Increasing reliance on outsourcing has made supplier selection a critical success factor for a supply chain/network. In addition to cost, the synergy among product components and supplier selection criteria should be considered holistically during the supplier selection process. This paper shows this synergy using coupled-attribute analysis. The key coupling attributes, including total cost, quality, delivery reliability, and delivery lead time of the final product, are identified and formulated. A max-max model is designed to assist the selection of the optional combination of suppliers. The results are compared with the individual supplier selection. Management insights are also discussed.
Managing the outsourcing process productively is the key to enhancing competitiveness because for every dollar an industrial company generates, 50 to 90 cents are spent on purchasing [
A company’s primary supply chain goal is to efficiently and effectively provide the required products for its customers. To meet the customer-specified criteria to achieve this aim, a company must choose the best suppliers in order to produce the best finished products. A number of publications have focused on the development of various methodologies to select individual suppliers [
First, the interdependencies between different products and components can affect the choice of suppliers. Synergies may apply when the suppliers that are selected aggregately for a group of products or components outperform the suppliers that are selected separately for individual products or components. With synergy, both buyers and suppliers can be more profitable. One research direction of this synergy is the combinatorial auction, which considers economies of scale and scope. The basic motivation of utilizing a combinatorial auction is the presence of complementarities among items supplied by different suppliers [
Second, the literature pointed out that a different production mode (i.e., made to order (MTO) or made to stock (MTS)) has different supplier selection criteria [
We believe that this work contributes to several areas. First, we aim to develop an analytical model considering the synergies among product components and supplier selection criteria under the production mode framework, thus enhancing the effectiveness of supplier selection. This paper integrates combinatorial optimization with coupling attributes of the final product, which is the real objective of the end user. It also investigates the balance between component attributes and its effect on the production mode when selecting a supplier. Second, we apply the model to a real case and show it to be an appropriate methodology for evaluating suppliers. The results let practitioners know the importance of balance between suppliers. We structure the rest of this paper as follows: Section
The supplier selection literature contains much research studying selection criteria. Dickson [
Several approaches and techniques have been developed to determine an effective supplier selection process. According to Chai et al. [
Much attention has been given to the coordination between procurement and production planning or intervention of suppliers to develop supply chain management systems. Cook et al. [
We evaluate the impacts of different supplier combinations on the finished product performance and identify the optimal combination with the highest performance level. To facilitate the presentation, we summarize Notation and Symbols Used in Section
Suppose that there are
Considering the productivity of supplier combination, the attributes of the final product can be classified as two types: (a) higher values, defined as outputs, which indicate better levels of performance such as product quality, and (b) lower price, defined as inputs, which indicate better levels of performance such as component cost.
For each
Each supplier combination
Constraints (
In this paper, there are two inputs: final product cost and total delivery lead time. In addition, there are two outputs: final product quality and delivery reliability of final product. Cost and quality are key factors in evaluating the performance of finished products, whereas delivery lead time and delivery reliability are key supply chain management performance indicators. Their formulae are given in Section
In this section, we define the inputs and outputs in models (
The purchasing cost of the finished product is
The quality of the finished product is related to its components. We treat the finished product as a system, which may be composed of unreliable components. In order to analyze the system reliability and other related characteristics, we use reliability block diagrams (RBDs). RBDs are widely used in engineering and science for describing the interrelations among components [
In a parallel system, at least one of the units must succeed for the system to succeed. Units in parallel are also referred to as redundant units. Redundancy is a very important aspect of system design and reliability in that adding redundancy is one of several methods for improving system reliability. For example, in a computer with a redundant array of independent disks (RAID), there are many hard disks. To put it another way, if disk A, disk B, or any of the
While many smaller systems can be accurately represented by either a simple series or parallel configuration, there may be larger systems that involve both series and parallel configurations in the overall system. Such systems can be analyzed by calculating the reliabilities for the individual series and parallel sections, respectively. Then, we combine them in an appropriate manner. Such a methodology is illustrated in the example shown in Figure
Product structure.
Delivery reliability (DR) of the final product depends on the delivery reliability of all materials/components. The finished product’s delivery reliability will be lowered when any material is not delivered on time. We define
The popular manufacturing/assembly/delivery mode is a hybrid control between made to order (MTO) and made to stock (MTS). There are inventory and backlog costs when using MTS. However, manufacturing/assembly/delivery time is needed when using MTO, which is infeasible when its time is greater than the customer cycle time. In general, the shorter the time from customer orders arriving to fulfil them, the better. A company will choose different kinds of suppliers after it has chosen a MTO/MTS production mode. The company that uses the just-in-time strategy (a kind of MTO) requests its strategic suppliers to be located nearby in order to fulfil its orders quickly and reliably. Sun et al. [
The case study is based on a real-life supplier selection problem in a large Chinese electronic OEM in Shenzhen. Its competitive advantages are low cost and short delivery lead time. The production of the electronic products is highly complicated and relies on the solid suppliers. After the analysis of component value and consumption, the key components are listed. We apply the proposed methodology to a finished product with seven key components to be purchased.
Figure
Supplier information.
Component | Amount/production mode | Supplier candidates | Quality |
Total delivery lead-time |
Cost (RMB) | Delivery reliability |
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1/MTS | S-11 | 98.5 | 5 | 24 | 99 |
S-12 | 99.7 | 2 | 26 | 98.5 | ||
S-13 | 99.8 | 14 | 36 | 98.8 | ||
S-14 | 99.2 | 7 | 34 | 99.5 | ||
S-15 | 99.6 | 5 | 12 | 99.5 | ||
S-16 | 99.7 | 8 | 35 | 98.8 | ||
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2/MTO | S-21 | 98 | 20 | 75 | 98 |
S-22 | 98.8 | 13 | 70 | 97 | ||
S-23 | 99.2 | 5 | 50 | 96.5 | ||
S-24 | 99.7 | 25 | 55 | 99 | ||
S-25 | 98.5 | 10 | 80 | 98 | ||
S-26 | 99.2 | 3 | 90 | 99.5 | ||
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1/MTS | S-31 | 99.2 | 8 | 150 | 99.5 |
S-32 | 99.5 | 4 | 120 | 99 | ||
S-33 | 98.8 | 5 | 130 | 98.6 | ||
S-34 | 98.5 | 25 | 90 | 98.5 | ||
S-35 | 98.8 | 12 | 80 | 99.5 | ||
S-36 | 99 | 20 | 65 | 98.7 | ||
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2/MTO | S-41 | 99.2 | 15 | 30 | 98.5 |
S-42 | 97.6 | 45 | 95 | 96.5 | ||
S-43 | 98.5 | 15 | 65 | 98.8 | ||
S-44 | 98.4 | 27 | 83 | 96.5 | ||
S-45 | 99.2 | 31 | 65 | 98.7 | ||
S-46 | 99.5 | 18 | 65 | 98.6 | ||
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2/MTO | S-51 | 99.2 | 8 | 200 | 98.5 |
S-52 | 99.2 | 5 | 200 | 98.6 | ||
S-53 | 98.5 | 12 | 250 | 98.3 | ||
S-54 | 98.7 | 13 | 180 | 99 | ||
S-55 | 98.7 | 12 | 160 | 99.5 | ||
S-56 | 98.8 | 16 | 210 | 99 | ||
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3/MTS | S-61 | 99.6 | 25 | 30 | 99.5 |
S-62 | 98.8 | 13 | 36 | 98.7 | ||
S-63 | 98.5 | 23 | 45 | 98.5 | ||
S-64 | 99.2 | 25 | 48 | 98.8 | ||
S-65 | 99.3 | 19 | 65 | 99.5 | ||
S-66 | 99.2 | 15 | 55 | 98 | ||
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3/MTO | S-71 | 98.8 | 12 | 35 | 98 |
S-72 | 99.5 | 18 | 30 | 98.5 | ||
S-73 | 98.5 | 18 | 40 | 99 | ||
S-74 | 97.7 | 21 | 32 | 98.5 | ||
S-75 | 99.5 | 5 | 38 | 99.5 | ||
S-76 | 98.4 | 13 | 35 | 97.5 |
We assume that the company can decide on how to produce or assemble each component/semifinished product/finished product, using either MTO or MTS. Although different potential suppliers can have different delivery lead times, the in-house production lead times for
As shown in Table
Top ten supplier combinations.
Number | Supplier combination | Coupling values for final product | Score | |||||||||
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Quality | Cost | Delivery lead-time | Delivery reliability | ||
85 | S-15 | S-23 | S-36 | S-41 | S-55 | S-61 | S-72 | 87.23% | 754 | 39 | 91.03% | 1 |
87 | S-15 | S-23 | S-36 | S-41 | S-55 | S-62 | S-72 | 85.14% | 772 | 34 | 90.30% | 0.9746 |
86 | S-15 | S-23 | S-36 | S-41 | S-55 | S-61 | S-75 | 87.23% | 778 | 39 | 91.95% | 0.9713 |
21 | S-12 | S-23 | S-36 | S-41 | S-55 | S-61 | S-72 | 89.19% | 782 | 39 | 90.12% | 0.9677 |
81 | S-15 | S-23 | S-36 | S-41 | S-52 | S-61 | S-72 | 87.67% | 794 | 39 | 90.21% | 0.9512 |
88 | S-15 | S-23 | S-36 | S-41 | S-55 | S-62 | S-75 | 85.14% | 796 | 35 | 91.22% | 0.9463 |
23 | S-12 | S-23 | S-36 | S-41 | S-55 | S-62 | S-72 | 87.05% | 800 | 35 | 89.39% | 0.9427 |
22 | S-12 | S-23 | S-36 | S-41 | S-55 | S-61 | S-75 | 89.19% | 806 | 39 | 91.03% | 0.9399 |
83 | S-15 | S-23 | S-36 | S-41 | S-52 | S-62 | S-72 | 85.57% | 812 | 35 | 89.48% | 0.9274 |
82 | S-15 | S-23 | S-36 | S-41 | S-52 | S-61 | S-75 | 87.67% | 818 | 39 | 91.12% | 0.9243 |
For comparison, individual supplier selection using data envelopment analysis (DEA) has been carried out. The top two suppliers selected by DEA are S-12 and S-15 for
The optimal supplier combination is number 85, which is the first row in Table
In this section, we reveal that the supplier selection is related to other components’ suppliers and production modes. We should balance all the attributes of different components. The best supply network should harmonize itself with the supply network structure, production modes, and supplier attributes. The performance of a supply network will decrease if the coordination between suppliers is worse. One study of the US food industry estimated that poor coordination among supply chain partners wasted $30 billion annually [
In this subsection, which discusses synergy among component attributes, we change the attribute values of components by a trial-and-error approach in order to determine how the supplier combinations are affected. We adjust two attribute values of components to study their relationships provided that the best supplier combination remains unchanged. The attribute value of the first component is manipulated at discrete points. We calculate the range of attribute values of the second component to keep the best supplier combination unchanged provided that the values of other components are not changed. Figure
Synergy effect among components
The four plots reveal the
The manufacturing company should decide which component is made to order or stock. Sun et al. [
We change a component’s production mode, for example, by setting
If the manufacturing company could obtain excellent, additional suppliers at a reasonable cost, it would change its production mode. Hewlett Packard manufactures its printers in the United States and delivers them to markets all over the world after several months of ocean shipping. In this situation, the total delivery lead time is so long that Hewlett Packard has to forecast the demand in advance and bear the risk of forecast error. After researching its supply chain and product design, Hewlett Packard successfully developed several of their printers around modular components to benefit from postponing the point of differentiation in their manufacturing and assembly processes. Finally, they postponed the last assembly into local markets [
We examine how the production mode will change when the production time of a component is changed. We divide the components into two groups: cost factors and production time. In order to find the relation between the lead time parameters and the optimal production mode, in one group these factors are fixed, and in another group they are varied. Table
What-if analysis based on the change of lead time of suppliers.
The lead time of components | The production mode change | ||
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Short |
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0 |
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0 |
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0 | |
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0 | |
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0 | |
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0 | |
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0 | |
Long |
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0 |
Table
The parameters of the supply network mode are integrated; thus, some functions of the supply network, such as supplier selection, should be considered from a whole system point of view. Most papers give the criteria based on a single supplier of product quality, price, and delivery time. In general, a supplier with a short delivery time should have a high price. However, this is a question of whether or not companies should pay higher prices for shorter delivery times. Based on our model, the total time to convert raw material to finished goods is related to the total production time of materials/components. Therefore, a supplier offering a shorter delivery time cannot always decrease the total time needed to convert raw materials to finished goods. For example, if a component is in a noncritical path of the supply network, the total time will be constant in a range of the component’s lead time. This allows the product manager to choose an external raw material vendor with lower capability (i.e., with lower production cost and longer processing lead time).
This paper aims to develop an analytical model that describes the synergies among product components and supplier selection criteria that enhance supplier selection effectiveness. A max-max model was designed to facilitate the selection of the optional combination of suppliers. The synergies are identified using coupled-attribute analysis.
This paper integrates combinatorial optimization with coupling attributes of the final product, which is the real objective of the end user. Four coupling attributes are identified, including final product cost, final product quality, delivery reliability, and delivery lead time of final products. This paper also investigates the balance among component attributes and the effect on the production mode when selecting a supplier. The production time is calculated under the defined supply structure, lead time of suppliers, and production mode. The effect of supplier selection on the production mode is measured by using a different experimental scenario.
The model is applied to a real case and is shown to be an appropriate methodology for evaluating suppliers. The real case demonstrated that the best supply network should harmonize itself with the supply network structure, production modes, and supplier attributes. The what-if analysis showed that the parameters of the supply network mode are integrated; thus, some functions of the supply network, such as supplier selection, should be considered from a whole system point of view.
Further research into the problem of supplier selection may encourage the development of experimental design or heuristics algorithms to explore how to improve the performance supplier combination among many supplier candidates or multiple components considering numerous levels of supplier attributes.
The number of components types
The number of suppliers for each component
The supplier
The vector represents the supplier combination as sequenced
Representing the value of the
The number of inputs of supplier combination
The number of outputs of supplier combination
A set of factor weights given to the
The author declares that he has no conflicts of interest.
This research was supported by Natural Science Foundation of Shaanxi (2017JM7009) and Young Talent Scheme of Xi’an Jiaotong University, China.