Reconfiguration of manufacturing supply chains considering outsourcing decisions and supply chain risks

Journal of Manufacturing Systems 52 (2019) 217–226

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Journal of Manufacturing Systems journal homepage: www.elsevier.com/locate/jmansys

Reconfiguration of manufacturing supply chains considering outsourcing decisions and supply chain risks

T

Qi Tiana,b, Weihong Guoa,

⁎

a b

Department of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ, 08854, USA The State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116024, China

ARTICLE INFO

ABSTRACT

Keywords: Supply chain reconfiguration Graph-based model Manufacturing outsourcing Cost model Optimization

In order to stay responsive to evolving customer demands and to meet the need for greater product customizations, many manufacturing enterprises are recognizing the need to quickly reconfigure their manufacturing systems and supply chains. Making reconfiguration decisions requires a system-level optimization that involves many factors such as manufacturing tasks, outsourcing decisions, supply chain configurations, as well as risks. This paper proposes a graph-based cost model to optimize the configuration of manufacturing supply chain networks to support reconfiguration decision-making. An optimization model is formulated to minimize the total cost of the manufacturing enterprise with the consideration of operating cost and reconfiguration cost. To effectively quantify the reconfiguration cost, a graph-based cost model is developed to characterize the relationship between the graphical similarity of two supply chain networks and the reconfiguration cost. Outsourcing decisions and supply chain risks are also considered in the proposed model. A case study on a supply chain for laptop computer assembly is presented to demonstrate the effectiveness of the proposed method.

1. Introduction Manufacturers strive to stay responsive to evolving customer demands and meet the need for greater product customizations. In today’s global manufacturing paradigm, the speed of responsiveness to market is becoming more and more important for manufacturing enterprises. Supply chain reconfiguration is an important and efficient strategy for achieving “exactly the capability and functionality needed, exactly when needed” [1]. Hence, the reconfiguration of supply chain networks along with manufacturing decisions has generated considerable attention over the years. Existing research on supply chain reconfiguration mainly focuses on providing new reconfiguration strategies. Lu et al. [2] introduced the concept of “agility” into supply chain system to improve supply chain rapidly responsive capability to market opportunity. Komoto et al. [3] developed a multi-objective reconfiguration method of supply chains by using discrete event simulation technology. Osman et al. [4] proposed a bilinear goal programming model for supply chain reconfiguration and employed a modified Benders decomposition method to solve the model. Kristianto et al. [5] developed a decision support system for integrating manufacturing and product design into the reconfiguration of the supply chain networks. Dev et al. [6] proposed a hybrid adaptive decision system integrating agent-based simulation with decision tree ⁎

learning for supply chain reconfiguration. Mondragon et al. [7] proposed a design process for the adoption of composite materials and supply chain reconfiguration by using a software tool. Jiang et al. [8] provided a co-design strategy for supply chain network and subassembly planning considering the reconfiguration of the supply chain structure for factory-in-a-box manufacturing. These studies provide important references for future works, while there is a lack of studies on effective models to quantify the reconfiguration cost. To fill this research gap, the authors’ recent work developed a graph-based method to characterize the similarity between supply chain configurations and to establish the relationship between the similarity index and reconfiguration cost [9]. This previous work has enabled an efficient calculation of reconfiguration cost; its model, however, is limited to simple manufacturing tasks in a relatively ideal setting without any consideration on supply chain risks, limiting the method’s generalization to real-world problems. Outsourcing has been a very popular strategy to reduce production costs and allow companies to focus on their core competencies when manufacturing tasks are complex. In addition to outsourcing, risk is also unneglectable as various risks exist in all stages of the manufacturing supply chains. In order to effectively consider outsourcing and risks in reconfiguration decision-making, a new graph-based cost model is developed in this paper to optimize the reconfiguration of manufacturing

Corresponding author. E-mail address: [email protected] (W. Guo).

https://doi.org/10.1016/j.jmsy.2019.04.005 Received 18 November 2018; Received in revised form 17 February 2019; Accepted 8 March 2019 Available online 03 July 2019 0278-6125/ © 2019 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

Journal of Manufacturing Systems 52 (2019) 217–226

Q. Tian and W. Guo

Nomenclature o i j m t Z TC MC PC RC L u, v t

u, v

St i, j

Si, j o, f

D po, f

o, t

po, t prm, t TCm, t MCm, t

PCt

Plant location index, o O Supplier index, i I Resource index, j J Outsourcing manufacturer index, m = 1, 2, 3, …, M Manufacturing task index, t = 1, 2, 3, …, T Total cost of the plant Transportation cost of the plant Manufacturing cost of the plant Procurement cost of the plant Reconfiguration cost of the plant Distance between u and v, (u, v) can be an (m, o) pair, (i, m) pair, or (i, o) pair Importance factor of semi-finished product produced by task t Transportation cost factor from u to v, (u, v) can be an (m, o) pair, (i, m) pair, or (i, o) pair, per unit raw semi-finished product per unit distance Quantity of the semi-finished product produced by task t Importance factor of resource j from supplier i Quantity of resource j supplied by supplier i Manufacturing cost per unit of the final product at plant o Demand for the final product Scrap rate of the final product at plant o Manufacturing cost per unit of the semi-finished product produced by task t at plant o Scrap rate of the semi-finished product produced by task t at plant o Net profit margin of the outsourcing manufacturer m that performs task t Transportation cost of the outsourcing manufacturer m that performs task t Manufacturing cost of the outsourcing manufacturer m

i, j m, t

pm, t SIM i

pt t

Dt t

SIMu, v

l u, v

l u, v

Cu, v u, v

ru, v

supply chains with the consideration of outsourcing and supply chain risks. Research on outsourcing includes manufacturing outsourcing, logistics outsourcing, and service outsourcing. In this paper, we focus on manufacturing outsourcing decisions. In this direction of research, Kumar et al. [10] developed a closed loop modelling framework which aided in selecting an effective manufacturing strategy considering key enablers and barriers to successful outsourcing. Pawar et al. [11] proposed a method to assess the holistic cost of uncertainty within outsourcing manufacturing supply chains, with a view to assisting firms to make more informed decisions when outsourcing. Hahn et al. [12] studied the issue of contract manufacturing at the strategic-tactical level aiming for robust decisions to accommodate stochastic manufacturing environments and immanent uncertainty of planning parameters. Jiang et al. [13] proposed a multi-objective algorithm for task scheduling and resource allocation in cloud-based disassembly with the consideration of disassembly tasks outsourcing. Kumari et al. [14] developed an automated self-adaptive multi-agent system for outsourcing SMEs manufacturing supply chain. Zulkiffli et al. [15] aimed to conceptually analyze outsourcing decisions with competitive capabilities and business performance among Malaysian SMEs. Ren et al. [16] developed a game-theoretic model of a dyadic supply chain to study the joint decisions on product line design and outsourcing. Han et al. [17] proposed a market equilibrium supply chain model for supporting selfmanufacturing or outsourcing decisions in prefabricated construction. As for existing studies on supply chain risk analysis, Ellinger et al. [18] examined the roles of learning orientation and supply chain integration as complementary parts of a knowledge deployment process that facilitates supply chain risk management. Aqlan et al. [19]

that performs task t Procurement cost to perform the task t Unit cost of resource j from supplier i Manufacturing cost per unit of the semi-finished product produced by task t at outsourcing manufacturer m Scrap rate of the semi-finished product produced by task t at outsourcing manufacturer m Row vector of similarity measures between the supply chain networks before and after reconfiguration Quality acceptance rate for resources provided by supplier i Scrap rate of the semi-finished product produced by task t Quality acceptance rate for the semi-finished product produced by task t Demand of semi-finished product produced by task t in order to meet the demand requirement of the final product Ratio of the number of semi-finished product produced by task t to the number of final products Similarity of the line segments between u and v before and after reconfiguration, (u, v) can be an (m, o) pair, (i, m) pair, or (i, o) pair Length of the line segment between u and v before reconfiguration, (u, v) can be an (m, o) pair, (i, m) pair, or (i, o) pair Length of the line segment between u and v after reconfiguration, (u, v) can be an (m, o) pair, (i, m) pair, or (i, o) pair Coefficient associated with SIMu,v Reconfiguration cost factor of the line segment between u and v, (u, v) can be an (m, o) pair, (i, m) pair, or (i, o) pair Risk factor of the line segment between u and v, (u, v) can be an (m, o) pair, (i, m) pair, or (i, o) pair

presented an approach and a software application for supply chain optimization under risk and uncertainty. Mani et al. [20] explored the application of big data analytics in mitigating supply chain social risk and demonstrated how such mitigation can help in achieving environmental, economic, and social sustainability. Truong et al. [21] compared the impact various risks have on the performance of manufacturing-oriented and service-oriented firms from a supply chain perspective. Chaudhuri et al. [22] studied the impact of internal integration, external integration, and supply chain risk management on manufacturing flexibility, and the moderating effect among them by using hierarchical regression. Shenoi et al. [23] simultaneously analyzed manufacturing risk and supply risk and its impact upon Chinese manufacturing supply chains business performance by using a twophased multi-method approach. These studies have greatly contributed to the knowledge base on manufacturing outsourcing and manufacturing supply chain risks. However, few of the existing methods consider the impact of manufacturing outsourcing and supply chain risks on supply chain reconfiguration, limiting their wider-adoption in modern manufacturing enterprises. The above review has revealed that there is a need to integrate manufacturing outsourcing and supply chain risks into supply chain reconfiguration decision-making. To address this need, an optimization model is formulated in this paper to minimize the total cost of the manufacturing enterprise; a graph-based cost model is proposed to efficiently quantify the relationship between the graphical similarity of two supply chain networks and the reconfiguration cost. The proposed method is demonstrated and validated in a case study on a supply chain for laptop computer assembly. 218

Journal of Manufacturing Systems 52 (2019) 217–226

Q. Tian and W. Guo

The remainder of this paper is organized as follows. Section 2 presents the optimization model, including the decision variables, the objective function and constraints, and the model assumptions. The quantification of reconfiguration cost is presented in Section 3, where the graph-based method is proposed to calculate the similarity between supply chain networks. Section 4 provides a case study to demonstrate the proposed method. Section 5 concludes the paper and discusses directions for future work.

The decision variable in expression (2) describes the selection of supplier i to provide resource j. The universal set of suppliers is I = {I1, I2, …, Ij, …, IJ} and Ij can be expressed as follows:

I1 = {1, 2, ..., n1}, I2 = {n1 + 1, n1 + 2, ..., n1 + n2}, ..., I j j 1

={ ={

ns + 1,

1, plant location o is selected zo = 0, otherwise

TC =

(1

(1)

(2)

x m , o, t ) ×

={

Ns + 2, ...,

Ns + 1, s=1

Ns},

x m, o, t ×

PC =

o, t

×

zo × o O t T

m, o

i, j

×

i, o

× St × xm, o, t )+

× Si, j × yi, j, t )

×

1

1

St po, t

i I j J

( L i, m ×

i, j

×

i I j J

PCt =

×

( i I j J

RC = f (SIM)

D + po, f

xm, o, t

TCm, t =

m, t

(7)

(8)

[(1 + prm, t )(TCm, t + MCm, t + PCt ) xm, o, t ]

m M

MCm, t =

×

m M

+ 1

1 i, j

St pm, t

× Si, j × yi, j, t )

i, m

i, j

× Si, j × yi, j, t

× Si, j × yi, j, t )

(9) (10) (11) (12) (13)

A common feature in Eqs. (7–9) is that each cost element consists of two parts, one for the cost related to outsourcing and the other related to the non-outsourcing manufacturing, i.e., performing a manufacturing task at the plant rather than at an outsourcing manufacturer. The x second line in Eqs. (7–9) has the same term (1 , which m M m , o, t ) will be 1 if task t is to be performed at the plant o. Thus, the cost related to non-outsourcing manufacturing (second line in Eqs. (7–9)) will be incurred when m M xm, o, t = 0 . As shown in Eq. (7), the transportation cost (TC) of the entire

Ns}, ..., JT

s=1

o, f

m M

s=1 T

Ns + 2, ..., s=1

zo ×

1

t

s=1 T 1

(Li, o ×

o O t T

(3)

t

i I j J

MC =

J1 = {1, 2, ..., N1}, J2 = {N1 + 1, N1 + 2, ..., N1 + N2}, ..., Jt Ns + 1,

(Lm, o × m M

m M

xm, o, t = 0 for a given o, then task t will be performed at

s=1 T 1

zo × o O t T

the plant o. It is reasonable to assume that once an outsourcing manufacturer is selected by plant o, the task to be carried out at this outsourcing manufacturer is then automatically specified, thus there is no need to introduce an additional decision variable for selecting task t for m. Similarly, the resource j needed to perform manufacturing task t is known once the task is determined. Thus, the selection of resource j for task t is not needed in the decision variables. The universal set of resource is J = {J1, J2, …, Jt, …, JT}, where Jt can be expressed as follows

={

(6)

where

m M

t 1

(5)

Min Z = TC + MC + PC + RC

For task t, it may be performed by some of the outsourcing manufacturers or it may be performed at the plant. Expression (1) describes the selection of outsourcing manufacturer m by plant o to perform task

t 1

ns}, s=1

The objective of the supply chain reconfiguration optimization problem in this paper is to minimize the total cost of the entire manufacturing enterprise. The objective function is given in Eq. (6) below. The total cost consists of four cost elements: transportation cost (Eq. (7)), manufacturing cost (Eq. (8)), procurement cost (Eqs. (9–12)), and reconfiguration cost (Eq. (13)).

Let o denote the location of the manufacturing plant, o O, where the final product is produced. Let i denote the index of the supplier and j denote the index of the resource, respectively, i = 1, 2, …, I, j = 1, 2, …, J. Let m denote the index of the alternative outsourcing manufacturer, m = 1, 2, …, M, which is responsible for producing a component (sub-system) of the final product and provide to the plant. Let t denote the index of the manufacturing/assembly task, which can be performed by either the plant or the outsourcing manufacturer(s), t = 1, 2, …, T. Based on the above notations, the decision variables are defined as:

t. In (1), if

ns + 2, ..., s=1

2.2. Objective function and constraints

2.1. Decision variables

1, supplier i is selected to provide j for task t 0, otherwise

s=1 J

where nj is the number of available suppliers to provide resource j. That is to say, for resource type 1 (j = 1), there are n1 suppliers (i = 1, 2, …, n1) available to provide it. The decision variable in expression (3) describes the selection of alternative location for plant o during reconfiguration.

In this section, we present the formulation of the optimization model for supply chain reconfiguration. The decision variables are defined in subsection 2.1. The objective function is presented in subsection 2.2, along with the constraints for reconfiguration, manufacturing outsourcing, and transportation risks. Model assumptions are described in subsection 2.3.

yi, j, t =

ns}, ..., IJ

s=1 J 1

s=1

1, plant o outsources task t to manufacturer m 0, otherwise

j

ns + 2, ...,

s=1 J 1

2. Model formulation

x m, o, t =

j 1

ns + 1,

(4)

where Nt is the number of resource types required to carry out task t. For example, to complete task 1 (t = 1), N1 types of resources (j = 1, 2, …, N1) are required; each resource type may represent a specific raw material or component. 219

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Q. Tian and W. Guo

manufacturing enterprise consists of two parts. The first line of Eq. (7) is the transportation cost of semi-finished products from outsourcing manufacturers to the plant. This cost applies to the outsourced task. It considers the distance between outsourcing manufacturer and plant Lm, o , the importance of the semi-finished product t , unit transportation cost m, o , and the quantity of the semi-finished products St . The second line of Eq. (7) is the transportation cost of sending resources from suppliers directly to the plant for tasks to be performed at the plant. This part of TC is determined by the distance Li, o , the importance factor i, j , unit transportation cost i, o , and the transportation quantity Si, j , which are related to the resource type, the supplier, and the plant. The second part of TC will only be incurred if a task is to be performed at the plant o, i.e., when m M xm, o, t = 0 . Eq. (8) shows the manufacturing cost (MC) in two parts. The first line of Eq. (8) is for the final product, which can only be produced at the plant and its manufacturing cost determined by the unit manufacturing cost of the final product in plant o, f , demand D for the final product, and scrap rate of the final product in plant, po, f . The cost in the second line of Eq. (8) will only be incurred when a task is performed by the plant. This cost is calculated from the unit manufacturing cost of the semi-finished product o, t , demand for the semi-finished product St , and scrap rate of the semi-finished product in plant po, t . Similarly, Eq. (9) shows the total procurement cost (PC) in two parts: one for semi-finished products from outsourcing manufacturers, the other for resources directly procured from suppliers to perform a certain task at the plant. When the plant procures semi-finished products from an outsourcing manufacturer m, the procurement cost should cover the total production cost at m, which consists of the transportation cost TCm, t , manufacturer cost MCm, t , and procurement cost PCt at m, as well as the net profit margin for m, denoted by prm, t . It is noticed that the reconfiguration cost of the outsourcing manufacturer will be considered with the reconfiguration cost of the plant together as a function of the similarity vector SIM in this paper. As a result, the RC of the outsourcing manufacturer is not included in Eq. (9) when calculating the total cost of a certain outsourcing manufacturer. Besides, there is a trade-off between the total cost of outsourcing manufacturers and the selection of the outsourcing manufacturers. For an outsourcing manufacturer m to be able to deliver task t, we assume that the net profit margin prm, t is a constant value in this paper. Therefore, it would be better if the total cost of the outsourcing manufacturer is set high, indicating that the outsourcing manufacturer can earn more profits. On the other hand, however, if the procurement cost of a semi-finished product from the outsourcing manufacturer is too high, the plant would prefer to select a different outsourcing manufacturer, or even choose to perform the task by itself. By defining the procurement cost in Eq. (9), the proposed optimization problem is able to consider this kind of trade-off in finding the optimal reconfiguration strategy. The transportation cost TCm, t , manufacturer cost MCm, t , and procurement cost PCt at outsourcing manufacturer m are described in Eqs. (10–12). Their calculations are similar to the second parts in Eqs. (7–9), while the cost factors are related to outsourcing manufacturer m instead of plant o. Eq. (13) gives the reconfiguration cost (RC) as a function of the similarity vector SIM between two supply chain networks before and after reconfiguration. The proposed method to determine SIM and reconfiguration cost will be explained in detail in Section 3. The constraints of the optimization model are shown as follows. The constraint for meeting the demand requirement of the semifinished product is:

[Si,j × yi, j, t × i I j J

i

× (1

pt )]

St

product pt . Here, pt = pm, t if task t is performed by outsourcing manufacturer m, and pt = po, t if task t is performed at plant o. The constraint for meeting the demand requirement of the final product is:

St ×

t

× (1

po, f )

Dt

(15)

In Eq. (15), the semi-finished products should meet the demand requirement of the final product, with the consideration of quality acceptance rate for semi-finished product t and scrap rate of the final product po, f , regardless of whether the final task is performed by outsourcing manufacturers or the plant. Here, Dt denotes the demand of semi-finished product produced by task t in order to meet the demand requirement of the final product, which can be calculated from the bill of materials, denoted as Dt = D/ t . The constraint for supplier selection is:

Dt , for Dt + 1

yi, j, t i I j j Jt

i,

j

(16)

Eq. (16) expresses the relationship between yi, j, t and Dt . If Dt > 0 , then semi-finished product produced by task t is needed, and at least one supplier for resource j should be selected to perform task t. If Dt = 0 , then semi-finished product produced by task t is not needed, and Eq. (16) will be reduced to i I j j Jt yi, j, t 0 . The constraint for plant location selection is:

z o = 1 for

i

(17)

o O

It indicates that one and only one alternative location o can be selected. The constraint for outsourcing manufacturer selection is:

x m, o, t

1 for

t

(18)

m M

It means that one and only one outsourcing manufacturer can be selected for a given task t, and once the outsourcing manufacturer m is selected, the task t to be performed at m should be automatically determined. The constraint for the relationship between x m, o, t and z o is:

x m, o, t

z o for

o,

m,

t

(19)

It indicates that if an alternative location o is selected for the plant, then outsourcing manufacturer m may or may not be selected to perform task t. But if location o is not selected, then the (o, m) pair should not be selected. The non-negative constraints in the model are: t,

m, o,

St , i, j, i, o, Si, j, o, f , D , po, f , o, t , po, t , prm, t , i, j, i,m, m,t , pm, t , i, pt , t , Dt , t

0.

(20) The above-formulated problem is a Mixed Integer Linear Programming (MILP) problem. Various optimization techniques, such as genetic algorithm [24], tabu search [25], evolutionary artificial neural networks [26], and randomized neighborhood search [27], can be utilized to solve the problem. For large-scale problems, heuristics [28] can be developed to improve computational efficiency. 2.3. Model assumptions Assumptions of the model are listed as follows:

• The higher the similarity between two supply chain networks has, the lower the reconfiguration cost is. • The larger the area of a region is, the higher the flourishing degree

(14)

It means that the resources provided by suppliers should meet the demand for producing semi-finished products, considering quality acceptance rate for resources i and scrap rate of the semi-finished

• 220

of the region is, and thus the higher the manufacturing cost, technical level, and travel conditions are, the lower the supply chain risks are. When u and v are in the same region ((u, v) can be an (m, o) pair, (i,

Journal of Manufacturing Systems 52 (2019) 217–226

Q. Tian and W. Guo

• • • • •

m) pair, or (i, o) pair), the cost factors related to (u, v) will be lower than the ones in different regions. Once an outsourcing manufacturer m is selected, the task t which will be performed by m is then automatically determined. One manufacturing task can only be handled by one outsourcing manufacturer or the central plant. For any given task, the resource types needed to perform the task are determined in advance. One and only one alternative location of the plant o can be selected. If a model factor depends solely on o, then it will assume the same value if two alternative locations of o are in the same region, while it will take on a different value if two alternative locations are in different regions.

before and after reconfiguration; (u, v) can be an (m, o) pair, (i, m) pair, or (i, o) pair, as shown in Eqs. (23–25). In order to relate SIM with reconfiguration cost, a coefficient vector is proposed in Eq. (26) to account for the cost factors: where Cm, o , Ci, m , and Ci, o are cost coefficients associated with SIMm, o , SIMi, m , and SIMi, o , respectively, which can be calculated as follows:

Cm, o =

Ci, m =

i, m

Ci, o =

× rm, o

×

i, j

× ri, m ×

(27)

Si, j i Ij

i, o

×

i, j

× ri, o ×

Si, j

× (1 + prm, t )

Si, j

RC = (ones (n)

SIM) × CT

(21)

3.3. Two illustrative examples for the proposed method In this subsection, we provide two illustrative examples to demonstrate how the reconfiguration cost is evaluated in supply chain reconfiguration considering outsourcing.

3.2. Calculation of reconfiguration cost based on graph similarity To develop the method to calculate reconfiguration cost RC, we first use the length ratio method to determine the similarity vector between two supply chain networks before and after reconfiguration. Eq. (22) shows the similarity SIM as a row vector

Example 1. Plant migration with replacing outsourcing manufacturer (s) As shown in Fig. 2, two plant locations (O1 and O2), three tasks (T1,

(22)

where SIMm, o , SIMi, m , SIMi, o are the similarity metrics considering the similarity of all the lines between outsourcing manufacturer m and plant o, supplier i and outsourcing manufacturer m, supplier i and plant o in the supply chain network after reconfiguration, respectively. The individual similarity metrics are expressed in Eqs. (23–25):

SIMi, m =

SIMi, o =

1

|lm, o

lm, o |/ lm, o for lm, o

1

|lm, o

lm, o |/ lm, o for lm, o = 0

1

|li, m

li, m |/ li, m for li, m

1

|li, m

li, m |/ li, m for li, m = 0

1

|li, o

li, o |/ li, o for li, o

1

|li, o

li, o |/ li, o for li, o = 0

(30)

where ones(n) is a row vector with all elements being 1; the length of ones(n) is the same as SIM. The detailed steps in calculating the reconfiguration cost RC for any given supply chain network after reconfiguration are listed in Table 1.

where l and l’ denote the graphs before and after transformation, respectively; SIM represents the similarity between the two graphs.

SIMm, o =

(29)

Here, elements in the coefficient vector considers the reconfiguration cost factors for (m, o), (i, m), or (i, o) as m, o , i, m , or i, o , the importance factors for semi-finished product (or resource) as t (or i, j ), as well as the risk factors as rm, o , ri, m , or ri, o . Besides, since one kind of raw resource may be provided by multiple suppliers, the weight based on the quantity of resource needed is also considered in Eqs. (28) and (29). Moreover, the net profit margin of the outsourcing manufacturer prm, t is also considered in Eq. (28). The rationale behind associating supply chain risks with the reconfiguration cost is that we treat the baseline supply chain network (before reconfiguration) as the reference, having a risk of baseline value 1. Thus, the risks in the reconfigured supply chain network are evaluated as the relative risks comparing to the baseline. Hence, it is convenient to associate the risks in a supply chain with reconfiguration cost. Based on SIM and C, reconfiguration cost RC can be calculated by:

Existing methods for graphical similarity measurement include the length ratio method, angle sum method, relative displacement method, etc. We adopt the length ratio method and extend it to the manufacturing supply chain context. The length ratio method is considered the most suitable method to measure the similarity between two supply chain networks due to its advantage in expressing the relationship between manufacturers and suppliers [9]. The illustration of the length ratio method is shown in Fig. 1 and the calculation of the similarity is expressed in Eq. (21).

SIM = [ SIMm, o SIMi, m SIMi, o ]

(28)

Si, j i Ij

j J

3.1. The length ratio method for similarity measurement

l |/ l

t

j J

In order to evaluate the reconfiguration cost for the entire manufacturing enterprise, the configurations of the supply chain networks before and after reconfiguration are compared. Subsection 3.1 presents the method to quantify the similarity between two supply chain networks using graphic similarity measurement. Subsection 3.2 then presents the proposed graph-based method for evaluating reconfiguration cost. The relationship between SIM and reconfiguration cost is clarified in the proposed method. Two illustrative examples are provided to in subsection 3.3 to demonstrate the proposed method.

|l

×

m, o j J

3. Graph-based model for reconfiguration cost

SIM = 1

(26)

C = [Cm, o Ci, m Ci, o ]

0 (23)

0 (24)

0 (25) Fig. 1. Illustration of the length ratio method.

where SIMu, v denotes the similarity of the line segment between u and v 221

Journal of Manufacturing Systems 52 (2019) 217–226

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Table 1 Procedure to calculate reconfiguration cost. Step 1: Step 2: Step 2.1: Step 2.2: Step 2.3: Step 3: Step 3.1: Step 3.2: Step 3.3: Step 4:

Identify the (m, o), (i, m), and (i, o) pairs in the supply chain network after reconfiguration; for each pair, determine the length of vector SIMm,o , SIMi,m , and SIMi,o , respectively. Obtain the SIM vector. For each (m, o) pair in the reconfigured supply chain network, compare the supply chain configurations before and after reconfiguration, and calculate SIMm,o by Eq. (23); For each (i, m) pair in the reconfigured supply chain network, compare the supply chain configurations before and after reconfiguration, and calculate SIMi,m by Eq. (24); For each (i, o) pair in the reconfigured supply chain network, compare the supply chain configurations before and after reconfiguration, and calculate SIMi,o by Eq. (25). Obtain cost coefficient vector C. For each (m, o) pair, calculate the corresponding Cm, o value by Eq. (27); For each (i, m) pair, calculate the corresponding Ci, m value by Eq. (28); For each (i, o) pair, calculate the corresponding Ci, o value by Eq. (29). Calculate RC by Eq. (30).

T2, and T3), four outsourcing manufacturers (M1 to M4), six type of resources (J1 to J6), and eight suppliers (S1 to S8) are considered. The relationship in o, t, m, j, and i are defined in Tables 2–4. Table 2 shows which outsourcing manufacturer or plant is able to perform which task, according to the facility, equipment, manpower, etc. at the manufacturer or plant. Table 3 shows what resources are needed to perform each task. Table 4 shows which supplier can provide which resource. In Fig. 2, the solid lines represent the original supply chain network before reconfiguration; the dashed lines represent the new supply chain network after reconfiguration; the dotted lines (S3-M2 and S4-M2) represent the existing alternative (i, m) pairs that are not selected by the plant in the original supply chain network. There are also dashed lines connecting M2 and S3, M2 and S4, M4 and S7, M4 and S8 in Fig. 2, but are hidden behind the other lines. In reconfiguration, we can see that the plant moves from O1 to O2. M1 is replaced by M2 to perform task T1 due to its relatively long distance from O2 after reconfiguration. Task T2 will be performed at O2 instead of M3 for lower cost, while task T3 will still be performed at M4. To calculate the reconfiguration cost for the new supply chain network, we first identify that there are two (m, o) pairs ((M2, O2) and (M4, O2)), four (i, m) pairs ((S3, M2), (S4, M2), (S7, M4), and (S8, M4)), and two (i, o) pairs ((S5, O2,) and (S6, O2)) in the network. Comparing with the original three (m, o) pairs and eight (i, m) pairs, we can obtain the SIM vector by using Eqs. (23–25). Specifically, for (m, o) pairs, SIM4,2 is between 0 and 1, while SIM2,2 is 0 since the outsourcing manufacturer M2 doesn’t have a relationship with the plant O1 in the original supply chain network. For (i, m) pairs, SIM7,4, SIM8,4, as well as SIM3,2, SIM4,2, are all 1, since they are identical to the structure of the original supply chain network. It is noticeable that (S3, M2) and (S4, M2) exist in the original supply chain network, but have no contribution towards the total cost of plant O1 since there is no relationship between M2 and O1. This explains why SIM3,2 and SIM4,2 are both 1 and have no contribution towards the reconfiguration cost for (i, m) pairs. As for (i, o) pairs, both of SIM5,2 and SIM6,2 are 0, since the transportation, logistics, demand frequency, and quantity, etc. would be completely different between the structure in the new supply chain network and the original one, although the same task is being performed. Then, by using Eqs. (26–29), we can easily obtain the coefficient vector C, thus, reconfiguration cost RC can be calculated by Eq. (30).

all 1 since they are identical to the original ones, and thus have no contribution to the reconfiguration cost. After calculating the similarity vector SIM, reconfiguration cost can be calculated according to Eqs. (26–30). 4. Case study In this section, the laptop computer manufacturing supply chain in the authors’ prior work is adopted to demonstrate and verify the effectiveness of the proposed method, considering manufacturing outsourcing and supply chain risks. The laptop case was provided by [29] and [30] for the first time and was employed in the authors’ prior study in [9], as shown in Fig. 4. 4.1. Case description and parameter setup There are 13 kinds of resources to assemble a laptop (i.e., j = 1, 2, …, 13). They can be divided into four sub-systems: display (j = 1, 2), main body (j = 3, 4, 5, 6, 8, 9, 10, 13), processor (j = 11, 12), battery (j = 7). Here, the first three sub-systems can be assembled by 3 tasks (t = 1, 2, 3) respectively, and the last sub-system will be provided by a supplier to the plant directly. Six outsourcing manufacturers are considered here (m = 1, 2, …, 6) to carry out the 3 tasks, M1 and M2 for task 1, M3 and M4 for task 2, and M5 and M6 for task 3. All of the three tasks can also be performed at the plant. The final product of the laptop

Example 2. Outsourcing manufacturer migration with replacing supplier(s). As shown in Fig. 3, during reconfiguration, the outsourcing manufacturer M2 moves to a new location (denoted as M2′), and supplier S4 in the original supply chain is replaced by supplier S7 due to the relatively short distance from M2′. In this case, in order to calculate reconfiguration cost, for (m, o) pairs, SIM2',1is a value between 0 and 1; for (i, m) pairs, SIM3,2' is between 0 and 1, while SIM7,2' is 0. SIM metrics of all other pairs in the supply chain network after reconfiguration are

Fig. 2. Plant migration with replacing outsourcing manufacturers. 222

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Table 2 Relationship between task t and outsourcing manufacturer m and plant o. m or o

t

M1 M2 M3 M4 O1 O2

T1

T2

T3

√ √ – – √ √

– – √ – √ √

– – – √ √ √

Note: If the task can be performed by the outsourcing manufacturer or plant, it will show “√”, otherwise “-”. Table 3 Relationship between task t and resource j. j

Fig. 4. Components of the laptop computer in case study.

t

J1 J2 J3 J4 J5 J6

T1

T2

T3

√ √ – – – –

– – √ √ – –

– – – – √ √

Table 5 Serial number of supplier i in four regions in the extended scenario.

Note: If the task needs resource j, it will show “√”, otherwise “-”. Table 4 Relationship between resource j and supplier i. i

S1 S2 S3 S4 S5 S6 S7 S8

j J1

J2

J3

J4

J5

J6

√ – √ – – – – –

– √ – √ – – – –

– – – – √ – – –

– – – – – √ – –

– – – – – – √ –

– – – – – – – √

j

Region 1

Region 2

Region 3

Region 4

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

1 3 5 7 8 11 × 14 15 17 18 20 23

2 4 6 × × × 4 6 × × × × ×

× × × 19 9 12 21 × 16 × 19 21 12

× × × × 10 × 13 × × 10 × 22 ×

Table 6 Location coordinates and cost factors of suppliers.

Note: If the resource can be provided by supplier i, it will show “√”, otherwise “-”.

Fig. 3. Outsourcing manufacturer migration with replacing suppliers.

223

(i, j)

LC

(1,1) (2,1) (3,2) (4,2) (5,3) (6,3) (7,4) (19,4) (8,5) (9,5) (10,5) (11,6) (12,6) (4,7) (13,7) (21,7) (6,8) (14,8) (15,9) (16,9) (10,10) (17,10) (18,11) (19,11) (20,12) (21,12) (22,12) (12,13) (23,13)

(2,6) (15,2) (3,8) (14,8) (4,2) (19,7) (10,7) (8,18) (10,2) (10,16) (16,16) (8,1) (10,19) (14,8) (17,15) (2,15) (19,7) (5,11) (9,13) (1,17) (16,16) (2,12) (9,9) (8,18) (6,6) (2,15) (14,12) (10,19) (4,8)

i, j

($/unit)

80 70 12 8 30 35 10 9 85 85 85 35 40 40 40 38 9 10 55 54 7 8 5 4 160 150 155 2 3

i, j

1.3 1.3 1.0 1.0 1.1 1.1 1.0 1.0 1.4 1.4 1.4 1.1 1.1 1.1 1.1 1.1 1.0 1.0 1.2 1.2 1.0 1.0 1.0 1.0 1.5 1.5 1.5 1.0 1.0

i

0.93 0.90 0.93 0.90 0.90 0.93 0.93 0.90 0.93 0.93 0.93 0.90 0.93 0.93 0.93 0.90 0.90 0.93 0.93 0.90 0.90 0.93 0.93 0.90 0.96 0.90 0.93 0.90 0.93

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can only be assembled by the plant. A total of 23 suppliers in four regions provide the 13 kinds of resources for the outsourcing manufacturers or the plant (i.e., i = 1, 2, …, 23). A total of 400 candidate locations of the plant in a 20 × 20 rectangular coordinate are considered (i.e., o = 1, 2, …, 400). The supplier indexes and regions are shown in Table 5. Here, one resource type may be provided by multiple suppliers; one supplier may also provide multiple resource types; the symbol “×” represents that there isn’t any supplier that can provide the specific resource type in the region. The location coordinates and cost factors of each supplier (i, j) that relate to suppliers are shown in Table 6. In Table 6, the 2-D array (i, j) shows the relationship between supplier and resource type. LC is the location coordinate of the suppliers in the 20 × 20 rectangular coordinate, as shown in Fig. 5. The original location of the plant is in O, whose coordinate is (5, 10). The location of the six outsourcing manufacturers (M1 to M6) are also shown in the figure, whose coordinates are (3, 7), (15, 5), (8, 6), (15, 11), (5, 15), and (7, 8), respectively. These coordinates are generated in simulation. It is assumed that region 3 is the least developed region, with relatively poor infrastructures and more challenging road conditions. The values of i, j , i, j , and i are adopted from [9]. The cost factors that only relate to o are listed in Table 7, including o, f and po, f . It is assumed that the values of o, f and po, f are determined according to the flourishing degree of the region, which is related to the size of the region. The larger the region is, the higher the flourishing degree is, the larger o, f and smaller po, f are. For the alternative location of the plant in the same region, the values of o, f and po, f will remain the same. As for the values of o, t and po, t , which not only relate to o, but also relate to t, they are listed in Table 8, along with other cost factors that relate to task t. Besides, for j = 7, t = 1, and po, t = 0 , because the resource is provided by the supplier for the plant to prepare for the manufacturing of the final product. In Table 8, a 2-D array (x, t) is used to express the relationship between tasks and manufacturers, where x can be either a plant in four regions (denoted as O1 to O4) or outsourcing manufacturers that can handle the associated task (i.e., M1 to M6). From the table, we can see that the assumption for o, f and po, f also applies to x, t , px , t , and t here. The larger the region is, the larger x, t , smaller px , t and larger t is. At the same time, for the plant and outsourcing manufacturers in the same region to perform the same task, it will have lower x, t and px , t but higher t for outsourcing manufacturers compared to the ones at the plant, which shows the advantages of outsourcing tasks to manufacturers to deliver semi-finished products. As for t , it relates to the value of i, j to perform the task, the more the resource types needed to perform a task and the larger the value of i, j is, the larger the value of t is. The net profit margin prm, t varies from 5% to 7%, which is determined according to the corresponding m, t : the larger m, t is, the larger prm, t is. For the values of i, o , i, m , i, o , i, m , ri, o , and ri, m , since they reflect the transportation, reconfiguration, and overall risks related to resources, the values of i, m , i, m , and ri, m are set to be the same values as i, o , i, o , and ri, o , respectively. The values of i, o and i, o are shown in Table 9. For ri, o , since region 3 is the least developed region with relatively poor infrastructure and road conditions, if the plant or supplier is in region 3, it will have a higher risk when providing or receiving resources. Specifically, if i and o are in the same region in region 1, 2 or 4 (e.g., i and o are both in region 1), ri, o will be 1; if i and o are in different regions but still in region 1, 2 or 4 (e.g., i in region 1 and o in region 2), ri, o will be 1.05; if i and o are both in region 3, ri, o will be 1.1; if one of the i or o is in region 3, and the other is in region 1, 2 or 4, ri, o will be set as 1.15. As for m, o , m, o and rm, o , they are related to semi-finished products. These factors will be larger due to the higher importance of semi-finished product comparing with resources, which are set as 1.2 times of the value of the corresponding factors for resources. The values of m, o and m, o are shown in Table 10. As for rm, o , it is set as 1.2, 1.26, 1.32,

and 1.38, respectively, corresponding to four scenarios determining ri, o . According to the components of the laptop computer in Fig. 4, one unit of each resource is needed to perform task 1–3, and one unit of semi-finished product produced by task 1–3 is needed for the final product assembly (i.e., t = 1). In this case study, the demand for the laptop computer is set as 100 (i.e., D = 100), thus Dt = 100. After that, the value of St can be determined according to Eq. (15) and Si, j can also be obtained based on St and Eq. (14). 4.2. Reconfiguration solutions Once all the parameters are determined, the initial solution can be obtained by minimizing the total cost while having the location of the plant unchanged, whose supply chain network is shown in Fig. 6. It can be seen that for task 1 and task 3, they are delivered by outsourcing manufacturers M1 and M6, respectively, due to their relatively smaller distance from suppliers to the plant, as well as the cost advantage compared with performing the tasks at the plant. However, for task 2, even the lowest cost for using outsourcing manufacturer M3 (as shown as the dotted lines in the figure) cannot compete with performing task 2 at the plant. Besides, we can see that all the suppliers selected to perform tasks are in region 1, due to relatively smaller distance from outsourcing manufactures or the plant except supplier (21, 7). Supplier (21, 7) is selected since it is the nearest one that can provide resource 7 to the plant in (5, 10) and has the lowest cost. The objective function value of the initial solution is $134,009. The detailed cost elements are listed in Table 11. We continue with the proposed optimization model to reach the optimal solution that minimizes the total cost. The configuration of the optimal supply chain network is shown in Fig. 7. In the optimal solution, the plant moves to (8, 6), whose location is the same as the outsourcing manufacturer M3. Task 1 and task 3 will still be performed at outsourcing manufactures M1 and M6, respectively. Task 2 will be performed by outsourcing manufacturer M3. The semi-finished products from M3 will be purchased and transported to the plant. To provide resource 7, the supplier at (4, 7) is selected in place of (21, 7) for a lower cost. Note that when calculating reconfiguration cost that relates to M3, since the relationships between M3 and the suppliers (i.e., (5, 3), (7, 4), (8, 5), (11, 6), (14, 8), (15, 9), (17, 10), and (23, 13)) are

Fig. 5. Location of suppliers, outsourcing manufacturers and the plant before reconfiguration. 224

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Table 7 Values of

o, f

and po, f .

Region

(dollars/unit)

o, f

1 2 3 4

po,f

290 260 230 260

0.04 0.07 0.10 0.07

Table 8 Values of factors that relate to task t. (x, t)

x,t

(O1, 1) (O2, 1) (O3, 1) (O4, 1) (M1, 1) (M2, 1) (O1, 2) (O2, 2) (O3, 2) (O4, 2) (M3, 2) (M4, 2) (O1, 3) (O2, 3) (O3, 3) (O4, 3) (M5, 3) (M6, 3)

Table 9 Values of

(dollars/unit)

35 30 25 30 25 20 105 95 85 95 95 85 45 40 35 40 25 35

i, o

and

0.04 0.07 0.10 0.07 0.01 0.04 0.07 0.10 0.13 0.10 0.04 0.07 0.04 0.07 0.10 0.07 0.07 0.01

In the same region In different regions

m, o

and

1.0 1.0 1.0 1.0 1.0 1.0 1.2 1.2 1.2 1.2 1.2 1.2 1.1 1.1 1.1 1.1 1.1 1.1

– – – – 5.5 5.0 – – – – 7.0 6.5 – – – – 5.5 6.0

Fig. 6. Supply chain network of the initial solution. Table 11 Cost elements and total cost in case study solutions. Solution

Initial Optimal i, o

(dollars/unit/unit distance)

2 2.5

i, o

Cost Element ($)

Total Cost Z = MC + PC + RC + TC

MC

PC

RC

TC

42,515 30,208

74,738 98,787

0 556

16,756 3,907

134,009 133,458

(dollars)

100 200

m, o .

Relative location of m and o In the same region In different regions

0.96 0.93 0.90 0.93 0.98 0.95 0.96 0.93 0.90 0.93 0.98 0.95 0.96 0.93 0.90 0.93 0.92 0.98

prm,t (%)

t

t

i, o .

Relative location of i and o

Table 10 Values of

px , t

m, o

2.4 3

(dollars/unit/unit distance)

m, o

(dollars)

120 240

identical to the dotted lines in the initial supply chain network, these (i, m) pairs will have no contribution to the reconfiguration cost. Besides, although the plant and M3 are at the same location, the (m, o) pair between them will have maximum impact when calculating reconfiguration cost. The optimal total cost of the manufacturing enterprise is $133,458. The cost elements and total cost of the initial and optimal solution are compared in Table 11. It is clear that the optimal solution has a lower transportation cost TC and manufacturing cost MC, but a higher procurement cost PC since task 2 is performed at outsourcing manufacturer M3 instead of at the plant. In summary, by outsourcing task 2 to manufacturer M3 in the optimal solution, although it will bring up procurement cost and an additional reconfiguration cost comparing to the initial solution, the decrease of manufacturing cost and transportation cost are more significant, which leads to a lower total cost in the optimal solution. When comparing the initial supply chain networks in this paper (outsourcing and risks considered, “with O&R”) to the ones in the authors’ previous work [9] (without outsourcing or supply chain risks, “without O&R”), we notice that the initial networks are almost identical

Fig. 7. Supply chain network of the optimal solution.

in supplier selection, but two outsourcing manufacturers M1 and M6 are selected to perform the tasks in “with O&R”. When comparing the optimal supply chain networks in “with O&R” versus “without O&R”, all three tasks in “with O&R” are performed by outsourcing manufacturers, which reflects the higher priority of manufacturing outsourcing in this paper. Moreover, supplier (21,7) is replaced by (4,7) in “with O&R”, 225

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which also shows the influence of supply chain risks on supply chain reconfiguration, since supplier (21,7) is located in a high-risk region (region 3). Further, because of the combined effects of outsourcing and risks, the plant location in the optimal solution is also different.

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5. Conclusion and discussion Making reconfiguration decisions requires a system-level optimization that involves many factors such as manufacturing tasks, outsourcing, supply chain configurations, as well as risks. In this paper, the optimization of manufacturing supply chain reconfiguration is studied, considering outsourcing decisions and supply chain risks. The reconfiguration cost is quantified by developing a graph-based model to characterize the relationship between reconfiguration cost and the similarity between two supply chain networks. Two illustrative examples are provided to demonstrate the proposed graph-based model. A manufacturing supply chain for laptop computer is employed as the case study to demonstrate the effectiveness of the proposed method. The proposed method can serve as a decision-support tool for the supply chain reconfiguration optimization problem. In this study, risks in supply chain are modelled as risk factors towards calculating the reconfiguration cost. Supply chain risk analysis, however, is a vast area. It will be of great interest to analyze the impact of risk propagation on supply chain reconfiguration in future works. Although investigating the optimization algorithms for the formulated mixed integer linear programming problem is beyond the scope of this work, it will be an interesting direction for future efforts. Solution algorithms with high computational efficiency will be needed to solve large-scale problems, especially when the supply chain configuration gets more complicated and more products are involved. Further, most of the cost factors used in our case study are determined from assumptions or domain knowledge; how to estimate these parameters from real data is another interesting yet challenging future direction. Acknowledgement This research is partially supported by Rutgers University Big Data Pilot Initiative Grant Award. References [1] Koren Y. The global manufacturing revolution: product-process-business integration and reconfigurable systems. John Wiley & Sons; 2010. [2] Lu CX, Zhang SS. Reconfiguration based agile supply chain system. IEEE International Conference on Systems, Man and Cybernetics (SMC). 2001. [3] Komoto H, Tomiyama T, Nagel M, et al. A multi-objective reconfiguration method of supply chains through discrete event simulation. Fourth International Symposium on Environmentally Conscious Design and Inverse Manufacturing. 2005. [4] Osman H, Demirli K. A bilinear goal programming model and a modified benders decomposition algorithm for supply chain reconfiguration and supplier selection. Int J Prod Econ 2010;124(1):97–105. [5] Kristianto Y, Gunasekaran A, Helo P, Sandhu M. A decision support system for integrating manufacturing and product design into the reconfiguration of the supply

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