An emergy-based sustainability evaluation method for outsourcing machining resources

An emergy-based sustainability evaluation method for outsourcing machining resources

Journal Pre-proof An emergy-based sustainability evaluation method for outsourcing machining resources Wei Cai, Conghu Liu, Shun Jia, Felix T.S. Chan...

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Journal Pre-proof An emergy-based sustainability evaluation method for outsourcing machining resources

Wei Cai, Conghu Liu, Shun Jia, Felix T.S. Chan, Minda Ma, Xin Ma PII:

S0959-6526(19)33719-9

DOI:

https://doi.org/10.1016/j.jclepro.2019.118849

Reference:

JCLP 118849

To appear in:

Journal of Cleaner Production

Received Date:

20 September 2018

Accepted Date:

10 October 2019

Please cite this article as: Wei Cai, Conghu Liu, Shun Jia, Felix T.S. Chan, Minda Ma, Xin Ma, An emergy-based sustainability evaluation method for outsourcing machining resources, Journal of Cleaner Production (2019), https://doi.org/10.1016/j.jclepro.2019.118849

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Journal Pre-proof Amount of words:6482

An emergy-based sustainability evaluation method for outsourcing machining resources Wei Cai a, Conghu Liu b, c *, Shun Jia d, Felix T.S. Chan e, Minda Ma f, Xin Ma g a. College of Engineering and Technology, Southwest University, Chongqing, 400715, China b. Sino-US Global Logistics Institute, Shanghai Jiao Tong University, Shanghai, 200030, China c. School of Mechanical and Electronic Engineering, Suzhou University, Suzhou, 234000 d. Department of Industrial Engineering, Shandong University of Science and Technology, Qingdao 266590, China e. Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hung Hum, Hong Kong f. School of Construction Management and Real Estate, Chongqing University, Chongqing, 400045, China g. School of Science, Southwest University of Science and Technology, Mianyang, 621000, China

Abstract: Outsourcing has become an effective production and management measure to increase resource utilization efficiency and production efficiency. However, performing a sustainability evaluation of the outsourcing faces some difficulty in constructing a unified dimensional evaluation model for integration of the quality, time, logistics cost and resources consumption. Therefore, this paper proposes a sustainability evaluation method for the outsourcing machining resources for improving resource utilization efficiency. Firstly, the scope and boundary are defined, and the processes of the method and preparation are illustrated. Then, four important emergy models are established including the production quality emergy, production time emergy, production logistics cost emergy and production resources consumption emergy models. On this basis, an integrated assessment is performed to acquire a best plan of outsourcing machining, and the corresponding evaluation indicators can be determined. Finally, results and discussion illustrate the practicability of the proposed method laying a foundation for sustainability evaluation and selection of the outsourcing machining resources. Keywords: Sustainability evaluation; Resource utilization efficiency; Outsourcing machining; Emergy

* Corresponding author. Sino-US Global Logistics Institute, Shanghai Jiao Tong University, Shanghai, 200030, China E-mail address: [email protected]

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Journal Pre-proof 1. Introduction In view of natural resource consumption, environmental degradation, resulting climate warming, policy makers heightened attention on ecological modernization, green growth, and low carbon development, with some sustainability strategies and methods (Cai et al., 2019a; Ma et al., 2018; Xu et al., 2018a, 2018b). A large number of methods and measures (i.e. various algorithm, tools) have been widely applied to the industry to improve the energy, resources, environment and economic performance (Ding et al., 2018; Ma et al., 2019). Manufacturing industry plays a pivotal role in the national economies of countries all around the world (Jiang et al., 2019; Cai et al., 2019b). Yet, the manufacturing industry consumes a huge amount of natural resource and exert considerable pressure on the environment, performing the transformation process from manufacturing resources to products (Balogun et al., 2018; Zhao et al., 2019; Jia et al., 2018). Therefore, due to the large amount of energy and resources usage in a low efficiency, the manufacturing has considerable potential of the energy conservation and emission reduction (Lv et al., 2019; Cai et al.,2017c). Outsourcing production has become a sustainability strategy for the manufacturing enterprises and benefits for coordinating energy and recourses of the manufacturing enterprises to enhance the core competitiveness and rapid response to the market. The outsourcing production performs some significant advantages among different manufacturing enterprises including the utilization and concordance of production equipment, production technologies and management methods (Cachon et al., 2002). The outsourcing production has been regarded as an effective production and management measure to improve the resource utilization efficiency and production efficiency and to decrease the production cost (Hoecht and Trott 2006). The research on outsourcing has aroused wide attentions. A measure of outsourcing was proposed to improve economic benefits by the reconfiguring the organization and reducing the transaction costs (Mccarthy and Anagnostou 2004). To guide managers to measure the sustainability of the international outsourcing decisions, a method was addressed for quantifying some impacts of the environmental, social and economic sustainability via using input output analysis (Moosavirad et al.,2014). Due to the certain risks of outsourcing with oftentimes difficult to assess, a risk evaluation framework of outsourcing e-procurement services through a strengths, weaknesses, opportunities and threats analysis was studied, and this method can derive overall risk magnitudes (Ramkumar et al., 2016). Strategic outsourcing decisions were as a measure for two competing manufacturers from perspectives of vertical and horizontal product differentiation on demand by the Subgame Perfect Nash Equilibria (Xiao et al., 2014). the capacity of a large number of outsourcing dimensions should be considered to developa dynamic programming-based algorithm and solved this problem in polynomial time (Zhang et al., 2015).A cooperative quality investment strategy and a simple proportional investment sharing scheme in the outsourcing were analyzed (Chen et al., 2015). These research results provide support for the optimization of the outsourcing process. Besides, at aspects of the resource commitment, innovation and collaboration, a value 2

Journal Pre-proof co-creation in an outsourcing arrangement between manufacturers and third-party logistics providers contributes to understanding how customers and other actors engage with the companies in collaborative value creation activities (Sinkovics et al., 2018). Through establishing an integrated multi-objective optimization framework for software maintenance, component evaluation and selection, it is a measure of understanding outsourcing, redundancy and customer to customer relationship (Gupta et al., 2019). For the cloud manufacturing (CMfg) environments, an outsourcing service selection method using artificial neural network (ANN) and shuffled frog leaping algorithm (SFLA) algorithms for cement equipment manufacturing enterprises promote the production efficiency (Wang et al., 2019). An analytic hierarchy process (AHP) was adopted based on 34 experts’ responses from Korean pharmaceutical industry to rank a set of different criteria substantial for establishing a new outsourcing relationship (Song 2019). A robust multi-criteria decision-making (MCDM) tool based on the integrated use of fuzzy Step-wise Weight Assessment Ratio Analysis and weighted fuzzy axiomatic design methods was proposed for use in the outsourcing provider selection problem (Perçin 2019). a two-stage decision-making framework was developed using MCDM-NLMIP model to elect supplier portfolio of key outsourcing parts (Zhou et al., 2019). To select the best outsourcing firm for waste of electrical and electronic equipment, a multi criteria decision making methodology based on hesitant fuzzy enveloped TOPSIS was proposed (Erdoğan and Kaya 2018). Analyzing drivers of outsourcing were important for establishing their selection criteria of 3rd party logistics service providers from the manufacturers working in different industries (Momeni and Vandchali 2017). Designing an interval-valued hesitant fuzzy-decision approach contributes to selecting IT outsourcing services’ activities considering services cost and risks (Ebrahimnejad et al., 2017). Using an integrated DEMATEL and Fuzzy ANP techniques was an effect method for evaluating and selecting outsourcing providers for a telecommunication company (Uygun et al., 2015). Furthermore, some studies examined the extent to which supply chain outsourcing objectives determine the selection criteria for 3PL service providers in India (Samgam and Shee 2017). selecting the outsourcing activities through the hybrid model of feature selection was considered based on Kohonen network and slack-based measure (Razi 2017). The vendor selection in mining industries depended on varieties of conflicting tangible and intangible criteria, the suitable vendor selection was analyzed for the production process in the green mining industries (Sivakumar et al., 2015). A novel hybrid MCDM approach offered a new way for outsourcing supplier selection and used in pipe and fittings manufacturing (Rezaeisaray et al., 2016). Machining are widely distributed and consume large amounts of energy and resources in low efficiency, which has considerable potential for energy saving and resources usage (Hu et al., 2018). The outsourcing machining is an approach of the low carbon manufacturing that helps to improve resource utilization efficiency and production efficiency. For the outsourcing machining, the leading enterprises give some of production processes with low value added to the outside of the professional machining resources to complete by means of principal/agent contract to achieve the low cost, high-quality, environmentally friendly protection and to enhance the core competitiveness of enterprises. For the leading enterprise, the outsourcing machining resources 3

Journal Pre-proof have the following drivers. Firstly, along with the trend of globalization of manufacturing industry, the selection of outsourcing resources is wide. Secondly, the quality and production efficiency of the outsourcing machining with uncertainty is difficult to control, resulting in affecting the production planning and product quality of the leading enterprise. Therefore, how to optimize the selection of outsourcing machining enterprise becomes a challenge to enhance the core competitiveness, rapid response to market changes, meet the diverse needs of customers, reduce energy conservation and emission reduction pressures for the leading enterprise. In face of the bibliographic review, current studies have great progresses for the evaluation of the outsourcing resources. However, studies on outsourcing machining resources have some problems that need to be resolved, in following three aspects: 

The existing studies for outsourcing machining resources mostly consider the quality, cost and production time. However, the integrated studies are insufficient for sustainability evaluation from perspectives of the energy, resource, environment and economic.



The complexity of machining processes results in difficulty in establishing evaluation system because of the lack of an effective method.



This is a problem about how to establish a unified dimensional evaluation model for integration of the quality, cost, energy and material in outsourcing machining.

To solve above problem of outsourcing machining resources, taking the mechanical parts as the object, a novel sustainability evaluation of outsourcing machining resources is proposed based on emergy method However, it is a difficulty to construct a unified dimensional evaluation model integrating the quality, cost, energy and material in the outsourcing machining. The emergy theory is an excellent economics analysis tool and can measure the performance of different dimension systems (Odum 1996). The evaluation of environmental sustainability was performed using the emergy indicators (Arbault and Rugani 2014). Sustainability assessment of different energy options for green buildings was based on emergy providing a novel way to integrate different environmental impacts and facilitate better system choices (Luo et al., 2015). An optimization of biodiesel supply network design using emergy sustainability index provides a significant help for the most sustainable design and planning (Ren et al., 2015). Sustainability evaluation was extended to the remanufacturing machining systems providing a new method for studying the sustainability evaluation of manufacturing systems (Liu et al., 2018). This paper offers both the theoretical and practical contributions. This proposed method can achieve the unity of measure for different performance indicators, which performs the quantitative evaluation of outsourcing machining resources to offer the analysis basis towards the circular economy. This method can clearly make a direction to the operator and managers about how to optimize the outsourcing machining resources, which improves the resource utilization efficiency in outsourcing for enterprise manager and realizes sustainable development of manufacturing enterprises.

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Journal Pre-proof 2. Materials and Method 2.1 Scope and definition The study aims at performing the sustainability evaluation of outsourcing machining resources using the emergy to promote resource utilization efficiency and production efficiency. The functional unit considered in this study is a mechanical part as a typical mechanical manufacturing product. The outsourcing machining cycle of mechanical part is, in principle, a cradle to grave exercise. However, in some cases cradle to gate, gate to gate, gate to cradle or, more recently, cradle to cradle approaches are possible (Song et al., 2016). In the case of the mechanical part, the approach can only be gate to gate. The gate is regarded as the raw material or semi-finished part (or workpiece), the other gate is regarded as the mechanical part that is qualified. Therefore, the whole outsourcing machining processes include the production quality, efficiency, cost and the consumption of energy, material, services and waste from the raw material or semi-finished workpiece to the mechanical part in the machining plant. With the emergy diagram (Almeida et al., 2018a; Almeida et al., 2018b) of outsourcing machining resource in Fig. 1, this paper can evaluate the sustainability of the outsourcing machining resources by emergy conversion efficiency, which is used to measure the production quality, efficiency and cost, energy consumption, material use and service, as well as emissions during the outsourcing machining process within the unit production time. Raw material

Renewable energy and resources

Energy

Product ion material

Seviers

Water Output Parts

Outsourcing machining Environmental Systems Industrial waste Degraded Energy

Fig.1. Emergy system diagram of outsourcing machining resource

For the outsourcing machining, some issues need to be considered. The leading enterprise has a number of outsourcing resources available for selection of the external task. Due to different machining resources, production capacity and management level, the ability outsourcing resources is different. Therefore, there are some hypothesis: 

There are a lot of outsourcing machining resources available, and all the outsourcing machining resources are certified by the leading enterprise quality.

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Journal Pre-proof 

For each external task, in a certain period of time, the production capacity and resources of the outsourcing machining resources are determined.



The information of the actual operation and production system of the outsourcing is clear.



The processing parameters and statistical data are accurate and reliable.

2.2 Preparation To perform the sustainability evaluation of outsourcing machining resources, it is necessary to analyze the outsourcing machining and evaluation method. Dimensions of the production quality, efficiency, cost and the consumption of energy, material, services and waste are entirely different. Meanwhile, the weight of each index by artificial or fuzzy processing is difficult to compare among different outsourcing resources, which results in difficulty in the evaluation and selection of outsourcing resources. Therefore, it is the first step to create a unified dimension model to quantify these indexes. The schematic diagram is shown in Fig 2.

Fig 2. Emergy-based sustainability evaluation method flow

In the evaluation flow for outsourcing machining resources, it can be found that the dimensions unification is the key to perform the evaluation through the emergy theory. Emergy theory originated in the late 1980s, is a new ecological economic value theory and system analysis method by the famous American ecologist Odum led creation. The total amount of available energy directly or indirectly put into operation in the process of product or service formation is the emergy value. Essentially, it's the embodied energy (Odum 1996). The method is to express different forms of energy in the biosphere by using the uniform dimension all transformed into solar energy, and the Solar energy is a unit of seJ (solar-equivalent Joule). For the energy, material, service and waste of outsourcing machining processes, using the emergy approach to convert different types of units into a uniform metric is easy-to-use, and it can still realize the measure of the quality, cost and efficiency of outsourcing products, which offers a 6

Journal Pre-proof quantitative method for the optimization selection of outsourcing machining resources. Due to difference in dimensionality for resources, products or services, resulting in different emergy values, the transformity can represent the energy level system of different types (Liu et al., 2016). The basic expression for the emergy is: 𝐸𝑀 = 𝑈𝐸𝑉 × 𝑁 (1) Where 𝐸𝑀 is the value of solar energy. 𝑈𝐸𝑉 (Unit Emergy Value, UEV) is the emergy conversion ratio of different materials. 𝑁 represents an input stream of different units (mass m or joule J). The energy flows in a system can be compared and calculated using the emergy method by calculating the emergy of different input streams. Emdollar value is the economic value of the energy flow (Arbault et al., 2014; Odum 1996). Emdollar value is the emergy in the economic market value reflected form, and its basic expression is: 𝐸𝑀 = 𝐸𝑀𝑅 × 𝑈

(2)

Where, 𝐸𝑀𝑅 is the emergy to money ratio, which represents the amount of emergy per unit currency (unit: sej/$), and 𝑈 is the monetary value of the economic system or economic activity. The emergy is an effective method for analyzing various types of resources, energy and service, and it could convert different types of units into a uniform metric. From the point of view of leading enterprises, the advantages and disadvantages of outsourcing machining resources can be quantified towards a circular economy. Therefore, it arises out of theoretical significance and practical value to establish an emergy-based sustainability evaluation model for outsourcing machining resources.

2.3 Data and modelling According to above analysis, the production quality, efficiency and cost, and energy consumption, material consumption, service consumption and waste are considered as evaluation objectives Therefore, this section establishes some emergy models to acquire the production quality emergy (PQEm), production time emergy (PTEm), production logistics cost emergy (PLEm), production resources emergy (PREm) and production integration emergy (PIEm) for the mechanical part in outsourcing machining, respectively. Meanwhile, the production total emergy (PTEm) of all items for the PQEm, PTEm, PLEm, PREm and PIEm are also an important index, which is necessary to be considered, to measure the sustainability of outsourcing machining.

2.3.1 PQEm In the production process, quality deviation of the mechanical part is frequently found because of the error of some links. The quality deviation results in varying degrees of quality loss. In this paper, the quality of the target is used (Liu et al., 2018), and we put forward the quality loss function of the mechanical part from the quantitative point of view to measure the quality loss of the mechanical part of different outsourcing resources. Realistically, a mechanical part generally has multiple processed quality attributes. If the 7

Journal Pre-proof mechanical part is represented as 𝑥, the processed quality attributes is represented as 𝑛. Thus, the quality loss of the average unit of the mechanical part is as follows. 𝑛

𝑙(𝑥)𝑞 =

∑𝑖 = 1𝑘𝑖[𝑓(𝑥𝑖) ― 𝑓0(𝑥𝑖)]2

(3)

𝑛

Where, 𝑓(𝑥𝑖) is the output of the quality value, 𝑓0(𝑥𝑖) is standard quality value. The more𝑓

(𝑥𝑖) is far away from𝑓0(𝑥𝑖), the greater the loss. Thus, it is better to the closer 𝑓(𝑥𝑖) get to target value 𝑓0(𝑚𝑥𝑖) or standard quality value𝑓0(𝑥𝑖) the better. The Eq. (3) can be used to measure the loss of the output of quality value through the comparison with the standard quality value 𝑓0

(𝑥𝑖) of the i-th quality attribute 𝑚𝑖. 𝑘𝑖 is quality loss constant and could be acquired by enterprise production data (Liu et al., 2018). The description is as follows. 𝑘𝑖 =

𝐵𝑖0

(4)

∆2𝑖0

Where, 𝐵𝑖0 is business loss while the mechanical is in failure, and ∆𝑖0 is functional limit of the quality attribute 𝑚𝑖. Therefore, the PQEm can be determined. 𝑃𝑄𝐸𝑚(𝑥) = 𝑙(𝑥)𝑞 ∙ 𝐸𝑀𝑅

(5)

Where, 𝑃𝑄𝐸𝑚(𝑥) is the production quality-emergy of the mechanical part.

2.3.2 PTEm Production efficiency that is delivery time is an important index for evaluating outsourcing resources ability, which affects the production schedule and inventory costs of the leading enterprises. All outsourcing machining resources to delivery time is different, and this paper adopt Just in time (JIT) production mode, namely to delivery time is determined. Early or late would affect the loss of money. The 𝑡(𝑥𝑖) is the delivery time of the mechanical part 𝑥𝑖. The enterprise to set the time is 𝑡0(𝑥𝑖). ∆𝑡(𝑥𝑖) = 𝑡(𝑥𝑖) ― 𝑡0(𝑥𝑖)

(6)

In this paper, the ∆𝑡(𝑥𝑖) can be affected by the production plan, scheduling, inventory and production capacity. The time loss monetary value of the leading enterprise is fitted by the multivariate nonlinear pure three equation. The average unit time loss function is as follows. 𝑛

𝑙(𝑥)𝑡 =

∑𝑖 = 1{𝑎0 + 𝑎1 ∙ ∆𝑡(𝑥𝑖) + 𝑎2 ∙ [∆𝑡(𝑥𝑖)]2 + 𝑎3 ∙ [∆𝑡(𝑥𝑖)]3} 𝑛

(7)

Where, 𝑎0, 𝑎1, 𝑎2 and 𝑎3 are fitting coefficients. The fitting coefficients can be easily obtained by using the Newton Gauss iteration method. The basic starting point of Gauss Newton method is the nonlinear model of linear approximation, least squares approximation for model estimation, and then iterative calculation, multiple regression coefficient correction, regression coefficients approaching optimal regression coefficients of nonlinear regression model, finally squared residuals of the original model and reached the minimum. On this basis, therefore, the PTEm can be determined. 𝑃𝑇𝐸𝑚(𝑥) = 𝑙(𝑥)𝑡 ∙ 𝐸𝑀𝑅 8

(8)

Journal Pre-proof 2.3.3 PLEm Production logistics cost for supply chain of outsourcing machining is one of important indicators to choose the outsourcing machining resources. The cost is the quotation of the enterprise mainly comprising transit stock cost, transportation cost, etc. It does not include Energy, material, service and waste consumption in the production process of outsourcing machining. If mechanical parts 𝑛 need to be processed, the average unit cost is as follows. 𝑛

𝑙(𝑥)𝑐 =

∑𝑖 = 1𝑐(𝑥𝑖)

(9)

𝑛

Where, 𝑐(𝑥𝑖) is the cost of 𝑥𝑖. On this basis, therefore, the PLEm can be determined.

(10)

𝑃𝐶𝐸𝑚(𝑥) = 𝑙(𝑥)𝑐 ∙ 𝐸𝑀𝑅

2.3.4 PREm Energy, material, service and waste for the mechanical part of resources consumption are important for measuring the sustainability and production capacity of outsourcing machining. In the outsourcing machining, the electricity and diesel are the main energy sources. Main materials are raw materials and semi-finished workpiece. The auxiliary materials are necessary such as water, alloy powder, lubricating fluid, etc. Main services include equipment, fixture, management personnel wages and welfare expenses, workshop buildings, etc., to maintain the production and operation. The waste comprises the waste gas, waste liquid and solid waste. To simplify the calculation, the production resources consumption emergy that includes the emergy of energy, material, service and waste for the mechanical part can be determined: 𝐸

𝑀

𝑆

𝑊

𝑖 𝑖 𝑖 𝑖 𝑃𝑅𝐸𝑚(𝑥) = ∑𝑖 𝑙(𝑥)𝑖e × 𝑈𝐸𝑉e + ∑𝑖 𝑙(𝑥)𝑖𝑚 × 𝑈𝐸𝑉m + ∑𝑖 𝑙(𝑥)𝑖𝑠 × 𝑈𝐸𝑉s + ∑𝑖 𝑙(𝑥)𝑖𝑤 × 𝑈𝐸𝑉w (11)

Where, 𝑙(𝑥)𝑖e, 𝑙(𝑥)𝑖𝑚, 𝑙(𝑥)𝑖𝑠 and 𝑙(𝑥)𝑖𝑤 are i-th function of the average unit energy consumption, material consumption, service generation and waste generation. 𝑈𝐸𝑉𝑖𝑒, 𝑈𝐸𝑉𝑖𝑚, 𝑈𝐸𝑉𝑖𝑠 and 𝑈𝐸𝑉𝑖𝑤 are i-th emergy conversion ratio of the average unit energy consumption, material consumption, service generation and waste generation. E, M, S and W are the total number of the average unit energy consumption, material consumption, service generation and waste generation.

2.4 Integrated assessment and indicators PIEm is an integrated assessment indicator to measure the sustainability of outsourcing machining. In terms of above analysis on the PQEm, PTEm, PLEm and PREm, the PIEm for the outsourcing machining can be simply acquired.

𝑃𝐼𝐸𝑚(𝑥) = 𝑃𝑄𝐸𝑚(𝑥) +𝑃𝑇𝐸𝑚(𝑥) +𝑃𝐿𝐸𝑚(𝑥) +𝑃𝑅𝐸𝑚(𝑥)

(12)

Selection of enterprises and processing workshops for outsourcing machining resources is important decision-making issues. Selecting a sustainable enterprise and processing workshop for the production capacity, production efficiency and resource utilization is an important safeguard for the processing. Therefore, the production quality, time and logistics cost, and resources consumption are make as the decision objectives from all the enterprises and 9

Journal Pre-proof processing workshops. These objectives can be represented by emergy including PQEm, PTEm, PLEm, and PREm. Through collecting the historical information of previous processed mechanical parts among different enterprises or processing workshops, the integrated evaluation method can be as an effective method for obtaining the best enterprise or processing workshop considering sustainability, which provides a processing selection.

(13)

𝑃𝐼𝐸𝑚(𝑥)𝑏𝑒𝑠𝑡 = min 𝑃𝐼𝐸𝑚(𝑥)

Emergy of formula (13) can realize the quantitative evaluation of the outsourcing resources. It provides decision support for optimizing the selection of outsourcing processing. The smaller emergy show the more comprehensive ability, the greater the possibility of selection.

3. Results and discussion 3.1 Background The emergy-based evaluation method for outsourcing machining resources is applied to a manufacturing enterprise of the car parts. The enterprise is mainly engaged in the production of auto parts and components, but its own is not good at heat treatment processing. With the rapid development of the enterprise, building a green supply chain to reduce energy and CO2 emission has become a focus of strategic decision-making. How to realize maximization of economic and environmental that is benefit for meeting the government, the market and the enterprise is a difficulty in managing. Therefore, under the environment of circular economy, it is vital to rescan, evaluate and select the outsourcing resources. In this case, an automobile spare parts processing is taken as an example. The heat treatment process of engine connecting rod has high energy consumption, large investment, and high professional and technical requirements. In order to concentrate on the core competitiveness, the enterprise decided to carry out the heat treatment of connecting rod outsourcing machining. We elaborate on the evaluation of the outsourcing machining resources, which can provide some decision-making supports and selection.

3.2 Results There are four outsourcing machining resources of the connecting rod, the processes of heat treatment processing is mainly used to quenched and tempered after forging, after normalizing and tempering, quenching and tempering. Forging temperature is generally controlled between 1000~ 1200℃ , the control of the final forging temperature within 1000℃ . The processing quality of the various outsourcing resources is shown in Tab. 1. Tab 1. PQE of the various outsourcing resources Outsourcing resources 1 2 3

Processing method Quenched and tempered after forging After normalizing and tempering Quenching and tempering

Metallographic microstructure (T-93)

Hardnes s HB

Decarburized layer /mm

σb/M pa

Level 3

245

< 0.20

988

Level 1

260

< 0.24

1100

Level 2

240

< 0.22

986

10

Surface quality 0.18% Micro crack 0.06% Micro crack 0.05% Micro crack

quality loss ($/pie ce) 1.81 1.42 1.48

Journal Pre-proof 4

Quenching and tempering

Level 2

242

< 0.21

1024

0.05% Micro crack

1.47

Because of the different production and operation management mode, the regional position and the contract agreement of four outsourcing machining resources, the delivery time and lost to the connecting rod as shown in Tab 2. Tab 2. PTEm of the various outsourcing resources Outsourcing resources

1

2

3

4

Delivery time (h)

[-24, 24]

[-18, 18]

[-12, 12]

[-8, 8]

Time loss ($/piece)

2.72

2.31

1.85

1.2

Due to the different distances of the four outsourcing resources, the logistics costs are different, as shown in Tab.3. Tab 3. PLEm of the various outsourcing resources Outsourcing resources

Mode of transport

Distance/km

Logistics Cost ($/piece)

1

Vehicle

30

3.78

2

Truck

60

5.51

3

Truck

55

5.36

4

Train

74

5.7

In the production process of outsourcing machining, the consumption of energy consumption, material consumption, service consumption and wastes of outsourcing resources 1 is shown in Tab 4. Tab 4. PREm of outsourcing resources 1 Category

Energy

Materiels

Services

Wastes

Item

Units(piece)

Type

Transformity (sej/unit)

References

Raw amount

Emergy

Diesel oil

J

N

1.07E+05

Pan et al., 2015

1.31E+07

1.40E+12

Coke

J

N

6.44E+04

Pan et al., 2015

1.34E+08

8.63E+12

Electric

J

N

2.78E+05

Pan et al., 2015

1.04E+07

2.89E+12

Alloy steel

g

N

1.96E+10

Liu et al., 2018

1.75E+02

3.43E+12

Steel-QT500

g

N

1.20E+10

Liu et al., 2018

4.15E+02

4.98E+12

Cleaning liquid

$

N

6.57E+12

Investigation

3.97E+00

2.61E+13

Water

g

R

2.25E+05

Pan et al., 2015

4.50E+02

1.01E+08

Jigs and Fixtures

$

F

6.57E+12

Investigation

2.30E-01

1.51E+12

Equipments

$

F

6.57E+12

Investigation

2.29E+00

1.50E+13

Labor

$

F

6.57E+12

Shen et al., 2019

2.09E+00

1.37E+13

Waste water

g

N

9.16E+09

Investigation

4.50E+02

4.12E+12

Waste oil disposal

$

N

6.57E+12

Investigation

2.67E+00

1.75E+13

Waste Solid

g

N

2.52E+08

Investigation

4.89E+00

1.23E+09

Waste gas

g

N

7.24E+08

Investigation

1.86E+00

1.35E+09

Footnote: Values of specific emergy are relative to the 12.00+E24 seJ /year baseline (Brown and Ulgiati, 2016).

On the basis of the actual production data collection, the project team has calculated the 11

Journal Pre-proof calculation data of the PQEm, PTEm, PLEm, PREm and PIEm of the outsourcing machining resources in Tab 5. Tab 5. The calculation data of outsourcing machining resources PLE

PLEm/

($/piece)

piece

1.79E+13

3.78

2.31

1.52E+13

9.72E+12

1.85

9.66E+12

1.2

Outsourcing resources

PQE ($/piece)

PQEm/ piece

PTEm ($/piece)

PTEm/ piece

PREm/ piece

PIEm/ piece

1

1.82

1.20E+13

2.72

2.48E+13

9.94E+13

1.54E+14

2

1.42

9.33E+12

5.51

3.62E+13

9.03E+13

1.51E+14

3

1.48

4

1.47

1.22E+13

5.36

3.52E+13

8.79E+13

1.45E+14

7.88E+12

5.7

3.74E+13

8.60E+13

1.41E+14

3.3 Discussion Through the mechanical processing data of each of the above mentioned, according to the evaluation model, the emergy of the unit part can be calculated as shown in Fig 3.

Fig 3. Emergy of the unit part for outsourcing resources According the emergy analysis on the unit part for outsourcing resources, the emergy value of the 4-th outsourcing part is minimum. The smaller the emergy, the better, therefore, the plan selection of the heat treatment outsourcing can be as the best alternative. This is consistent with the actual selection result. Compared with literature (Yin 2010; Karcz et al., 2014; Li et al., 2016), the automobile parts manufacturing enterprise has obtained good application effect by using the emergy-based evaluation method for machining outsourcing resources towards a circular economy. According to the Tab.3 and Fig.3, there are multiple alternatives including the enterprises and processing workshops in actual outsourcing. But due to limitation of outsourcing machining resources in the study, evaluation and selection of the other outsourcing machining resources are neglected. In the application process, the best enterprise and processing workshop can be selected on this basis of above analysis by the proposed integrated assessment. Results from the automobile parts manufacturing enterprise indicate that there are some ways to the optimal selection of outsourcing machining resources. Firstly, the enterprise gives some processing processes, which is energy consumption and low value-added, to outsourcing resources. It has solved the technical bottlenecks restricting the low carbon manufacturing, and improved the quality of products for focusing on its core competitiveness. Secondly, it integrates quantitative 12

Journal Pre-proof assessment of quality, time, cost, energy, material, service and waste from the point of view of the emergy, and the decision support is provided for the selection of outsourcing machining resources with more accurate and reasonable. The enterprise focus on their own advantages in the design and manufacture of auto parts products, more concentrate on their core competitiveness of the upgrade, to provide customers with a higher quality of service. Moreover, it has achieved good economic, resource and environmental benefits to confirm, evaluate and optimize the selection of machining outsourcing resources. It has improved resource efficiency and quality of manufacturing systems for enhancing the core competitiveness and innovation ability of manufacturing enterprises.

4. Conclusions Performing the sustainability evaluation of outsourcing machining resources is significant measure to increase resource utilization efficiency and production efficiency, to enhance the rapid response ability to market changes. The results of the study were summarized as follows. First, previous studies on outsourcing machining resources related to sustainability evaluation were analysed. In view of the following problems, a new sustainability evaluation method was proposed based on emergy. The proposed method can overcome existing deficiencies. It not only considers the energy, resource, environment and economic but also provides a unified dimensional evaluation model for integration of the quality, cost, energy and material in outsourcing machining. Some evaluation indicators, which includes production quality emergy (PQEm), production time emergy (PTEm), production logistics cost emergy (PLEm), and production resources consumption emergy (PREm) and, and their models were presented, laying a significant foundation for the sustainability evaluation for outsourcing machining resources. Then, a sustainability integrated assessment (PIEm) was proposed offering an important strategy for selecting enterprises and processing workshops for outsourcing machining resources from perspectives of the energy, resource, environment and economic. The proposed method was applied to the heat treatment for connecting rod as an automobile part, it indicates that the evaluation method can reflect the characteristics of outsourcing systems, to provide decision-making basis for supporting optimization and selection of outsourcing resources. Through the analysis, the automobile parts manufacturing enterprise had obtained good application effect by using the emergy-based evaluation method for machining outsourcing resources towards a circular economy. For the sustainable evaluation, optimization and selection of outsourcing machining resources, data collection for establishing more fundamental databases, we will further study the optimization model and methods for the sustainability of outsourcing machining resources.

Acknowledgments This work was supported in part by Anhui Science and Technology Innovation Strategy and Soft Science Research Project (No. 201806a02020041) and the Hong Kong Scholars Program (XJ2019059). 13

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Journal Pre-proof Highlights: 

Proposing a sustainability evaluation method for outsourcing machining resources



Considering the quality, time, logistics cost and resources in outsourcing machining



Establishing a unified dimensional emergy model



Presenting an integrated assessment method



Illustrating the practicability of the proposed method in real application