Effects of geographic dispersion on supply chain performance

Abstract

This paper empirically examines how the geographic dispersion of the firm’s supply chain impacts supply chain performance at the firm level. The research draws on a subset of 109 large manufacturing firms from the Finland State of Logistics survey. Generalized linear modelling is utilised as the data analysis method. Our analysis shows that increased geographic dispersion of the upstream and downstream supply chain operations results in higher costs of warehousing, inventory carrying and logistics administration, in the decline of perfect orders, and increase in order fulfilment cycle time. In contrast, geographically dispersed production network enables improved service performance as closer proximity to customers enables shorter order cycle times but, on the other hand, inventory costs tend to increase. Further, geographic dispersion in all tiers of the supply chain affects negatively asset utilisation since both the inventory days of supply and cash-to-cash cycle time increase. As a practical implication, managers should be acutely aware of the possibly large performance implications of geographically dispersing supply chains. The paper helps in the identification of supply chain design and performance management priorities for internationalising and global companies. Keywords: international supply chain, geographic dispersion, supply chain performance, supply chain design

1. Introduction

Globalisation and increasing international trade in manufactured products and components (WTO, 2005) imply geographically dispersed organisational structures, such as the multinational corporation (MNC). While it has been shown that diversification brings economic performance benefits to MNCs (Rumelt, 1982), there are also downsides to the increasing complexity of the firm's international business. For example, as critical internationalisation thresholds are reached, i.e. in terms of geographic and product diversification, profit margins begin to erode, implying diminishing levels of economic performance (Geringer et al., 1989; Geringer et al., 2000).

In addition to the complexity of managing internationally diversified information and revenue flows, the physical characteristics of the international network implies uncertainty for material flow that may affect both the financial and non-financial performance of supply chain operations to a serious degree (Prater et al., 2001; Arvis et al., 2010). Relevantly, Levy (1995, 356) has suggested that disruptions in international supply chains generate substantial expedited shipping costs, high inventories, and lower demand fulfilment, that is, relational, not locational factors, “raise the cost of geographically dispersing value chain activities”. It follows that “managers will frequently underestimate the costs of international sourcing” (Levy, 1995, 356). Therefore, in our research, geographic dispersion of the supply chain is identified as one potential determinant of supply chain operations’ performance at the firm level.

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In aiming to shed light on the implications of geographically diversifying and expanding the supply chain, we specify our research question (RQ) to be as follows: How does geographic dispersion of the firm’s supply chain impact supply chain performance at the firm level? With this research set-up, we contribute to the literature-base linking supply chain design and performance (e.g. Stock et al., 2000; Narasimhan and Kim, 2002; Bozarth et al., 2009). We limit our scope on studying the impact on individual firms, and do not, therefore, cover total effects in the inter-organisational supply chain, which would add significantly, and perhaps unnecessarily, to the complexity in data collection.

The research data is a subset of 109 large manufacturing firms collected by the authors in connection with the Finland State of Logistics 2009 study (Solakivi et al., 2009). Generalised linear modelling is utilized as the data analysis method.

This article is structured so that the next (second) section specifies the theoretical constructs of the research including considerations on measurement. After that, the literature is reviewed on the impact of supply chain geographic dispersion on supply chain performance, a focal topic of the research providing grounds for hypothesis formulation in the third section. The fourth section on research methodology describes the data collection process, the sample, and elaborates the utilised data analysis method. In the fifth section, the results are discussed, and finally, conclusions bring the paper to a close.

2. Theoretical constructs of the research

2.1. International supply chains

In supply chain management (SCM) literature, the international dimension has been covered through treatises on facility location (Bhutta, 2004), sourcing (Trent and Monczka, 2003), global supply chain design and optimisation (Everett et al., 2010), as well as strategy formulation (Christopher et al., 2006).

In addition, the literature also emphasise the role of environment in supply chain strategy and operation, and how logistics and SCM is affected by complexity and uncertainty implied by the global environment. That is, one is concerned about the fit of the design with the intended context. In this vein, Luo et al. (2001) propose that logistics is culture, economic system, and infrastructure related, and therefore different in Western developed countries in comparison to other countries of the world.

Meixell and Gargeya (2005) reviewed literature on decision support models for the location related design of global supply chains and conclude that location specific variables such as tariffs and duties, non-tariff trade barriers, currency exchange rates, and corporate income tax rates are often incorporated. As an example of such a model, Bhatnagar and Sohal (2005) establish the relationship of diverse location, uncertainty and manufacturing practice related factors, to supply chain competitiveness. Such factors as labour, infrastructure, business environment, political stability, proximity to markets, proximity to suppliers, key competitors’ location, supply chain uncertainty as well as manufacturing practices affect the operational measures of supply chain competitiveness. Fraering and Prasad (1999) further identify policy measures through which authorities may increase a country’s attractiveness in terms of manufacturing and logistics

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operations. Countries with high tariffs, a volatile currency, poor infrastructure, and a poor setting for SCM should be approached with caution in terms of location decisions.

Bello et al. (2004) argue that the adoption of supply chain innovations in international marketing channels is often constrained by the institutional context. Elements, especially those of a regulatory (e.g. laws and government requirements), normative (e.g. society’s values and norms) and cultural-cognitive (socially mediated framework providing templates and scripts for action) nature may make or break the attempt of exchange partners to form a suitable institutional arrangement for the facilitation of a supply chain innovation adoption. Relevantly to the international context, Tan (2001) elaborates on the conditions conducive to supply chain management by pointing out some institutional elements. In particular, change in corporate culture towards the appreciation of long-term system wide benefits and competitiveness is required, as are the business building activities of mutual trust and commitment.

To summarise, global supply chains that span countries and continents are subject to varying environments, institutions and operating conditions, implying longer and unpredictable lead times, co-ordination challenges and uncertainty in all aspects of supply chain management (Prater et al., 2001; Meijboom and Vos, 1997; Arvis et al., 2010). Understanding the effect of these conditions on the supply chain and related strategy execution, such as lean or agile, is one of the key questions in global supply chain management, or managing individual supply chains in specific foreign markets.

2.2. Geographic dispersion

In contemporary business settings, distance between people and organisations, and the consequent implications, is a topic of interest to practitioners and researchers. For example in geographically dispersed global companies, management of innovation and product development teams can be a significant challenge (O´Leary and Cummings, 2007) with negative performance implications on work processes and effectiveness (Cramton and Webber, 2005; Gibson and Gibbs, 2006). Geographical dispersion also affects corporate decision making in terms of employment adjustments and divestments, as more proximate employees and entities are protected (Landier et al., 2009).

While logistics and supply chain related literature on geographic dispersion per se seems scarce, geography related decisions have been prevalent from early on in the form of classic problems on warehouse location and production-distribution system design (e.g. Baumol and Wolfe, 1958). This resultant body of literature has been thoroughly reviewed by Owen and Daskin (1998), who point out the long-term and strategic nature of location decisions as these set the constraints for medium and short term supply chain decisions (Chopra and Meindl, 2001).

Although geographic dispersion is only one dimension of the aggregate supply chain design problem, it is becoming more relevant in the globalised world with more potential sourcing, production, and distribution contexts for firms, as well as with increasingly relevant demands for location diversification, or hedging, as a supply chain risk management strategy (Manuj and Mentzer, 2008). However, as a downside for location diversification, Hendricks et al. (2009) have found that geographically diversified firms experience a more negative stock market reaction as supply chain disruptions occur.

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Measurement of geographically dispersed business structures has been addressed in prior literature. Dörrenbächer (2000) reviews measurement concepts and their use in the sphere of corporate internationalisation. Individual structural indicators may be used, such as the number of countries the company is active in, the amount/proportion of value-added abroad, and the amount/proportion of sourcing abroad. Performance indicators, such as the amount of foreign sales or operating income abroad, may also be used. Composite indicators employ ratios, such as the Transnationality Index by UNCTAD (1995), assessing the degree to which corporations are engaged in international activities as compared to their total level of activities. The index (TNi) is formulated as follows:

TNi=( A f

A+

S f

S+

E f

E )3

(1)

where A stands for total assets, S for total sales, E for total employment, and f specifies the variable to denote foreign accumulation of the same. Dörrenbächer (2000) also reviewed indicators of regional diversification. For example, the Ietto-Gillies (1998) network spread index (NSi) allows cross-case comparison of companies’ global networks at the time of measurement, and also the examination of within-company evolution over time, and is formulated as follows:

NSi=n/n¿ (2)

where n stands for the number of foreign countries where a company currently has affiliates, and n* denotes the number of foreign countries in which a company may in theory have affiliates (basically the number of nation states in the world).

On a more of a micro level, e.g. in the innovation team management context, O´Leary and Cummings (2007) point out three literature based dimensions of geographic dispersion: spatial (geographic distance among team members), temporal (time difference among team members) and configurational, with the latter categorised into site, isolation, and imbalance characteristics. Site characteristics refer to the locations where team members work, implying more coordination complexity.

For survey use, Stock et al. (2000) developed an indicator of network geographic dispersion to test whether globally dispersed network organisations adopting enterprise logistics practices are able to achieve higher levels of organisational performance. The respondents were asked to specify the percentage of suppliers, production facilities, distributors and customers in several regions of the world. Dispersion indicators were calculated for these supply chain tiers, according to the following formulation:

DISP=1−|Europe %−25|+|Asia %−25|+|N . America %−25|+|Other %−25|150

(3)

The DISP formulation can be applied to other regional categorisations, and other tier structures, such as purchasing dispersion, own production capacity dispersion, and sales dispersion.

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2.3. Supply chain performance

Supply chain performance is a subset of the broader concept of organizational performance (Chow et al., 1994). The set of performance measures should be compatible to the goals of the company and the environment it operates in (Caplice and Sheffi, 1995; Neely et al., 1995; Griffis et al., 2004). Accordingly, Chow et al. (1994) conclude that performance is a multi-dimensional concept and leave it up to researchers and managers to find a set of measures that collectively capture most of the performance dimensions considered relevant for the purpose. Furthermore, because literature discusses supply chain performance measurement on multiple levels of aggregation, the comparison of different metrics is not always straightforward.

Cost associated to logistics operations is one class of the supply chain performance measures but they are neither well established in accounting nor in statistical terms. Beamon (1999) distinguishes between operating costs and inventory costs. Transportation, warehousing, inventory carrying costs, and administration cost (or administration overhead) in turn seem to be the most commonly used cost categories in survey based studies (Klaus and Kille, 2007; Töyli et al., 2008; Davis Database, 2009). Apart from these four, the cost categories tend to vary. Transportation costs have been widely reported as part of logistics cost already in the seventies (Lambert and Armitage, 1979). Transportation costs are considered directly related to the size of the shipment and the distance transported (Bowersox et al., 1986), although they do not increase in direct proportion to distance due to the carriers’ economies of scale (Coyle et al., 2003).

The total cost associated with inventory (Gunasekaran et al., 2001) can be further broken into: 1) capital cost, i.e. opportunity cost of [the resources used for] warehousing, capital and storage; 2) value of incoming inventory and work in progress; 3) value of finished goods in transit; 4) storage space costs, i.e. service costs of stock management, handling, storage space costs; 5) insurance and taxes; 6) risk costs of pilferage, deterioration and damage; 7) cost of scrap and rework; 8) and cost associated with shortage of inventory resulting in lost sales and/or lost production (Lambert and LaLonde, 1976; Gunasekaran et al., 2001; Coyle et al., 2003). Inventory carrying cost includes capital cost, storage space cost, inventory service, cost and inventory risk cost (Lambert and LaLonde, 1976; Coyle et al., 2003).

Logistics administration costs have often been classified as sales and marketing overheads (LeKashman and Stolle, 1965; Lambert and Armitage, 1979). Logistics administration costs include costs of communications and order processing, shipping documents, (LeKashmann and Stolle, 1965), scheduling shipments, tracking and counting inventory, expediting orders (Cooper and Kaplan, 1988), to name a few.

Customer service performance required to retain customers in today’s competitive environment consists of reliable, on-time delivery and accurately filled orders (Coyle et al., 2003). This can be measured, e.g., by percentage of orders delivered on time (Chow et al., 1994; Beamon, 1999; Gunasekaran et al., 2001; Griffis et al. 2004; Morgan, 2004) as well as by perfect order fulfilment (Brewer and Speh, 2000; Gunasekaran et al., 2001; Griffis et al. 2004; Shepherd and Günter, 2006; Töyli et al., 2008). Time is also increasingly important competitive weapon and can be, in many cases, seen as part of customer service performance or as its own class of measures. Order fulfilment cycle time (Gunasekaran et al., 2001), also known as total order cycle time, marks the time between the receipt of the customer order and the delivery of the goods, and can be

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further broken into 1) order entry time (through forecasts or actual order); 2) order planning time (design, communication and scheduling time); 3) order sourcing, assembly and follow up time, and 4) finished goods delivery time (Gunasekaran et al., 2001).

Cash-to-cash cycle or cash conversion cycle (Stewart, 1995; Brewer and Speh, 2000; Farris and Hutchinson, 2002) describes the average days required to turn ‘the dollar invested in raw material into sales’ (Stewart, 1995). It is calculated as follows: inventory days of supply + days of accounts receivable – days of accounts payable. This measure and its components illustrate the asset utilization efficiency and its components have sometimes been coined as operational metrics (see Töyli et al. 2008). Similarly, Gunasekaran et al. (2001) essentially discuss cash-to-cash cycle, although they call it total cash flow time.

2.4. Operationalisation of the theoretical constructs

Our opetrationalisation of the constructs is shown in Error: Reference source not found. Logistics cost components were classified following Töyli et al. (2008) as transportation cost (TRAN), warehousing cost (WARE), cost of capital tied in the inventory (INV), and logistics administration costs (ADMIN). The costs were measured as percentages from turnover that, according to Stewart (1995), is a robust base for analysis.

Customer service performance was measured through perfect order fulfilment (POF) and order fulfilment cycle time (OFCT) as was done, e.g., in Töyli et al. (2008). Asset utilisation included the usual cash-to-cash cycle time (CCC) and its widely used sub-component inventory days of supply (DOS).

Insert Table 1 here

For the measurement of geographic dispersion of the supply chain, we use the percentage share of purchases, production capacity, and sales in six geographical areas of the world: Finland (home), other EU (including Norway, Iceland, and Switzerland), Russia, North and South America, Asia, and Other. This regional configuration was formed from the Finnish foreign trade point of view. According to Finnish customs (2009), 54.6% of Finnish exports was within the European Union, 15.5% to other European countries, 14.0 percent to Asia, and 8.8 percent to North America. Because Russia is the third largest individual destination of Finnish exports, Russia was considered as an individual country.

The three geographic dispersion measures for the purchasing (PURCHDISP), production (PRODDISP) and sales networks (SALESDISP) were calculated based on Stock et al. (2000) as follows:

DISP=1−|Home%−100

6|+|EU%−100

6|+|Rus%−100

6|+|Ame%−100

6|+|Asia%−100

6|+|Other%−100

6|

10006 (4)

The geographic dispersion measures range between zero and unity, the former meaning the network is concentrated completely in one region, and the latter implying an evenly spread network in all six regions.

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2.5. Hypothesis on impact of geographic dispersion on supply chain performance

In order to provide grounds for a set of research hypotheses, we cover literature on the possible relationships between geographic dispersion and supply chain performance. The hypotheses are summarised in Error: Reference source not found, with argumentation and elaborations in the following. Positive sign indicates an expected positive relationship between the dependent and the independent, while the contrary holds in the case of a negative sign.

Insert Table 2 here

International purchasing is typically more complex than domestic due to for example lengthy pipelines of goods, increased regulation and currency fluctuations (Trent and Monczka, 2003). Same applies to the distribution side of operations (Milgate, 2001). Increased transit times and distances in international operations may add to a company’s inventory carrying cost, depending on the ownership of the in-transit inventory. Longer transit times, loading and unloading related to multimodal transportation, and customs procedures may also increase variability of lead time causing need for increased safety stock to ensure customer service level (Dollar, 1990; Young et al., 2009; Arvis et al., 2010). Thus, we expect dispersion of sales (SALESDISP) and purchasing tiers (PURCHDISP) in the supply chain to increase transport (TRAN), warehousing (WARE) and inventory costs (INV).

In terms of geographic dispersion of in-house production capacity (PRODDISP), we expect transport costs (TRAN) to decrease with increased dispersion, as more facilities reduce the average distance to suppliers and customers (Lambert and Armitage, 1979; Chopra and Meindl, 2001). However, in terms of warehousing (WARE) and inventory costs (INV), increased dispersion is expected to increase costs (Error: Reference source not found), in line with the square root law (Maister, 1976; Chopra and Meindl, 2001). It follows that inventory days of supply (DOS) is expected to increase as well.

Increased administrative effort typical for international operations, such as supplier development, on-site due diligence, and other supplier relationship management actions is likely to increase the administrative overhead costs, or transaction costs in general (Choi and Krause, 2006). Managing the network of additional parties in a global supply chain, such as freight forwarders, customs brokers and government agencies, represents a significant cost to the company (Dollar, 1990; Young et al., 2009). In this vein, Stock et al. (2000), as well as Narasimhan and Kim (2002), establish potentially costly internal and external supply chain integration as prerequisites for successful international market diversification. Further, Stringfellow et al. (2008) have proposed the impact of geographic interaction distance on the invisible, communication-related costs in offshoring service work. Therefore, we expect increase in dispersion in all tiers of the supply chain to increase administration costs (ADMIN; Error: Reference source not found).

Prater et al. (2001) consider vast geographic expanses, border crossings, and varying political and regulatory contexts in international supply chains to be sources of uncertainty, and Arvis et al. (2007) point out the dire situation of landlocked countries, which suffer from unpredictable transit transportation times, a problem that may be

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common in many ports and other border crossing points as well (e.g. Haralambides and Londono-Kent, 2004). It follows that we expect increased geographic dispersion in the sales tier (SALESDISP) to lengthen order fulfilment cycle times (OFCT). Dispersion in the purchasing tier (PURCHDISP) may have similar effect in cases where the decoupling point has been positioned in the upstream. With dispersed production capacity (PRODDISP), however, the average distance to customers becomes shorter, allowing faster response, i.e. shorter OFCT.

Due to the inherent uncertainty in the international context, we expect the opposite relationship signs from increased geographic dispersion in terms of perfect order fulfilment, that is, increase in dispersion in upstream (PURCHDISP) and downstream (SALESDISP) reduces the share of perfectly fulfilled orders (POF), while dispersion in in-house production capacity (PRODISP) increases the share.

Though extending payment time may not often be viable in international business and collaborative supplier relationships, regional and cultural differences may affect the cash-to-cash cycle time: for example more advanced payment methods and a culture of prompt payment may reduce the average accounts payable (Farris and Hutchinson, 2002; Naula et al., 2006). We hypothesise that the increased geographic dispersion in supply chain tiers, and therefore exposure to nationally varying standards, increases customer payment time, and may increase payment times to suppliers, due to the higher level of competition in international markets in this sphere (Min, 1994: payment terms is a criteria in supplier selection). This depends on the actual bargaining power of the manufacturer over suppliers (Kraljic, 1983). As inventories are expected to increase with increase in geographic dispersion, we expect cash-to-cash cycle (CCC) to increase as dispersion increases.

3. Research methodology

3.1. Data

The empirical data analysed in this research consists of a sub-sample of all 109 large manufacturing companies from the Finnish logistics survey1 data of Solakivi et al., (2009). For this article, a company was identified as large based on the European Commission definition; thus, the included companies are enterprises with over 250 employees, and either an annual turnover over 50 million euros, or an annual balance sheet over 43 million euros.

The analysed sample covers over 80 % of the total turnover of the Finnish manufacturing industry. Most of the respondents (45 %) have a turnover between 100.1-500 million euro and 6.4 % have over 5 billion euro turnover. The survey questions related directly to our operationalisations of logistics costs, supply chain performance, and geographical dispersion of operations.

1 The Finnish logistics survey was done through www-survey over the period from October to December in 2008. An invitation to participate was sent to 26,311 personal email addresses of all non-student members of the Finnish Association of Purchasing and Logistics (LOGY), Finnish Transport and Logistics (SKAL), and to the members of Federation of Finnish Enterprises and regional Chambers of Commerce, active in the industries covered in the survey. In total 2,705 responses were received resulting in the response rate of 13.9%. From these 37% (n=996) were manufacturing (including construction companies).

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3.2. Method selection

The properties of the research variables set the constraints for the deployment of statistical methods. Logistics costs variables were highly skewed, as depicted in Error: Reference source not found, and even after applying typical transformations like logarithms not normally distributed. Previous research has also shown the tendency of cost variables to high skewness and to cause complications in standard statistical analysis, such as in the OLS regression analysis (Dodd et al., 2006). OFCT showed skewness and the previous research on the nature of delay (Rose, 1999), lead time (Sivakumar and Arivarignan, 2009), and cycle time (Chow, 1980), has suggested gamma, Erlangian, and exponential distributions as models, respectively. The latter are special cases of gamma. CCC is the only dependent variable that visually resembles normal distribution.

In order to find out the best-fitting distribution, we tested several candidates (normal, gamma, Weibull, exponential). In case of CCC, a constant was added to transform all values positive and POF was converted into the %-share of customer orders not on time, not at the right place, with incorrect documentation, in wrong quantity, or with damage, i.e. 100%-POF (100-POF). Because the parameter spaces of all the distributions are not nested, the likelihood ratio test is not legitimate to use in our comparisons. Instead, we applied the Schwarz criterion (Schwarz, 1978) that is based on Bayesian approach, which states that it is most appropriate to select the model with the highest posterior probability. Rather than being a test, Schwarz criterion is an information criterion for ranking models without reference to statistical significance of the difference (see Schwarz, 1978). Although it is usually impossible to calculate posterior probabilities directly, the following approximate procedure can be applied:

1. For each model calculate SC=log L ( x|φ )−d log √T

, where )|( xL is the maximum likelihood function value, d the number of independent parameters, and T the sample size.

2. Select the model with the largest SC.

There is some evidence from financial data that the Schwarz criterion and the likelihood ratio test lead to similar ranking order of models, for which both criteria are legitimate to use (see Töyli et al., 2004).

The results indicate that Gamma distribution is the best fitting model in case of TRAN, WARE, ADMIN, and the second best after exponential distribution for INV, 100-POF, OFCT, and DOS. When Gamma is the second best model, the difference to its special case of exponential distribution is always smaller than “one parameter” in the Schwarz criterion. Normal distribution is the best fitting model for CCC. As a result, we chose to use Gamma distribution to model TRAN, WARE, INV, ADMIN, 100-POF, OFCT, DOS, and assumed CCC normally distributed.

For data analysis we applied generalized linear modelling (GLZ) with gamma as the random component and log as the link function for all Gamma distributed variables and GLZ with normal distribution and identity as the link function for CCC. In fact, GLZ provides a single estimation model within which any number of differing statistical

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models can be accommodated (Hair et al., 2010, 462). These GLZ models are composed of three elements: the variate (linear combination of independent variables), random component (the probability distribution assumed to underlie the dependent variable, e.g. normal or gamma), and the link function (specifying the type of transformation needed to specify the desired model, e.g. identity or log) (Hair et al., 2010, 462).

Having tested various multivariate models in an attempt to explain costs, Dodd et al. (2006), conclude on the highest suitability of generalized linear modelling (GLZ) for the task, and specifically, of the use of gamma distribution as the random component, with log as the link function. The nature of DOS and 100-POF and the results of Dodd et al. (2006), point also towards the usefulness of using GLZ with gamma as the random component and log as the link function in these cases.

In GLZ, the omnibus test (Likelihood ratio χ2) indicates statistically significant improvement from intercept-only model, the Wald χ2-test statistic is used to test the hypothesis whether the β coefficients are significant in the model, or non-zero. The coefficients indicate the magnitude of change in dependent variable with a unit increase in a dependent variable, ceteris paribus.

In our analysis the relationship between each performance variable and the three potential geographic dispersion related independent variables (SALESDISP, PRODDISP, PURCHDISP) was investigated. In the first phase all three independents were included in the model that was then improved with trial-and-error basis by shedding independents that do not demonstrate statistical significance, i.e. p values are below 0.1. Independents are also tested singularly with a dependent variable; however, models with more than one independent are prioritised.

4. Results and discussion

This section presents the results of the data analysis, by first providing descriptive overview of the research variables, and then moving on to the examination of the dependency between geographic dispersion and the performance of the supply chain operations. The dispersion variable means indicate the highest average dispersion in SALESDISP, and then in PURCHDISP and PRODDISP in descending order (Error: Reference source not found).

This seems logical, as the Finnish market is a relatively small home market, and the companies are required to seek growth in foreign markets, while highly dispersed international purchasing is not a goal in its own right. This same applies to foreign production capacity, possibly even to a greater degree due to the risk of investment and control challenges.

Insert Table 3 here

The average logistics costs per category range between 7.64% of turnover for transport and 1.77% of turnover for administration. The share of imperfect orders is 7.55% on average and the average order cycle time is some 46 days. However, the standard deviation of OFCT is quite high, i.e. at about 100 days. Respondent companies hold 52 days of inventory supply on average, while the average cash-to-cash cycle time is 50 days.

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Error: Reference source not found presents the GLZ models with statistically significant improvement from the intercept only model (omnibus test), and with non-zero coefficients (test of model effects). Statistical significance thresholds for both were set at 0.1.

Insert Table 4 here Based on the estimated models, three cost variables and four other performance variables can be explained with the dispersion variables, resulting in fourteen different models (Error: Reference source not found). In interpreting the model coefficients, it should be noted that as the independent dispersion variables range from 0 to 1, a unit increase in this context implies a strategic level move from one region network configuration (such as 100% purchasing in one region) to a configuration with equal shares in the six previously defined regions of the world. Each dependent variable is elaborated in the following.

Effects on logistics costs are as follows. Warehousing costs as a percentage of turnover (WARE) appears to increase by 0.74 (β) as the geographic dispersion of the sales network (SALESDISP) increases by one unit, i.e. from 0 to 1, or from no dispersion to full dispersion. Further, a unit increase in PURCHDISP, implies an 1.81 increase in warehousing costs. Inventory costs as a percentage of turnover (INV) behave in a similar manner to warehousing costs, increasing by 1.49 and 2.53, as SALESDISP and PURCHDISP increases by one unit, respectively. Additionally, INV increases by 1.32 with a unit increase in PRODDISP. Administration costs increase by 1.02 and 2.19 with a unit increase in SALESDISP and PURCHDISP, respectively.

In terms of service performance implications, imperfect order fulfilment (i.e. 100-POF), increases with both the increase of SALESDISP and PURCHDISP. As in the case of logistics costs, statistically significant models could be achieved singly, but not with both independents. The effect of PURCHDISP increase on 100-POF is larger (2.49), in comparison to SALESDISP (1.31).

In contrast to the previous dependents, order fulfilment cycle time in days can be explained by all the dispersion variables, in a statistically significant single model. As SALESDISP and PURCHDISP increase by one unit, the OFCT in days increases by 3.29 and 2.90, respectively. However, in the case of PRODDISP, the relationship is negative, the coefficient being -1.88.

In terms of asset utilisation, and indicated by a single model, inventory days of supply increase by 1.15 and 1.31, with unit increase in SALESDISP and PURCHDISP, respectively. Based on another model, DOS increase by 1.08 with a unit increase in PRODDISP.

Further, in terms of the cash-to-cash cycle, measured in days, a positive relationship between the independents SALESDISP and PURCHDISP and the dependent CCC is observed in the same model, with the coefficients at 54.72 and 82.90, respectively. However, PRODDISP also proves to possess some explanatory power, as in a single independent model set-up, CCC increases some 74.7 days as the independent increase from 0 to 1.

The above described results are summarised in Error: Reference source not found. Hypotheses that lack statistically significant results are shaded; however, the observed

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effect direction is nevertheless indicated. In white cells asterisks or the lack of it indicates the level of statistical significance.

Insert Table 5 here

The insights of our results fall into three main categories, namely the direction of the relationship between the independent and the dependent variables, the magnitude of the relationship between the independent and the dependent variables, and the relative dominance of the independent variables in explaining the dependent variables. In answering the research question, i.e. how does geographic dispersion of the firm’s supply chain impact supply chain performance at the firm level, each of the categories will be discussed in turn.

In general, the signs of the statistically significant relationships are as hypothesised, i.e. the expected effects are aligned with the observed effects, confirming our hypotheses. On the logistics cost side, increased geographic dispersion of the upstream and downstream supply chain results in higher costs of warehousing and inventory carrying. Further, the administrative burden of managing geographically dispersed customers and suppliers is evident from the results, as higher costs. As expected, a dispersed production network also seems to increase the cost of inventory holding.

Geographic dispersion of the upstream and downstream network has a detrimental effect on the service performance of the supply chain, that is, the share of perfect orders declines and order fulfilment cycle time increases. In contrast, geographically dispersed production network enables improved responsiveness, as closer proximity to customers enables shorter order cycle times.

Further, geographic dispersion in all tiers of the supply chain has a negative effect on asset utilisation, as increases are experienced in both the inventory days of supply, and the cash-to-cash cycle time, most probably due to multiple sites with raw material, component and finished goods inventories, and the varying diligence and promptness in customer payments that the company subjects itself with increased exposure to the international business environment.

The results that are not statistically significant naturally do not allow robust conclusions, however, it is interesting to note that while most of the relationships behave as expected, transport costs do not, indicating a tendency for decreasing with increased geographic dispersion in up and downstream tiers of the supply chain. Hopefully, further research can address and clarify this point.

The magnitude of the effect of supply chain geographic dispersion on performance seems relatively moderate in absolute terms. However, the validity of such a statement naturally depends on the level of competition and margins in an industry. Additionally, it should be noted that costs are measured as percentage of turnover, and by nature, the level of logistics costs categories as share of turnover is relatively low in absolute terms (see Error: Reference source not found; Solakivi et al., 2009).

The dispersion variables range from 0 to 1 and a unit increase in the independents would imply a strategic change from one region sales to equally dispersed sales in six regions of the world, i.e. Finland, other EU, Russia, North and South America, Asia, and other. Error: Reference source not found describes the effect of this unit increase in

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dispersion variables on performance variables, by putting the effect in perspective through comparison with the median level of each performance variable among companies with zero dispersion (i.e. one region purchasing, production, or sales).

Insert Table 6 here Error: Reference source not found serves as the extreme example on the

magnitude of effects when dispersion increases from 0 to 1. The presentation provides a reference point for the models’ coefficients, and it seems that, as compared to the median performance values from zero-dispersed companies, some of the effects may in fact be on average quite substantial. For example, companies with fully dispersed purchasing may end up paying multiple times the inventory costs and administration costs, and suffering more than triple the amount of imperfect orders, as compared to companies with one region purchasing. Other effects are also potentially quite large between one region oriented companies in comparison to global ones, such as the potentially major cash-to-cash cycle time increases. Additionally, the average order fulfilment cycle time may be more than twice as long for global customer base, in comparison to the average cycle time in one region sales.

Of the three geographic dispersion dimensions, purchasing and sales seem to have equally strong effect on firm level supply chain performance, even based on the subcategories of cost, service performance, and asset utilisation (Error: Reference source not found). Especially at the supply side of operations, the fact that companies often source from low-cost contexts may play a role, as overall trade logistics performance may often be lower than average in these low income and low labour cost countries.

While the dispersion of production network appears to have a relatively more limited impact on supply chain performance, it is the only independent that may balance detrimental performance effects with an improved level of service performance, i.e. in the form of shorter order fulfilment cycle times.

5. Conclusions

In theoretical terms, this research has demonstrated the validity of prior literature in predicting the performance effects of increasing geographic dispersion of the supply chain. However, the extant literature has not explicitly addressed the topic. This research has provided explicit evidence on the dispersion-performance relationship, as well as new insight on the relative importance of dispersion management in different tiers of the supply chain, from the point of view of focal company performance. Based on our results, in internationalising and global companies, the management of supply chain performance should be high on the agenda, especially in terms of logistics costs and cash-to-cash cycle time.

In practical terms, this research has demonstrated the challenges in managing international or global supply chains, and essentially, provides general guidelines for the design of international supply chains. Based on our results, managers should be acutely aware of the possibly large performance implications of geographically dispersing supply chains, for example in the context of internationalisation of sales, and aim, when feasible, for consolidation in for example the supply base. Such development aims should naturally be balanced with the drive to geographically diversify sourcing as a risk

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management strategy. In this context, our research sheds light on the possible cost effects of the diversification strategy.

In further research, geographic dispersion measures could be enhanced or complemented by including a measure of logistics friction in the network by the means of the Logistics Performance Index (Arvis et al., 2010), representing the aggregate reliability of for example the supply network. The potential of this approach has been pointed out by Bozarth et al. (2009), whose results underscore the significance of suppliers’ delivery performance in contrast to mere geographic location or nationality. It is also important to clarify the behaviour of transport costs in globally dispersed supply chains, due to their large contribution to the overall level of logistics costs.

The usual caveats apply for this research also. One should exercise caution in interpreting results due to the cross-sectional nature of the data, and the concentrated geographic origin of the respondents. Data is also based on single respondents per organisation, resulting in possible bias. The scope is further limited to the impact on individual firms and, therefore, total effects in the inter-organisational supply chain are not covered. Despite these limitations, the research provides novel insights from large manufacturing organisations that enable improved evaluation of performance implications due to internationalisation in purchasing, production or distribution.

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