexibility and quality) contribute to higher performance improvement for Locals. Companies that have decided to focus on local SCs benefit more from focusing attention on how products are distributed in their market. Similarly, on the supply side, Locals benefit from investing in their suppliers and trying to make them more competitive. Interestingly, these investments are not significant for Barons. On the contrary, Barons show a significant negative effect from coordination with suppliers and customers on quality performance. This means that companies belonging to the Barons cluster that have invested in these improvement programs have not performed as well as those investing in other areas. Finally, Shoppers do not experience greater improvements from any specific improvement programs. This means that any SC improvement program can increase performance to the same degree.

6. Conclusion In this paper, we have investigated the moderating effect of global SC configuration on the relationship between the investment on SC improvement programs and performance improvement. The main results provided by our analyses indicate that

global SC configuration, in terms of percentage of sourcing, manufacturing and sales outside the continent, affects the relationship between action programs and performance. In fact, some relationships are significant only for one configuration. Therefore, we conclude that the overall SC configuration is a major contingent factor that must be considered when evaluating investments in SC improvement programs, in addition to other more traditional variables such as company size or country GNI, for which we have controlled our results. These findings correspond with some previous contributions (Golini and Kalchschmidt, 2010, 2011) but also provide a relevant extension. Previous research focused only on one dimension of globalization at a time (either sourcing or sales), whereas in this paper, we have considered the three dimensions together (sourcing, manufacturing and sales). Moreover, previous contributions considered only a very limited set of performance (either inventory or lead time), but in this paper we have considered a broad range of performance, covering the whole range of main operational performance. Another key outcome of our analysis is our finding that, in general, SC improvement programs provide benefits in all of the considered performance measures. However, some differences emerge between companies with different SC configurations. The Locals cluster shows significant results for programs directed both upstream (supplier development) and downstream (distribution strategy). The Barons cluster, characterized by local sourcing and manufacturing and global sales, shows only a negative effect of coordination activities (both with customers and suppliers) on quality. The Shoppers cluster, beyond a high positive effect from SC investments, does not show any other specific effect. Finally, Globals do not show any significant impact from SC investments; but this could be due to the small size of this cluster in our sample, but also to the high complexity of this configuration, which makes investment less effective. This predominant effect of improvement programs for Shoppers can be justified by the major complexity of managing global distribution rather than global sourcing which somehow reduces the benefits of improvement programs or discourages companies from investing in that direction (Meixell and Gargeya, 2005; Steinle and Schiele, 2008).

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Table A1 Correlation among the variables (nnsig. o .01; nsig o .05). Flexibility Cost

Quality Delivery Lead time

Supply strategy

Supplier development

Coordination w/suppliers

Distribution strategy

Coordination w/customers

Risk management

Flexibility Cost Quality Delivery Lead time

1 .448nn .503nn .566nn .537nn

.448nn 1 .484nn .469nn .589nn

.503nn .484nn 1 .550nn .490nn

.566nn .469nn .550nn 1 .620nn

.537nn .589nn .490nn .620nn 1

.267nn .214nn .144nn .254nn .277nn

.326nn .273nn .283nn .261nn .297nn

.274nn .235nn .163nn .273nn .260nn

.291nn .309nn .242nn .362nn .386nn

.222nn .252nn .156nn .275nn .294nn

.241nn .272nn .216nn .249nn .306nn

Supply strategy Supplier development Coordination w/suppl. Distribution strategy Coordination w/customers Risk management

.267nn .326nn

.214nn .144nn .273nn .283nn

.254nn .261nn

.277nn .297nn

1 .581nn

.581nn 1

.538nn .597nn

.397nn .441nn

.388nn .456nn

.443nn .530nn

.274nn

.235nn .163nn

.273nn

.260nn

.538nn

.597nn

1

.544nn

.622nn

.533nn

.291nn

.309nn .242nn

.362nn

.386nn

.397nn

.441nn

.544nn

1

.631nn

.565nn

.222nn

.252nn 156nn

.275nn

.294nn

.388nn

.456nn

.622nn

.631nn

1

.577nn

.241nn

.272nn .216nn

.249nn

.306nn

.443nn

.530nn

.533nn

.565nn

.577nn

1

nn

Correlation is significant at the .01 level (2-tailed).

We can conclude that our results contribute to existing research on global SC management by providing new insights with a comprehensive analysis based on broad empirical evidence. At the same time, our results are also relevant for practitioners because they illuminate a crucial topic for operation managers. Global SCs are a challenge for managers and so far, it seems that Shoppers, characterized by high levels of global sourcing and local distribution, have benefitted most when companies have invested money and efforts into improvement programs. Therefore, for both researchers and practitioners, more work is needed in order to find ways to improve performance for Barons, characterized by local sourcing and global distribution. Understanding which programs affect which performance elements, given a specific global SC configuration, will provide managers with vital information as they make strategic decisions. In our work, we found that Locals benefit particularly from distribution strategy and supplier development, while Barons are negatively affected by coordination with suppliers and customers. In another key message for practitioners, size does not appear to be a critical issue, which means that smaller firms can also achieve benefits by improving their SC. Our examination of the country’s income level revealed an unexpected result: companies belonging to the Locals in emerging countries are achieving higher benefits. This is both a positive message for managers in those countries and a warning for those operating in higher income nations. In the end, we would also like to highlight some of the limitations of this work. First, the sample is not representative of a complete population and replication is therefore needed to validate and extend these results. Even if the dataset comes from consolidated research testing, examination of these relationships in other contexts and industries is also important. In addition, other potentially relevant contextual variables have not been considered; specifically, it would be interesting to integrate SC strategic objectives in order to consider this effect. Moreover, a broader spectrum of SC improvement programs could be considered. Finally, we have considered performance improvement rather than absolute values. Therefore, we may have overlooked the fact that well performing companies may not need further improvement.

Appendix A Correlation among the variables is shown in Table A1.

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