The optimization of reverse logistics cost based on value flow analysis – a case study on automobile recycling company in China

Abstract

With the excessive consumption of resources and environmental damage in the economic development, the reverse logistics’ demand for the Internet of Things’ function of logistics monitoring and environmental monitoring has been further highlighted under the background of recycle economy practice. The Internet of Things’ integration on material flow, value flow and information flow can not only improve the efficiency of the logistics system, but also reduce the cost of logistics system. In this paper, we introduce the value flow analysis of circular economy into the cost accounting, analysis and optimization of enterprise reverse logistics. We also take into account the external costs (secondary pollution and environmental benefits of recycling) in the reverse logistics cost accounting. Ultimately, this paper aims to formulate a cost accounting and an optimization model of reverse logistics. Based on the case of an automobile recycling company in Hunan Province, China, it makes a proposal on how an enterprise may optimize its costs and achieve a coordinated development of society, economy and environment.

Keywords

Circular economy value flow analysis reverse logistics cost optimization

1 Introduction

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Nowadays, the excessive consumption of resources and environmental destruction is a major threat to the sustainable development of human society, and is also an impediment for the economic development. The concept of circular economy, characterized by resource conservation and recycling, has drawn more and more attention. With the improvement of people’s environmental awareness and the acceleration of environmental legislation, more and more enterprises have joined in the industry of reverse logistics. Developing reverse logistics is a way to enhance company’s’ competitive advantages, and it’s also one of the important ways to implement green supply chain’s innovation [1]. Especially under the background of the Internet of Things, mining the Internet of Things’ function on logistics monitoring, environmental protection and industrial monitoring becomes an inevitable trend of environmental information. As a developing country, China’s resource utilization is low, and environmental pollution and ecological damage are very serious in the course of rapid economic development. Therefore, the circular economy has become an urgent requirement in today’s Chinese society. China is a major consumer of cars with car ownership accelerating and the number of scrapped cars rapidly rising year by year. Moreover, decreasing the environmental impact, increasing the degree of social responsibility, and considering the economic motivations of organizations are three significant features in designing a reverse logistics network under sustainability respects [2]. In order to reduce environmental pollution and waste of resources, disposing properly scrap cars becomes a problem under the current context of widespread concern, the Internet of Things has opened up a innovative branch for tracking information of scrap cars’ reverse logistics. According to the statistics, in 2011, China’s ownership of cars exceeded 100 million with more than 4 million cars scrapped. By 2020, we expect that 14 million cars will be scrapped each year. If not properly disposed in a timely manner, these cars will represent a huge waste of resources and cause environmental pollution. The Chinese government attaches great importance to this and promulgated a “scrap car recycling management approach” in 2001. In 2006, the NDRC and the Ministry of Science and State Environmental Protection Administration jointly issued an “automotive products recycling technology policy.” However, due to its non-implementation, China’s auto scrap recycling market is still very complicated. Therefore, the government introduced a series of incentives to help improve the efficiency of recycling scrap cars.

In recent years, reverse logistics gradually attracted the attention of scholars and enterprises. The study of reverse logistics focused on the following aspects: the reverse distribution of waste materials in the supply chain, reverse logistics and inventory management [3 –5]; the product remanufacturing management, remanufactured product pricing strategy [6 –8]; the reverse logistics network design and planning and reverse logistics network efficiency evaluation [9 –17]. Chinese scholars started to study the reverse logistics later than that in many developed countries [18 –20]. They focused more on discarded household appliances than on cars and seldom brought environmental aspects into the cost accounting of reverse logistics, optimization and control. In the current situation, a study on environment and resource issues is necessary, and with the state vigorously promoting the development of circular economies, research on the scrap car recycling industry is very relevant. Given the limitations of traditional methods of cost accounting, the current research on the reverse logistics of automobile recycling companies needs to further discuss and refine [21 –24]. Using the Internet of Things to innovate waste reverse logistics process, and setting up a reverse logistics informationized processes, becomes an effective tool to reduce the recycling enterprise’s operating costs, improve the efficiency of reverse logistics and monitor the environmental destruction and pollution in the reverse logistics [25 –27]. Therefore, this paper analyzes the concept of the value flow of circular economy to further expand the reverse logistics cost and the analysis methods and to seek a reasonable method of reverse logistics management; namely, it is to balance the economic and environmental benefits in the process of reverse logistics. We seek to provide information to support the reverse logistics management and decision-making, to reduce costs and improve operational efficiency. With a domestic scrap car recycling business as a case study, this paper puts forward cost optimization management proposals to enhance the efficiency of resource use, reduce environmental damage and promote social, economic and environmental development [28 –30].

2 Enterprise reverse logistics based on the value flow analysis of circular economy

2.1 The value flow analysis of circular economy

The value flow analysis of circular economy is proposed by the research team lead by Professor Xu Xiao, using the science of resources concepts and meanings for reference, and integrating the environment and ecological concerns into the economic system. A value flow analysis system of circular economy divides the value of resources into the efficient use of resources, the loss of waste value, the environmental damage value and the resource value added. The value accounting of resource flow is based on the material flow analysis. Material flow analysis is based on the principle of balancing the material circulation and tracking the volume of input and output to improve the efficiency of resource use and conserve energy. The value accounting of resource flow is based on the management theory about materials and energy investment, production, consumption and transfer into a product in the manufacturing process. Learning the methods of cost gradually carried forward to track changes in the number of physical resources provides the process with an accounting of resource volume and information value. The value flow accounting of circular economy is more in line with the requirements of circular economy and sustainable development than the current accounting model. It pays attention to the resource output direction and flow of qualified products and waste and distribution of the value of resource flow to accurately determine the effective use value of resources and loss value [31, 32].

The enterprise resource flow has a positive flow (positive flow), as well as reverse circulation (counter current), constituting a bidirectional structural analysis model of resources flow shown in Fig. 1.

Fig.1

The bidirectional structural features of enterprise resource flows.

As is shown in Fig. 1, the resources flow can be subdivided into two categories: the forward resource flows (solid lines) and the reverse flow of resources (dashed lines). Forward resource flow is the resource flow (effective resource flow) with one-way linear flow characteristics that ultimately cause disposable resources to be consumed or transformed into products in the production process. The initial investment in the process includes natural ore, fuel and other raw auxiliary materials (Yi) and energy (Ei). The reverse resources flow means the resources flow in reflow production systems or natural systems, such as renewable resource streams, secondary resource flows (Rs) and the waste of resources flow (Rw). Among them, the flow can be divided into consumption of renewable resources flow (Rc) from the outer enterprise and productive renewable resources (Rp) inside which can be divided into the manufacturing return resource flow (Rm) and the functional return resources stream (Ru) categories. Manufacturing return flow of resources means the products, semi-finished products and scrap materials. Falling below a certain level that needs to re-processed in the production process, the behavioral characteristics of remanufacturing are important to improve the productivity of resource use; functional return resources streams mean the medium class resources have a fixed and single function that does not change with the process changes; they can be reused in the process, such as water return or a secondary use stream, which has the behavioral characteristics of reuse or recycling and help to reduce the material consumption and costs. In addition, the consumption of renewable resources includes recycling resources that had been scrapped after circulation and consumption; this has recycled behavioral characteristics within the social level, and it is a fundamental way to ease the pressure on resources, thus achieving a circular economy and sustainable development.

In enterprise resource flow, there is a forward flow as well as a reverse flow, constituting a two-way flow of resources in a structural analysis model. At the same, the closed loop supply chains of circular economy can be divided into forward logistics and reverse logistics to meet the needs of reduction, reuse and recycling. The analysis model of resources value flow can record the enterprise’s generation of waste, recycling and final volume of disposal and disposal costs, so it can be adapted to the requirements of reverse logistics cost accounting.

2.2 The connotation of reverse logistics based on circular economy

Reverse logistics is a kind of logistics activity containing the product return, material substitution, recycling, waste disposal and re-manufacturing. It is in the opposite direction from conventional forward logistics. Its meaning expands with the growing importance of social and economic development and environmental protection. Reverse logistics is produced in every aspect of forward logistics, and is involved in all stages of production, sales, service and other businesses.

A supply chain constituted by forward logistics and reverse logistics, which together form a closed-loop structure, includes internal and external logistics. As shown in Fig. 1, forward logistics (solid line) represent normal demand and commodity trading activities through procurement, production, distribution and consumption to meet the needs of customers. It is the main channel of the logistics flow. Another flow direction is called reverse logistics (dashed flow line shown in Fig. 2).

Fig.2

The forward logistics and reverse logistics in closed loop supply chains.

2.3 The constitution of reverse logistics costs based on circular economy

The cost of reverse logistics mainly refers to the monetary manifestations of the expense and material consumption incurred to protect the environment and regain use value and save costs. Included in these calculations are costs that arise from activities including recycling, reuse and reasonable disposal of materials, the original use-value, substandard materials, packaging materials and related information. Traditionally, the costs of reverse logistics include: ➀ recovery cost; ➁ detection and classification costs; ➂ product disassembly costs; ➃ the cost of remanufacturing; ➄ the cost of waste disposal; ➅ transportation costs; and ➆ management costs. Management costs contain publicity expenses making the reverse logistics run smoothly, office expenses, travel expenses and labor costs (base salary, bonuses, allowances, employee benefits and other costs), expenses spent in the collection, consolidation, analysis and transmitting of logistics information as well as the system management costs of reverse logistics [33 –36]. However, in the circular economy, the reverse logistics motto “reduce, reuse, recycle”, should also consider more contents: the value gains of reduction in environmental damage provided by recycling; and the environmental costs of secondary pollution in reverse logistics. The recycling of waste materials, can provide resource utilization and reduce environmental damage. However, recycling and remanufacturing will also produce secondary pollution and exert some negative effects on the environment.

3 The optimization model of enterprise reverse logistics cost based on the value flow analysis of circular economy

According to the requirements of circular economy, an enterprise must reduce resource consumption while reducing waste emissions so as to achieve more effective environmental management objectives. It must also carry out internal damage assessment while evaluating the effect on the environment. If the reverse logistics is planned and implemented properly, they should reduce production resource depletion and environmental value losses. Therefore, in cost accounting of reverse logistics, to adapt to the requirements of circular economy, the resource-saving and environmental benefits resulting from the planning of reverse logistics can be seen as revenue. The efficiency accounting of reverse logistics can be combined with the binary accounting and analysis model in enterprise resource value transfer analysis to calculate the external environment damage value of enterprise resource depletion and waste emissions, along with the loss of the value of resource waste. First, calculate the cost of each cost center. The positive and negative products costs are equal to each unit price multiplied by the number of units purchased. Waste disposal costs equal unit price of disposition multiplied by the amount of waste disposal. The amount of waste multiplied by its environmental impact factor coefficient can yield the external damage value of waste. The difficulty of external environmental damage value accounting is to determine the units of the loss. After a comprehensive comparison of environmental impact assessment methods, this paper selects the LIME, life-cycle impact assessment method based on endpoint modeling which was developed in Japan.

In the entire closed-loop value chain, the reverse logistics chain is divided into two parts: resource recycling logistics of internal production and the logistics of consumers recycling of outer product. We assume that both reverse logistics will set up a processing center for resource recycling, testing and reproduction. Nodes of the reverse logistics chain include collection centers, detect disassembly centers, the centers of remanufacture, waste disposal centers; the process is shown in Fig. 3.

Fig.3

The flow chart of reverse logistics.

In the background of circular economy, the optimization model of reverse logistics cost based on value flow analysis adjusts the cost accounting of traditional reverse logistics to achieve sustainable development requirements. These combine resource consumption, environmental impact with economic performance, helping solve coordination problems of resource consumption, environmental protection and financial performance and promote sustainable development.

3.1 The calculation of reverse logistics cost

The model of cost accounting is formulated according to the composition and characteristics of reverse logistics:

$\begin{matrix} TC & = & {TC}_{1} + {TC}_{2} + {TC}_{3} + {TC}_{4} + {TC}_{5} \\ + {TC}_{6} + {TC}_{7} + {TC}_{8} - {TR}_{9} \end{matrix}$ (1)

Among them: TC₁—collection cost

TC₂—test cost

TC₃—takedown cost

TC₄—remanufacturing cost

TC₅—waste disposal costs

TC₆—transportation cost

TC₇—fixed cost

TC₈—environmental damage cost of waste

TC₉—environment benefits

➀ The reverse logistics starts from recycling, the costs of which are presented by TC₁. Here we discussed the two cases: the resource recycling logistics in internal production logistics, because waste recycling does not have to pay the purchase costs of the internal production process, and the purchase cost can be considered 0; the outer consumer recycling of products. We calculate this as follows:

${TC}_{1} = \sum_{i}^{I} C_{i} \cdot N_{i}$ (2)

C_i represents unit operating costs (RMB/T) in the recycling collection center i; N_i represents the recycling quantity (T).

➁ Testing costs:

${TC}_{2} = \sum_{i}^{i} \sum_{j}^{j} C_{j} \cdot N_{ij}$ (3)

C_j represents the unit operating costs (RMB/T) of detection center j; N_ij represents the number of items from collection p center i to the testing center j.

➂ Takedown costs:

${TC}_{3} = \sum_{j}^{J} \sum_{r}^{R} C_{r} \cdot N_{jr}$ (4)

C_r means the unit operating costs (RMB/T) of takedown center r; N_jr represents the number of items from testing center j to disassembly center r.

➃ The cost of remanufacturing is calculated as: ${TC}_{4} = \sum_{r}^{R} \sum_{h}^{H} C_{h} \cdot N_{rh} \sum_{h}^{H} C_{h} \cdot N_{rh}$ (5)

C_h means the unit operating costs (RMB/T) of remanufacturing center l; N_rh represents the number of items from takedown center r to remanufacturing center h.

➄ Waste disposal costs

$\begin{matrix} {TC}_{5} & = & \sum_{j}^{J} \sum_{g}^{G} C_{g} \cdot N_{jg} + \sum_{r}^{R} \sum_{g}^{G} C_{g} \cdot N_{rg} \\ + \sum_{h}^{H} \sum_{g}^{G} C_{g} \cdot N_{hg} \end{matrix}$ (6)

C_g represents the unit cost of waste disposal (RMB/T); N_jgj represents the amount of waste that cannot be recycled after detection by testing center; N_rgr represents the amount of waste that cannot be recycled after demolition; N_hg expresses the amount of newly-generated waste in remanufacturing center h.

➅ Transportation costs

$\begin{matrix} {TC}_{6} \\ = \sum_{i}^{I} \sum_{j}^{J} {DC}_{ij} \times N_{ij} + \sum_{j}^{J} \sum_{r}^{R} {DC}_{jr} \times N_{jr} \\ + \sum_{r}^{R} \sum_{h}^{H} {DC}_{rh} \times N_{rh} + \sum_{j}^{J} \sum_{g}^{G} {DC}_{jg} \times N_{jg} \\ + \sum_{r}^{R} \sum_{g}^{G} {DC}_{rg} \times N_{rg} + \sum_{h}^{H} \sum_{g}^{G} {DC}_{hg} \times N_{hg} \end{matrix}$ (7)

DC_ij represents unit transportation costs (RMB/T) from collection center i to testing center j; DC_jr represent unit transportation costs from testing center j to disassembly center r; DC_rh represents unit transportation costs from disassembly center r to remanufacturing center h; DC_jg indicates the unit transportation costs from testing center j to waste disposal center g; DC_rgr represents the unit transportation costs from takedown center r to waste disposal center g; DC_hg represents unit transportation costs from remanufacturing center h to waste disposal center g.

➆ Fixed costs ${TC}_{7} = \sum ({FC}_{i} + {FC}_{j} + {FC}_{r} + {FC}_{h} + {FC}_{g})$ (8)

FC_i represents fixed costs (RMB) of collection center i; FC_j represents fixed costs of detection center j; FC_r represents fixed costs of disassembly center r; FC_h represents fixed costs of remanufacturing center h; FC_g represents fixed costs of waste disposal center g.

➇ External environmental damage costs of waste

$\begin{matrix} {TC}_{8} \\ = L \times (\sum_{j}^{J} \sum_{g}^{G} N_{jg} + \sum_{r}^{R} \sum_{g}^{G} N_{rg} + \sum_{h}^{H} \sum_{g}^{G} N_{hg} \end{matrix}$ (9)

L represents a comprehensive evaluation factor of the environmental impact (RMB/T).

➈ Gains resulting from the lessening of environmental damage caused by the recycling.

$\begin{matrix} {TC}_{9} & = & L \times (\sum_{i}^{I} N_{i} - \sum_{j}^{J} \sum_{g}^{G} N_{jg} \\ - \sum_{r}^{R} \sum_{g}^{G} N_{rg} - \sum_{h}^{H} \sum_{g}^{G} N_{hg}) \end{matrix}$ (10)

Considering a variety of factors affecting the cost of logistics, this paper will establish the control and decision model of reverse logistics cost to help companies determine the optimal solution to control the total cost of reverse logistics.

3.2 The optimization model of reverse logistics cost on the value flow analysis of circular economy

In the construction of reverse logistics, the position of the various nodes has a great impact on the operating efficiency of logistical operations. To help enterprises make better construction decisions, this paper re-formulates the optimization model of reverse logistics cost to minimize total operational cost. It also considers the various impact factors of reverse logistics. By accounting for and analyzing every cost of reverse logistics chain, the best cost plan will be found.

Model assumptions:

To simplify the model, we make the following assumptions:

The recycling quantity of waste products, the processing capacity of the various facilities, investment and operating costs and transportation costs between various facilities are determined to be known quantities.

We do not consider the material loss in the process of recycling.

The expansion of logistics facilities refers not only to the enterprise scale expansion of its logistics facilities, but also to the cost of inputs helpful to improving productivity, such as the production line and technology.

The new logistics facilities are set in known alternative geographic locations.

Fixed costs include storage charges, utility bills and other items generated by the logistics node facility.

Model:

The control model of reverse Logistics cost is as follows:

$\begin{matrix} min Z \\ = [\sum_{i}^{I} C_{i} \cdot N_{i} y_{i} + \sum_{i}^{I} \sum_{j}^{J} C_{j} \cdot N_{ij} y_{i} y_{j} + \sum_{j}^{J} \sum_{r}^{R} C_{r} \cdot N_{jr} y_{j} y_{r} + \sum_{r}^{R} \sum_{h}^{H} C_{h} \cdot N_{rh} y_{r} y_{h} \\ + \sum_{j}^{J} \sum_{g}^{G} C_{g} \cdot N_{jg} y_{j} y_{g} + \sum_{r}^{R} \sum_{g}^{G} C_{g} \cdot N_{rg} y_{r} y_{g} + \sum_{h}^{H} \sum_{g}^{G} C_{g} \cdot N_{hg} y_{h} y_{g} + \sum_{i}^{I} \sum_{j}^{J} {DC}_{ij} \cdot N_{ij} y_{i} y_{j} \\ + \sum_{j}^{J} \sum_{r}^{R} {DC}_{jr} \cdot N_{jr} y_{j} y_{r} + \sum_{r}^{R} \sum_{h}^{H} {DC}_{rh} \cdot N_{rh} y_{r} y_{h} + \sum_{j}^{J} \sum_{g}^{G} {DC}_{jg} \cdot N_{jg} y_{j} y_{g} + \sum_{r}^{R} \sum_{g}^{G} {DC}_{rg} \cdot N_{rg} y_{r} y_{g} \\ + \sum_{h}^{H} \sum_{g}^{G} {DC}_{hg} \cdot N_{hg} y_{h} y_{g} + \sum_{i}^{I} {FC}_{i} y_{i} + \sum_{j}^{J} {FC}_{j} y_{j} + \sum_{r}^{R} {FC}_{r} y_{r} + \sum_{h}^{H} {FC}_{h} y_{h} + \sum_{g}^{G} {FC}_{g} y_{g} \\ + L \times (\sum_{j}^{J} \sum_{g}^{G} N_{jg} + \sum_{r}^{R} \sum_{g}^{G} N_{rg} + \sum_{h}^{H} \sum_{g}^{G} N_{hg}) \\ - L \times (\sum_{i}^{I} N_{i} - \sum_{j}^{J} \sum_{g}^{G} N_{jg} - \sum_{r}^{R} \sum_{g}^{G} N_{rg} - \sum_{h}^{H} \sum_{g}^{G} N_{hg})] \end{matrix}$ (11) $s . t : \sum_{j} N_{jr} = (1 - α) \sum_{i} N_{ij}$ (12) $\sum_{r} N_{rh} = (1 - β) \sum_{j} N_{jr}$ (13) $\sum_{j} N_{je} = α \sum_{i} N_{ij}$ (14) $\sum_{r} N_{rf} = β \sum_{j} N_{jr}$ (15) $\sum_{h} N_{hg} = μ \sum_{r} N_{rh}$ (16) ${LI}_{i} \leq \sum N_{i} \leq {UI}_{i}$ (17) ${LJ}_{j} \leq \sum N_{ij} \leq {UJ}_{j}$ (18) ${LR}_{r} \leq \sum N_{jr} \leq {UR}_{r}$ (19) ${LH}_{h} \leq \sum N_{rh} \leq {UH}_{h}$ (20) $\begin{matrix} {LG}_{g} \leq \sum_{j}^{J} N_{jg} + \sum_{r}^{R} N_{rg} + \sum_{h}^{H} N_{hg} \leq {UG}_{g} \end{matrix}$ (21) $\sum y_{i} \leq I$ (22) $\sum y_{j} \leq J$ (23)

$\sum y_{r} \leq R$ (24) $\sum y_{h} \leq H$ (25) $\sum y_{g} \leq G$ (26) $y_{i}, y_{j}, y_{r}, y_{h}, y_{g} \in {0, 1}$ (27) $i \in I, j \in J, r \in R, h \in H, g \in G$ (28) $N_{i}, N_{ij}, N_{jr}, N_{rh}, N_{jg}, N_{rg}, N_{hg} \geq 0$ (29)

Target formula (11) means maximizing profits in enterprises with reverse logistics; constraint Equations (12–16) describe the conditions of flow conservation; formulas (17–21) limit the capacity of logistics facilities; formulas (22–26) indicate the limit number of logistics facilities; formulas (27–29) provide for the range of each variable.

Symbol description:

➀ Decision variables

y_i, y_j, y_r, y_h, y_g are 0-1 variables that indicate whether the new or expanded nodes exists; yes means 1, no means 0;

➁ Parameter description

α represents scrap rate in a testing center; β represents the scrap rate of a demolition center; μ represents the waste generation rate of a remanufacturing center; I, J, R, H and G are the maximum number of collection center, detection center, then the center of manufacture and waste processing center respectively; Pi means the purchase price of waste recycling products;

LI_i and UI_i represent the minimum and maximum processing capacity of collection center i; LJ_j and UJ_l represent the minimum and maximum processing capacity of center j; LR_r and UR_r denote the minimum and maximum processing capacity of disassembly center r; LH_h and UH_h denote the minimum and maximum processing capacity of remanufacturing center h; LG_g and UG_g denote the minimum and maximum processing the capacity of waste disposal center g;

The model above is a MILP model, which can be solved with LINGO software, specialized in solving the linear programming problems. By solving the model, we can derive the optimal cost of reverse logistics planning and then implement a further cost estimation, obtaining the amount of binary values that include the internal and external costs of consumption. Finally considering the economic and environmental benefits, the cost-optimized operating mode can be selected.

4 Case study

With a professional scrap car recycling company D in the Hunan Province of China as an example, this paper gives a study on its reverse logistics decisions. The main services of Company D are recycling, dismantling and remanufacturing waste cars. It can be regarded as a third-party logistics enterprise. Company D intends to further expand the disposal scrap of its car recycling business to respond to the government’s call for energy conservation. This company has signed a recycling contract with a local car manufacturer that is responsible for the recycling, dismantling and disposal processes of scrap cars, as shown in Fig. 4. This enterprise recycles, dismantles and crushes the metal body of scrap cars in three steps, and has two recycling centers and a center for dismantling and metal grinding. The company signed a cooperation agreement with a manufacturer and a metal processing plant that remanufacture the metal components and parts. The company currently recycles 5,000 tons of used cars every year, at an annual total cost of RMB11,105,875.5 (this figure has been adjusted by the value of the external environmental damage) (Tables 1–5).

Fig.4

The flow chart of scrapped vehicle dismantling.

Table 1

Scrap car recycling capacity of each expanded or newly-built collection center (Ni) Unit: T

Collection center	I ₁	I ₂	I ₃	I ₄
N _i	2037	2437	3000	2718

Table 2

Minimal processing capability (LJj) and maximum processing capacity (UJj) for each expaned or newly-built dismantling center Unit: T

Dismantling center	J ₁	J ₂	J ₃	J ₄
LJ _j	1600	2100	2500	3800
UJ _j	1900	2700	4500	5100

Table 3

Minimum processing capacity (LRr) and maximum processing capacity (URr) for each expanded or newly-dismantled metal-grinding center Unit: T

Metal grinding center	R ₁	R ₂	R ₃
LR _r	1800	2200	2900
UR _r	1600	2600	5800

Table 4

Unit transport costs from recycling center to dismantling center Unit: RMB/T

Dismantling center	Collection center
	I ₁	I ₂	I ₃	I ₄
J ₁	12	17	32	22
J ₂	18	16	15	14
J ₃	10	8	26	15
J ₄	32	15	24	30

Table 5

Unit transportation costs from dismantling center to metal-grinding center Unit: RMB/T

Metal grinding center	Dismantling center
	J ₁	J ₂	J ₃	J ₄
R ₁	5	17	47	22
R ₂	13	28	14	24
R ₃	8	10	6	23

Due to the increase in used cars and the rising business volume, the company’s operating facilities cannot meet the business needs, and the company decided to expand production capacity. It plans to add logistics services in the local region by either expanding the original facility or building some new facilities in a new location. In calculating the actual operational processes of the company, we adjust the previous cost control model appropriately (based on the business realities, adding the metal grinding center instead of a testing center, counting only the relevant freight of remanufacturing and waste treatment sectors and not counting operating and fixed costs). After dismantling about 40% of the scrap cars and sending them into the remanufacturing process, and sending 50% into the metal grinding process, the solid waste output rate of dismantling and crushing is 10%). The company can use the adjusted cost control and decision model to re-plan the structure of its used car recycling logistics and optimize the total logistics costs (Tables 6–9).

Table 6

Unit transportation costs from dismantling center to remanufactured center Unit: RMB/T

Remanufacturer center	Dismantling center
	J ₁	J ₂	J ₃	J ₄
H	30	7	44	23

Table 7

Unit transportation costs from metal-crushing center to metal-processing plants Unit: RMB/T

Metal processing plants	Metal grinding center
	R ₁	R ₂	R ₃
M	14	17	27

Table 8

Unit transportation costs from dismantling center or metal-crushing center to waste-disposal site Unit: RMB/T

Waste disposal site	Dismantling center				Metal grinding center
	J ₁	J ₂	J ₃	J ₄	R₁	R ₂	R ₃
G	18	20	17	11	14	17	27

Table 9

Fixed costs and unit operation costs for each expanded or newly-build center Unit: RMB

	I ₁	I ₂	I ₃	I ₄
FC _i	87500	80800	918000	825000
C _i	620	680	590	660
	J ₁	J ₂	J ₃	J ₄
FC _j	107000	125000	113000	156000
C _j	1350	1250	1300	950
	R1	R2	R3
FC _r	83000	93000	94200
C _r	500	430	400

There are four alternative recycling locations currently: I₁ and I₂ are the original collection centers that can be extended, and I₃ and I₄, are new candidate sites of enterprises that create new collection centers. There are four alternative dismantling centers: J₁ and J₂ are the existing scrap vehicle dismantling centers that can be expanded and J₃ and J₄ are candidates for new dismantling centers. There are three alternative metal grinding centers, where R₁ is considering the expansion of the existing metal grinding center and R₂ and R₃ can be considered new metal grinding centers. Remanufacturer H and metal processing plant M will join in the cooperation. Solid waste is transported to waste disposal site G set up by the government. It is difficult to set parameters such as unit operational costs directly from reported statistical data because of the confidentiality of the business’ records. All the related parameters were completed using data learned and estimated in interviews. In general, the environmental Impact Evaluation coefficient L has estimated an value of 650 (LIME value here is calculated using the weighted average of the Japanese standards with appropriate adjustments). Other estimated parameters are as follows:

With the LINGO software, the result expands collection centers I₁ and I₂, dismantles center J₂ and builds new dismantling center J₄ and new metal grinding center R₂. According to the new operating program, the largest operational capacity reached was 9,474 tons, an increase of 89% over the original. The minimum added cost is RMB7,466,430, an increase of only 67% more than the original. To achieve the goal of reducing the cost, the traffic volume between the various selected locations is as shown in Fig. 5.

Fig.5

The logistics processes and volume of company D.

The total added cost has been reduced by the environment benefits. The diminishing portion can be a reference for government subsidies policy. The results also demonstrate that operational cost accounts for the majority of the total recycling cost, and the operational cost of the dismantling center makes up most of the operational cost (nearly 47%). Therefore, the most efficient way to further reduce the system’s total cost is to reduce the operational cost of the dismantling center.

5 Conclusions

Reverse logistics, as an important aspect of logistics, when open up a new field for the sustainable development of the economy and resources, not only conformed to the requirements of circular economy, but also become an important application field of supply chain logistics process visualization under the background of the Internet of Things. This paper, introducing the concepts of value flow accounting of circular economy into the cost calculations of reverse logistics, aims to accomplish certain goals: to consider the internal cost and external damage value factors of reverse logistics management in the business cost decision and to formulate a reverse-flow logistics model based on the value flow analysis of circular economy. In addition, with the reverse logistics development of a scrap car recycling business as a case study, our paper redesigns the reverse logistics network to optimize costs, thus reducing an enterprise’s expenses and helping to accomplish its business operations in a way that benefits both the economy and the environment.

Although this paper explored the cost of reverse logistics optimization problem from the perspective of circular economy, with the significant impact of Internet of Things technology on logistic system cost, and the uncertainty of reverse logistics on the recycling quantity and quality, Developing Internet of Things’ integrating function in the aspect of material flow and value flow is in urgent need. For the supply chain enterprises, it is still need to improve the function of the system and the effective use of the advantages of Internet of Things, to realize the coordinate ascension of economic benefit and environment benefit.

Footnotes

Acknowledgments

I would like to thank all the seminar participants at Central South University for their valuable comments and discussions. I also appreciate the two anonymous referees who gave much sound advice on this paper. I would like to extend my thanks to my wife for her work checking grammar and editing this paper. All remaining errors are my own.

This research work was supported by the National Natural Science Funds of China (No. 71303263), the Major Program of the National Social Science Fund of China (11&ZD166, 15ZDA020), the State Key Program of National Natural Science of China (No. 71431006), the Key Projects of Philosophy and Social Sciences of the Ministry of Education (No. 13JZD0016), the Innovation Driven Program of Central South University (2015CX010), the Doctoral Fund of the Ministry of Education (20130162120045).

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