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Contents lists available at ScienceDirect
Int. J. Production Economics
Int. J. Production Economics 120 (2009) 125–138
0925-52
doi:10.1
� Cor
E-m
(H. Yan
Amrik.S
journal homepage: www.elsevier.com/locate/ijpe
The impact of IT implementation on supply chain integrationand performance
Gang Li a, Hongjiao Yang a,�, Linyan Sun a, Amrik S. Sohal b
a School of Management, The State Key Lab for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xianning West Road 28#, Xi’an,
Shann Xi 710049, PR Chinab Department of Management, Monash University, Australia
a r t i c l e i n f o
Article history:
Received 1 September 2007
Accepted 1 July 2008Available online 17 October 2008
Keywords:
IT implementation
Supply chain integration
Empirical study
73/$ - see front matter & 2008 Elsevier B.V. A
016/j.ijpe.2008.07.017
responding author. Tel./fax: +86 29 82664643
ail addresses: [email protected] (G. Li), recha
g), [email protected] (L. Sun),
[email protected] (A.S. Sohal).
a b s t r a c t
The implementation of information technology (IT) for supply chain management (SCM)
is becoming more and more important in the context of an increasingly globalized and
competitive economy. IT, by providing timely, accurate, and reliable information, has
greatly improved supply chain performance (SCP). This study aims to investigate the
relationship among three factors: IT implementation, supply chain integration (SCI), and
SCP. It presents a conceptual structure model in which IT implementation can affect SCP
either directly or indirectly, via SCI. Data collected from 182 Chinese companies are
analyzed using structural equation modeling. The results suggest that IT implementa-
tion has no direct effect on SCP, but instead that it enhances SCP through its positive
effect on SCI. These findings highlight the importance for companies to promote SCI and
implement IT as an enabler.
& 2008 Elsevier B.V. All rights reserved.
1. Introduction
In recent decades, the development of informationtechnology (IT) has rapidly changed the conditions fordoing business around the world. With its power toprovide timely, accurate, and reliable information, IT hasled to better performance of both the focal firm and thepartners in the supply chain (Jin, 2006). IT, as aninfrastructure support both inside the organization itselfand within its upstream, has been recognized as a criticalfactor in the improvement of supply chain management(SCM) (Gupta and Capen, 1996; Koh and Saad, 2006).
Although there are many definitions in the literature,SCM is primarily concerned with managing relationshipswith suppliers and customers in order to deliver the bestcustomer value at the lowest cost (Stevens, 1989). SCMemphasizes effective and efficient flows of both informa-tion and physical items to meet customer requirements,
ll rights reserved.
.
starting from the source of supply of raw materialsthrough to the consumption of the product by the end-customer. The management of these processes requiresclose collaboration among the different parties in thesupply chain, including raw materials suppliers, manu-facturers, distributors, and retailers, in order to achievethe ultimate goal of satisfying customer requirements andreducing costs. By making possible the sharing of largeamounts of information along the supply chain, includingoperational, logistical, and strategic planning data, IT hasenabled real-time integration of supply chain partners,provided organizations with forward visibility, and im-proved production planning, inventory management, anddistribution. Thus, virtually all companies in today’smarket place either have implemented, or are in theprocess of implementing IT in order to streamline SCMactivities (Olhager and Selldin, 2004; Zhang et al., 2005).
Not only are more and more businesses investing in IT,but also, more and more research is being devoted toinvestigating the impact of IT implementation on supplychain performance (SCP). While the payoff from investingin IT has been a subject of long standing academicresearch and intense discussion, there is as yet no clear
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consensus reached. Some studies have found that overallIT capability is positively linked to organizational perfor-mance (Bharadwaj, 2000; Kearns and Lederer, 2003;Wamba et al., 2008); others have found that investmentin IT can give a firm a significant competitive advantage(Earl, 1993; Kathuria et al., 1999). On the other hand, anumber of other empirical studies have failed to find aclear IT effect, a phenomenon which has been called the‘‘IT paradox’’ (see Weill, 1992; Hitt and Brynjolfsson, 1996;Lee and Barua, 1999; Devaraj and Kohli, 2003; Poirier andQuinn, 2003). As a result, despite the widespreadimplementation of IT, it still remains elusive whether ITimplementation has a direct positive effect on SCP.
Another open question concerns the manner in whichIT implementation affects SCP. Most of the researchexamining the benefits of IT has focused on broad,overarching firm performance metrics (Bharadwaj et al.,1999; Brynjolfsson and Yang, 1996; Dehning et al., 2003).While these studies do provide insights into the overallbenefits of IT implementation, the underlying analyses areaffected by a considerable amount of measurement‘‘noise’’ attributable to (1) the indirect path between ITimplementation and these overarching performance me-trics and (2) a recognition that these overarchingperformance metrics are affected by numerous factorsother than the focal IT implementation (Dehning andRichardson, 2002). These qualifications notwithstanding,the literature supports the conclusion that supply chainintegration (SCI) can greatly improve SCP (Stevens, 1989;Lee et al., 1997; Anderson and Katz, 1998; Hines et al.,1998; Johnson, 1999; Vickery et al., 2003; Stank et al.,2001). These benefits derive from the fact that IT makespossible information sharing and other forms of colla-boration between customers and suppliers. Examplesinclude jointly developed demand forecasting (Koloczyc,1998; Aviv, 2001) and vendor-managed inventory (VMI),also referred to as direct shipment or automatic replen-ishment (Cetinkaya and Lee, 2000; Kulp et al., 2004). Buteven if the implementation of IT does not have a directeffect on SCP, it may have an indirect effect via its impacton the processes developed for SCI. This possibility hasdrawn less attention in the literature. There are fewempirical studies that have confirmed the indirect impactof IT implementation on SCP.
This study seeks to extend our understanding of how ITimplementation impacts on SCI, and SCP. We develop a set ofhypotheses based on the literature to empirically test the directimpact of IT implementation on SCP and the indirect impact ofIT implementation on SCP mediated by SCI.
The remainder of this paper is organized as follows.First, we develop a conceptual model of the relationship
IT Implementation
Supply CIntegrati
H3(+)
H1(+)
Fig. 1. The proposed co
among IT implementation, SCI, and SCP. Second, a briefliterature review and a set of three research hypothesesrelated to the conceptual model are presented. Next, theresearch methodology and empirical results are discussed.This is followed by a discussion of the results and theirimplications for managers. Finally, the limitations andconclusions of this study are presented.
2. Literature review and research hypotheses
2.1. Conceptual model development
We propose a conceptual model of the relationshipsamong IT implementation, SCI, and SCP (see Fig. 1).According to this model, the implementation of IT canimprove SCP not only directly, but also indirectly, via itsimpact on SCI. The present study takes SCP rather thanfinancial performance as the dependent variable. This isbecause the direct effect of IT implementation on SCP islikely to be more significant than its effect on financialperformance. ‘‘SCP’’ in turn includes the dimensions ofcost, quality, flexibility, and delivery (Chen and Paulraj,2004; Kathuria, 2000). ‘‘IT implementation,’’ as opposedto ‘IT,’ refers specifically to the technical capability toacquire, process, and transmit the information needed formore effective decision making. This definition not onlymeasures the degree of a firm’s proactive adoption andimplementation of advanced IT to enforce speed, quality,and quantity of information transferred, but also mea-sures the degree of its embeddedness of IT across thesupply chain to coordinate its business processes with itssupply chain partners. The third term, ‘‘SCI’’, refers to theability of a firm to integrate exchange-related activitieswithin functional departments and with supply chainpartners. Integration within functional departments re-quires cross-functional planning, coordination, and shar-ing of integrated databases. Integration with supply chainpartners requires the coordination of operational, logis-tical, and planning data to improve production planning,inventory management, and distribution.
2.2. SCP and IT implementation
The impact of IT on organizational performance hasbecome one of the major preoccupations of both managersand researchers. Studies have ranged from the investiga-tion of the alignment of specific IT applications with theorganizational competitive priorities and alignment withstrategic objectives (Kathuria et al., 1999; Kearns andLederer, 2003) to comparisons of the effectiveness of
hain on
Supply Chain Performance
H2(+)
nceptual model.
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specific IT applications (Raghunathan, 1999; Hendrickset al., 2007) and method of IT use (Subramani, 2004). Ingeneral, IT has been widely recognized as a critical factor inthe supply chain because of the contribution it can maketo improve the performance of both the individual firmand the supply chain as a whole. However, research on thedirect impact of IT on specific performance measures hasyielded inconsistent results (Sanders, 2007).
While a considerable number of studies argued that ITimplementation has vast potential for improving a firm’sfinancial performance (Mukhopadhyay et al., 1995; Bhar-adwaj, 2000; Dehning and Richardson, 2002; Hendricksand Singhal, 2003; Dehning et al., 2006), a few empiricalstudies have been more equivocal in finding the perfor-mance effect of IT (Weill, 1992; Yosri, 1992; Hitt andBrynjolfsson, 1996). This phenomenon has been called the‘‘productivity paradox of information technology’’ (Limet al., 2004), and numerous explanations have been offeredfor it, such as management’s failure to leverage the fullpotential of IT (Dos Santos and Sussman, 2000), ineffectiveimplementation (Stratopoulos and Dehning, 2000), poormeasures of performance (Bharadwaj et al., 1999), and thepresence of a time lag between IT investment and itsactual impact on performance (Devaraj and Kohli, 2000).
Most previous studies have focused primarily on thetotal level of IT spending by firms over several years and theimpact of this spending on financial performance. Fewstudies have attempted to examine the direct effects of IT onSCP, even though the latter effect is likely to be moresignificant than the indirect effect on financial performance.In addition, earlier studies have tended to measure IT as astand-alone resource, focusing on the level of IT spending,without considering the alignment between IT and theoverall business processes which are a firm’s strategicemphasis (Wu et al., 2006). These two shortcomings inprevious studies may explain the existence of the ‘‘produc-tivity paradox’’. And it is in an attempt to remedy the secondthat the study reported here takes IT alignment into accountin assessing the impact of IT implementation on SCP.
Reducing environmental uncertainty has become one ofthe most important objectives in SCM (Chen and Paulraj,2004). By providing real-time and accurate informationregarding product availability, inventory levels, shipmentstatus, and market needs, IT implementation can reduceenvironmental uncertainty and improve supply chainefficiency (Radstaak and Ketelaar, 1998). It has come tobe widely believed that the implementation of IT along asupply chain is a significant factor determining success inSCM and has increasingly become a necessity for enhan-cing SCP (Lai et al., 2006; Handfield and Nichols, 1999).
On the basis of the foregoing considerations, wepropose the following hypothesis:
H1. IT implementation has a positive effect immediatelyon SCP.
2.3. SCP and SCI
Mentzer (2001) provides a holistic definition of SCM:
SCM is defined as the systemic, strategic coordination ofthe traditional business functions and the tactics across
these business functions within a particular companyand across businesses within the supply chain, for thepurposes of improving the long-term performance of theindividual companies and the supply chain as a whole.
This view recognizes that the successful managementof a supply chain requires going beyond the boundaries ofa single company, and requires the integration of businessprocesses among partners along the chain. The theoreticalfoundation for SCI can be traced to the value chain model(Porter, 1980, 1985), and specifically, its notion of linkageswithin a firm’s value chain and the linkages among thefirms in the value chain. It is believed that all of theindividual organizations that comprise the supply chainshould ultimately be managed as a single entity or onecomplete system, which can lead to superior performance(e.g. Tan et al., 1998; Frohlich and Westbrook, 2001). Thisrequires integration and coordination across individualfirm functions and throughout the supply chain.
Previous studies, both empirical and theoretical, havecome to the consensus that SCI can improve firmperformance (Stevens, 1989; Lee et al., 1997; Metters,1997; Anderson and Katz, 1998; Hines et al., 1998;Johnson, 1999; Frohlich and Westbrook, 2001). SCI isenhanced by sharing information about key processingactivities. With high degree of SCI, manufacturers canreact more flexibly to individual customer demands, todecreased delivery times, and to reduced inventories, allof which can make the supply chain more efficient (Clarkand Lee, 2000; Barrat, 2004). In contrast, lack of integra-tion has been shown to create the classic magnification ofdemand up the supply chain, known as the ‘‘bullwhipeffect,’’ with resulting alternations between excess in-ventory and stock-outs (Lee and Billington, 1992).
At present, more and more companies that are directlylinked in a supply chain attempt to exploit intensiveintegration across individual firm functions with theirsupply chain partners, thereby permitting each companyto deliver products quickly and reliably, enhance respon-siveness, shorten lead times, improve performance, andeliminate the bullwhip effect (Lee et al., 1997). Thedevelopment of the channel partnership between P&Gand Wal-Mart is a good example. With the channelpartnership, both companies have improved profitabilityand their joint business revenues have grown from $375million in 1988 to over $4 billion dollars in 2002 (Micheal,2002). Similarly, the collaborative relationship betweenSears and Michelin using CPFR (Collaborative Planning,Forecasting, and Replenishment) has resulted in a 25%reduction in inventories for both companies (Steerman,2003). And General Motors’ new collaborative relationshipwith its suppliers has reduced vehicle development cycletimes from four years to 18 months (Gutman, 2003).
As the preceding examples suggest, enhanced integra-tion across an individual firm’s functions and alongits supply chain can be expected to impact manydimensions of performance, including cost, quality, deliv-ery, flexibility, and profits. We therefore propose thefollowing hypothesis.
H2. Supply chain integration has a positive effect on SCP.
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2.4. IT implementation and SCI
More than ever before, today’s IT has permeated thesupply chain at every point, transforming exchange-related activities and the linkages between those activities(Palmer and Griffith, 1998). IT has vast potential tofacilitate integration and coordination among supplychain partners through the sharing of information ondemand forecasts and production schedules that dictatesupply chain activities (Karoway, 1997).
A company’s supply chain divides it into a sequence ofprimary activities: inbound logistics, operations, out-bound logistics, marketing and sales, and service, alongwith support activities. Among these activities, some areinternal and some are external to the organization, allwith the primary goal of creating value to the end-customer (Handfield and Nichols, 1999). This goal isaccomplished through integration of activities betweenlinked organizations, and should result in reduced costsdue to the elimination of operational duplication andresource waste (Andraski, 1998; Stank et al., 2001). Inorder to achieve this reduction, it requires engaging inintegration that is both internal and external to theorganization.
Many researchers have indicated that SCI needs to beachieved across organization boundaries, linking externalsuppliers, carrier partners, and customers. Higher levels ofintegration are characterized by increased logistics-related communication, greater coordination of the firm’slogistics activities with those of its suppliers and custo-mers, and more blurred organizational distinctions be-tween the logistics activities of the organization and thoseof its suppliers and customers (Stock et al., 2000).Successful integration requires fluent flow of accurateand timely information across these supply chain part-ners. The ability to manage the information flow is one ofthe critical weapons of today’s leading edge organizations.
IT has the potential to manage the information flowand to provide links that support communication andcollaboration along the supply chain (Brandyberry et al.,1999). Implementation of IT in SCM can integrateand coordinate the flow of materials, information, andfinances among suppliers, manufacturers, wholesalers,retailers and end-consumers. Here, IT serves as a keyenabler of SCI through the capture, organization, andsharing of vital information regarding key businessprocesses, both within and outside an organization’sboundaries (Clemons et al., 1993; Frohlich and Westbrook,2001; Sanders and Premus, 2002; Vickery et al., 2003;Kelle and Akbulut, 2005).
The argument that IT improves SCI is further supportedby transaction cost economics. Transaction cost econo-mists hold that cooperation and coordination among firmsis limited by the transaction cost of managing theinteraction (Coase, 1937; Williamson, 1975; Stoeken,2000). As transaction costs increase, market transactionefficiency decreases, which may result in higher marketprices. The determinants of transaction costs are transac-tion frequency, asset specificity, uncertainty, boundedrationality, and opportunistic behavior. Since IT has thepower to provide timely, accurate, and reliable informa-
tion, it provides managers with a convenient, low costalternative to traditional face-to-face communication, onewhich decreases information uncertainty and transactionfrequency. IT has also proved to be an effective meansfor decreasing both coordination costs, including thedirect cost of integrated decisions (Nooteboom, 1992),and transaction risk, which is the risk of being exploited inthe relationship (Clemons et al., 1993).
On the basis of the foregoing, we propose the followinghypothesis:
H3. IT implementation has a positive effect on SCI.
3. Research methodology
3.1. Sampling and data collection
Data for this study were obtained through a survey of182 companies in China, which, as a global factory, playsan important role in many supply chains. Given China’ssize, it is extremely difficult to obtain data from all parts ofthe country. The survey conducted for this study wascarried out mainly in three typical large manufacturingcities: Beijing, Shanghai, and Shenzhen. These three cities,which have benefited from a high degree of economicreform and marketization, are representative of the mostdeveloped areas in China.
For the sake of the accuracy and completeness of theresponses, the sampled companies were selected onthe basis of recommendations from local universitiesand government officials. The highest-ranking officers(e.g. president, CEO, vice president, or senior manager)of the targeted companies were contacted first, afterwhich, contact was made with a middle-manager (e.g.supply chain manager, logistics manager, or procurement/purchasing manager) responsible for the company’ssupply chain activities. Since all of our respondents werecorporate managers familiar with their company’s supplychain activities, it is reasonable to expect that therespondents could offer a deep insight into the supplychain activities and be knowledgeable about the contentof the inquiry.
According to Miller et al. (1997), two criteria wheresubjective data may be reliable and valid are:(a) questions do not require recall from the distant past,and (b) informants are motivated to provide accurateinformation. We promised confidentiality of data andhighlighted the usefulness of the project. In addition, theywere informed that they would receive a benchmarkingreport after the data were collected. Therefore, weattempted to minimize response bias in subjective dataobtained from respondents.
The survey was conducted from June to September2005. During the data collection process, we first calledevery sampled company to inform them of our purposebefore sending our questionnaires. The questionnaireswere then sent to the respondents via email or postalmail, with a postage-paid return envelope to the completesample of all 400 companies. Ten days later, follow-uptelephone calls were used to remind them of answeringthe questionnaires. At last, a total of 308 questionnaires
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Table 1Profile of the respondent companies
Characteristics of firms No. (share in total)
Nature of ownership
State-owned 29 (15.93%)
Private 38 (20.88%)
Joint-venture 58 (31.88%)
Unmarked 57 (31.32%)
Number of formally enrolled employee
1–100 54 (29.67%)
101–500 46 (25.27%)
500–1000 19 (10.44%)
Over 1000 48 (26.37%)
Unmarked 15 (8.24%)
Level of turnover (in 10 million RMB)
Below 2 27 (14.84)
2–13 61 (33.52)
13–33 21 (11.54)
33–66 13 (7.14%)
66–132 7 (3.85%)
132–330 8 (4.40%)
330–660 3 (1.65%)
Above 660 26 (14.29%)
Unmarked 16 (8.79%)
G. Li et al. / Int. J. Production Economics 120 (2009) 125–138 129
were returned (effective response rate of 77.0%), but 126of them were not useable because of significant datamissing and incompleteness. The final usable samplecontains 182 usable responses, yielding a usable responserate of 45.5%. The profiles of the usable respondentcompanies and their characteristics are displayed inTable 1.
To assess non-respondent bias, we compared theresponses of early and late respondents to test for theirsignificant differences (Armstrong and Overton, 1977).The first 75% (n ¼ 136) of the responses were classified as‘‘early respondents’’. The last 25% (n ¼ 46) of them wereclassified as ‘‘late respondents’’ and were deemed repre-sentative of firms that did not respond to the survey.At the 5% significance level, no differences between the‘‘early’’ and ‘‘late’’ respondents were detected, suggestingthat non-response bias was not a problem with regard tothe data collected in this study.
3.2. Construct measures
We followed the procedures suggested by Churchill(1979) in scale development. First, the domain of eachconstruct was clearly defined in terms of what would beincluded or excluded. Second, the literature was surveyedto locate any relevant scales. Measures were adopted oradapted from the existing literature where appropriate. Ifnone were available or appropriate, new measures weredeveloped.
In this study, the scales for IT implementation werederived from the measurement of IT in Chen and Paulraj(2004), which emphasizes the usage of IT. SCI scales wereobtained by modifying the degree of logistics integration
proposed by Chen and Paulraj (2004). For SCP, measuredby six items, scales were adopted from Stank et al. (2001).In addition to adaptation and modification of scales fromexisting literature, we have also added new items basedon interviews with industrial managers. Specifically, wedeveloped three items which emphasize IT alignment tocomplement the scale for IT implementation. Besides,being different from the traditional five-point Likert scale,we provide five detailed descriptions based on interviewswith industrial managers for every item, and eachdescription represents a certain level (marked 1–5). Whenanswering questionnaires, the informants can just draw‘‘O’’ on the description which is in accordance with thefact of their companies. The survey items are summarizedand shown in Appendix A.
3.3. Reliability analysis
Reliability is operationalized by the internal consis-tency method that is estimated using Cronbach’s alpha(Hull and Nie, 1981). Typically, reliability coefficients of0.70 or higher are considered adequate. Therefore, analpha value of 0.70 is considered as the critical value.
As shown in Table 2, Cronbach’s alpha values of thefactors are well above the critical value and ranged from0.87 to 0.88. These results suggest that the theoreticalconstructs exhibit good psychometric properties.
3.4. Unidimensionality
Assessing unidimensionality means determiningwhether indicators reflect one, as opposed to more thanone, construct (Gerbing and Anderson, 1988). There aretwo implicit conditions for establishing unidimensional-ity. First, an empirical item must be significantly asso-ciated with the empirical representation of a construct.Second, it must be associated with one and only oneconstruct. Only when a measure satisfies both of theseconditions, it can be considered unidimensional.
In this study, unidimensionality was established usingconfirmatory factor analysis (CFA). The CFA results for IT,SCI, and SCP are shown in Table 3. It can be seen from Table 3that all the measurement models have acceptable fitindices, which prove the unidimensionality of the con-structs. Moreover, the convergent and discriminant valid-ities established in the following section, further solidifiesthe extent of unidimensionality of the constructs.
3.5. Convergent validity
In order to perform meaningful analysis of the causalmodel, the measure needs to display certain empiricalproperties. The first of them is convergent validity, whichmeasures the similarity or convergence between theindividual items measuring the same construct.
One way to test convergent validity is to use CFA. InCFA, convergent validity can be assessed by testingwhether each individual item’s standardized coefficientfrom the measurement model is significant, namelygreater than twice its standard error (Anderson and
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Table 2Summary measurement results
Factors Number of items Mean S.D. Cronbach’s alpha Range of item-to-total
correlations
IT Implementation 5 2.91 1.02 0.87 0.66–0.74
Supply chain integration 5 3.06 0.85 0.87 0.61–0.75
Supply chain performance 6 2.99 0.88 0.88 0.76–0.83
Table 3Results from confirmatory factor analysis for measurement model
Factors and scale items Standardized coefficient Standard error t-Value
IT implementation (w2/d.f. ¼ 3.71, RMSEA ¼ 0.12, GFI ¼ 0.96, NFI ¼ 0.96, NNFI ¼ 0.94, CFI ¼ 0.97, IFI ¼ 0.97)
IT1: Electronic data Interchange (EDI) coverage 0.704 0.093 8.143a
IT2: Usage of bar Coding/automatic identification and data capture 0.716 0.104 8.047a
IT3: Effective usage of computers in operations and decision-making 0.771 0.075 7.448a
IT4: Open standards and unique identification codes 0.815 0.092 6.721a
IT5: Decision-making systems and support to supply chain partners 0.775 0.078 7.399a
Supply chain integration (w2/d.f. ¼ 1.90, RMSEA ¼ 0.07, GFI ¼ 0.98, NFI ¼ 0.98, NNFI ¼ 0.98, CFI ¼ 0.99, IFI ¼ 0.99)
SCI1: Strategies for optimizing logistics system resources based on DFL 0.664 0.076 8.433a
SCI2: Understanding of market trends and accuracy of demand forecasting 0.703 0.067 8.167a
SCI3: Accuracy and adaptability of SCM planning 0.761 0.065 7.598a
SCI4: Control and tracking of inventory: accuracy and visibility 0.814 0.063 6.763a
SCI5: Process standardization and visibility 0.814 0.065 6.753a
Supply chain performance (w2/d.f. ¼ 1.24, RMSEA ¼ 0.04, GFI ¼ 0.98, NFI ¼ 0.98, NNFI ¼ 0.99, CFI ¼ 0.99, IFI ¼ 0.99)
SCP1: Just-in-time 0.741 0.079 8.019a
SCP2: Inventory turnover and cash-to-cash cycle time 0.790 0.048 7.469a
SCP3: Customer lead time and load efficiency 0.702 0.064 8.329a
SCP4: Delivery performance and quality 0.738 0.050 8.050a
SCP5: Supply chain inventory visibility and opportunity costs 0.723 0.074 8.170a
SCP6: Total logistics cost 0.799 0.060 7.345a
a Significance at the level of pp0.01.
Table 4Discriminant validity tests
Factors IT SCI SCP
IT implementation (IT) –
Supply chain integration (SCI) 37.78a –
Supply chain performance (SCP) 84.80a 68.73a –
Chi-square differences between fixed and free models.a Significance at the level of pp0.00.
G. Li et al. / Int. J. Production Economics 120 (2009) 125–138130
Gerbing, 1988). In addition, according to Bollen (1989), thelarger t-values or the standardized coefficients are, thestronger the evidence that the individual items representthe underlying factors is. The results of CFA reveal thatthe standardized coefficients for all items greatly exceedtwice their standard errors, and that the standardizedcoefficients for all variables are large (40.6) and significant(all the t-values are larger than 2). Therefore, all items aresignificantly related to their underlying theoretical constructs.
3.6. Discriminant validity
In addition to convergent validity, discriminant validityis another important test to ensure adequacy of themeasurement model. Discriminant validity measures theextent to which individual items intending to measureone latent construct do not at the same time measure adifferent latent construct (DeVellis, 1991).
In this study, discriminant validity is established usingCFA. Models were constructed for all possible pairs oflatent constructs. These models were run on each selectedpair, (1) allowing for correlation between the two
constructs, and (2) fixing the correlation between theconstructs at 1.0. A significant difference in Chi-squarevalues for the fixed and free solutions indicates thedistinctiveness of the two constructs (Bagozzi et al.,1991). For the three constructs of IT, SCI, and SCP, a totalof three different discriminant validity checks wereconducted. As shown in Table 4, all the three Chi-squaredifferences between the fixed and free solutions in Chi-square are significant for statistical significance at po0.00confidence level. This result provides strong evidence ofdiscriminant validity among the theoretical constructs.
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SCI
SCP
IT
IT1
IT2
IT3
IT4
IT5
SCI1 SCI2 SCI3 SCI4 SCI5
SCP1 SCP2 SCP3 SCP4 SCP5 SCP6
0.70
0.77
0.83
0.79
0.69
0.73 0.69 0.74 0.80 0.81
0.76 0.68 0.72 0.73 0.820.77
0.89*
-0.10
0.99*
Fig. 2. Standardized results of structural equation model. Significant at: *po0.01.
G. Li et al. / Int. J. Production Economics 120 (2009) 125–138 131
4. Results
In the study, we use structural equation modeling toestimate the causal relationship among the differentconstructs with linear structural relations (LISREL) pro-gram. The results of the structural equation model testedare shown in Fig. 2. The overall model fit indices are asfollows: w2d:f : ¼ 152:54=101 ¼ 1:51, root mean squareerror of approximation (RMSEA) ¼ 0.048, goodness of fitindex (GFI) ¼ 0.90, normed fit index (NFI) ¼ 0.92, non-normed fit index (NNFI) ¼ 0.97, and comparative fit index(CFI) ¼ 0.97. These indices are better than the thresholdvalues suggested by Hu et al., 1992. Therefore, thestructural equation model is well within the suggestedrange and can be supported.
Since the structural equation model is satisfactory, itcan be served as the basis of evaluation for ourhypotheses. The results indicate that SCI has a positiveand significant effect on SCP (b ¼ 0.99, t ¼ 12.74, po0.01),and IT implementation has a positive and significant effecton SCI (b ¼ 0.89, t ¼ 9.39, po0.01). Thus, hypotheses H2and H3 are supported. However, our analysis found nosignificant effect of IT implementation on SCP therefore,hypothesis H1 is not supported (b ¼ �0.10, t ¼ �0.62,po0.01).
To assess the mediation effect of SCI on the relationshipbetween IT implementation and SCP, two alternative modelsare estimated (Venkatraman, 1989). First, the construct ofSCI is removed and only the direct effects of IT implementa-tion and SCP are estimated. The results, with its fit indices ofw2=d:f : ¼ 1:43, RMSEA ¼ 0.049, GFI ¼ 0.94, NFI ¼ 0.95,NNFI ¼ 0.98, and CFI ¼ 0.98, indicate that the direct effectof IT implementation on SCP is positive and significant(b ¼ 0.85, t ¼ 9.91, po0.01). Second, the path between ITimplementation and SCP is removed from the originalmodel, where only the indirect effect of IT implementationon SCP via SCI integration is remained. From the results ofthis specific model, with its fit indices of w2=d:f : ¼ 1:50,RMSEA ¼ 0.053, GFI ¼ 0.90, NFI ¼ 0.92, NNFI ¼ 0.97, and
CFI ¼ 0.97, it can be indicated that both the direct effectsof IT implementation on SCI (b ¼ 0.89, t ¼ 9.33, po0.01)and SCI on SCP (b ¼ 0.97, t ¼ 10.56, po0.01) are positiveand significant. Hence, we conclude that the effect of ITimplementation on SCP is mediated by SCI.
5. Discussion and implications
5.1. Discussion
The purpose of this study was to propose and test amodel of the relationship among three factors: ITimplementation, SCI, and SCP. A number of importantfindings emerge that have both theoretical and manage-rial implications.
First, a significant contribution of this study is theempirical test of theoretical assumptions in the existingliterature on the impact of IT implementation on SCI andits performance. Although some empirical studies havebeen conducted to test the relationship between IT andSCP (Devaraj and Kohli, 2003; Jin, 2006; Aaker andJacobson, 1994, 2001; Allenby and Rossi, 1991), as far aswe are aware, this is the first study that explores theantecedents of IT implementation, SCI and SCP. Whilemore and more companies are seeking efficient ways toimprove SCP and often make large investments in ITsystems, it is not clear whether IT implementation has adirect effect on SCP. This study proposes a new modelwhere SCI bridges IT implementation and SCP. This modelhelps to reveal the impact of IT implementation on SCPand verifies the general finding that SCI has a positiveeffect on SCP.
Second, this study provides answers to two questions:(1) Does IT implementation have a direct effect on SCP? Ashypothesized in H1, IT implementation has a direct,positive effect on SCP. It is perhaps surprising that wefound there to be no significant relationship. However,this may be explained by the answer of the secondquestion: (2) How the implementation of IT affects SCP. As
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hypothesized in H2 and H3, SCI mediates the relationshipbetween IT implementation and SCP. The results reportedthat IT implementation affects SCI directly and SCI has apositive effect on SCP. This is partly in line with previousscholars (e.g., Devaraj et al., 2007) who have suggestedthat supplier integration leads to better SCP. It indicatedthat SCI is not synonymous with IT implementation.Rather, IT implementation is a separate construct thatimproves SCI. Supply chain integration is a result ofhuman interactions which can be supported, but notreplaced by IT (Sanders, 2007). This is an important pointfor managers when they consider investing in various ITinitiatives. Based upon the findings of this study, priorityshould be given to IT investments that improve SCI.Any competitive advantage gained from IT will result fromthe improvement of SCI, but not from the IT investmentper se.
The third contribution of this study relates to themeasurement of IT implementation. Most previous stu-dies measured IT by measuring financial investment in IT;in addition, they measured IT facility usage from second-ary data, which did not take account of the impact of ITadaptation, implementation and use by supply chainpartners. The lack of IT alignment among supply chainpartners often frustrates the potential beneficial effects ofIT implementation by individual companies (Kearns andLederer, 2003; Seggie et al., 2006). This study sought tointegrate the usage of IT and IT alignment into acomprehensive framework, which allows better captureof the nature of IT implementation and its affect on SCM.
5.2. Managerial implication
The findings of this study provide insights into thedesign of effective strategies for IT implementation andSCI in order to enhance SCP.
A key finding is that SCI is affected by IT implementa-tion, and SCI mediates the relationship between ITimplementation and SCP. This finding has a number ofimplications for managers. First, it underscores theimportant role IT implementation plays in the functioningof supply chain organizations. Based upon this finding, ITefforts that promote SCI should be given higher priority.The proactive adoption, implementation, and utilizationof IT systems, such as electronic data interchange (EDI),bar coding, enterprise resource planning (ERP), customerrelationship management (CRM) and decision supportsystems (DSS), and the alignment of IT philosophies,patterns and practices among supply chain partnersshould result in better SCI.
Second, although the finding that SCP can best beimproved by the integration of the supply chain partnersis not new, it validates the important role played by SCI.The significant impact of SCI on SCP suggests thatcompanies should invest in strategies that promotecollaboration and integration across the members of thesupply chain. Since IT implementation has been shown topromote SCI, companies should also consider implement-ing these types of ITs. Third, this finding suggests that IT isnot an actual source of competitiveness but a source of
competitive necessity. SCM emphasizes the global andlong-term benefits to all entities participating in the chainthrough cooperation and information sharing. A com-pany’s efficient communication with downstream andupstream business entities is a necessity, rather than asource that can boost its competitive advantage. Today,more and more companies are deploying and utilizing ITto improve communications and decrease the responsetime to market fluctuations. Implementing IT has becomea necessity, not a choice (Jin, 2006). Companies shouldinvest in IT capability if they want to enhance the SCIintensity. At the same time, companies should not seek tojustify investments in IT in terms of their potential directimpact on SCP.
Another contribution of this study is the IT measure-ment, which focus on the adoption, implementation and ITalignment among supply chain partners. The implication isthat managers should not assume that all investments areequally effective. The same level of investment does notguarantee the same result. In the complex environment ofthe supply chain, the successful implementation of SCIprojects is not so much a technological problem as it is amanagement problem, requiring a thorough study of thebusiness conditions for all companies involved. Companiesshould have the processes and procedures in place tocapture the full potential of IT implementation. Thedifferent business contexts of the individual supply chainpartners have to be aligned to the supply chain. Partici-pants in the supply chain have to transform theircollaboration patterns and build an open and uniform ITframework to support IT implementation throughout thesupply chain. In the long run, as supply chain membersalign their philosophies on SCM, adopt a win–wincollaboration pattern for their business interaction, andadopt an open framework (e.g. architecture, standard) forIT implementation, IT implementation will be better ableto enhance SCI and boost SCP.
6. Limitations and future research
Several limitations of this study need to be noted, aswell as some directions for future research.
This study focused on the impact of overarching ITs,and not on any specific IT system. Also, this study did notclassify the various supply chains in the samples, as hasbeen proposed by Fisher (1997), who suggested thatsupply chains could be categorized into efficient supplychains and responsive supply chains, and that supplychains facing different environmental dynamism shoulduse different supply chain practices. Furthermore, pre-vious studies have proposed many different classificationsof ITs (Barki et al., 1993; Kendall, 1997). But regardless ofclassification, it can be assumed that some ITs have a moredirect and significant impact on integration and perfor-mance for some kinds of supply chains than others. Giventhe high cost of IT implementation, it may be importantfor future work to consider the impact of different types ofITs on different types of SCI and SCP. A model for matchingITs with supply chain characteristics is needed, so thatmanagers can more easily find the best form of ITimplementation.
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Second, in a web of dyadic supply chain relationships,there are numerous factors that can contribute to SCI andSCP. Furthermore, it remains unanswered whether invest-ment in IT implementation will lead to greater enhance-ments in SCI and SCP outcomes than will other investmentalternatives such as vertical integration. Future researchshould explore such alternatives to help managers find thebest way to enhance SCI and SCP.
Third, the data for this study came from companieslocated in the developed areas of China. Companies inother parts of the country may have different businessconditions, different cultures, different leadership styles,and so on. All of which might affect company strategies onIT and SCM. Therefore, future research is needed todetermine whether the findings reported here are validin other areas (e.g. the underdeveloped areas of China).
Finally, there are several limitations on the surveyprocedures. Like many other studies, this study used asingle informant from each company. A dataset withmultiple informants could enhance the validity of thefindings. Also, this study uses cross-sectional data whichis static in nature. Although the causal interrelations wereanalyzed and could imply temporal aspects, collectingdata over time from informants can offer richer implica-tions. Future research might be undertaken to test thefindings of this study using time-series data.
The topic of SCI is still in its infancy. There are manyopportunities for future research in this area. We call uponmanagers and researchers to take up these challenges.
7. Conclusions
With the accelerated development of science andtechnology, the capability of IT has dramatically increased.
Because IT has been proved to have the potential toimprove SCP, more and more practitioners invest inIT. With the growing awareness of the benefits of ITimplementation, it is important to understand how itimpacts on SCP. Earlier studies have viewed IT as anenabler of integration, which is the foundation of SCM. ITimplementation has had a particularly profound impacton supply chain organizations. Its power to providetimely, accurate, and reliable information has createdthe expectation that IT will always improve SCI and SCP.The present study focused on the relationship among ITimplementation, SCI, and SCP, using empirical datacollected in China. Our findings show that IT implementa-tion has no direct effect on SCP, but rather, that itcontributes to the improvement of SCP through itspositive impact on SCI. These findings contribute todeepening our understanding of the impact of IT im-plementation on SCP. In addition, these findings shouldhighlight the importance for managers to promote SCI andimplement IT as an enabler.
Acknowledgments
The authors wish to thank the anonymous referees fortheir valuable suggestions, and in particular, the authorsacknowledge the contributions of professor De-bi Caoof Keio University. This research was supported by theNational Natural Science Foundation of China (Nos.70433003, 70701029), the National Social Science Foun-dation of China (No. 08XJY016) and the Research Fund forthe Doctoral Program of Higher Education of China (No.20070968063).
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Appendix A. Construct measurement
Construct Item Level 1 Level 2 Level 3 Level 4 Level 5
IT
implementation
(IT)
IT1: Electronic data
interchange (EDI)
coverage
Company is not
electronically linked to any
customer or supplier.
EDI links are set up with
some customers or
suppliers at their request.
EDI is used with over 50% of
customers or suppliers.
Proprietary EDI standards are
used in most cases.
In addition to Level 3, EDI is
integrated with the company’s
internal systems so that manual
re-entry of data is not necessary
in most cases.
EDI is used for nearly all
transactions and is integrated
with internal systems. Open
standards for EDI are adopted or
in-process of adoption.
IT2: Usage of bar
coding/automatic
identification and data
capture (AIDC)
Bar codes or other forms of
automatic identification and
data capture (AIDC) are not
utilized.
Bar codes are utilized in
some activities such as
inspection, but the data are
not used for other purposes.
Bar codes are utilized in some
activities, such as inspection,
and the data are shared with
internal systems to
synchronize the material and
information flow.
Extending the scope of Level 3,
bar codes are used as a means to
accelerate innovation of the
logistics system, in addition to
synchronizing the material and
information flow.
The best mix of bar codes, 2-
dimensional symbols, IC tags and
other AIDC methods is linked
with EDI, and used to support
innovation of the logistics system
at the supply chain level.
IT3: Effective usage of
computers in
operations and
decision-making (ERP,
supply chain planning
software, etc.)
PCs are not utilized
anywhere in the business.
PCs are used to support
some business operations
and activities.
Most routine business
operations and activities are
computerized (e.g. accounting,
production, etc.) but are not
integrated with each other.
In addition to Level 3, decision
support systems and other IT
tools are utilized for logistics
planning and optimization.
ERP, SCP, CRM and other IT tools
are utilized for planning and
optimization of the entire supply
chain. Outsourcing and other
means are considered for
increasing the effective use of IT
and related resources.
IT4: Open standards
and unique
identification codes
Company has no awareness
of open standards and
unique identification codes.
Company understands the
importance of open
standards and unique
identification codes for
improving the efficiency of
logistics processes.
To exploit the potential of IT,
unique identification codes are
used within the company and
process simplification is also
carried out.
In addition to Level 3, usage of
unique identifiers is extended to
suppliers and/or customers. Open
standards for EDI and other IT
applications are adopted or under
consideration.
In addition to Level 4, unique
identification codes are extended
to both suppliers and customers.
Company is actively working
towards adoption of open
standards for EDI and other IT
applications.
IT5: Decision- making
systems and support to
supply chain partners
No knowledge or interest in
the decision-making
processes and systems used
by suppliers or customers.
Has a general
understanding of how a
supplier or customer makes
its decisions, but does not
know the details of the
systems used.
Understands the systems used
by a supplier or customer, but
has made no proposals or
efforts to bring about a win-
win solution.
Exploring ways to modify or
integrate the systems of the
company and its suppliers or
customers in order to realize win-
win solutions.
Have succeeded in implementing
a win-win solution with supply
chain partners, and actively
provide proposals and support to
partners to improve their systems
and innovate the supply chain.
Supply chain
integration (SCI)
SCI1: Strategies for
optimizing logistics
system resources based
on DFL
Efficient utilization of
logistics facilities and
resources is not seen as a
problem. No improvement
strategy exists.
Importance of optimizing
logistics system resources is
recognized, but there is no
strategic plan or review.
Strategic plan exists for review
of transportation modes and
inventory allocation among
plant, distribution center,
transfer center. Optimization
efforts are making progress.
In addition to Level 3, suppliers
and customers are involved in
efforts to optimize logistics sterns
resources.
Clear strategy exists for
collaboration and optimization
across the supply chain, including
product re-design based on
design for logistics, and use of
other approaches such as joint
distribution and category
management.
SCI2: Understanding of
market trends &
accuracy of demand
forecasting
Rely on the experience and
judgment of the sales
department to predict
market trends and forecast
demand.
Demand forecasting for
certain products is based on
a quantitative sales history
combined with the
judgment and experience of
the sales department.
Demand forecasting for key
products is based on an
analysis of market trends and
quantitative sales history, and
includes the input of sales and
related departments.
Level 3 approach is extended to
all products, and forecasts for key
products are broken down into
items or categories. Demand
forecasting system is in place.
Level 4 approach is carried out
jointly with supply chain
partners. Demand forecasts can
be revised dynamically for
changing market conditions.
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SCI3: Accuracy and
adaptability of SCM
planning
Planning for sales,
replenishment and delivery
is carried out separately,
without consideration of
inventory availability.
Plans for sales,
replenishment and delivery
are intended to be
coordinated with each
other on a monthly basis,
but in practice tills is only
partially achieved.
Plans for sales, replenishment
and delivery are supposed to
be coordinated with each other
on a weekly basis, but
individual departments may
make their own adjustments
during the week.
Linkage of weekly plans between
departments is done on a rolling
basis. Plan adjustments for
customers can be done on a daily
basis.
Linkage of daily plans between
departments is done on a rolling
basis. Plan adjustments for
suppliers or customers can be
done on an hourly basis.
SCI4: Control and
tracking of inventory
(product/parts/WIP):
accuracy and visibility
No tracking or visibility of
inventory/WIP status.
Management action is taken
after-the-fact.
For most items, inventory
status is tracked on a daily
basis, and supply is
adjusted to meet demand
on a monthly basis.
A system is in place which
enables the company to
manage and track its own
inventory and replenishment
activities on a daily basis.
A system is in place which
enables the company to manage
and track inventory and
replenishment activities for itself
and its suppliers on a daily/
hourly basis
Inventory and replenishment
activities are managed and
tracked throughout the entire
supply chain, including suppliers
and customers. Information is
strategically shared.
SCI5: Process
standardization and
visibility
Little standardization of
work methods or use of unit
loads. Some process
activities are treated as a
‘‘black-box’’.
Work methods are mostly
standardized, but the
overall work flow is not
completely visible.
Work methods are
standardized and unit loads are
used but interface activities
with suppliers and customers
are not made sufficiently
visible.
Work flow, including interface
activities with suppliers and
customers, is standardized and
made visible. There is continuous
improvement of work activities
within the company
In addition to Level 4,
partnerships are established for
each business unit and the entire
supply chain is made visible.
Process innovation is continually
pursued.
Supply chain
performance
(SCP)
SCP1: Just-In-Time
(elimination of idle
time and setup time
through information
sharing and
synchronization of
material and
information flow)
JIT philosophy is not part of
the company’s approach or
practices.
Company recognizes the
importance of JIT
philosophy, but has not
implemented JIT practices
in production,
replenishment, material
handling, or delivers.
JIT practices such as setup time
reduction, lot-size reduction,
load consolidation or floor-
ready merchandise are
implemented, but they are not
synchronized with other
activities.
Some JIT activities are
synchronized (e.g. picking
sequence is determined from
delivery plan, delivery trucks
allocated based on picking
sequence, etc).
JIT activities are synchronized
throughout the material flow and
involve suppliers and customers.
SCP2: Inventory
turnover & cash-to-
cash cycle time
Neither inventory turnover
nor cash-to-cash cycle time
is measured. Inventory
turnover is low, and cash
flow is poor.
Inventory turnover is
known at the aggregate
level for each facility, but
inventory management is
not linked to cash flow.
Inventory turnover for each
supplier and individual
product is measured with
accuracy at the week-level and
actual performance level of
less than 12 turns/yean
Inventory turnover for each
supplier and SKU is measured
with accuracy at the day-level
and actual performance level of
12+ turns/year. Inventory
management is linked with cash
flow.
Exceeds Level 4, with inventory
measured with accuracy at the
hours-level and actual
performance of 24+ turns/year.
Cash-to-cash cycle time is less
than 10 days.
SCP3: Customer lead
time (from order
placement to receipt)
and load efficiency
Lead time from order
placement to receipt is long.
Company receives frequent
requests from customer to
shorten lead time.
Lead times for different
customer categories are
known, but orders with
short lead time are covered
by on-hand inventory. Little
effort made to reduce lead
times.
Lead time is known and
managed for each customer or
item category, and is linked to
truck allocation planning to
increase load efficiency.
In addition to Level 3, average
lead time is less than 2 days.
Continuous efforts made to
further reduce lead times.
In addition to Level 4, achieves
load efficiency of 80% or higher.
SCP4: Delivery
performance and
quality
On-time delivery rate (on-
time deliveries/total orders)
and order fulfillment
accuracy (accurate
deliveries/total orders) are
not known. Company faces
many customer complaints.
On-time delivery rate and
order fulfillment accuracy
are measured, but actual
performance level is less
than 95%.
Performance is between 95 and
99% for both rates. To improve
performance, efforts are made
to collect data on the root
causes of late deliveries, stock
outs, miss deliveries, damage,
etc.
Performance exceeds 99% for
both rates. Based on data about
root causes, error prevention
measures such as mistake-
proofing are implemented on an
ongoing basis.
In addition to Level 4, suppliers
and customers are involved in
improvement efforts. While
maintaining high performance,
efforts to improve efficiency, such
as elimination of incoming
inspections, are promoted.
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SCP5: Supply chain
inventory visibility &
opportunity costs
Only on-hand inventories
within one’s own facility or
company are known.
Opportunity cost of lost sales
is not known or estimated.
Inventory levels within the
company are known. Some
estimation is made of the
opportunity cost of lost
sales.
Inventory levels are known for
the company and its
immediate suppliers or
customers. Some estimation is
made of opportunity cost of
lost sales for the company only.
Inventory levels are known for
the company and its immediate
suppliers and customers. Some
estimation is made of
opportunity cost of lost sales for
the company only.
Inventory levels are known
throughout the entire supply
chain. Estimation is made of
opportunity cost of lost sales at
the end demand level.
SCP6: Total logistics
cost (transportation
costs, inventory
holding costs, order
management costs,
administrative costs,
etc.)
Order management costs and
product manufacturing costs
are known, but logistics-
related costs are not well-
defined or separated out.
Most logistics-related costs
for the company are known
at an aggregate level (e.g.
own transportation costs,
freight payments to outside
carriers, inventory holding
costs).
In addition to Level 2, logistics-
related costs are broken down
to individual supplier and
customer level well enough
that they can be utilized in
revenue management.
Total logistics costs (transport,
inventory holding, order mgmt,
admin, costs, etc) are broken
down for each supplier and
customer. Using activity-based
costing approach, this
information is used in revenue
management and system
improvement and innovation.
In addition to Level 4, total
logistics costs throughout the
supply chain are known and
shared among supply chain
members. Win-win scenarios for
cost reduction are developed
from the viewpoint of supply
chain optimization.
Note: Electronic data interchange (EDI): The computer-to-computer exchange of structured information, by agreed message standards, from one computer application to another by electronic means and with a
minimum of human intervention. There are two types of EDI standard. Proprietary standard and open standard. UN/EDIFACT is used as the international open standard. Recently, Web EDI and XML EDI are also
used as a simple standard.
Automatic identification and data capture (AIDC): The methods of identifying objects, collecting data about them, and entering that data directly into computer systems for the synchronization of material and
information flow. Technologies typically considered as part of AIDC includes bar codes, QR codes, biometrics, OCR, RFID (IC tags).
Enterprise resource planning (ERP): Management information systems that integrate and automate many of the business practices and information associated with the operations and accounting of a company.
Supply chain planning (SCP): Management information system for planning and optimization of the entire supply chain. It integrates and supports many of the business planning and decision making by
synchronizing material and information flow of the supply chain.
Unique identification codes: Unique code for cargos and products through departments, organizations, and the whole country, which prevents re-entering and re-handling, IT plays an important role for utilizing
EDI or AIDC technologies.
Design for logistics (DFL): General term of a measure/approach for product and load redesign that goes up to replenishment and distribution processes restructuring, in order to enhance efficient logistics while
coping with diversification and constant changes.
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