Exploring geographical dispersion in Thailand-based food supply chain (FSC) Pichawadee Kittipanya-ngam, Yongjiang Shi and Mike J. Gregory Department of Engineering, Institute for Manufacturing, University of Cambridge, Cambridge, UK Abstract Purpose – The purpose of this paper is to explore the key influential factors and their implications on food supply chain (FSC) location decisions from a Thailand-based manufacturer’s view. Design/methodology/approach – In total, 21 case studies were conducted with eight Thailand-based food manufacturers. In each case, key influential factors were observed along with their implications on upstream and downstream FSC location decisions. Data were collected through semi-structured interviews and documentations. Data reduction and data display in tables were used to help data analysis of the case studies. Findings – This exploratory research found that, in the food industry, FSC geographical dispersion pattern could be determined by four factors: perishability, value density, economic-political forces, and technological forces. Technological forces were found as an enabler for FSC geographical dispersion whereas the other three factors could be both barriers and enablers. The implications of these four influential factors drive FSC towards four key patterns of FSC geographical dispersion: local supply chain (SC), supply-proximity SC, market-proximity SC, and international SC. Additionally, the strategy of the firm was found to also be an influential factor in determining FSC geographical dispersion. Research limitations/implications – Despite conducting 21 cases, the findings in this research are based on a relatively small sample, given the large size of the industry. More case evidence from a broader range of food product market and supply items, particularly ones that have significantly different patterns of FSC geographical dispersions would have been insightful. The consideration of additional influential factors such as labour movement between developing countries, currency fluctuations and labour costs, would also enrich the framework as well as improve the quality and validity of the research findings. The different strategies employed by the case companies and their implications on FSC location decisions should also be further investigated along with cases outside Thailand, to provide a more comprehensive view of FSC geographical location decisions. Practical implications – This paper provides insights how FSC is geographically located in both supply-side and demand-side from a manufacturing firm’s view. The findings can also provide SC managers and researchers a better understanding of their FSCs. Originality/value – This research bridges the existing gap in the literature, explaining the geographical dispersion of SC particularly in the food industry where the characteristics are very specific, by exploring the internationalization ability of Thailand-based FSC and generalizing the key influential factors – perishability (lead time), value density, economic-political forces, market opportunities, and technological advancements. Four key patterns of FSC internationalization emerged from the case studies. Keywords Supply chain management, Food industry, Geographic dispersion, Internationalization, Food supply chain, Thailand Paper type Research paper The current issue and full text archive of this journal is available at www.emeraldinsight.com/1463-5771.htm This article is part of the special issue: “Supply chain networks in emerging markets” guest edited by Harri Lorentz, Yongjiang Shi, Olli-Pekka Hilmola and Jagjit Singh Srai. Due to an administrative error at Emerald, the Editorial to accompany this special issue is published separately in BIJ Volume 19, Issue 1, 2012. BIJ 18,6 802 Benchmarking: An International Journal Vol. 18 No. 6, 2011 pp. 802-833 q Emerald Group Publishing Limited 1463-5771 DOI 10.1108/14635771111180716
Transcript
Exploring geographicaldispersion in Thailand-based
food supply chain (FSC)Pichawadee Kittipanya-ngam, Yongjiang Shi and Mike J. Gregory
Department of Engineering, Institute for Manufacturing,University of Cambridge, Cambridge, UK
Abstract
Purpose – The purpose of this paper is to explore the key influential factors and their implications onfood supply chain (FSC) location decisions from a Thailand-based manufacturer’s view.
Design/methodology/approach – In total, 21 case studies were conducted with eightThailand-based food manufacturers. In each case, key influential factors were observed along withtheir implications on upstream and downstream FSC location decisions. Data were collected throughsemi-structured interviews and documentations. Data reduction and data display in tables were usedto help data analysis of the case studies.
Findings – This exploratory research found that, in the food industry, FSC geographical dispersionpattern could be determined by four factors: perishability, value density, economic-political forces, andtechnological forces. Technological forces were found as an enabler for FSC geographical dispersionwhereas the other three factors could be both barriers and enablers. The implications of these fourinfluential factors drive FSC towards four key patterns of FSC geographical dispersion: local supplychain (SC), supply-proximity SC, market-proximity SC, and international SC. Additionally, the strategyof the firm was found to also be an influential factor in determining FSC geographical dispersion.
Research limitations/implications – Despite conducting 21 cases, the findings in this research arebased on a relatively small sample, given the large size of the industry. More case evidence from abroader range of food product market and supply items, particularly ones that have significantlydifferent patterns of FSC geographical dispersions would have been insightful. The consideration ofadditional influential factors such as labour movement between developing countries, currencyfluctuations and labour costs, would also enrich the framework as well as improve the quality andvalidity of the research findings. The different strategies employed by the case companies andtheir implications on FSC location decisions should also be further investigated along withcases outside Thailand, to provide a more comprehensive view of FSC geographical location decisions.
Practical implications – This paper provides insights how FSC is geographically located in bothsupply-side and demand-side from a manufacturing firm’s view. The findings can alsoprovide SC managers and researchers a better understanding of their FSCs.
Originality/value – This research bridges the existing gap in the literature, explaining the geographicaldispersion of SC particularly in the food industry where the characteristics are very specific, by exploringthe internationalization ability of Thailand-based FSC and generalizing the key influential factors –perishability (lead time), value density, economic-political forces, market opportunities, and technologicaladvancements. Four key patterns of FSC internationalization emerged from the case studies.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1463-5771.htm
This article is part of the special issue: “Supply chain networks in emerging markets” guestedited by Harri Lorentz, Yongjiang Shi, Olli-Pekka Hilmola and Jagjit Singh Srai. Due to anadministrative error at Emerald, the Editorial to accompany this special issue is publishedseparately in BIJ Volume 19, Issue 1, 2012.
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Benchmarking: An InternationalJournalVol. 18 No. 6, 2011pp. 802-833q Emerald Group Publishing Limited1463-5771DOI 10.1108/14635771111180716
IntroductionInternational supply chain (SC) management has increasingly caught much attention ofresearchers and practitioners along with the rising trend of globalisation in the lastdecades (Snow et al., 1992; Arntzen et al., 1995; Chopra and Meindl, 2007; Christopher et al.,2006; Harland et al., 1999; Meixell and Gargeya, 2005; Nagurney and Matsypura, 2005;Narasimhan and Mahapatra, 2004; Srai and Gregory, 2008; Supply Chain Council, 2009).Firms have been purchasing and/or (out)sourcing their raw materials and/or productsfrom overseas, shifting manufacturing sites to the Far East or Latin America, or evenexpanding their markets outside the country of origin (Christopher et al., 2006; Ferdows,1997; Flaherty, 1996; Gattorna and Walters, 1996). This is partly because of thesignificantly lower wages and fewer regulations that have lured manufacturing firms tomigrate from developed economies to emerging economies (Christopher et al., 2006).Gattorna and Walters (1996) argued that though labour cost savings and less regulationsappeared to be the key drivers of SC internationalisation, there were also other driverssuch as: opportunistic development (event led), following customers, geographicaldiversification (to reduce risks), exploiting product life cycle (PLC) differences, pursuingpotential abroad, defensive reasons, and global logic.
Despite the abundant benefits of SC internationalisation, moving suppliers,manufacturing operations, or market destinations to emerging economies also broughtnew challenges. SC in emerging economies is required to handle more diverseenvironmental conditions such as increasing time and geographical distance, politicaland economic instability, exchange rate fluctuation, dynamic changes in the regulatoryenvironment and policies, and increasing uncertainties (Acar et al., 2010; Dorneir et al.,1998; Flaherty, 1996; Meixell and Gargeya, 2005; Perron et al., 2010). Additionally,it has been argued that suboptimal SC decisions, for example, off-shoring to lowlabour cost countries, might turn out to increase the total cost in the SC as a whole(Christopher et al., 2006). International SC is hence far more difficult to manage thandomestic ones due to the increasing complexity and uncertainty on the global platform.Hence, firms need to have deeper understanding of their business environment in orderto effectively decide on geographical locations for their SCs.
The growth of internationalisation in SC was witnessed (Christopher et al., 2006) inalmost every industry, even in the food industry where localisation is rather strong incomparison with other industry (Sterns and Peterson, 2001; van Hoek, 1999). However,the research on food supply chain (FSC) and its internationalisation is still a “recentphenomenon” (Soman, 2008) and there are many opportunities for the food industry tobenefit from SCM research and that are yet to be explored (Cunningham, 2001; Mena andStevens, 2010). This is partly because the food industry has specific characteristics suchas product perishability, long PLC, non-modular product structure, product safety andtraceability, product temperature sensitivity, and seasonality, etc. (Christopher et al.,2009; Entrup, 2005; Fuller, 1994; Karkkainen, 2003; Soman et al., 2004; van der Vorse et al.,2001; der Vorst and Beulens, 2002; van Hoek, 1999) that place specific requirements on SC.These characteristics are argued to have implications on geographical location decisionsof FSC, e.g. high perishability, safety regulations, or even macroeconomic policies such asWorld Trade Organization (WTO) trade agreements on subsidies removal, could limitgeographical dispersion of FSC (The Economist Intelligence Unit Limited (EIU), 2005;Raynolds, 2004; Ruquet, 2006; van Hoek, 1999). However, sustainability policy and“green” concerns such as food miles and global warming, could prevent geographical
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dispersion of FSC and support localization instead (Lang et al., 2004). Theseconsiderations place FSC in complex and dynamic environmental conditions (Mena andStevens, 2010; Soman, 2008; Van Donk and van der Vaart, 2005).
To date, the existing literature has explored issues in international or global SCdecisions such as global SC optimization modeling (Acar et al., 2010; Arntzen et al.,1995; Narasimhan and Mahapatra, 2004; Nagurney and Matsypura, 2005; Vidal andGoetschalackx, 1997), global SC strategies (Christopher et al., 2006), manufacturinglocation decisions (Bhatnagar and Sohal, 2005; Melo et al., 2009), FSC configuration anddesign (Van der Vorst and Beulens, 2002; van Hoek, 1999). However, most of theresearch was quantitative, lacking a qualitative perspective. Moreover, none of themcould comprehensively explain the geographical dispersion of SC in the food industry,taking into consideration the specific characteristics of the food industry. Coupled withthe fact that Thailand, an emerging market, is ranked among the top 20 countries infood manufacturing and exporting for the global market (WTO, 2009), this paper seeksto explore how Thailand-based FSC is geographically dispersed and what drives FSCtowards particular patterns of geographical dispersion by taking into account specificcharacteristics of the food industry. Case study method was chosen to provide richunderstanding of the research context (FSC in emerging economies), which isconsidered relatively new, complex, and dynamic.
This paper is structured in five sections. First, it begins by setting out the researchscope and objective. Second, the relevant literature on SC and its internationalisation ingeneral as well as the relevant literature on FSC in particular are reviewed. Third, theresearch context is defined, along with the research question identification and theconceptual framework development. The research approach is then identified, whichleads to the fourth section of this paper – the conduct of field work and cross-caseanalysis. In this section, multiple-case data are presented and analysed, followed by apresentation of the key findings. Finally, the fifth section concludes with researchlimitations and future work suggestions.
Literature reviewSC and its internationalisationWith the rise of globalization over the last decades, the literature on SC and itsinternationalisation has been increasingly witnessed from various perspectives,e.g. facility location decisions (Bhatnagar and Sohal, 2005; Ferdows, 1997; Nagurney andMatsypura, 2005; Lorentz, 2008; Vidal and Goetschalackx, 1997), international sourcingand purchasing (Kotabe et al., 2009; Peterson et al., 2000; Trent and Monczka, 1998), andinternationalisation process ( Johanson and Vahlne, 1977, 1990). International SC isarguably far more difficult to manage than domestic SC (Flaherty, 1996; Meixell andGargeya, 2005). Flaherty (1996) compared international SC with domestic SC and notedthat international SC had rising issues of greater geographic distance and timedifferences, multiple national markets, multiple locations of operations, diversity ofsupply and demand conditions. The literature review, presented below, serves as afoundation of the key factors influencing SC geographic dispersion decisions.
The location decisions of SC have been researched through three perspectives:sourcing, manufacturing, and distribution to the market (Flaherty, 1996). First, sourcingperspective involves decisions to either source raw materials/finished productsfrom overseas or local suppliers (Bozarth et al., 1998; Trent and Monczka, 2005).
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Historically, cost has been regarded as the primary determinant of sourcingdecisions (Flaherty, 1996; Peterson et al., 2000; Trent and Monczka, 1998). This factorbecomes important once labour costs are a substantial part of production and unit costs(Kotabe et al., 2009). With substantial economies of scale, global demand for the sameproducts can be meet through a single source globally. However, it has been suggestedthat in making sourcing decisions, total logistics costs, customs, duties, handling,and damages during transit costs as well as the trade-off impact on performance suchas delivery lead time and additional inventory should be taken into account(Bozarth et al., 1998).
Currently, a number of additional factors are being considered for sourcing decisionsas firms now aim to “exploit both the firm’s and suppliers’ competitive advantages andthe locational advantages of various countries in global competition” (Kotabe et al.,2009). For example, international sourcing can bring product and process technologyfrom overseas suppliers to the firm and that consequently improves the product offering,product quality, and reduces problems caused byshorter PLCs (Bozarth et al., 1998;Kotabe et al., 2009). In some cases, firms are forced to source internationally due toscarcity of raw materials in local markets and where local sourcing is feasible(Dornier et al., 1998; Kotabe et al., 2009). This situation occurs often in the agriculturaland food industry where weather seasons impact the production of raw materials(Bourlakis and Weightman, 2004; Georgiadis et al., 2005; Kittipanya-ngam, 2010).
Second, the key factors determining manufacturing location decision originated frominternational trade and capital movement in early 1960s (Dunning, 1979; Hymer, 1972;Vernon, 1966). Ferdow (1997) asserted that most foreign-owned plants aimed to benefitonly from tariff and trade concessions, cheap labour, capital subsidies, and reducedlogistics costs, which resulted in limited range of resource utilisation. Therefore,he proposed a strategic mix roles for foreign factories: source, lead, contributor, offshore,outpost, and server; to position foreign-owned factories as competitive weapons in theirinternational production network. These roles can change according to the firm’sbusiness environment and strategy.
Dorneir et al. (1998) had proposed four factors influencing the internationalisation ofmanufacturing operations: global market forces, global cost forces, technological forces,and political-macroeconomic forces. “Global competition” is suggested as one of thosefactors (Shi and Gregory, 1998; McCormick and Stone, 1990). More specifically, Bhatnagarand Sohal (2005) explored the impact of facility location factors, SC uncertainty, andmanufacturing practices on SC competitiveness. They suggested factors for facilitylocation decisions as: cost, infrastructure availability, business services, labour, politicalstability, proximity to markets, proximity to suppliers, and location of key competitors.
Finally, firms consider whether or not the geographical dispersion of their logisticsand market channels is an option to compete on a global platform (Yip et al., 1993). Thetheory of international PLC, where firms extend their PLC through marketinternationalisation once its domestic PLC reaches maturity and decline stage, hasbeen implemented along with the rise of globalisation and the advancement ofinformation technology (Douglas and Craig, 1993; Vernon, 1966). However, this theory isno longer enough in making decisions as firms are suggested to trade-off between thefollowing criteria: cost (economy of scale, logistics costs, inventory costs, macro-economiccosts, i.e. customs, duties, subsidies), quality (quality reliability, innovation), flexibility(economy of scope), national differences and locational advantages, control over its
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chain such as customer service level and delivery time (Dornier et al., 1998; Goshal, 1993;Waters, 2002). Product characteristics and requirements such as demand predictability(Dornier et al., 1998) and product diversification (Goshal, 1993) are also suggested as keycriteria in this decision making. “If customization and fast response drive the industry,then economy of scale is less important” (Dornier et al., 1998). This implies that firms needto prioritise their criteria to match their product and process nature with consumer andmarket requirements.
Dornier et al. (1998) suggested three alternatives in making a geographical expansiondecision: market focus, product focus, and process focus. “Market focus” refers to settingup a facility close to the targeted markets to achieve quick response in delivery lead timeand product offerings that are more localized. Whereas “product focus” and “processfocus” are driven by economy of scale (cost effectiveness) and the facility may be locatedfurther away from destination markets. For “product focus”, a specific product categoryis allocated to each particular facility and finished products are shipped to destinationmarkets while “process focus” means that each facility is responsible for a particularmanufacturing process with specific manufacturing technology. Process focus isdriven by high product quality and technology requirement. Consequently, productmodularity encourages the concept of process focus as it divides manufacturingprocess into modules and delay manufacturing processes (Dornier et al., 1998; Waters,2002). Dornier et al. (1998) categorised these “focuses” by two dimensions of productcharacteristics: market requirements (PLC and demand predictability) andmanufacturing complexity (product complexity). However, the key criteria of thesetwo dimensions and the downstream chain configuration were not well defined,reflecting the need for further exploration in such issues.
Recently, it was suggested that product demand volatility, inventory-holding costs,and delivery lead time play a significant role in determining the internationalisationstrategies of logistics (Harrison and van Hoek, 2005; Table I). It has been suggestedthat products with high volatile demand should have globally centralized inventory inorder to save inventory-holding costs from inaccurate demand forecasts; whereas it issuggested that the inventory system of low volatile demand product be decentralizedlocally. This is partly because inventory-holding costs accounts for a significantamount of total SC costs, given approximately 55-75 per cent of total costs account forraw materials, components, and subassemblies, whereas transportation costs accountfor only 2-5 per cent of total costs (Urban, 2002). Additionally, to make SC locationdecisions, Lovell et al. (2005) took into account the required delivery lead time
Internationalisationstrategies of logistics
Demandpredictability Inventory location Speed
A Predictableproducts
Local inventory, preconfiguredSC, high inventory level
Fast moving withdirect shipment
B Less predictableproducts
Medium inventory levelawaiting for final configuration(postponement)
Medium velocity
C Unpredictableproducts
Global inventory, low inventorylevel (make to order)
and service level and incorporated these costs in SC and trade-off with the productvalue and the product value, which is the ratio of product value and its physical weightor size. Three SC models were suggested (Figure 1). They argued that products withhigh demand uncertainty and high value density should have inventory that wasglobally centralized and that product with low demand uncertainty and low valuedensity should have inventory that was decentralized locally. In the case of lowdemand uncertainty and high value density, transportation should be speeded up toincrease service level. Finally, they asserted that in case of high demand uncertaintyand low value density of the product, it was impossible to internationalise the SC.
To summarise, to geographically disperse in supply-side (sourcing perspective),firms commonly considers the following factors: unit costs, total logistics costsincluding duties and tariff, and the trade-off between total logistics costs, delivery leadtime, and inventory level. Similarly, in demand-side (logistics and market perspective),firms often consider the aforementioned factors in making decisions on geographicaldispersion of their market. Demand volatility, inventory holding costs, delivery leadtime, and product value density are added into consideration from the demand-side dueto the nature of product demand in the market per se. From the manufacturingperspective, the firm’s geographical dispersion decisions involve fundamentally cheaplabour cost, logistics costs, market proximity and availability, technological forces,infrastructure, and political-macroeconomic factors. Making decisions in eithersupply-side or demand-side would impact the manufacturing perspective. Therefore,decisions on geographical dispersion in supply-side and demand-side should not bemade separately. However, ironically, none of the existing literature provides such acomplete view for decisions makers in geographical dispersion in both supply-side anddemand-side. As a result, this paper aims to fulfill this research gap.
FSC and its characteristicsThe food industry, particularly the food processing industry, is not dominated by asmall number of multinational firms with few facility locations worldwide (Regmi andGehlhar, 2005). Rather, food manufacturing activities tend to be close to consumer basesas it is argued that “consumer-driven changes are increasingly pushing food suppliers tomeet consumer demand and preferences at a local level even though the food industrybecomes more global” (Bolling and Gehlhar, 2005). EIU (2005) has also pointed out thatglobal food industry is fragmented with Nestle and Unilever, the two largest food
Figure 1.Relationship between
demand uncertainty andproduct value density
Source: Lovell et al. (2005)
Globallycentralizedinventory
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manufacturers in the world, holding only 3.2 and 2.7 per cent global market share,respectively, in 2003. The specific characteristics of food products, e.g. temperaturesensitivity, limited shelf-life, food safety and traceability, and sustainability policies andpublic concerns has also contributed to limit the ability of FSC internationalisation(Karkkainen, 2003; Bourlakis and Weightman, 2004), leading to increased fragmentationof the food industry (Lawson et al., 1999). More specifically, food product perishabilityand value density (sensitivity to distribution costs) of the product are stressed as the keydeterminants of FSC geographical dispersions (van Hoek, 1999).
The scarcity and specific location of suppliers (raw materials) as well as the uniquequality of raw materials in the international market are also pointed out key enablers forinternationalisation of market (Dornier et al., 1998). The case of MacDonald’s in Moscowis a good example (Cockburn, 2000). Owing to the shortage and high variability in thequality of beef and cheese, MacDonald’s started a beef farm and a dairy plant to supplyits outlets. This exemplifies how raw material and food product characteristics couldinfluence the geographical dispersion decisions of FSC.
There is also evidence that most multinational food manufacturers are now movingtowards regionalization with their focused-factory strategy (Christopher, 2005).Concurrently, food retailers are also expanding beyond their home bases intooverseas markets. This geographical spread of both food manufacturers and foodretailers is mainly encouraged by the deregulation and liberalisation of internationaltrade and technological advancements including process and product, and informationtechnologies (Gattorna and Walters, 1996; Van der Vorst et al., 2001). Eversheim et al.(1997) argued that despite several forces driving internationalisation of FSC, e.g. costefficiency, market expansion, or technological advancements; macroeconomic andpolitical forces (i.e. trade liberalisation, international trade policies, and trade/non-tradebarriers) remain an important factor of the globalisation/de-globalisation in food andagricultural supply networks (Raynolds, 2004; Regmi, 2005).
Several food firms, including multinational and small-/medium-scale foodmanufacturers, and multiple food retailers, have expanded globally through exportingand global sourcing activities, international joint ventures, foreign direct investment,merger and acquisition (M & A), etc. (Sterns and Peterson, 2001; Moreira, 2004; Bollingand Gehlhar, 2005). Moreira and Gerry (2003) observed three globalisation patternsof transnational food corporations: production-driven (Nestle, Unilever, Kraft Foods,Philip Morris, Nabisco, Dole, Del Monte, Chiquita, Cargill, etc.), commercial-driven(Carrefour, Tesco, Sainsbury, Royal Ahold, Metro Group, Aldi, and Wal-Mart), andspeculative-driven organisations. Global sourcing and M & A strategies were alsonoticed in all these three globalisation patterns (Moreira and Gerry, 2003).
Lorentz (2008) examined the factors for facility location decisions in the foodindustry in Russia and suggested the following factors as key criteria: infrastructuralpotentials and risks, costs, potential market availability, availability of raw materials,and competitive situation. However, his research did not take into account severalfactors specifically to the food industry, e.g. perishability and limited lead time.
In summary, the existing literature of SC in the food industry has argued thatgeographical dispersion is limited due to its local preference, raw material seasonality,high quality standard, high perishability, and low value density (Cockburn, 2000;Dornier et al., 1998; van Hoek, 1999). However, to date, there is no existing literature thatdeliberately demonstrates how FSC is geographically dispersed and how the product
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and raw material characteristics impact FSC geographical location decisions. Even thedescription of characteristics such as value density is not yet well defined and explored(Lovell et al., 2005; van Hoek, 1999). Consequently, this paper seeks to further explorethese issues in FSC to fulfill such research gaps in the food industry.
Table II summarised the key drivers towards SC internationalisation from theliterature. Five key drivers were identified, SC lead time allowance (perishability),profit and costs, uncertainties and risks, market opportunities, and technologicaladvancement. These drivers provided a guide in the data collection of the case studiesconducted. These drivers could be either barriers or enablers to SC internationalisationdepending on their degree. For instance, short lead time would be a barrier tointernationalisation whereas long lead time would enable SC internationalisation.
Research designResearch contextThe term “supply chain” in this research refers to a linear chain based on amanufacturer’s view, taking into account the inputs (major agri-food materials) andlocation of suppliers as well as the outputs (food products) and the location of markets.Each case company could also offer several case studies because to survive in the currentbusiness environment companies “will need not just one supply chain solution butmany” (Aitken et al., 2005). Because each case company could provide different productsto different market locations, in this research, geographic location of a particularproduct-market FSC is the unit of analysis. Reasons for choosing firms in Thailand inparticular were:
Key drivers Key authors
SC lead time allowance (perishability)Delivery lead timeCustomer order lead timeProduction lead time
Blackburn and Scudder (2009), Bourlakis and Weightman(2004), Bozarth et al. (1998), Christopher et al. (2006, 2009),Harrison and van Hoek (2005), Kittipanya-ngam (2010),Lovell et al. (2005), Waters (2002) and van Hoek (1999)
Value densityTransportation costsInventory-holding costsUnit costs including labour costs, rawmaterial and component costsTransaction and administrative costsProduct value
Blackburn and Scudder (2009), Bozarth et al. (1998),Dorneir et al. (1998), Flaherty (1996), Harrison andvan Hoek (2005), Lovell et al. (2005) and Trent andMonczka (2005)
Uncertainties and risksDemand uncertainyPolitical and economic risksRaw material scarcity and seasonalitySafety and traceabilityPublic policies
Bhatnagar and Sohal (2005), Dorneir et al. (1998), Lovellet al. (2005), Roth et al. (2008) and Shi and Gregory (1998)
Dorneir et al. (1998), Ghemawat and Hout (2008), Kotabeet al. (2009), Shi and Gregory (1998) and Yip et al. (1993)
Technology advancement Dorneir et al. (1998), Kotabe et al. (2009) and Shi andGregory (1998)
Table II.Emerging key drivers
from the literature
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. Thailand is an emerging economy ranked amongst the world’s top 20 food tradingcountries (WTO, 2009). For instance, Thailand has been the world largestexporters of canned pineapple and frozen shrimps, the world second largestexporter of seafood, and a top ten exporter of chicken meat (National FoodInstitute of Thailand, 2008).
. Taking Thailand as a geographical base of manufacturing firms, the complexityof SC in relation to upstream suppliers’ and downstream customers’ locationscould be simplified.
Research question and the conceptual frameworkGiven the lack of literature to help with geographical location decisions in SC,particularly in the food industry where specific characteristics drive the SC towardsparticular requirements such as perishability limits geographical dispersion andincreases transportation and inventory costs, there is a need to further explore:
RQ1. How do FSCs of manufacturing firms geographically dispersed?
Answering this question involved creating a framework to investigate thegeographical dispersion in FSC. Figure 2 shows the conceptual framework adaptedfrom Flaherty’s (1996) typical configuration patterns of international SC. Four patternsof FSC geographical dispersion include:
(I) local SC – local sourcing for local market;
(II) supply-proximity SC – local sourcing for export;
(III) market-proximity SC – international sourcing for local market; and
(IV) international SC – international sourcing for international market.
The key factors which drive SC towards different patterns are drawn from theliterature and shown in Table II as SC lead time allowance, profit and costs,uncertainties and risks, market opportunities, and technological advancement. Thesedrivers provide a guide in the data collection of the case studies conducted. Thesedrivers could be either barriers or enablers to SC internationalisation depending
Figure 2.The conceptualframework for FSCgeographical dispersion Suppliers
(Agri-food materials)
Cus
tom
ers
(foo
d pr
oduc
ts)
Geographicallydispersed
I. Local SC
II. Supply-proximity SC
III. Marketproximity SC
IV. International SC
Geographicallyconcentrated
Geographicallydispersed
Geographicallyconcentrated
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on their degree. For instance, short lead time would be a barrier to internationalisationwhereas long lead time would enable SC internationalisation.
Research methodology and case study designThis research intends to be exploratory in nature with the aim of understanding howFSC of manufacturing firm is geographically dispersed through the keyinternationalisation patterns and their key drivers. The case study method is chosenbecause it is appropriate for exploratory research with no control of the event(Eisenhardt, 1989; Yin, 2009). Additionally, this research question begins with “how”,which is suggested to fit well in case study approach (Eisenhardt, 1989; Yin, 2009). As theunit of analysis of this research is the geographic location of a particular product marketand the key drivers of such geographical location decisions, multiple case study methodis used to generate cross-case view of geographical location and its key drivers(data dimensions), in order to compare and contrast the differences and similarities.
The selection of cases is based on replication sampling rather than statistical orlogic sampling meaning this research gathers diversified data in order to maximiseopportunities to discover variations amongst the evidence and to classify categoriesinto different patterns (Glaser and Strauss, 1967; Strauss and Corbin, 1998). The FSCsof leading Thailand-based manufacturers were chosen in order to allow case evidenceto be compared and contrasted within similar context. This replication strengthens theinternal validity of the research as it provides comparable cases (Mena et al., 2009).Nonetheless, the researcher recognizes the pitfall of this selection, e.g. generalizability(external validity) of the research.
Case studies which demonstrated differences in patterns of geographical location andshelf life were chosen in order to allow the ability to strengthen the generalizability of theresearch. The final criterion, accessibility to case companies, was also critical to thecase sampling strategy. The required number of cases for this type of research is stilldebatable academically. Eisenhardt (1989) suggested that four to ten cases in caseresearch method were understood to be typical. However, the sample size is not initself valuable or critical to qualitative research, but rather multiple comparisons ofcommonalities and differences amongst cases (Strauss and Corbin, 1998). Strauss andCorbin (1998) argued that the optimal number of cases would be reached when nosignificant changes could be made, e.g. the dimensions and subdimensions of theframework are saturated. In this research, the optimal number of cases was reachedwhen the cases stopped adding new elements to the framework or leading to significantchanges to the framework. As a result, 21 case studies with eight Thailand-based foodmanufacturers across 16 various food products sold to 21 market locations, wereconducted. Each of which involves particular product-market FSC geographicallocations taking into account the supplier’s location to the customer’s location anddifferent key factors influencing these location decisions. These cases were conductedduring years 2007-2009 by the main researcher only.
Given the scope of research, unit of analysis, and case selection guidelines, 21 casestudies (Table III) are selected for the exploratory empirical study and these cases arenot used for the testing and verification purposes. Noted that the first and secondcolumns in Table III indicate the case number (1-16) and the case companies. Therewas more than one case conducted within a case company. Cases 3-5, 12, and 15 containdifferent product-market locations; therefore, letters (a) and (b) were added to indicate
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CT
hai
larg
est
food
con
glo
mer
ate,
the
larg
est
anim
alfe
edan
dsh
rim
pp
rod
uce
rin
the
wor
ld,
Asi
a’s
larg
est
pou
ltry
pro
du
cers
(£2.
8b
illi
onin
2008
)
Fre
shp
oult
rym
eat
Loc
alm
ark
etL
ocal
sup
ply
Fou
rto
fiv
ed
ays
at0-
48C
2C
hil
led
read
ym
eal
Loc
alm
ark
etL
ocal
sup
ply
Fou
rto
fiv
ed
ays
at0-
48C
3aF
roze
nre
ady
mea
lL
ocal
mar
ket
Loc
alsu
pp
lyN
ine
to12
mon
ths
at2
188C
3bO
ver
seas
mar
ket
Loc
alsu
pp
lyN
ine
to12
mon
ths
at2
188C
4a 4bC
omp
any
KB
igg
est
Th
aisn
ack
man
ufa
ctu
rer
and
top
3b
igg
est
Th
aisn
ack
exp
orte
r(£
18m
illi
onin
2008
)
Pro
cess
edp
ean
uts
Loc
alm
ark
etIn
tern
atio
nal
sup
ply
Nin
eto
12m
onth
sat
248C
Ov
erse
asm
ark
etIn
tern
atio
nal
sup
ply
Nin
eto
12m
onth
sat
248C
5a 5bC
omp
any
ML
ead
ing
man
ufa
ctu
rer
ofin
stan
tn
ood
les
and
bak
ery
inT
hai
lan
d(£
115.
6m
illi
onin
2008
)
Inst
ant
noo
dle
Loc
alm
ark
etIn
tern
atio
nal
sup
ply
Nin
eto
12m
onth
sat
248C
Ov
erse
asm
ark
etIn
tern
atio
nal
sup
ply
Nin
eto
12m
onth
sat
248C
6 7C
omp
any
RL
ead
ing
fres
hfr
uit
and
veg
etab
lep
rod
uce
r(£
27m
illi
onin
2006
)F
resh
read
y-t
o-co
okv
eget
able
sO
ver
seas
mar
ket
Loc
alsu
pp
lyF
ive
tote
nd
ays
at0-
48C
Rea
dy
-to-
eat
sala
dO
ver
seas
mar
ket
Loc
alsu
pp
lyF
ive
tote
nd
ays
at0-
48C
8 9C
omp
any
BIn
tern
atio
nal
lead
ing
Th
aire
stau
ran
tch
ain
,T
hai
food
man
ufa
ctu
rer
and
exp
orte
r
Cu
rry
pas
teO
ver
seas
mar
ket
Loc
alsu
pp
lyN
ine
to12
mon
ths
at248C
Fre
shre
ady
-to-
cook
veg
etab
les
Ov
erse
asm
ark
etL
ocal
sup
ply
Fiv
eto
ten
day
sat
0-48
C
10 11C
omp
any
TT
hai
lead
ing
food
and
bev
erag
em
anu
fact
ure
r(£
93m
illi
onin
2008
)F
resh
fru
itju
ice
Loc
alm
ark
etIn
tern
atio
nal
sup
ply
On
eto
two
day
sat
0-48
C
Pas
teu
rise
dfr
uit
juic
eL
ocal
mar
ket
Inte
rnat
ion
alsu
pp
lyT
hre
eto
fou
rw
eek
sat
0-48
C
(continued
)
Table III.List of case studies andtheir selection criteria
BIJ18,6
812
Cas
esC
omp
any
An
nu
alre
ven
ue
Foo
dp
rod
uct
Ma
rket
loca
tion
Su
pp
lier
’slo
cati
onP
rod
uct
shel
fli
fe
12a
12b
UH
Tju
ice
Loc
alm
ark
etIn
tern
atio
nal
sup
ply
Nin
eto
12m
onth
sat
248C
Ov
erse
asm
ark
etIn
tern
atio
nal
sup
ply
Nin
eto
12m
onth
sat
248C
13C
ann
edp
inea
pp
leO
ver
seas
mar
ket
Loc
alsu
pp
lyN
ine
to12
mon
ths
at248C
14C
omp
any
BT
Lea
din
gA
sian
bak
ery
chai
nst
ores
(£11
9.7
mil
lion
in20
09)
Fre
shb
read
sL
ocal
mar
ket
Inte
rnat
ion
alsu
pp
lyT
wo
tofo
ur
day
sat
248C
15a
Com
pan
yN
Wor
ldla
rges
tm
ult
inat
ion
alfo
odm
anu
fact
ure
r(£
62.5
bil
lion
in20
08)
Sol
ub
leco
ffee
Loc
alm
ark
etL
ocal
sup
ply
Nin
eto
12m
onth
sat
248C
15b
Ov
erse
asm
ark
etL
ocal
sup
ply
Nin
eto
12m
onth
sat
248C
16Y
ogh
urt
Loc
alm
ark
etL
ocal
sup
ply
Tw
oto
thre
ew
eek
sat
0-48
C
Table III.
Exploringgeographical
dispersion
813
the differences amongst these cases. Owing to the exploratory nature of this research,these cases are not intended for purposes of testing or verifying.
Case data on:. geographical locations of suppliers and customers; and. key factors influencing location decisions were collected through two sources of
evidence: semi-structured interviews and documentations.
Semi-structured interviews allow the flexibility to ask questions about the key factorsthat emerged during the research while keeping the focus on research boundary(Bernard, 1995). SC directors, purchasing directors, sales and marketing directors of thecase companies were key informants and each interview was audio recorded. Eachinterview lasted at least one hour. Sometimes more than one interview with similarinformants was conducted in order to ensure that the interviews thoroughly coveredthe research focus. The interviews were transcribed and checked by the interviewersagain in order to ensure the correct understanding of the interview content and datacollected. Documentations were mainly used to obtain key facts and background of thecases and allowed confirmation of facts after the interviews.
The individual case analysis of this research includes displaying the data, explainingand generalizing, and reviewing the conceptual framework including four patterns ofSC internationalisation and their five key drivers. Within-case analysis allows theresearcher to become familiar with the data and their preliminary patterns (Eisenhardt,1989). It began since the first round of case data collection. Data display and analysis,which include data reduction[1], data display[2], and visualizing conclusions (Miles andHuberman, 1994), were used in within-case analysis. Once new cases were added,cross-case analysis was conducted to observe different patterns of data across cases(Eisenhardt, 1989). The purpose of the cross-case analysis was to identify the genericcategories while preserving the uniqueness of individual case data (Miles andHuberman, 1994). This analysis was aided by cross-case displays including contentanalytic summary tables (Miles and Huberman, 1994), which allowed the researcher tovisually compare and contrast the data dimensions across cases. Revisiting theliterature, reviewing the conceptual framework, and consultations with case companies,practitioners, and academics, helped in data reduction and consistency, which is part ofcase data analysis (Miles and Huberman, 1994). This process was repeated multipletimes. Pattern matching technique (Yin, 2009) was applied to observe the key patterns ofFSC geographical dispersion. Cross-case data analysis was then presented in tableformats to allow visualization for conclusions.
Case studies and cross-case analysisThe first part of this section presents the overall view of the 21 case data. The secondpart tabulates the data for cross-case analysis and elaborates on the analysis acrosscase studies. The final part then concludes the key findings of the research from thecase analysis.
Case data presentationBased on the conceptual framework, case data presentation involved capturinggeographical locations of each FSC and the key drivers of geographical dispersion ofeach case study SC from both supply-side and demand-side perspectives. Each case
BIJ18,6
814
demonstrated diversified decisions in their both supply-side and demand-sideaccording to different key drivers.
The conceptual framework was tested during the case study conducted across a rangeof SCs (Table IV). The cases allowed minor refinements of the conceptual framework interms of the practical application and developing unambiguous attribute definitions.Additionally, these cases also allowed the exploration of the implications of the keydrivers on FSC geographical dispersion patterns, extending discussions to explorepotential opportunities through FSC internationalisation. The case studies includeddifferent types of product characteristics ranging from commodity products (cannedpineapple) to innovative products (frozen ready meal, fruit juice) and to highly perishableproducts (fresh poultry meat, chilled ready meal, ready-to-eat salad, fresh ready-to-cookvegetables, and fresh breads). The case companies included Thailand-based multinationalcorporations (MNCs) as well as Thailand-based local firms. This allowed the conceptualframework to be assessed across a wide range of application environments.
The 21 case studies set out in Table IV illustrates the fieldwork approach usedacross the different SCs studied. These illustrative cases set out the key findings acrosscases in terms of framework refinement and emerging inter-relationships between FSCgeographical dispersion patterns and the five key drivers.
Cross-case analysisThe cross-case analysis part is used to compare and contrast the results from two casestudies and identify differences and similarities. The results can offer new insights toboth practitioners and researchers. Practitioners can better observe the key enablersand barriers in their own SC internationalisation with greater clarity as well as usethem as the key decision criteria on location decisions in SC. Researchers on the otherhand also have a rich tool to support comparative study of SC geographical dispersionand their key characteristics. More study is still needed to ensure more reliable meansof capturing the patterns of SC internationalisation as well as their key drivers.However, the preliminary findings in this research are worthwhile mentioning.
From supply-side location decision perspective, perishability (SC lead timeallowance) of raw materials appears to strongly influence the geographical location ofsuppliers since high perishable raw materials require limited delivery lead time andlimited inventory keeping time. Consequently, manufacturing firms tend to locategeographically near their suppliers. For example, case 16 (yoghurt)’s major raw materialis fresh unprocessed milk, which can only last for less than seven to ten days at 0-48C.Therefore, the manufacturing firm located its cooling centre and pasteurisation plantnearby local farms in order to ensure high product quality and freshness. This is alsopartly because the suppliers’ infrastructure could not support sufficient temperaturecontrol and monitoring at all times, risking product spoilage during transportation.In addition to perishability, the availability or scarcity of raw materials locally alsoappears to impact the geographical location decisions in supply-side of FSC. Forexample, the raw materials in 5a-b (wheat flour) is rare in Thailand (locally); hence, themanufacturing firm is forced to source from overseas from wherever its total logisticscosts and unit costs are best optimised. The firm eventually decided to source the wheatflour from USA and Australia.
Furthermore, value density of raw materials also plays an important role in decisionmaking in supply-side geographical location of FSC. Though the availability and
Exploringgeographical
dispersion
815
Cas
esP
rod
uct
sS
up
ply
-sid
elo
cati
ond
ecis
ion
fact
ors
Dem
and
-sid
elo
cati
ond
ecis
ion
fact
ors
1F
resh
pou
ltry
mea
tH
igh
per
ish
abil
ity
ofli
ve
bir
ds
(2)
Hig
hp
eris
hab
ilit
yof
pro
du
cts
(2)
Low
un
itco
sts
ofli
ve
bir
ds
(2)
Hig
hlo
gis
tics
cost
sfo
rov
erse
asm
ark
et(2
)S
hor
td
eliv
ery
lead
tim
eal
low
ance
du
eto
the
deh
yd
rati
onof
liv
eb
ird
s(2
)Im
por
tre
stri
ctio
ns
du
eto
avia
nfl
u(2
)
Loc
alm
ark
etav
aila
bil
ity
(2)
Sh
ort
del
iver
yle
adti
me
allo
wan
ced
ue
toh
igh
per
ish
abil
ity
(2)
Ex
por
tre
stri
ctio
ns
du
eto
avia
nfl
u(2
)2
Ch
ille
dre
ady
mea
lH
igh
per
ish
abil
ity
ofm
ajor
raw
mat
eria
ls(2
)H
igh
per
ish
abil
ity
ofp
rod
uct
s(2
)L
owu
nit
cost
slo
call
y(2
)H
igh
log
isti
csco
sts
for
over
seas
mar
ket
(2)
Low
log
isti
csco
stlo
call
yd
ue
tolo
cal
avai
lab
ilit
y(2
)L
ocal
mar
ket
avai
lab
ilit
y(2
)S
hor
td
eliv
ery
lead
tim
eal
low
ance
du
eto
hig
hp
eris
hab
ilit
y(2
)S
hor
td
eliv
ery
lead
tim
ed
ue
toth
ep
eris
hab
ilit
y(2
)Im
por
tre
stri
ctio
ns
du
eto
avia
nfl
u(2
)3a
Fro
zen
read
ym
eal
(for
loca
lm
ark
et)
Hig
hp
eris
hab
ilit
yof
maj
orra
wm
ater
ials
(2)
Low
un
itco
sts
loca
lly
(2)
Low
per
ish
abil
ity
ofp
rod
uct
sd
ue
tofr
eezi
ng
tech
nol
ogy
(þ)
Low
tota
llo
gis
tics
cost
sfo
rlo
cal
mar
ket
s(2
)L
owlo
gis
tics
cost
loca
lly
du
eto
loca
lav
aila
bil
ity
(2)
Loc
alm
ark
etav
aila
bil
ity
(2)
Sh
ort
del
iver
yle
adti
me
allo
wan
cefr
omcu
stom
ers
(2)
Sh
ort
del
iver
yle
adti
me
du
eto
the
per
ish
abil
ity
(2)
3bF
roze
nre
ady
mea
l(f
orex
por
tm
ark
et)
Hig
hp
eris
hab
ilit
yof
maj
orra
wm
ater
ials
(2)
Low
un
itco
sts
loca
lly
(2)
Low
per
ish
abil
ity
ofp
rod
uct
sd
ue
tofr
eezi
ng
tech
nol
ogy
(þ)
Low
log
isti
csco
sts
toov
erse
asm
ark
ets
(þ)
Low
log
isti
csco
stlo
call
yd
ue
tolo
cal
avai
lab
ilit
y(2
)O
ver
seas
mar
ket
avai
lab
ilit
yan
dlo
wu
nit
cost
ofp
rod
uct
ion
inT
hai
lan
d(þ
)S
hor
td
eliv
ery
lead
tim
ed
ue
toth
ep
eris
hab
ilit
y(2
)L
ong
del
iver
yle
adti
me
allo
wan
ce(þ
)4a
Pro
cess
edp
ean
uts
(for
loca
lm
ark
et)
Low
per
ish
abil
ity
ofra
wp
ean
uts
du
eto
free
zin
gte
chn
olog
y(þ
)L
owp
eris
hab
ilit
yof
pro
du
cts
(þ)
Low
tota
llo
gis
tics
cost
sfo
rlo
cal
mar
ket
s(2
)L
owto
tal
log
isti
csco
stfr
omov
erse
as(þ
)L
ong
del
iver
yle
adti
me
allo
wan
ced
ue
tolo
wp
eris
hab
ilit
y(þ
)L
ocal
mar
ket
avai
lab
ilit
y(2
)
(continued
)
Table IV.Cross-case datapresentation
BIJ18,6
816
Cas
esP
rod
uct
sS
up
ply
-sid
elo
cati
ond
ecis
ion
fact
ors
Dem
and
-sid
elo
cati
ond
ecis
ion
fact
ors
Not
enou
gh
avai
lab
ilit
yof
raw
pea
nu
tslo
call
yan
dse
ason
alit
ysh
orta
ge
(þ)
Sh
ort
del
iver
yle
adti
me
allo
wan
cefr
omcu
stom
ers
(2)
FT
Aal
low
ance
wit
hC
hin
a(þ
)4b
Pro
cess
edp
ean
uts
(for
exp
ort
mar
ket
)L
owp
eris
hab
ilit
yof
raw
pea
nu
tsd
ue
tofr
eezi
ng
tech
nol
ogy
(þ)
Low
per
ish
abil
ity
ofp
rod
uct
s(þ
)L
owlo
gis
tics
cost
sto
over
seas
mar
ket
s(þ
)L
owto
tal
log
isti
csco
stfr
omov
erse
as(þ
)L
ong
del
iver
yle
adti
me
allo
wan
ced
ue
tolo
wp
eris
hab
ilit
y(þ
)O
ver
seas
mar
ket
avai
lab
ilit
yan
dlo
wu
nit
cost
ofp
rod
uct
ion
inT
hai
lan
d(þ
)N
oten
oug
hav
aila
bil
ity
ofra
wp
ean
uts
loca
lly
and
seas
onal
ity
shor
tag
e(þ
)L
ong
del
iver
yle
adti
me
allo
wan
ce(þ
)
FT
Aal
low
ance
wit
hC
hin
a(þ
)5a
Inst
ant
noo
dle
(for
loca
lm
ark
et)
Low
per
ish
abil
ity
ofw
hea
tfl
our
(þ)
Low
per
ish
abil
ity
ofp
rod
uct
s(þ
)L
owto
tal
log
isti
csco
stfr
omov
erse
as(þ
)L
owto
tal
log
isti
csco
sts
for
loca
lm
ark
ets
(2)
Sca
rcit
yof
wh
eat
flou
rlo
call
y(þ
)L
ocal
mar
ket
avai
lab
ilit
y(2
)L
ong
del
iver
yle
adti
me
allo
wan
ced
ue
tolo
wp
eris
hab
ilit
y(þ
)S
hor
td
eliv
ery
lead
tim
eal
low
ance
from
cust
omer
s(2
)5b
Inst
ant
noo
dle
(for
exp
ort
mar
ket
)L
owp
eris
hab
ilit
yof
wh
eat
flou
r(þ
)L
owp
eris
hab
ilit
yof
pro
du
cts
(þ)
Low
tota
llo
gis
tics
cost
from
over
seas
(þ)
Low
log
isti
csco
sts
toov
erse
asm
ark
ets
(þ)
Sca
rcit
yof
wh
eat
flou
rlo
call
y(þ
)L
ong
del
iver
yle
adti
me
allo
wan
ced
ue
tolo
wp
eris
hab
ilit
y(þ
)
Ov
erse
asm
ark
etav
aila
bil
ity
and
low
un
itco
stof
pro
du
ctio
nin
Th
aila
nd
(þ)
Lon
gd
eliv
ery
lead
tim
eal
low
ance
(þ)
6F
resh
read
y-t
o-co
okv
eget
able
sH
igh
per
ish
abil
ity
offr
esh
lyh
arv
este
dv
eget
able
s(2
)L
ocal
avai
lab
ilit
yof
the
raw
mat
eria
lsat
low
cost
s(2
)L
owto
tal
log
isti
csco
sts
loca
lly
(2)
Sh
ort
del
iver
yle
adti
me
allo
wan
ced
ue
toh
igh
per
ish
abil
ity
(2)
Hig
hp
eris
hab
ilit
yof
pro
du
cts
(2)
Hig
hp
rofi
tm
arg
in(v
alu
ed
ensi
ty)
ofp
rod
uct
sd
ue
top
rod
uct
scar
city
inth
eov
erse
asm
ark
et(þ
)O
ver
seas
mar
ket
avai
lab
ilit
yan
dlo
wu
nit
cost
ofp
rod
uct
ion
inT
hai
lan
d(þ
)S
hor
td
eliv
ery
lead
tim
ed
ue
toh
igh
per
ish
abil
ity
(2)
(continued
)
Table IV.
Exploringgeographical
dispersion
817
Cas
esP
rod
uct
sS
up
ply
-sid
elo
cati
ond
ecis
ion
fact
ors
Dem
and
-sid
elo
cati
ond
ecis
ion
fact
ors
7R
ead
y-t
o-ea
tsa
lad
Hig
hp
eris
hab
ilit
yof
fres
hly
har
ves
ted
veg
etab
les
(2)
Hig
hp
eris
hab
ilit
yof
pro
du
cts
(2)
Loc
alav
aila
bil
ity
ofth
era
wm
ater
ials
atlo
wco
sts
(2)
Hig
hp
rofi
tm
arg
in(v
alu
ed
ensi
ty)
ofp
rod
uct
sd
ue
top
rod
uct
scar
city
inth
eov
erse
asm
ark
et(þ
)L
owto
tal
log
isti
csco
sts
loca
lly
(2)
Sh
ort
del
iver
yle
adti
me
allo
wan
ced
ue
toh
igh
per
ish
abil
ity
(2)
Ov
erse
asm
ark
etav
aila
bil
ity
and
low
un
itco
stof
pro
du
ctio
nin
Th
aila
nd
(þ)
Sh
ort
del
iver
yle
adti
me
du
eto
hig
hp
eris
hab
ilit
y(2
)8
Cu
rry
pas
teL
owp
eris
hab
ilit
yof
key
ing
red
ien
ts(þ
)L
owp
eris
hab
ilit
yof
pro
du
cts
(þ)
Loc
alav
aila
bil
ity
ofk
eyin
gre
die
nts
atlo
wco
st(2
)L
owlo
gis
tics
cost
sto
over
seas
mar
ket
s(þ
)L
owto
tal
log
isti
csco
stlo
call
y(2
)O
ver
seas
mar
ket
avai
lab
ilit
yan
dlo
wu
nit
cost
ofp
rod
uct
ion
inT
hai
lan
d(þ
)L
ong
del
iver
yle
adti
me
allo
wan
ce(þ
)9
Fre
shre
ady
-to-
cook
veg
etab
les
Hig
hp
eris
hab
ilit
yof
fres
hfr
uit
s(2
)H
igh
per
ish
abil
ity
ofp
rod
uct
s(2
)L
ocal
avai
lab
ilit
yof
trop
ical
fru
its
loca
lly
(2)
Low
tota
llo
gis
tics
cost
slo
call
y(2
)S
hor
td
eliv
ery
lead
tim
eal
low
ance
du
eto
hig
hp
eris
hab
ilit
y(2
)
Hig
hp
rofi
tm
arg
in(v
alu
ed
ensi
ty)
ofp
rod
uct
sd
ue
top
rod
uct
scar
city
inth
eov
erse
asm
ark
et(þ
)O
ver
seas
mar
ket
avai
lab
ilit
yan
dlo
wu
nit
cost
ofp
rod
uct
ion
inT
hai
lan
d(þ
)S
hor
td
eliv
ery
lead
tim
ed
ue
toh
igh
per
ish
abil
ity
(2)
10F
resh
fru
itju
ice
Low
per
ish
abil
ity
ofw
inte
rfr
uit
sd
ue
tofr
eezi
ng
tech
nol
ogy
(þ)
Low
tota
llo
gis
tics
cost
from
over
seas
(þ)
Lon
gd
eliv
ery
lead
tim
eal
low
ance
du
eto
low
per
ish
abil
ity
(þ)
Sca
rcit
yof
win
ter
fru
its
loca
lly
(þ)
Hig
hp
eris
hab
ilit
yof
pro
du
cts
(2)
Hig
hlo
gis
tics
cost
sfo
rov
erse
asm
ark
et(2
)L
ocal
mar
ket
avai
lab
ilit
y(2
)S
hor
td
eliv
ery
lead
tim
eal
low
ance
du
eto
hig
hp
eris
hab
ilit
y(2
)
11P
aste
uri
sed
fru
itju
ice
Low
per
ish
abil
ity
ofw
inte
rfr
uit
sd
ue
tofr
eezi
ng
tech
nol
ogy
(þ)
Low
tota
llo
gis
tics
cost
from
over
seas
(þ)
Lon
gd
eliv
ery
lead
tim
eal
low
ance
du
eto
low
per
ish
abil
ity
(þ)
Hig
hp
eris
hab
ilit
yof
pro
du
cts
(2)
Hig
hlo
gis
tics
cost
sfo
rov
erse
asm
ark
et(2
)L
ocal
mar
ket
avai
lab
ilit
y(2
)S
hor
td
eliv
ery
lead
tim
eal
low
ance
du
eto
hig
hp
eris
hab
ilit
y(2
)S
carc
ity
ofw
inte
rfr
uit
slo
call
y(þ
)
(continued
)
Table IV.
BIJ18,6
818
Cas
esP
rod
uct
sS
up
ply
-sid
elo
cati
ond
ecis
ion
fact
ors
Dem
and
-sid
elo
cati
ond
ecis
ion
fact
ors
12a
UH
Tju
ice
(loc
al)
Low
per
ish
abil
ity
ofw
inte
rfr
uit
sd
ue
tofr
eezi
ng
tech
nol
ogy
(þ)
Low
per
ish
abil
ity
ofp
rod
uct
s(þ
)
Low
tota
llo
gis
tics
cost
from
over
seas
(þ)
Low
tota
llo
gis
tics
cost
sfo
rlo
cal
mar
ket
s(2
)L
ong
del
iver
yle
adti
me
allo
wan
ced
ue
tolo
wp
eris
hab
ilit
y(þ
)S
carc
ity
ofw
inte
rfr
uit
slo
call
y(þ
)
Loc
alm
ark
etav
aila
bil
ity
(2)
Sh
ort
del
iver
yle
adti
me
allo
wan
cefr
omcu
stom
ers
(2)
12b
UH
Tju
ice
(ex
por
t)L
owp
eris
hab
ilit
yof
win
ter
fru
its
du
eto
free
zin
gte
chn
olog
y(þ
)L
owto
tal
log
isti
csco
stfr
omov
erse
as(þ
)L
ong
del
iver
yle
adti
me
allo
wan
ced
ue
tolo
wp
eris
hab
ilit
y(þ
)S
carc
ity
ofw
inte
rfr
uit
slo
call
y(þ
)
Low
per
ish
abil
ity
ofp
rod
uct
s(þ
)L
owlo
gis
tics
cost
sto
over
seas
mar
ket
s(þ
)O
ver
seas
mar
ket
avai
lab
ilit
yan
dlo
wu
nit
cost
ofp
rod
uct
ion
inT
hai
lan
d(þ
)L
ong
del
iver
yle
adti
me
allo
wan
ce(þ
)
13C
ann
edp
inea
pp
leH
igh
per
ish
abil
ity
offr
esh
pin
eap
ple
s(2
)L
owp
eris
hab
ilit
yof
pro
du
cts
(þ)
Loc
alav
aila
bil
ity
offr
esh
pin
eap
ple
atlo
wco
sts
(2)
Low
tota
llo
gis
tics
cost
slo
call
y(2
)S
hor
td
eliv
ery
lead
tim
eal
low
ance
du
eto
hig
hp
eris
hab
ilit
y(2
)
Low
log
isti
csco
sts
toov
erse
asm
ark
ets
(þ)
Ov
erse
asm
ark
etav
aila
bil
ity
and
low
un
itco
stof
pro
du
ctio
nin
Th
aila
nd
(þ)
Lon
gd
eliv
ery
lead
tim
eal
low
ance
(þ)
An
ti-d
um
pin
gre
gu
lati
onso
no
loca
lm
ark
etex
plo
rati
on(þ
)14
Fre
shb
read
sL
owp
eris
hab
ilit
yof
bre
adfl
our
du
eto
free
zin
gte
chn
olog
y(þ
)L
owto
tal
log
isti
csco
stfr
omov
erse
as(þ
)L
ong
del
iver
yle
adti
me
allo
wan
ced
ue
tolo
wp
eris
hab
ilit
y(þ
)S
carc
ity
ofb
read
flou
rlo
call
y(þ
)
Hig
hp
eris
hab
ilit
yof
pro
du
cts
(2)
Hig
hlo
gis
tics
cost
sfo
rov
erse
asm
ark
et(2
)L
ocal
mar
ket
avai
lab
ilit
y(2
)S
hor
td
eliv
ery
lead
tim
eal
low
ance
du
eto
hig
hp
eris
hab
ilit
y(2
)
15a
Sol
ub
leco
ffee
(for
loca
l)L
owp
eris
hab
ilit
yof
gre
enco
ffee
bea
ns
(þ)
Low
per
ish
abil
ity
ofp
rod
uct
s(þ
)
(continued
)
Table IV.
Exploringgeographical
dispersion
819
Cas
esP
rod
uct
sS
up
ply
-sid
elo
cati
ond
ecis
ion
fact
ors
Dem
and
-sid
elo
cati
ond
ecis
ion
fact
ors
Loc
alav
aila
bil
ity
ofg
reen
coff
eeb
ean
sat
low
cost
s(2
)L
owto
tal
log
isti
csco
sts
loca
lly
(2)
Imp
ort
qu
ota
ofg
reen
coff
eeb
ean
s(2
)
Low
tota
llo
gis
tics
cost
sfo
rlo
cal
mar
ket
s(2
)L
ocal
mar
ket
avai
lab
ilit
y(2
)S
hor
td
eliv
ery
lead
tim
eal
low
ance
from
cust
omer
s(2
)15
bS
olu
ble
coff
ee(f
orex
por
t)L
owp
eris
hab
ilit
yof
gre
enco
ffee
bea
ns
(þ)
Low
per
ish
abil
ity
ofp
rod
uct
s(þ
)L
ocal
avai
lab
ilit
yof
gre
enco
ffee
bea
ns
atlo
wco
sts
(2)
Low
log
isti
csco
sts
toov
erse
asm
ark
ets
du
eto
AS
EA
NF
TA
(þ)
Low
tota
llo
gis
tics
cost
slo
call
y(2
)Im
por
tq
uot
aof
gre
enco
ffee
bea
ns
(2)
Ov
erse
asm
ark
etav
aila
bil
ity
and
low
un
itco
stof
pro
du
ctio
nin
Th
aila
nd
(þ)
Lon
gd
eliv
ery
lead
tim
eal
low
ance
(þ)
16Y
ogh
urt
Hig
hp
eris
hab
ilit
yof
raw
mil
k(2
)H
igh
per
ish
abil
ity
ofp
rod
uct
s(2
)L
ocal
avai
lab
ilit
yof
raw
mil
kat
low
cost
s(2
)H
igh
log
isti
csco
sts
for
over
seas
mar
ket
(2)
Low
tota
llo
gis
tics
cost
slo
call
y(2
)L
ocal
mar
ket
avai
lab
ilit
y(2
)S
hor
td
eliv
ery
lead
tim
eal
low
ance
du
eto
hig
hp
eris
hab
ilit
y(2
)S
hor
td
eliv
ery
lead
tim
eal
low
ance
du
eto
hig
hp
eris
hab
ilit
y(2
)
Note:
Geo
gra
ph
ical
dis
per
sion
:p
osit
ive
imp
act
(þ)
and
neg
ativ
eim
pac
t(2
)
Table IV.
BIJ18,6
820
scarcity locally could influence the value density of raw materials, there are other factorssuch as unit costs and total logistics costs which should also be taken into account invalue density. For example, the raw material of cases 6 and 7 (freshly harvestedvegetables) is available locally at low unit costs. To transport the raw material overseasand allow the product to be produced at manufacturing plants overseas would raise totallogistics costs tremendously due to its high perishability and requirements on shortdelivery lead time and temperature control. This hence demonstrates low value densityof the raw material to be transported anywhere but locally.
Market opportunities such as availability and geographical location of cost-effectiveraw materials could also drive SC towards different patterns and decisions. Examplescan be seen in cases 4 and 5 where the raw materials cannot be found at the cost effectiveprice locally. Case 4’s raw materials are raw peanuts which are seasonal and rare to findin Thailand due to their unpopularity and low profit margins among local farmers.Therefore, the company in case 4 internationally sourced raw peanuts from its partner inChina at a cheaper cost and with greater reliability all year round. Case 5’s raw materials,which are wheat flour, could not be found or produced in sufficient amounts locally.Therefore, the case company of case 5 sourced the raw materials from USA andAustralia.
Finally, uncertainties and risks from economic and political policies, e.g. internationaltrade agreements, and raw material scarcity or seasonality could also influencegeographical decisions on suppliers’ location in FSC. Cases 1 and 15a-b demonstratedthe economic and political policies’ impact on geographical location decision insupply-side FSC. The major raw material of case 1 (live birds), for example, is notallowed to be imported or exported across countries contaminated by avian flu.Therefore, the manufacturing firm is forced to buy live birds locally for local supply inthe domestic market only. The major raw material of cases 15a-b (green coffee beans),on the other hand, is abundantly available. However, to protect local coffee farmers,Thai government sets an import quota of green coffee beans to protect local farmersfrom lower prices overseas. Hence, the manufacturing firm is forced to source the rawmaterial locally if the firm wants to manufacture the product locally to take advantageof low labour costs.
Finally, technological advancements are possible key enablers of upstream SCinternationalisation. Examples can be seen in cases 4a-b where their raw material (rawpeanuts) is slightly perishable and scarce during the rainy season (seasonal shortage).Although the normal shelf life of raw peanut is four to six weeks at room temperature(18-248C), it can be extended to six to eight months by freezing it at 2108C. Hence, theraw material can be kept in stock for production all year round, thanks to freezing,temperature-controlled packaging, and transportation technologies.
From demand-side perspective, similarly, perishability and value density ofproducts, economic and political policies and technological advancements demonstratedstrong influences on FSC geographical locations on demand-side. Example of the impactof perishability can be seen in case 1 (fresh poultry meat) where the product’s deliverylead time is limited to less than one day after production. Though in some cases freshpoultry meat is frozen to extend the product shelf life, allowing longer delivery leadtimes, this case’s customers strictly require fresh product without prior freezing.Therefore, high perishability of the product limits geographical dispersion of marketlocations to be nearby the manufacturing firm. Additionally, due to the avian flu
Exploringgeographical
dispersion
821
restriction, trading of raw poultry meat is not allowed internationally. This furtherdemonstrates the impact of economic and political policies on FSC geographical locationin demand-side. On the contrary to cases 1, 6 and 7 (fresh ready-to-cook and ready-to-eatvegetables) are similarly perishable but they can be traded internationally to markets(EU and UK markets) far away from their production bases (Thailand). This is becauseof the scarcity of the products in the overseas markets, low unit costs, and relatively lowtotal logistics costs in comparison to the perceived value of overseas customers. Thesefactors increase the value density of the products such that they are able to transportedthrough airfreight (high transportation cost) within limited delivery lead time to marketsfar away though their value density locally in Thailand is relatively low.
The key influential factors on FSC geographical location decisions are drawn fromthe case studies. Table V depicted the generalised key factors and their frequency ofemergence from the cases. According to Table V, four influential factors are generalisedas perishability, value density, economic and political forces, and technologicaladvancements. It is observed that the factors that influence geographical dispersion ofFSC the most are perishability and value density of both raw materials and products.In other words, the manufacturing firms basically seek to trade-off between limitedcost and available time in their FSC operations.
First, perishability could influence geographical location decisions on bothsupply-side and demand-side of FSC as perishability determines delivery lead timeallowance in FSC. Case evidence demonstrates that, typically, the higher theperishability, the nearer the manufacturing firm is to their suppliers or customers dueto short delivery lead time allowance.
Second, value density also demonstrates its influence on FSC geographical locationdecisions, in addition to perishability. Theoretically, value density refers to the ratiobetween the value of an item and physical weight or size (Lovell et al., 2005). Practically,case companies refer to the value density as the comparison between the perceived valueof the buyer and the costs, e.g. unit cost, total logistics costs, and other costs incurred inFSC. Typically, the higher value density, the further the manufacturing firm is to thesuppliers or customers due to higher total logistics cost allowances. Cases 6 and7 demonstrate that despite the high perishability of their products, their high valuedensity allows the manufacturing firm to pursue overseas customers far away easily viaairfreight.
Third, uncertainties and risks from economic and political forces, internationaltrade regulations, safety and traceability issues, and scarcity or seasonality of products
CaseSupply-side location
decisionDemand-side location
decision
Lead time (delivery lead time allowance/perishability) (2 ) 9/(þ ) 12 (2 ) 9/(þ ) 12Product value density (total logistics cost, unit cost,availability and scarcity, and cost savings incomparison with the perceived value) (2 ) 12/(þ ) 9 (2 ) 10/(þ ) 11Economic-political forces (2 ) 4/(þ ) 2 (2 ) 1/(þ ) 2Market opportunities (2 ) 11/(þ ) 4 (2 ) 10/(þ ) 11Technological advancements (þ ) 4 (þ ) 1
Table V.Key factors influencinglocation decisions in FSC
BIJ18,6
822
in markets could restrict or enable the geographical dispersion of FSC. Often, bilateraland multilateral free trade agreements (FTA) encourage geographical dispersion ofboth suppliers and markets within FSC, e.g. FTA between Thailand and China in cases4a and b or ASEAN FTA in case 15b. However, these economic and political forcescould also limit the geographical dispersion of FSC. For example, avian flu restrictionson international trade in case 1.
Market opportunities such as availability of local or international market share couldalso drive SC towards different patterns and decisions. Ansoff (1957) proposed fourmarket-product strategies of a firm through two dimensions: product growth andmarket growth. The four market-product strategies are market penetration strategy(gaining existing product share in an existing market), market development strategy(gaining existing product share in a new market segment), product developmentstrategy (gaining new product market share in an existing market), and diversification(gaining new product market share in a new market segment). These strategiesultimately aim to extend the PLC either though different product development ordifferent market exploration. Examples can be seen in cases 3-5 where the products aresold through both local and international markets in order to extend the PLC from theirmaturity in local markets to their introduction or growth stages in international markets.
Finally, technological advancements can enable the geographical dispersion of FSC,e.g. transportation technology (airfreight) in cases 6 and 7, shelf life extension throughfreezing and packaging technology in case 3b. It is hardly observed that technologicaladvancements have negative impact on FSC geographical dispersion in the casestudies.
Key findingsThe key findings from the within-case and cross-case analysis are as follow.
To begin, though the existing literature emphasised the importance of trade-offsbetween delivery lead time and costs in SC (Harrison and van Hoek, 2005) or thepotential impacts of eco-political issues and technology, the studies were notspecifically conducted within the food industry whose specific characteristics such asperishability played an important role in limiting the ability to geographically dispersein FSC (van Hoek, 1999). Additionally, despite the recognition of its impact on SCgeographical dispersion, not much attention yet has been given to the value density inthe literature (Lovell et al., 2005). This research, based on the multiple-case studyanalysis, provided in Tables IV and V, empirically demonstrated four key influentialfactors and their implications on the geographical dispersion of both supply-side anddemand-side FSC: perishability, value density, economic-political factors, andtechnological advancements. Based upon the case analysis, the different degree ofthese influential factors would lead to different degree of FSC geographical dispersion.
According to the conceptual framework in Figure 2, four key patterns of FSCgeographical dispersion were proposed: local SC, supply-proximity SC, market-proximitySC, and international SC. Through the cross-case analysis, each case fitted within thesefour key patterns (Figure 3), validating four key patterns proposed earlier. Additionally,the key characteristics of each pattern could be identified through cross-case analysis andare described below.
Type I. Local SC. Local SC is a chain which a manufacturing firm locallysources its supplies for use locally, though the motives behind this localisation of FSC
Exploringgeographical
dispersion
823
could be different. From the case studies, manufacturing firms with local SC werefound to have two motives.
First, the manufacturing firms needed to be localised due to the limitations such as,e.g. high perishability, low value density, or economic-political forces. For instance, theraw material (live birds) and product (fresh poultry meat) of cases 1 and 2 (fresh poultrymeat as the raw material and chilled ready meal as the product) and case 16 (fresh rawmilk as the major raw material and yoghurt as the product) are highly perishable. Theseraw materials and products require short delivery lead time, resulting in high logisticscosts if the manufacturing firms were to explore distant markets overseas (low valuedensity). Therefore, their FSCs appear to be geographically close to both suppliers andcustomers in order to ensure raw materials’ and products’ freshness at the lowest costs.This perishable type of FSC clearly requires a minimum degree of inter-firmcollaboration, e.g. demand forecast information accuracy to a certain extent which issufficient to prevent product spoilage from high inventory of perishable products. Case2’s FSC, for instance, implemented enterprise resource planning software to increase theinformation transparency in its FSC. Similarly, case 16’s FSC implementedVendor-managed inventory scheme to synchronise its order fulfilment processeswith its customers. These tools would ensure minimum inventory level of perishableproducts through increased demand forecast accuracy from demand informationtransparency.
Second, firms adopt local SC because of their objectives to serve the local market. Forexample, case 15a (soluble coffee)’s product is non-perishable; therefore, long deliverylead time is allowed for the markets far away from its production base, e.g. case 15b.However, due to the local market opportunities, the firm decided to serve the localmarket.
Type II. Supply-proximity SC. Supply-proximity SC is a chain which a manufacturingfirm locally sources its supplies and makes produces products for international marketconsumption. This type of FSC seeks to take advantage of the low cost environments incertain countries or the availability of unique raw materials locally.
Figure 3.Four key patterns of FSCgeographical dispersionwith case evidence Suppliers
Case 4b (processed peanuts)Case 5b (instant noodle)Case 12b (UHT juice)
BIJ18,6
824
Taking the supply-side perspective, the manufacturing firms are geographicallylocated close to their suppliers due to several reasons, e.g. high perishability of rawmaterials, scarcity of raw materials overseas, high logistics costs if located far awayfrom suppliers, economic and political restrictions to locally source, etc. Examples can beseen in cases 3b, 6-9, 13, and 15b. For instance, the raw materials of cases 6-9 are mainlyfreshly harvested tropical vegetables, fruits, and herbs, all of which are rare to find ordifficult to grow in the overseas markets, e.g. EU and UK markets, due to theunfavourable weather conditions. As such, manufacturing firms locate themselvesgeographically close to their suppliers to optimise their FSCs through the low labour andtotal logistics costs available in Thailand and because of the high perishability of theraw materials.
Taking the demand-side perspective, the manufacturing firms can earn high profitsfrom products that have high product value and value density, compared to the totalcosts of the products in FSC. For example, case 3b (frozen ready meal) is manufacturedat low unit costs in Thailand and can be transported to overseas markets, e.g. EU,UK, USA, and Asia Pacific markets via low cost transportation (sea), thanks to thenon-perishability of the product. The manufacturing firm mainly gain its profits fromthe difference between the labour costs in the markets (developed countries) it serves andits production base (Thailand). Additionally, the high value-added (ready meal) nature ofthe product increases the product value perception, allowing the product to be sold evenin Asia Pacific markets where labour costs are similar to that of Thailand. Thisexemplifies the role product value addition plays as a key enabler for geographicaldispersion in FSC.
Additionally, highly perishable product can also be merchandised internationallydue to the long lead time allowance, perishable products, e.g. firms in cases 6, 7, and 9could explore distant markets because of high product value density, which allowssufficient profit margins to pay for quick transportation to distant markets. Customersof cases 6, 7, and 9 are willing to pay high price for those products due to productscarcity in their markets (EU and UK). Hence, product value density in overseasmarkets is relatively high, compared to Thailand. This reflects the fact that productvalue and value density are relative terms and will vary with different geographicallocations.
Type III. Market-proximity SC. Market-proximity SC is a chain which amanufacturing firm internationally sources its supplies and produces products forlocal market consumption. Based on the cross-case evidence, the raw materials of thisFSC type tend to be rare to find or expensive to grow locally; hence, it makes more sensefor manufacturing firms to internationally source their raw materials. For example, theraw materials of cases 10-12 are winter fruits, which are rare or expensive to grow inThailand’s tropical climate. However, to lower transportation costs, the raw materialswere either frozen or semi-processed into an aseptic form in order to extend the deliverylead time allowance and reduce space for transportation. Technological advancementshave enabled the raw materials to be internationally transported economically, evenwith their limited value density. Similarly, the raw materials of cases 4a, 5a, and 14 areraw peanuts, wheat flour, and bread flour, which are rare or expensive to grow inThailand. Therefore, the manufacturing firms purchase these materials internationallyfrom the most cost competitive sources.
Exploringgeographical
dispersion
825
Taking the demand-side perspective, manufacturing firms have two motives forbeing located geographically close to their customers. First, the manufacturing firms areobliged to serve local markets due to limitations such as, e.g. high perishability, lowvalue density, or economic-political forces. For instance, the products of cases 10, 11, and14 are all highly perishable (fresh fruit juice, pasteurised fruit juice, and fresh breads).The total logistics costs to transport these products to overseas markets are high due toshort delivery lead time allowance and high risk of product spoilage in case of demandforecast errors. Therefore, these products are only merchandised locally due to their highperishability and low value density. Second, is the firm’s objective to serve local markets.For example, products in cases 4a, 5a, and 12a are all non-perishable; therefore, longdelivery lead time is allowed for manufacturing firms to explore the markets far awayfrom their production base. However, due to the local market availability, the firmdecided to serve the local market.
Type IV. International SC. International SC is a chain which a manufacturing firminternationally sources its supplies and produces products for international marketconsumption. This type of FSC seeks to take advantage of low cost countrymanufacturing and serve the international market platform. The manufacturing firmsthat fall into this type of FSC are very flexible. They can choose their manufacturinglocations where they are best optimised as their raw materials and products arenon-perishable. Additionally, the value density of both raw materials and products arehigh enough to merchandise in distant markets.
Taking the supply-side perspective, based on the cross-case evidence, the rawmaterials of this FSC type tend to be rare to find or expensive to grow locally; hence,it makes more sense for manufacturing firms to internationally source their rawmaterials. Examples can be seen in cases 4b, 5b, and 12b where the firms’ raw materialsare raw peanuts, wheat flour, and winter fruits. Raw peanuts can be found inThailand but their volume/year is not enough to meet annual consumption. Hence, themanufacturing firm has to source raw peanuts from China where raw peanuts areabundant at low costs. Similarly, wheat flour and winter fruits are rare to find inThailand and the manufacturing firms have to purchase them from overseas. Owing tothe non-perishability of these products, long delivery lead times and thus long distancetransportation is viable.
In terms of demand-side perspective, the manufacturing firms earn profitmargins from the high product value and value density of their products, comparedto the total costs of the products in FSC. For example, cases 4b, 5b, and 12b aremanufactured at low unit costs in Thailand and are transported to overseas markets,e.g. EU, UK, USA, and Asia Pacific markets via low cost transportation (sea), thanks tothe non-perishability of the product. The manufacturing firms mainly gain profits fromthe difference between the labour costs of the markets they serve (developedcountries) and their production base (Thailand). Though several overseas markets arealso developing countries with labour costs similar to Thailand, the case productsdistinguish themselves through their product uniqueness, e.g. unique recipes andflavours of processed peanuts and instant noodle. This increases the product value andvalue density in overseas markets, which enables the geographical dispersion of FSC’smarket. This also emphasises the role of product value addition as a key enabler forgeographical dispersion in FSC.
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Conclusions and limitationsThis paper seeks to answer the research question: how FSC is geographicallydispersed. To answer the research question, key relevant literature were reviewed andthe framework presenting four patterns of FSC geographical dispersion wasconceptualised. A total of 21 case studies with eight food manufacturing firms based inThailand were conducted to observe:
. key influential factors for FSC geographical location decisions and theirimplications; and
. key patterns of FSC geographical dispersion and their characteristics.
As a result, this research first identified four key influential factors as perishability(delivery lead time allowance), value density, uncertainties and risks, marketopportunities, and technological advancements. The first four factors could influenceFSC geographical dispersion in both positive and negative aspects whereas the lastfactor – technological advancements – is found to have only positive effects on FSCgeographical dispersion, e.g. packaging technology, shelf life extension technology,and transportation technology (Table V).
Second, this research, throughout cross-case analysis, tested four key patterns ofFSC geographical dispersion: local SC, supply-proximity SC, market-proximity SC, andinternational SC. The key characteristics of each pattern were also explored andexamined. Location decisions of FSC are either based on the limitations of four keyinfluential factors or the manufacturing firm’s objective itself to serve such marketlocations. It is also found that product value and value density, which partly determinedecisions on FSC geographical dispersion, are relative terms and could vary acrossdifferent geographical locations. One product may be perceived as a low value productin Thailand but could be perceived as a high value product in other markets. Productuniqueness also plays an important role in determining this value.
The key findings of this research can support SC managers and researchers in FSCto better understand how FSC is geographically dispersed. Theoretically, this paperfulfilled the research gaps reviewed in the literature section. First, the research findingsdemonstrated five key drivers of FSC geographical dispersion covering both insupply-side and demand-side, which lacked in the existing literature. Second, theresearch findings combined the linkages between five key drivers and theirimplications on FSC geographical dispersion as well as demonstrated the key patternsof FSC geographical dispersion, which had never been done before in the existingliterature. Third, researchers would have a rich tool to support a comparative study ofSC geographical dispersion and their key characteristics. Practically, the findings ofthis research as a whole allow SC managers to better understand their own SCsthrough identifying their key drivers, their impacts (negative or positive), and theirimplications on FSC geographical location decisions. Practitioners can better observethe key enablers and barriers in their own SC internationalisation with greater clarityas well as use them as the key decision criteria on location decisions in SC. Thesewould also help manufacturing firms to best fit their new product design with theirexisting SC locations. More studies are still needed to ensure more reliable means ofcapturing the patterns of SC internationalisation as well as their key drivers. However,as this research takes the manufacturing firm’s view to simplify the research context,future work will take into consideration the manufacturing location decisions,
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altogether with supply-side and demand-side decisions to provide more comprehensiveguidelines for SC managers and researchers on this matter.
Despite conducting 21 cases, the findings reported in this research are stillpreliminary and they are based on a relatively small number of cases. More case evidencefrom a broader range of food product market and supply items, particularly the ones thatmight have significantly different patterns of FSC geographical dispersions as well asthe consideration of additional influential factors such as labour movement betweendeveloping countries themselves, increase of currencies and labour costs, would enrichthe framework and key findings of this research. For example, the fluctuation in currencyexchange rates and increasing labour costs would force case companies to reconsider iftheir locations of suppliers, manufacturing sites, and customers are still cost effective.Different strategies of the case companies and their implications on FSC locationdecisions should also be further investigated as well as the use of cases outside Thailand.More cases in other geographical locations may provide more diversified key influentialfactors, which would enhance the comprehensiveness and validity of the research.
Notes
1. Data reduction is a way to analyse data through writing summaries, coding, teasing out,themes, making clusters or partitions. It simplifies the data complexity and transforms datainto written-up field notes (Miles and Huberman, 1994).
2. Data display is a way to analyse data through many types of matrices, graphs, charts, andnetworks. It has a clear implication on data reduction itself (Miles and Huberman, 1994).
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About the authorsPichawadee Kittipanya-ngam is a Doctoral Researcher at the Institute for Manufacturing (IfM),University of Cambridge, UK. Her research focuses on supply chain strategy, context, andconfiguration in the food industry, particularly in the UK and Thailand. Her practical experienceis in food supply chain operational management in food manufacturing and the food serviceindustry. Pichawadee Kittipanya-ngam is the corresponding author and can be contacted at:[email protected]
Dr Yongjiang Shi is a University Lecturer and Research Director in the Centre forInternational Manufacturing where he is working on an EPSRC project – Global ManufacturingVirtual Network (GMVN). He joined the Cambridge Manufacturing Research Group in 1994.He gained his PhD at Cambridge for work on international manufacturing networkconfigurations and has taken a leading role in the conceptualisation and delivery of thecentre’s research programme.
Professor Mike J. Gregory is Head of the Manufacturing and Management Division of theUniversity Engineering Department and of the Institute for Manufacturing (IfM). Mike Gregory’swork is mainly on the interface between engineering, management and policy and he is activelyinvolved in the institute’s work in these areas. He is heavily engaged in ensuring closeuniversity/industry links and directs, with senior colleagues, the Institute’s InnovativeManufacturing Research Centre. His external activities include a Chairman of UKManufacturing Professors Forum, a Member Executive Committee of ESRC AdvancedInstitute of Management, and a Chairman of the General Engineering Panel of the 2001 UKResearch Assessment Exercise.
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