Import Duty

Import duties are tariffs paid to the relative government as goods pass into tax areas. Issues of import duties can be divided into the following three dimensions.

1. Charge condition

As situation depicted in Figure 4-1, the charge condition of import duty is that for the same country original flows are in international logistics zones and destination flows are in tax areas, while for different countries destination flows are in tax areas.

Country A

international logistics zone

tax area ^FA

FA

Country B

international logistics zone

tax area

FA

FA

Figure 4-1 Charge condition of import duty 2. Import from low duty rate country

Since duty rates may differ between countries for the same goods, enterprises can reduce costs by importing goods from countries with lower duties. As Figure 4-2 shows, import duties from country ^B ($80=$800*10%) are lower than from country ^A ($400=$800*50%) for the same goods ($800). Consequently, assuming all other conditions are equal, the enterprise can save import duties by importing via the low duty country.

3. Import duty and product forms

Duty rates change with respect to product form, and manufacturers must then determine the most advantageous trade-off between import duty and processing cost. For instance, assuming country ^B requires the finished products in Figure 4-3, manufacturers must decide whether to (1) convert raw materials into finished products in country ^A and then import the finished products to country ^B or (2) import raw materials from country

A and then convert the raw materials into finished products in country ^B.

Figure 4-3 Import duty and product forms 4.2 Value Added Tax

Assessment of value added tax (VAT) is based on the incremental increase in the value of goods from raw materials to finished products. For each transaction, VAT is levied on the increased value of a product after input from previous chain members. Value added tax is generally formulated as follows:

where ^VATcost implies the cost of VAT; ^ps, ^pi, ^po represent the prices associated with sale, input and export, respectively; ^qs, ^qi, ^qo denote the quantities associated with sale, input and export, respectively; ^VAT indicates VAT rate (%) on the value of goods; ^DRT signifies VAT drawback rate (%) on the value of goods.

The first term in Eq. (1) represents sales VAT, and the second term denotes input VAT. Sales VAT can be offset by input VAT. Further, the third term is regarded as export VAT which refers to the VAT imposed on certain exported goods in some countries, e.g. China. Thus, governments adopt strategies for regulating the VAT drawback rate for exports. For example, a country may increase the VAT drawback rate to promote the exporting of certain goods (e.g. mechanical and electrical products) whereas a country may decrease the VAT drawback rate for goods that were restricted to exporting (e.g. natural resources).

As Figure 4-4 illustrates, according to a DC in tax areas or in international logistics zones, the charge condition of sales VAT is that destination flows are in tax areas for the same country, while the charge condition of input VAT is that neither original nor destination flows are in international logistics zones. Further, the charge condition of export VAT

is that, in the same country, original flows are in tax areas, and destination flows are in international logistics zones; for different countries, both original and destination flows are not in international logistics zones.

Consequently, international logistic zones enable enterprises to avoid government regulation strategies of export VAT.

Figure 4-4 Charge condition of VAT

4.3 Corporate Tax

Corporate tax is the tax paid by enterprises on the profit they earn. For tax savings, goods completely manufactured in international logistics zones

are exempt from corporate tax. Nevertheless, manufacturers must identify the most advantageous trade-off between corporate tax and other costs (e.g., import duties).

Products can be manufactured primarily in international logistics zones in order to save corporate tax. As Figure 4-5 illustrates, an example of the tax-saving approach concerning corporate tax for the requirement of finished products in country C . Goods are transformed from raw materials into semi-products in country A, then the semi-products are shipped from country A to country B for further transformation from semi-products into finished products. Finally, the finished products are shipped from country B to country C. Accordingly, the tax-saving route (A
B
C) saves $750 over that of direct shipment (A
C).

Income of enterprise A =$10,000-$6000=$4,000

Furthermore, two possibilities exist with regard to paying corporate tax:

(1) paying corporate tax to multiple governments in the various jurisdictions where the enterprise operates; (2) paying corporate tax to one government based on headquarter location. To facilitate model formulation, this study focuses on situation (1).

CHAPTER 5 MODELING

Given the problem statement, a tax savings model is formulated to derive after-tax solutions that maximize profit in the emerging global production-distribution network. The proposed model is based on models developed by Vidal and Goetschalckx (1998), Vidal and Goetschalckx (2000), Vidal and Goetschalckx (2001), Fandel and Stammen (2004) and Vila et al. (2006). Nevertheless, once the after-tax model considers the emerging global production-distribution network, determining the optimal tax savings route and manufacturing procedure for each order is difficult.

Furthermore, three principal tax factors—import duty, value added tax and corporate tax—are considered simultaneously in the proposed model.

Before formulating the proposed model, basic notations and definitions are presented. This chapter is divided into three sections: (1) notations and definitions, (2) objective function and (3) constraints.

5.1 Notations and Definitions

All the notations and definitions for sets, decision variables and parameters are summarized as follows.

1. Sets

FA Set of internal and external supply chain members.

Herein, FA^V: set of vendors (abbreviated as V ); FA^M: set of manufacturing centers (abbreviated as M ); FA^Ds: set of simple processing DCs (abbreviated as Ds; FA^Dd: set of deep processing DCs (abbreviated as Dd); FA^Dn: set of non-bonded DC (abbreviated as Dn); FA^B: set of brand companies (abbreviated as B); FA^I: set of all internal supply chain members (abbreviated as I); FA^DC: set of all DCs, including deep processing DCs, simple processing DCs and non-bonded DCs (abbreviated as DC); FA_C: set of chain members in international logistics zones (abbreviated as C); FA_T: set of chain members in tax areas (abbreviated as T).

G Set of types of goods. Herein, G^r : set of modular components (abbreviated as r); G^s: set of semi-products (abbreviated as s ); G^p : set of finished products (abbreviated as p).

N Set of countries.

SN Set of simple and deep process product lines. Herein,

SNrs: set of product lines involving transformation of modular components r into semi-products s (abbreviated as rs); SN^rp: set of product lines involving transformation of modular components r into finished products p (abbreviated as rp ); SN^sp: set of product lines involving transformation of semi-products s into finished products p (abbreviated as sp); SN^ss : set of product lines involving simple processing of semi-products s (abbreviated as ss ); SN^pp : set of product lines involving simple processing of finished products p

(abbreviated as pp); SN_I : set of product lines of inbound flow (abbreviated as I ); SN_O: set of product lines of outbound flow (abbreviated as O); SN^R: set of product lines of sum of corresponding modular components (abbreviated as R); SN^S: set of product lines of sum of corresponding semi-products (abbreviated as S); SN^P: set of product lines of sum of corresponding finished products (abbreviated as P).

Ω Set of transportation modes. Here, Ω(θ λ^x, ^y) is the set of

available transportation modes between a given chain member θ in the country x∈N and another given chain member λ in the country y∈N.

2. Decision variables

gotr_{θ αβ}x Binary decision variable indicates whether goods transformation occurs at a given chain member θ in country x∈N when transferring ancestor goods α into descendant goods β.

x f

gt_{θ α} Binary decision variable indicates whether goods α is in progress in a product line f at a given chain member θ in country x∈N.

oi_θx Operating income of a given chain member θ in country

x∈N for the period of analysis (dollar/unit of time).

ord_{θ α}x Number of nodes visited on the transfer path from the origin up to node θ^x for goods α (i.e., the visit number of the θ^xth node).

x ym

qu_{θ λ α} Binary decision variable representing whether goods α∈G

is shipped from a given chain member θ in country x∈N

to another given chain member λ in country y∈N, using transportation mode m∈Ω(θ λ^x, ^y).

3. Parameters

BR_λy Required finished products for a given chain member λ in country y∈N (units of p/unit of time).

BOM_αβ Units of ancestor goods α∈G required to make one unit of descendant goods B∈G (α-units/ β-unit).

BN A big number.

CSF Cycle stock factor (%).

COT_θx Corporate tax rate (%) of country x∈N of a supply chain member θ.

CPRICE_α International contract price of goods α∈G (dollar/unit of goods α )

DRT_{θ α}x Value added tax drawback rate (%) on the value of goods α∈G of country x∈N of supply chain member θ.

x y

DUTY_{θ λ α} Import duty rate (%) on the value of goods α∈G shipped from a given chain member θ in country x∈N to another given chain member λ in country y∈N.

E_θx Exchange rate of country x∈N of supply chain member θ (monetary units of the respective country/dollar).

FIX_θx Fixed cost associated with a given chain member θ in country x∈N (monetary units of country of member θ per unit of time).

x ym

FS_{θ λ} Frequency of goods shipments from a given chain member θ in country x∈N to another given chain member λ in country y∈N , using transportation mode m∈Ω(θ λ^x, ^y)

(units of time ).

H Holding cost ($/($. unit of time)).

IV_{θ α}x Inventory value of goods α∈G, given in monetary units of a given chain member θ in country x∈N per unit of goods α .

NODE Number of DC nodes.

y x

PROC_{λ θ α} Procurement cost (including total cost and taxes) of goods

α∈G shipped from a given chain member λ in country

y∈N to another given chain member θ in country x∈N

(monetary units of country of member λ/unit of goods α ).

PPA_θx Simple processing capacity of finished products in a given chain member θ in country x∈N (finished product units/unit of time).

PPC_θx Simple processing cost of finished products in a given chain member θ in country x∈N (monetary units of country of member θ/unit of finished product).

RSA_θx Capacity to transform goods associated with a given chain member θ in country x∈N for transferring modular components into semi-products (semi-product units/unit of time).

RSC_θx Cost of transforming goods associated with a given chain member θ in country x∈N for transferring modular components into semi-products (monetary units of country of member θ/ unit of semi-products).

RPA_θx Capacity to transform goods associated with a given chain member θ in country x∈N for transferring modular components into finished products (finished product units/unit of time).

RPC_θx Cost of transforming goods associated with a given chain

member θ in country x∈N for transferring modular components into finished products in country x∈N

(monetary units of country of member θ/ unit of finished products).

SPA_θx Capacity to transform goods associated with a given chain member θ in country x∈N for transferring semi-products into finished products (finished product units/unit of time).

SPC_θx Cost of transforming goods associated with a given chain member θ in country x∈N for transferring semi-products into finished products (monetary units of country of member θ/ unit of finished products).

SSA_θx Simple processing capacity of semi-product in a given chain member θ in country x∈N (semi-product units/unit of time).

SSC_θx Simple processing cost of semi-product in a given chain member θ in country x∈N (monetary units of country of member θ/unit of s).

SSF_{θ α}x Safety stock factor of goods α∈G at a given chain member θ in country x∈N.

TP_{θ α}x Transfer price of goods α∈G shipped from a given chain member θ in country x∈N (monetary units of country of member θ/unit of goods α ).

x ym

TRC_{θ λ} Transportation cost per weight unit of goods shipped from a given chain member θ in country x∈N to another given chain member λ in country y∈N , using transportation mode m∈Ω(θ λ^x, ^y) (monetary units of country of member θ/weight unit).

x ym

TT_{θ λ} Average transportation time from a given chain member θ in country x∈N to another given chain member λ in country y∈N , using transportation mode m∈Ω(θ λ^x, ^y)

(units of time).

VC_θx Capacity of a given chain member θ in country x∈N for supplying modular components (modular component units/unit of time).

VAT_{θ α}x Value added tax rate (%) on the value of goods α∈G of country x∈N of supply chain member θ.

W_α Weight of a unit of goods α∈G (weight units/unit of goods).

5.2 The Objective Function

The objective function maximizes global after-tax profit in dollars for the period of analysis. The after-tax profit of internal supply chain members involved in the objective function are expressed in Eq. (2). The operating income variables oi_θx are free variables since operating income may be positive, zero or negative. Accordingly, each variable is treated as the difference between a plus non-negative variable (operating profit) oi_θ⁺x =oi_θx

and a minus non-negative variable (operating loss) oi_θ⁻x = −oi_θx(Vidal and Goetschalckx, 1998; Vidal and Goetschalckx, 2001; Fandel and Stammen, 2004; Vila et al., 2006). corresponding aggregate costs cost_θx (=

24

∑

= ) from the respective aggregate revenues revenue_θx (=πθ¹x+πθ²x), as Eq. (3) demonstrates.

,

x x x x

x I

oi_θ⁺ −oi_θ⁻ =revenue_θ −cost_θ ∀θ ∈FA (3) Trading with internal supply chain members and brand companies

produces the corresponding aggregate revenue, as expressed in Eqs. (4) and (5), respectively. Here, transfer price TP_{θ λ α}x y is given to avoid costly auditing and litigation. An effective method for obtaining market-driven transfer prices was proposed in Lakhal et al. (2005).

1 corresponding aggregate costs in terms of transforming modular components into semi-products (Eq. (6)), transforming modular components into finished products (Eq. (7)), transforming semi-products into finished products (Eq. (8)), simple process of semi-products (Eq. (9)), simple process of finished products (Eq. (10)), transportation cost of trading with internal VAT trading with internal members (Eq. (18)), sales VAT trading with brand companies (Eq. (19)), input VAT trading with vendors (Eq. (20)), input VAT

trading with internal members (Eq. (21)), export VAT trading with internal members in the same country (Eq. (22)), export VAT trading with brand companies in the same country (Eq. (23)), export VAT trading with internal members in different countries (Eq. (24)), export VAT trading with brand companies in different countries (Eq. (25)), import duty trading with internal members in the same country (Eq. (26)), import duty trading with brand companies in the same country (Eq. (27)), import duty trading with internal members in different countries (Eq. (28)), import duty trading with brand companies in different countries (Eq. (29)). Note that Eqs. (20) and (21) are minus items as mentioned in Chapter four. Furthermore, the term

x ym x ym x x ym

TT_{θ λ} CSF FS_{θ λ} SSF_{θ α} TT_{θ λ}

 + × + 

  in Eqs. (13) and (14) is the total

time required to calculate inventory costs (Vidal and Goetschalckx, 2000).

Herein, the first term is the time required to measure the pipeline inventory;

the second term is the time required to measure the cycle inventory; the third term is the time required to measure the safety stock (Vidal and Goetschalckx, 2000). The gamma distribution was adopted in the safety stock for modeling stochastic lead times and inventory problems (Vidal and Goetschalckx, 2000).

{ }

20

Given that corresponding logistics conditions are limited by operating requirements, eleven groups of constraints are the following: flow conservation of deep and simple process, inbound flow conservation, outbound flow conservation, identifying goods transformations, maximum goods transformation, assignment of goods, brand company requirements, capacity of chain members, subtour breaking constraints, binary constraints, and non-negative constraints. These constraints are further elaborated below.

1. Flow conservation of deep and simple process

As Figure 3-2 shows, deep process, including transforming modular components into semi-products, transforming modular components into finished products and transforming semi-products into finished products, are

expressed as Eqs. (30), (31) and (32), respectively.

Simple process involving simple processing of semi-products and finished products are expressed as Eqs. (33) and (34), respectively.

{ }

2. Inbound flow conservation

Figure 3-2 shows three inbound flows: modular components, semi-products, and finished products. Consequently, the corresponding inbound flow constraints are expressed as Eqs. (35), (36) and (37),

3. Outbound flow conservation

As Figure 3-2 shows, two outbound flows are semi-products and finished products. Regarding finished products, manufacturing centers only can

convey finished products to DCs, while DCs convey finished products to brand companies or other DCs. Consequently, the corresponding outbound flow constraints are expressed as Eqs. (38), (39) and (40), respectively.

( ) ( , )

4. Identifying goods transformations

For the sake of rational goods transformations and assignments, the expression gotr_{θ αβ}x represents good transformations, including

transformations from modular components into semi-products, from modular components into finished products and from semi-products into finished products. Accordingly, the corresponding constraints on goods transformations are expressed in Eqs. (41), (42) and (43), respectively.

{ }

5. Maximum goods transformation

Equations (41), (42) and (43) ensure only that if goods transformation occurs, the sum of gotr_{θ αβ}x equals or exceeds one. Consequently, it is

necessary to limit the maximum number of goods transformations, including those from modular components into semi-products, from modular components into finished products, and from semi-products into finished products. Thus, these constraints are expressed as Eqs. (44), (45) and (46), respectively.

6. Assignment of goods

Each modular component can only be used once. Only one of two manufacturing procedures, including from modular components into either semi-products or finished products, can be used. Therefore, the corresponding constraint is given by Eq. (47).

{ ^{, ,} } ^{s x} { ^{, ,} } ^{p x 1,} constraint is given by Eq. (48).

{ ^, } ^{p x 1,} assignment process. Accordingly, the corresponding constraint is given by Eq. (49).

7. Brand company requirements

To meet brand company requirements, the corresponding constraint is given by Eq. (50).

8. Capacity of chain members

In addition to vender capacity to supply modular components (Eq. (51)), there are five capacities of internal supply chain members for goods transformation, including from modular components into semi-products, from modular components into finished products, from semi-products into finished products, simple process of semi-products and simple process of finished products. Accordingly, the corresponding constraints on five capacities of internal supply chain members are expressed as Eqs. (52), (53),

(54), (55) and (56), respectively.

9. Subtour breaking constraints

Since goods can transfer among DCs, Eq. (57) prohibits a formation of

Constraints denoted by Eqs. (58), (59), (60), (61) and (62) indicate that those variables are binary.

11. Non-negative constraints

Constraints denoted by Eqs. (63) and (64) indicate that operating income variables are non-negative variables.

0,

x

x I

oi_θ⁺ ≥ ∀θ ∈FA (63)

0,

x

x I

oi_θ⁻ ≥ ∀θ ∈FA (64)

CHAPTER 6 NUMERICAL ILLUSTRATION

The numerical illustration discussed includes the following: (1) the basic scenario, (2) sensitivity analysis, (3) extended scenarios, (4) discussion.

6.1 The Basic Scenario

To test the applicability and the solvability of the proposed model, a simplified numerical study was conducted by interview. Figure 6.1 depicts the global network used in the numerical study and Table 6.1 outlines the main characteristics of the basic scenario. It should be noted that country 1 has a lower logistics cost (such as deep processing costs) and greater processing capacity (such as deep processing capacity) than countries 2 and 3 in the basic scenario.

The scenario considered in this study involves a simplified case. In this study, country 1 can be taken to represent China, country 2 can be regarded as Taiwan, and country 3 can be treated as Hong Kong. The scenario involves brand companies requesting global manufacturers to distribute three orders of finished products to FA₁₁^B and two orders to FA₁₂^B. FA₁₁^B

and FA₁₂^B are DCs or warehouses owned by the brand companies. Vendors (FA₁^V , FA^V₂ ) send modular components to manufacturing centers (FA₃^M), deep processing DCs (FA₇^Dd, FA₈^Dd) or non-bonded DCs (FA₉^Dm, FA₁₀^Dn) to

transform modular components into semi-products or finished products.

Semi-products or finished products can then be transferred between various kinds of DCs (FA₄^Ds, FA₅^Ds, FA₆^Ds, FA₇^Dd, FA₈^Dd, FA₉^Dm, FA₁₀^Dn) to identify the optimal tax savings routes and manufacturing procedures. Notably, internal supply chain members in international logistics zones (FA₄^Ds, FA₅^Ds,

FA₇Dd, FA₈^Dd) are tax exempt.

Figure 6.1 Global network used for the numerical study

Table 6.1 Main characteristics of the basic scenario

Ω={air transportation: 1, sea transportation: 2, truck:

3}

Note: per order of 100 goods.

Figure 6.2 displays five main patterns of the numerical results of logistics behavior. First, modular components were shipped from vendor (No.2) to deep processing DC (No.7) or deep processing DC (No.8).

processing DC (No.8) to transform modular components into semi-products.

Third, semi-products were shipped from deep processing DC (No.7) to another deep processing DC (No.8), and simple processing of semi-products then took place at deep processing DC (No.8). Fourth, semi-products were shipped from deep processing DC (No.7) to non-bonded DC (No.9) or from deep processing DC (No.8) to non-bonded DC (No.10) for further transformation of semi-products into finished products. Finally, finished products are shipped from non-bonded DC (No.9) to brand company (No.11) or from non-bonded DC (No.10) to brand company (No.12). Furthermore, some internal supply members operate at a profit (No.7, No.9, No.10), and others operate at a loss (No.3, No.4, No.5, No.6, No.8).

4

Figure 6.2 Numerical results for logistics behavior

More precisely, Table 6.2 shows the numerical results of logistics behavior, and an example of the steps involved in created the finished product (NO.33) for the requirement of brand company (NO.12). Modular components (NO.6 and NO.10) were first shipped from vender (NO.2) to deep processing DC (NO.8), and the goods were then transformed from modular components (NO.6 and NO.10) into semi-product (NO.23) at deep processing DC (NO.8), as depicted in Figure 6.3. Meanwhile, modular

More precisely, Table 6.2 shows the numerical results of logistics behavior, and an example of the steps involved in created the finished product (NO.33) for the requirement of brand company (NO.12). Modular components (NO.6 and NO.10) were first shipped from vender (NO.2) to deep processing DC (NO.8), and the goods were then transformed from modular components (NO.6 and NO.10) into semi-product (NO.23) at deep processing DC (NO.8), as depicted in Figure 6.3. Meanwhile, modular

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