supply chain planning for a multinational enterprise: a performance analysis case study

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Supply chain planning for amultinational enterprise: aperformance analysis case studyBehnam Fahimnia a , Edward Parkinson b , Nikolaos P. Rachaniotis c

, Zubair Mohamed d & ark Goh ea UTS Business School, Management Discipline Group, University ofTechnology Sydney , Sydney , Australiab School of Computer Science, The University of Adelaide ,Adelaide , Australiac School of Social Sciences, Hellenic Open University , Patra ,Greeced Department of Management , Western Kentucky University ,Bowling Green , KY , USAe The Logistics Institute – Asia Pacific, National University ofSingapore , SingaporePublished online: 05 Jul 2013.

To cite this article: Behnam Fahimnia , Edward Parkinson , Nikolaos P. Rachaniotis , ZubairMohamed & ark Goh (2013) Supply chain planning for a multinational enterprise: a performanceanalysis case study, International Journal of Logistics Research and Applications: A Leading Journalof Supply Chain Management, 16:5, 349-366, DOI: 10.1080/13675567.2013.813445

To link to this article: http://dx.doi.org/10.1080/13675567.2013.813445

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International Journal of Logistics: Research and Applications, 2013Vol. 16, No. 5, 349–366, http://dx.doi.org/10.1080/13675567.2013.813445

Supply chain planning for a multinational enterprise:a performance analysis case study

Behnam Fahimniaa*, Edward Parkinsonb, Nikolaos P. Rachaniotisc, Zubair Mohamedd

and Mark Gohe

aUTS Business School, Management Discipline Group, University of Technology Sydney, Sydney,Australia; bSchool of Computer Science, The University of Adelaide, Adelaide, Australia; cSchool of Social

Sciences, Hellenic Open University, Patra, Greece; dDepartment of Management, Western KentuckyUniversity, Bowling Green, KY, USA; eThe Logistics Institute – Asia Pacific, National University of

Singapore, Singapore

(Received 27 August 2012; final version received 5 June 2013)

Motivated by a real world supply chain planning problem, this paper examines the impacts of exchange ratevolatility and different shipping pricing structures on the overall performance for a multinational enterprise(MNEs). A unified optimisation model is developed that minimises the major costs incurred in an MNE,incorporating important factors such as manufacturing localisation, exchange rates, and quantity-basedshipping pricing structures offered by transport vendors. Numerical results from the model implementationshow that (a) shipping pricing structures can have pronounced impacts on manufacturing/assembly anddistribution decisions both in the parent and host countries, (b) different shipping pricing structures mightnot be equally profitable as the cost benefits may only offset the losses from a discriminatory tariff orsubstantial storage expenses, and (c) increased manufacturing in the host country can be decelerated to alarge extent by the localisation policy in the parent country and tight transport and storage limitations.

Keywords: supply chain planning; multinational enterprise; economies of scale; exchange rate volatility;manufacturing localisation; case study.

1. Introduction

Advances in technologies and willingness of countries to embrace global commerce are acceler-ating the pace of global trade. To respond to the growing global demand, firms are exporting theirgoods and services, forging alliances with foreign affiliates, and expanding their own operationsoverseas (Gammeltoft, Filatotchev, and Hobdari 2012; Ge and Ding 2011). Companies conduct-ing business transactions across national borders are called multinational enterprises (MNEs).An MNE operates in more than one country with its manufacturing plants, assembly plants,warehouses, and end-users positioned in various geographical locations worldwide. The UnitedNations has identified over 60,000 MNEs worldwide, among which the 500 firms featured in theFortune Global 500 account for about 50% of global trade (both exports and imports) and over80% of the world’s foreign direct investment (Rugman 2005). Foreign direct investment repre-sents the flow of investment funds from home country to host countries where the production ofgoods or service provision takes place.

*Corresponding author. Email: [email protected]

© 2013 Taylor & Francis

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350 B. Fahimnia et al.

Corporations typically maximise their profit through increased revenue and reduced operat-ing cost (Fahimnia, Luong, and Marian 2012). The latter is attained through the optimisation ofresources such as labour, raw material, manufacturing, transportation, and sourcing along withseeking to mitigate exchange rate volatility (ExRV) and political risks (Fahimnia, Luong, andMarian 2008; Larson and Halldorsson 2004). An MNE derives competitive advantage by identify-ing world markets for its products and developing effective manufacturing and logistics strategiesto support its global marketing strategy (Christopher 2005).

The purpose of this research, based upon a real world case study, is to develop a supply chain(SC) planning model for an MNE. The case company, headquartered inAustralia, is engaged in theproduction and distribution of fitness equipment and home gyms in China and Australia. A unifiedoptimisation model is developed to simultaneously minimise the major costs of the MNE includingproduction, assembly and transportation costs, and tariffs. The model incorporates several factorsthat are critical for SCs operating under international trade regulations, such as manufacturinglocalisation as well as the economies of scale (EoS) in transport (i.e. shipping pricing structures).

The paper is organised as follows. Section 2 reviews the extant literature of SC planning forMNEs. Section 3 presents the unified optimisation model. Section 4 concerns the implementationof the proposed model to solve the real world SC planning problem that motivated this research.Section 5 analyses the obtained numerical results and highlights the practical insights and man-agerial implications. Concluding remarks along with directions for future research are presentedin Section 6.

2. Literature review

In the business world of today, companies are no longer limited by borders and hence need aglobal perspective to prosper. The challenge for companies pursuing international manufacturingand distribution strategies is to identify the most appropriate production and distribution strategiesthat minimises the overall SC cost (Sweeney and Park 2010). Companies pursuing internationaltrade must consider the current trends in the global economy such as the integration of maturemarkets, adoption of common currencies, growth potential in the emerging markets, regionaltrade agreements, and the gradual shift towards lowering and abolishing tariffs and quotas ininternational trade (Choi, Narasimhan, and Kim 2012; Zhang, Huang, and Liu 2011).

Many firms have relocated facilities to where they can gain the most cost advantage (Peck 2006;Tang 2006). However, the transportation of goods plays an important role in reducing costs andsatisfying demand in a timely manner. Domestic firms for years have transformed their SCs andhave attained double-digit reductions in cost per unit handled in transportation (Heaney 2011).Many of these domestic SCs have obviously gone global evidenced by the increasing number ofshipments flowing across borders as well as the increasing imbalance between supply and demand.However, the international dimension brings ample complexity due to longer lead-times, lead-time variability, as well as increased number of suppliers, partners, carriers, logistics channels, andend-users .(Manuj and Sahin 2011; Susarla and Karimi 2012).As a result, a very pressing businessissue facing SC executives is the impact of increasing SC complexity combined with rising SupplyChain Management (SCM) costs (Heaney 2011; Melacini, Creazza, and Perotti 2011).

In a survey of 141 firms, Min and Galle (1991) found that the advantages achieved throughgoing global are negated by an increase in transportation costs and payment systems. Looking atthe international purchasing strategies of these firms, the survey shows that while the firms havedemonstrated improvements in quality and cost dimensions, they still have to balance the achievedbenefits against the transport and distribution costs. A similar result was obtained in a more recentsurvey study of 56 companies reporting that many benefits derived from the optimisation of adomestic SC are negated by a poorly performing global SC (Heaney 2011).

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International Journal of Logistics: Research and Applications 351

Despite the vast body of knowledge available on domestic facility location and distributionstrategies in the context of SCM, no discrete literature exists on investigating the actual charac-teristics and performance behaviours of today’s global SCs exposed to volatile exchange rates,inflation, and transport outsourcing issues. For example, Mohamed (1999) and Bhutta et al. (2003)present mathematical models to study the impact of exchange rates and investment decisions onMNEs. None of these studies solves real world case problems. At the local level, Kirca and Kok-salan (1996) consider a production–distribution problem for a domestic facility and find that it isdifficult to show profitability in a high inflationary condition and that work-in-process inventorycan significantly affect a firm’s profitability. The study again fails to implement the developedmodel in a multinational context.

A second drawback in the existing literature of SC modelling is that, in their majority, thepublished models do not consider a realistic set of real world characteristics in an aggregatedsense (Fahimnia et al. 2013). Such characteristics include, but not limited to, production andstorage capacity, multiple transportation modes and routes, carrier capacity, and shipping coststructure. Yan, Yu, and Edwin Cheng (2003) and Kazemi, Fazel Zarandi, and Moattar Husseini(2009) consider the production capacity for manufacturing of goods at each facility of a domesticSC. Wu (2008) studies a firm’s decision for outsourcing transportation (i.e. the use of self-ownedfleet vs. hiring outside vendors). Even though Wu models a two-country SC, neither the differentshipping pricing structure nor the explicit inclusion of exchange rates is investigated.

One primary concern of SCM is the optimisation of the production–distribution network thatmust be accomplished in an integrated fashion (Fahimnia, Molaei, and Ebrahimi 2011; Kazemi,Fazel Zarandi, and Moattar Husseini 2009). For an MNE, the integration should not only considerthe incorporation of suppliers, transport providers, production and assembly facilities, outboundlogistics, distributors and retailers, but it should also explicitly address the impacts of exchangerates, tariffs, quotas, regional regulations, and financing of operations on the global SC integration.Such inclusions usually result in complex, and in many cases intractable, optimisation models(Fahimnia, Ebrahimi, and Molaei 2012). This has made SCM scholars considering only a subsetof real world cost elements when developing SC planning and optimisation models (Abdinnour-Helm 1999; Chan, Chung, and Wadhwa 2005; Jayaraman, Srivastava, and Benton 1999; Meixelland Gargeya 2005; Wilson 1995).

In terms of modelling approach and solution method, SC planning and optimisation have beenaccomplished using a wide range of exact and heuristic techniques including linear program-ming (Wilson 1995), integer programming (Dhaenens-Flipo 2000), mixed-integer programming(Mohamed 1999; Vidal and Goetschalckx 1997), multiobjective integer programming and fuzzyalgorithms (Petrovic, Roy, and Petrovic 1998), and genetic algorithms (Abdinnour-Helm 1999;Chan and Chung 2005; Chan, Chung, and Wadhwa. 2005; Jayaraman, Srivastava, and Benton1999; Kazemi, Fazel Zarandi, and Moattar Husseini 2009). In this paper, we develop a sim-ple, yet useful, Mixed Integer Linear Programming (MILP) model that explicitly incorporatesimportant characteristics of an MNE. These characteristics include, but not limited to, aggregateproduction/assembly capacity limits in local and foreign facilities, storage capacity of local andforeign warehouses, exchange rates, shipping pricing structures, manufacturing localisation, andtariffs.

3. Model development

Figure 1 shows the schematic view of the supply network under investigation. Multiple sub-assembly parts (i) are produced in different manufacturing sites (m) to supply the assembly plants(a) where the finished products (j) are formed. Finished products are distributed to the local andforeign warehouses (w) and from there to the end-users (e) in various geographical locations.

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Figure 1. Scope of the proposed multinational SC planning problem.

The model aims to determine the tactical planning decision, including production, assembly, anddistribution allocation strategies for the next planning horizon of T (comprising t time periods)to minimise the overall SC cost, under the following key assumptions:

(1) The variety of products and their constituting parts are assumed to be known.(2) Strategic decisions (i.e. SC configuration) are outside the scope of this research. Therefore,

the number and location of plants, warehouses, and end-users are assumed to be known.(3) Demand is deterministic and aggregate demand for all product types at local and foreign

markets are known for all periods.(4) Production, assembly, transport, and storage capacity limits are deterministic and constant.

Input parameters, continuous decision variables, and binary variables used for model formula-tion are given in Tables 1–3, respectively.

Table 1. Model parameters.

I The number of sub-assembly parts i, i = 1, . . . , I

J The number of product types j, j = 1, . . . , J

M The number of local and foreign manufacturing plants m, m = 1, . . . , M

A The number of local and foreign assembly plants a, a = 1, . . . , A

W The number of local and foreign warehouses w, w = 1, . . . , W

E The number of end-users e, e = 1, . . . , E

T The planning horizon comprising t time periods, t = 1, . . . , T

Djet Demand for j in e in t

PCimt Production cost of i at m in t

ACjat Assembly cost of j at a in t

Omt Fixed cost of opening and operating m in t

O′at Fixed cost of opening and operating a in t

(Continued).

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Table 1. Continued.

O′′wt Fixed cost of opening and operating w in t

αje Localisation rate (%) for j in e

βij Units of i required per units of j (BOM)

γij Product/part correlation constant (= 1 if i is a component used in j; 0 otherwise)

λme Plant/end-user correlation constant (= 1 if m and e are located in the same country; 0 else)HTimat Highest rate (for lowest quantity) transportation cost for shipping i from m to a in t

HT′jawt Highest rate (for lowest quantity) transportation cost for shipping j from a to w in t

HT′′jwet Highest rate (for lowest quantity) transportation cost for shipping j from w to e in t

Gimat EoS coefficient for the shipment of i from m to a in t

G′jawt EoS coefficient for the shipment of j from a to w in t

G′′jwet EoS coefficient for the shipment of j from w to e in t

LXimat Lower-bound quantity for EoS transportation for shipping i from m to a in t

UXimat Upper-bound quantity for EoS transportation for shipping i from m to a in t

LYjawt Lower-bound quantity for EoS transportation for shipping j from a to w in t

UYjawt Upper-bound quantity for EoS transportation for shipping j from a to w in t

LZjwet Lower-bound quantity for EoS transportation for shipping j from w to e in t

UZjwet Upper-bound quantity for EoS transportation for shipping j from w to e in t

GLimat Lower discount rate (medium EoS) for shipping i f from m to a in t

GHimat Higher discount rate (large EoS) for shipping i from m to a in t

GL′jawt Lower discount rate (medium EoS) for shipping j from a to w in t

GH ′jawt Higher discount rate (large EoS) for shipping j from a to w in t

GL′′jwet Lower discount rate (medium EoS) for shipping j from w to e in t

GH ′′jwet Higher discount rate (large EoS) for shipping j from w to e in t

TCimat Tariff cost of i from manufacturing m to a in t

TC′jawt Tariff cost of j from a to w in t

TC′′jwet Tariff cost of j from w to e in t

EXmt Exchange rate of currency of the country where m is located in t

EX′at Exchange rate of currency of the country where a is located in t

EX′′wt Exchange rate of currency of the country where w is located in t

SCjwt Storage cost for holding j in w during t

PMaximt Maximum capacity of m for producing i in t

QMaxjat Maximum capacity of a for assembling j in t

XMinimat Minimum allowed distribution quantity for i from m to a in t

XMaximat Maximum distribution capacity for i from m to a in t

YMinjawt Minimum allowed distribution quantity for j from a to w in t

YMaxjawt Maximum distribution capacity for j from a to w in t

ZMinjwet Minimum allowed distribution quantity for j from w to e in t

ZMaxjwet Maximum distribution capacity for j from w to e in t

SMaxjwt Maximum holding capacity of w for j in t

Table 2. Continuous decision variables.

Pimt Quantity of i produced at m in tQjat Quantity of j assembled at a in tXimat Quantity of i shipped from m to a during tYjawt Quantity of j shipped from a to w during tZjwet Quantity of j shipped from w to e during tSjwt Amount of j stored at w at the end of t

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Table 3. Binary decision variables.

Fmt ={

1 If manufacturing plant m operates in period t0 Otherwise

F ′at =

{1 If assembly plant a operates in period t0 Otherwise

F ′′wt =

{1 If distribution centre w is open in period t0 Otherwise

Table 4. Auxiliary variables.

Binary auxiliary variables Aimat , A′imat , A′′

imat , Bjawt , B′jawt , B′′

jawt , Cjwet , C′jwet , C′′

jwet

Continuous auxiliary variables Kimat , K ′imat , K ′′

imat , Ljawt , L′jawt , L′′

jawt , Njwet , N ′jwet , N ′′

jwet

We also define a set of auxiliary variables (Table 4) that is used for formulating the shippingpricing structures (EoS in transport).

Using the above parameters and decision variables, the objective function of the MILP modelis presented in three segments (V1, V2, and V3). Segment 1 (Equation (1)) formulates productionand assembly costs in the local and foreign plants. Terms 1 and 2 of Equation (1) representthe fixed costs of opening and operating manufacturing and assembly plants, respectively. Themanufacturing and assembly costs are expressed in Terms 3 and 4, respectively

V1 =∑

m

∑t

(Omt · Fmt)EXmt +∑

a

∑t

(O′at · F ′

at)EX′at

+∑

i

∑m

∑t

(Pimt · PCimt)EXmt +∑

j

∑a

∑t

(Qjat · ACjat)EX′at . (1)

Segment 2 of the objective function (Equation (2)) is concerned with transportation and storagecosts, a three-stage shipment structure (EoS in transport). In Equation (2), Term 1 representsthe opening and operating costs at warehouses. Terms 2–4 express the transportation costs fromthe manufacturers to the assembly plants (Term 2), from the assembly plants to the warehouses(Term 3), and from the warehouses to the end-users (Term 4). Term 5 represents the storage costat warehouses

V2 =∑

w

∑t

(O′′wt · F ′′

wt)EX′′wt +

∑i

∑m

∑a

∑t

(Ximat · HTimat)GimatEXmt

+∑

j

∑a

∑w

∑t

(YjawtHT′jawt)G

′jawtEX′

at +∑

j

∑w

∑e

∑t

(ZjwetHT′′jwet)G

′′jwetEX′′

wt

+∑

j

∑w

∑t

(SjwtSCjwt)EX′′wt . (2)

The unit cost of transportation is a function of the shipment lot size in the sense that trans-portation costs are discounted when larger quantities are transported (EoS rule). The EoScoefficients (Gimat , G′

jawt , and G′′jwet) in Equation (2) are defined in Equations (3)–(5). It should

be noted that GLimat , GHimat , GL′jawt , GH ′

jawt , GL′′jwet , and GH ′′

jwet are set equal to 1 when EoS is

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unavailable

Gimat =

⎧⎪⎨⎪⎩

= 1 Ximat < LXimat

= GLimat LXimat ≤ Ximat

= GHimat UXimat ≤ Ximat

< UXimat ∀i, m, a, t, (3)

G′jawt =

⎧⎪⎨⎪⎩

= 1 Yjawt < LYjawt

= GL′jawt LYjawt ≤ Yjawt

= GH ′jawt UYjawt ≤ Yjawt

< UYjawt ∀j, a, w, t, (4)

G′′jwet =

⎧⎪⎨⎪⎩

= 1 Zjwet < LZjwet

= GL′′jwet LZjwet ≤ Zjwet

= GH ′′jwet UZjwet ≤ Zjwet

< UZjwet ∀j, w, e, t. (5)

Segment 3 of the objective function (Equation (6)) expresses the tariff costs (defined as a percent-age of product value). Tariff cost for the transportation of parts from manufacturers to assemblyplants is represented in Term 1 of Equation (6). Terms 2 and 3 express the tariffs for the shipmentof finished products from the assembly plants to the warehouses and from the warehouses to theend-users, respectively. Tariffs are obviously equal to zero for local transports

V3 =∑

i

∑m

∑a

∑t

(XimatTCimat)EXmt +∑

j

∑a

∑w

∑t

(YjawtTC′jawt)EX′

at

+∑

j

∑w

∑e

∑t

(ZjwetTC′′jwet)EX′′

wt . (6)

The summation of Equations (1), (2), and (6) forms the global objective function formulationpresented in Equation (7). The goal in the proposed model is to minimise the value of the globalobjective function presented in Equation (2)

V = V1 + V2 + V3. (7)

Using the auxiliary decision variables outlined in Table 4, Equations (8)–(13) present theformulation of the proposed three-stage EoS in transport

Gimat = Aimat + GLimatA′imat + GHimatA

′′imat ∀i, m, a, t,

G′jawt = Bjawt + GL′

jawtB′jawt + GH ′

jawtB′′jawt ∀j, a, w, t,

G′′jwet = Cjwet + GL′′

jwetC′jwet + GH ′′

jwetC′′jwet ∀j, w, e, t, (8)

in which

Aimat + A′imat + A′′

imat = 1 ∀i, m, a, t,

Bjawt + B′jawt + B′′

jawt = 1 ∀j, a, w, t,

Cjwet + C′jwet + C′′

jwet = 1 ∀j, w, e, t. (9)

Assuming that

Ximat = Kimat + K ′imat + K ′′

imat ∀i, m, a, t,

Yjawt = Ljawt + L′jawt + L′′

jawt ∀j, a, w, t,

Zjwet = Njwet + N ′jwet + N ′′

jwet ∈ ∀j, w, e, t, (10)

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then

0 ≤ Kimat < LXimatAimat ∀i, m, a, t,

LXimatA′imat ≤ K ′

imat < UXimatA′imat ∀i, m, a, t,

UXimatA′′imat ≤ K ′′

imat < MA′′imat ∀i, m, a, t, (11)

0 ≤ Ljawt < LYjawtBjawt ∀j, a, w, t,

LYjawtB′jawt ≤ L′

jawt < UYjawtB′jawt ∀j, a, w, t,

UYjawtB′′jawt ≤ L′′

jawt < MB′′jawt ∀j, a, w, t, (12)

0 ≤ Njwet < LZjwetCjwet ∀j, w, e, t,

LZjwetC′jwet ≤ N ′

jwet < UZjwetC′jwet ∀j, w, e, t,

UZjwetC′′jwet ≤ N ′′

jwet < MC′′jwet ∀j, w, e, t. (13)

The production, assembly, transport, and storage capacity constraints for the proposed model arepresented in Equations (14)–(28). Equation (14) expresses the localisation constraint for eachproduct type in each country. The degree of localisation (i.e. the fraction of parts and productswhich must be produced locally as per the local government restrictions) may vary in differentcountries and from one industry to another

∑m

∑t

Pimtγijλme ≥(∑

t

Djet

)αje.βij ∀i, j, e. (14)

Complete satisfaction of market demand at the end of the planning horizon is expressed inEquation (15). The quantity of every product type assembled during the entire planning horizonis set equal to the market demand for that product

∑a

∑t

Qjat =(∑

e

∑t

Djet

)∀j. (15)

Production and assembly capacity constraints are given in Equations (16) and (17), respectively

Pimt ≤ PMaximt ∀i, m, t, (16)

Qjat ≤ QMaxjat ∀j, a, t. (17)

The distribution capacity constraints at manufacturing plants, assembly plants and warehousesare expressed in Equations (18)–(20), respectively

XMinimat ≤ Ximat ≤ XMax

imat ∀i, m, a, t, (18)

Y Minjawt ≤ Yjawt ≤ Y Max

jawt ∀j, a, w, t, (19)

ZMinjwet ≤ Zjwet ≤ ZMax

jwet ∀j, w, e, t. (20)

Equation (21) imposes the storage capacity constraint at the warehouses

Sjwt ≤ SMaxjwt ∀j, w, t. (21)

Inventory balance at the warehouses is formulated in Equation (22)

Sjw(t−1) +∑

a

Yjawt =∑

e

Zjwet + Sjwt ∀j, w, t. (22)

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Equation (23) ensures the complete satisfaction of demand at each period∑w

Zjwet = Djet ∀j, e, t. (23)

Restrictions on the values of the continuous decision variables are given in Equations (24)–(29),where L is a large positive number

0 ≤ Pimt ≤ FmtL ∀i, m, t, (24)

0 ≤ Qjat ≤ F ′wtL ∀j, a, t, (25)

0 ≤ Ximat ≤ FmtL and 0 ≤ Ximat ≤ F ′atL ∀i, m, a, t, (26)

0 ≤ Yjawt ≤ F ′atL and 0 ≤ Yjawt ≤ F ′′

wtL ∀j, a, w, t, (27)

0 ≤ Zjwet ≤ F ′′wtL ∀j, w, e, t, (28)

0 ≤ Sjwt ∀j, w, t. (29)

The resulting MILP model has T [JA + IM(1 + A) + JW(A + E + 1)] continuous variables and atotal of T(M + A + W) binary variables. The number of constraints in this model is J(1 + IE) +T [JW(3 + 3E + 3A) + IM(2 + 3A) + J(E + 2A)].

4. Case study implementation

The case company (hereafter referred to as AFA) is an MNE headquartered in Australia. AFAis engaged in the production and distribution of fitness equipment and home gyms in Australiaand China. There are two production plants and two assembly plants in each country. A total offive warehouses (three in China and two in Australia) supply nine end-users, four of which arelocated onshore. A number of product types are produced by AFA, ranging from the old-fashionedweight benches to the latest stack machines (i.e. single-station and multistation machines) andplate-loaded machines (e.g. Smith machines and power cages). In this case study, we focus onanalysing the production and distribution strategies for AFA’s 3 main products including a heavyduty weight bench (comprising 6 parts), a single-station stack machine (comprising 8 parts) anda heavy duty power cage (comprising 10 parts). The planning horizon is one year comprising 12one-month periods.

With three product types (J = 3), an average of eight subassembly parts (I = 8), two manu-facturing plants (M = 2), two assembly plants (A = 2), five warehouses (W = 5), nine end-users(E = 9) and twelve time periods (T = 12), the proposed MILP model for this case study willhave 2808 continuous variables, 108 binary variables, and 8721 constraints. The proposed model

Table 5. Available shipping pricing structures (EoS offers) for AFA in 2012.

EoS 1 EoS 2 EoS 3 EoS 4 EoS 5 EoS 6 EoS 7 EoS 8 EoS 9 EoS 10 EoS 11 EoS 12 EoS 13

Lower-boundquantity

∞ 80 80 80 90 90 90 100 100 100 120 120 120

Upper-boundquantity

∞ 350 350 350 300 300 300 230 230 230 190 190 190

Lower discountrate

0.7 0.85 0.9 0.8 0.85 0.9 0.8 0.85 0.9 0.8 0.85 0.9 0.8

Upper discountrate

0.7 0.6 0.55 0.65 0.6 0.55 0.65 0.6 0.55 0.65 0.6 0.55 0.65

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Table 6. Numerical results for the shipping pricing structures.

EoS scenariosSC cost components(AUD) 1 2 3 4 5 6 7 8 9 10 11 12 13

Manufacturing 1,054,941 1,036,498 1,040,155 1,055,207 1,050,003 1,041,935 1,057,932 1,034,894 1,010,763 1,051,465 992,745 968,678 1,035,804Assembly 418,023 393,443 392,504 411,430 408,167 401,055 412,739 401,748 381,403 413,807 370,024 348,463 402,168Transportation 679,137 766,732 756,194 721,902 667,210 653,557 679,561 673,286 679,477 668,015 847,113 948,725 713,009Storage 575,408 583,959 588,458 575,476 575,541 581,750 572,338 559,147 567,544 568,391 398,800 227,539 539,417Tariff 50,220 31,420 30,535 48,610 41,580 35,690 46,855 36,925 20,450 46,795 68,823 106,405 47,540Overall SC costs 2,777,728 2,812,051 2,807,846 2,812,625 2,742,500 2,713,986 2,769,424 2,705,999 2,659,637 2,748,472 2,677,505 2,599,811 2,737,938

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(presented in Section 3) was coded in CPLEX optimiser (developed by IBM ILOG Optimisa-tion Studio) to solve the integrated production–distribution planning problem that motivated thisresearch. Numerical results from the model implementation are documented in two scenariosreflecting the model attributes for various shipping pricing structures (i.e. EoS discounts offeredby the transport vendors) as well as the ExRV.

4.1. Numerical results for EoS scenarios (shipping pricing structures)

Each year, AFA receives different EoS offers from the available transport providers through whichit develops the SC shipment structures for the next planning horizon. AFA has a high bargainingpower that enables the company to maximise the benefit that can be gained from the availableEoS offers. For the next planning horizon (2012–2013), AFA will have 13 primary shipping offersin terms of the EoS structures. Table 5 outlines the available shipment structures offered by theexternal transport vendors. Lower-bound quantities populate LXimat , LYjawt , and LZjwet (Table 1).Similarly, UXimat , UYjawt , and UZjwet are filled with upper-bound values provided in Table 5.The lower and upper discount rates indicate at what rate the transport costs are discounted forthe lower- and upper-bound quantities, respectively. GLimat , GL′

jawt , and GL′′jwet are populated by

the lower discount rates provided in Table 5 and upper discount rates are allocated to GHimat ,GH ′

jawt , and GH ′′jwet . It is assumed that a uniform shipping pricing structure is used in all routes

for the transportation of products from the manufacturers to the end-users. Hence, identical dis-count rates and quantities are used for the shipment of products from the manufacturers to the

Figure 2. Comparison of overall SC cost for the shipping pricing structures (EoS scenarios).

Table 7. ExRV scenarios for AUD/CNY.

Scenario Description

ExRV 1 Flat base rate of AUD against CNYExRV 2 15% rise in AUDExRV 3 30% rise in AUDExRV 4 45% rise in AUDExRV 5 Steady rise in AUD (0 to +45%)ExRV 6 Normal distribution of AUD (0 to +45%)ExRV 7 10% drop in AUDExRV 8 20% drop in AUDExRV 9 30% drop in AUDExRV 10 Steady drop in AUD (0 to −30%)ExRV 11 Normal distribution of AUD (0 to −30%)ExRV 12 Steady rise in AUD (0 to +45%) followed by steady drop in AUD (0 to −30%)ExRV 13 Steady drop in AUD (0 to −30%) followed by steady rise in AUD (0 to +45%)

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Table 8. Numerical results for the ExRV scenarios.

ExRV scenariosSC cost components(AUD) 1 2 3 4 5 6 7 8 9 10 11 12 13

Manufacturing 1,051,465 958,859 841,318 775,909 887,704 904,626 1,084,753 1,122,680 1,151,301 1,051,465 1,081,337 932,420 1,022,622Assembly 413,807 363,416 308,827 282,785 327,450 341,513 428,021 446,536 462,467 413,807 423,640 358,832 393,360Transportation 668,015 774,504 989,859 1,022,244 928,876 830,327 635,597 613,014 604,767 668,015 653,778 778,527 729,112Storage 568,391 564,276 444,340 411,422 475,734 547,574 591,363 590,785 604,769 570,959 589,407 556,497 550,620Tariff 46,795 18,184 21,340 27,790 18,785 8030 54,155 69,400 73,120 46,795 56,410 20,780 51,840Overall SC costs 2,748,472 2,679,238 2,605,683 2,520,151 2,638,550 2,632,069 2,793,889 2,842,414 2,896,424 2,751,041 2,804,571 2,647,056 2,747,554

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assembly plants, from the assembly plants to the warehouses, and from the warehouses to theend-users.

‘EoS1’ in Table 5 specifies the unit shipping cost for different transport quantities (i.e. ‘EoS1’is the base cost for no discounted quantity). We classify the available shipping options into 13scenarios to enable the comparison of different shipping pricing structures as the selection of eachEoS offer may affect the overall SC performance differently. It should also be noted that the actualdetailed offers presented by the external transport providers have been slightly different from thesimplified data given in Table 5.

The first stage of the model implementation is to determine how AFA can benefit optimallyfrom the available EoS offers. The objective, thus, is to determine the transport offer among aset of given EoS shipment choices that minimises the total SC cost. The model determines theoptimum portfolio of transport providers which may include the use of local and foreign transportproviders exclusively or in combination. The model outputs are recorded for the 13 transportoffers outlined in Table 5. The numerical results are presented in Table 6 and visually depicted inFigure 2. Table 6 shows that the overall SC cost for each scenario includes the manufacturing andassembly costs, transportation and storage costs and tariffs. The transportation costs and tariffsare further disaggregated into their constituting components including the costs incurred for theshipment of products from the manufacturers to the assembly plants, from the assembly plants tothe warehouses, and from the warehouses to the end-users.

4.2. Numerical results for ExRV scenarios

The second stage of the model implementation seeks to determine the impact of ExRV on theSC performance. Recent fluctuation in the value of Australian dollar (AUD) has received con-siderable attention in the literature and several models have been proposed to predict its trendsand behaviours (Sanidas 2005). We demonstrate how changes in the rate of AUD against ChineseYuan (CNY) will affect the overall SC performance as well as the production and distributionallocation strategies in AFA.

Despite the general stability of Australia’s economy and political system, the history of theAUD fluctuations against CNY has shown relatively high volatility. For the past three years, thecomparative rate of the AUD against CNY has varied from −30% to +45% compared to itscurrent rate of 6.57 (i.e. one AUD is equal to 6.57 CNY). As such, AFA is keen to understandhow various ExRV scenarios can affect its production allocation and distribution strategies for thenext planning horizon. We develop 13 ExRV scenarios expressing the projected fluctuations inthe AUD against CNY for the next planning horizon. Table 7 provides the details of the proposedExRVs.

Figure 3. Comparison of overall SC cost for the ExRV scenarios.

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The model was utilised to monitor the model outputs for each ExRV scenario. Table 8 sum-marises the numerical results. All results are recorded for the currently adopted EoS structure atAFA (i.e. EoS10). Figure 3 compares the overall SC cost for the projected ExRV scenarios. Themodel outputs reflect how the SC cost performance is affected (including production, distribu-tion and storage costs) under different AUD/CNY rate movements, when the AUD versus CNYfollows different stochastic patterns. Sensitivity analysis of the numerical results is presented inSection 5.

5. Analysis of numerical results

This section analyses the numerical results obtained from the model implementation for theAFA case problem. AFA is currently adopting the shipping pricing strategy EoS10 (refer toTable 6). For this EoS structure, Figure 4 shows the percentage contribution of SC cost compo-nents. Transportation costs are disaggregated into three components including the costs incurredfor the shipment of products from manufacturers to assembly plants, from assembly plants towarehouses, and from warehouses to end-users. While production and assembly costs are sig-nificant contributors (i.e. 38% and 15%, respectively), distribution costs including transport andstorage expenses represent 45% of the overall SC cost (i.e. 24% and 21%, respectively). Therelatively low tariff rates for 2012 scheduled by the Department of Foreign Affairs and Tradein Australia as well as the existing SC configuration and strategies encouraging less overseastransports would justify the minor contribution of tariffs in the overall SC cost (i.e. only less than2%). The transportation cost is more significant downstream SC (i.e. towards the end-users) suchthat the cost of shipments from the warehouses to the end-users is the costliest among the threetransportation cost components (constituting about 42% of the overall 24% transportation cost).Likewise, more than half of the overall tariff cost is allocated to the transportation of productsfrom the warehouses to the end-users indicating the higher fraction of cross-border shipmentsdownstream SC.

The numerical results for the 13 shipping pricing structures (Table 2 and Figure 6) show thatEoS4 and EoS12 have the worst and best outputs, respectively. EoS1 considers a uniform shippingprice and disregards the cost benefits that may be achieved through discounted EoS structures.AFA’s current shipping pricing structure is EoS10. Figure 5 provides a comparative illustrationfor evaluating the SC performance in the four aforementioned EoS scenarios.

Figure 4. Contribution of SC cost components under the current EoS structure at AFA (EoS 10).

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Figure 5. Comparison of SC cost components for the four selected shipping pricing structures.

Figures 2 and 5 demonstrate that AFA can yield a 5.4% reduction in the overall SC costthrough the implementation of EoS12 compared to the current EoS10. The cost saving is achievedprimarily through effective warehouse/storage management. In this case, the transportation costis increased to a large extent (i.e. about 50% increase) when compared to EoS10. However, EoS12requires significantly less storage space by getting the best use of medium and large shipments(i.e. 90% high-volume shipments), whereas in EoS1, EoS4, and EoS10, a blend of low, mediumand high-volume transports in all three SC echelons is used. This finding is consistent with theresult of the recent survey by Heaney from Aberdeen Group (2012) indicating that warehousemanagement is deemed essential by 94% of participating companies in the survey. According tothe report, effective storage management can result in up to 98% on-time delivery performance,3.7% reduction in labour costs per unit, and 2.6% decrease in the warehouse operating costs.

In the worst case scenario (EoS4), AFA may incur an extra 2.3% increase in the overall SCcost when compared with the current EoS10. This would make EoS4 a less efficient strategycompared to EoS1 (overall SC cost is 1.2% greater in EoS4). Based on the rationality of tariffrates, effectiveness of warehouse storage management and the range of available shipping pricingoffers, not only are the various EoS offers unequally profitable, but also MNEs may or may noteven benefit from the available EoS offers.

From the numerical results of the first stage of this case study (Table 6, Figures 2 and 5), wearrive at following important implications:

• The choice of EoS structure can significantly affects the production and distribution strategiesacross the SC. Not only can this affect the manufacturing and assembly costs, but it also hasimpacts on the transportation, storage and the tariff costs.

• Tariffs can drive the effective use of EoS in transport. The cost benefits achieved through EoScan theoretically only offset the losses from a discriminatory tariff.

• When examining various EoS structures, the optimal SC cost occurs where the manufacturingand assembly costs are at minimum.

Table 8 and Figure 3 show the numerical results for the ExRV scenarios. The corresponding impacton each SC cost component for the ExRV scenarios is illustrated in Figure 6. Not surprisingly,AFA experiences an increase in its manufacturing volume in China when the Australian currencyis stronger (ExRV 2–6). In the extreme case of ExRV 4, where the value of AUD against CNY isincreased by 45%, the overall SC cost is reduced by 8.3% over a planning horizon of 12 months.In this case, manufacturing cost is reduced by 26.2%, while a 56% increase can be observed intransport cost (Figure 7). The two significant influencers are the localisation policies in both thehost and parent countries and the restrictions on storage and transport capacities. Depending onthe upward movement pattern of the AUD (a steady rise or a normal distribution pattern), there

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Figure 6. Comparison of SC cost components for the ExRV scenarios.

Figure 7. Contribution of SC cost components for the three selected ExRV scenarios.

will be up to 4% reduction in the overall SC costs that is half of the extreme case of ExRV 4 (seeExRV 5 and ExRV 6 in Figure 3).

The numerical results in Figures 6 and 7 suggest that a 30% depreciation of the AUD leadsto about 5.4% reduction in the overall SC cost. The tendency to utilise local manufacturing andassembly causes the distribution costs (transport and storage costs) reduced by 2.2%, but inreturn, manufacturing and assembly costs (the major cost contributors) are increased by 10.1%.The findings suggest the insignificant effects of exchange rates on the overall SC costs when AFAfaces a steady decline in the AUD (see ExRV 10 in Figure 3). This may be encouraged by theeffective transport and storage management. Further, comparing ExRV 11, ExRV 12 and ExRV13 in Figure 3 provide insights on how storage availability as well as the existing production–distribution capacity restrictions at AFA can have impacts on the resiliency of the SC against theAUD depreciation.

6. Conclusions

An integrated optimisation model was developed in this paper that minimises the major costsincurred in an MNE considering several factors that are critical for SCs operating under an inter-national trade regime. The proposed model explicitly incorporates aggregate production/assemblyand distribution capacity limits, economies of scale (EoS) in transport, exchange rates, localisationpolicies and tariffs. Model analysis was focused on investigating the impact of two vital factors onthe production and distribution allocation strategies in MNEs, including shipping pricing structureand exchange rate volatility (ExRV).

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The numerical results from the model implementation in a real world case study show thepronounced impacts of shipping pricing structures on manufacturing, assembly and distributiondecisions both in the parent and host countries. It was found that different shipping pricing struc-tures offered by transport providers may not be equally profitable for MNEs since the cost benefitsachieved through EoS may only offset other consequent SC expenses like a discriminatory tariff orsignificant storage expenses. Our analysis on ExRV scenarios shows that increased manufacturingin the host country (generally encouraged by the rise in the currency of the parent country) can becurtailed to a large extent by localisation policy in the parent country as well as tight transport andstorage limitations. Further, a strong and sudden depreciation in the currency of the parent countrycan lead to more significant corresponding impact on the SC cost performance compared to otherstochastic scenarios including steady, normal distribution and blended depreciation patterns.

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Appendix

Glossary

EoS The cost advantages that an enterprise obtains due to increased order size in procurement,production or transportation volume

ExRV The degree to which exchange rate changes over timeMNE An organisation that operates (conducts business transactions) in more than one countrySC An integrated system that forwards products and services from suppliers to the end-usersSCM The process of integrating decisions and utilising the available resources to satisfy the market

demand both efficiently (cost minimisation objective) and effectively (service minimisationobjective)

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supply chain planning for a multinational enterprise: a performance analysis case study

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