The study of production and inventory policy of manufacturing/remanufacturing environment in a closed-loop supply chain

Abstract

The life cycle of electronic and electrical equipment has been ruthlessly shortened due to the rapid promotion of new products. Such a phenomenon has thereby resulted in serious environmental pollution and damages to the natural ecology. In order to reduce electronic wastes and increase recycling products, a manufacturing/remanufacturing simulation model was constructed through a series of inventory management policies. The model included the use of the traditional supply chain structures for the forward movement of goods to consumers, as well as a number of specialised operations required to perform reverse supply chain activities. Such a model took into account all the supply chain members such as the suppliers, manufacturers, logistics centres and customers at the same time. Finally, a case study on the application of the model was conducted.

Keywords:

• remanufacture
• recycling
• closed‐loop supply chain management
• end-of-life management, inventory

1. Introduction

The life cycle of electronic and electrical equipment has been ruthlessly shortened due to the rapid promotion of new products. Such a phenomenon has thereby resulted in serious environmental pollution and damages to the natural ecology. In response to these environmental problems, governments around the world have taken the initiative to reinforce environmental acts, to assist firms in inventing and managing green designs, as well as to encourage people to purchase energy saving products that yield low pollution. One of the myriad legislations in California, the USA, established a funding system for the collection and recycling of certain electronic waste (EWRA 2003). Likewise, an Environmental Directive commonly known as ‘Waste Electrical and Electronic Equipment (WEEE) Directive’ was passed by the European Union to necessitate individual European countries to mandate recycling and recovery schemes and forbid landfill placements (WEEE 2002). All of these governmental regulations have set requirements and targets for the collection, recycling, recovery and environmentally friendly treatment of electrical and electronic equipment upon their expiration. These new requirements have extended the responsibilities of the producers beyond mere production and distribution by holding them accountable for the post-treatment of expired products. It has also promoted the act of recycling, through which the materials in the used products are reused or remanufactured and reappear in a new form (Thierry et al. 1995).

In order to reduce electronic wastes, producers are to extend the capacity of their traditional forward logistic distribution chain. They have to consider the total effects that a product has on the environment from the production process to the end-of-life moment of the product. According to the moving direction of the logistics, the supply chain can be divided into the forward and the reverse logistics (Fleischmann et al. 1997). The forward logistics means acquiring the original material products from the suppliers and increasing their additional values by creating values in them through corporate managerial functions. On the contrary, the reverse logistics process suggested by Stock (1998) involves product return, source reduction, recycling, material substitution, item reuse, waste disposal, reprocessing, repairing and remanufacturing. From the perspective of logistics engineering, the reverse logistics management has been viewed as a systematic business model that makes the best out of a company's thinking and management model and thereby facilitates the supply chain management and maximises profits.

The integration of a forward and reverse logistics system could be viewed as a closed-loop supply chain. It includes the use of the traditional supply chain structures of the forward movement of goods to consumers and the application of specialised operations for the reverse supply chain activities (Beomon 1999, Kooi et al. 1996, Guide et al. 2003).

A remanufacturing firm needs to overcome three limitations. They are the limited access of cores leaving the use phase, limited feasibility of product remanufacturing and limited market demand for the secondary output from remanufacturing. It also has to face the fact that the market demand for remanufactured products and disposal of used products does not always go hand in hand (Geyer and Jackson 2004).

Van der Laan et al. (1999) proposed the PUSH and PULL control strategies to plan and control the inventory for remanufacturing durable products. Following suit, Fleischmann et al. (1997) in his paper presented an overview of quantitative approaches for production and inventory control in reverse logistics. Thierry et al. (1995) by means of a simple example tried to address a separate aspect of remanufacturing. That is, he claimed that automotive parts remanufacturing was the key determiner of models for inventory control. Deniel et al. (1997) provided a report on scheduling policies for remanufacturing shops using information from turbine jet engine remanufacturing, whereas Krikke et al. (1999) considered a number of alternatives for the design of a reverse logistics network for photocopiers in Western Europe. Toktay et al. (2000) pondered on the problem of predicting return flows for single-use cameras. Based on the research concept of Geyer and Jackson (2004), it is difficult to anticipate the feasibility of product remanufacturing and to determine the limitation of the market demand for remanufactured products. Since the actual system is very complex and involves risks of various kinds, most previous research could have been limited in their capability to provide a solution. To be more realistic, a holistic and complete model should take into account all the supply chain members such as the suppliers, manufacturers, logistics centres and customers at the same time.

The purpose of this research is thereby to simulate and construct a manufacturing/remanufacturing model by means of different inventory management strategies and variables. The aim is to seek a better performance. In the research, four different inventory management strategies were used in reference to Goggin and Browne's (2000) research strategies. They are (1) recovery to stock (RTS), (2) reassembly to order (RATO), (3) disassembly to stock (DATS) and (4) recovery to order (RTO)/collect to order (CTO). Definition of each can be referred to as below:

1.	RTS. The end-items may return to their original forms. Components or materials of the product are completely recovered based on the expected demand of recovery products in an RTS environment.
2.	RATO. Recovered products are reassembled in response to customers' orders.
3.	DATS. Certain parts are disassembled and reassembled based on customers' orders and specifications.
4.	RTO/CTO. Used products are recovered or collected in response to customers' orders.

Since demands tend to prevail over returns, both manufacturing and remanufacturing processes were used in the research to control the inventory (Teunter and Vlachos 2002). Figure 1 gives a graphical representation of the inventory system in the above practical situation. Additionally, a simulation system was performed to explore the different performances with respect to different inventory policies in the remanufacturing processes.

Figure 1 Hybrid inventory system with manufacturing and remanufacturing (and with or without a disposal option for returned remanufacturing items).

2. System description

The number of steps required to go from the vendor of raw materials to the end-users constitutes a key parameter for a forward supply chain. In order to effectively and simultaneously plan and control manufacturing/remanufacturing operations, a few things were to transpire. First, it was assumed that the remanufacturing process would take place in a normal production environment. The integration of the forward and reverse supply chains would not only include the information flow of the forward supply chain (namely, new materials/parts or new product purchases and sales) but also incorporate managerial activities such as product recycling, remanufacturing, materials/parts recovery and final waste disposal. Meanwhile, the manufacturing orders should have had been brought about by a standard (s, Q) policy (Isotupa 2006; reorder Q when inventory position drops to s) in the manufacturing/remanufacturing system. Moreover, the system used in the research adopted three multiple types of policies. They were (1) remanufacturing to stock (RMTS), (2) RATO and (3) remanufacture to order (RMTO). Secondly, by changing the system variables of recycling rate (RCR), demand rate (DR) and reuse rate (RUR), the performance indices were measured. Finally, a sensitivity analysis was carried out to evaluate the effects of the cycle time (CT), the possession rate of remanufactured products (PRR), the average waste (AW) and the total cost of remanufacturing (TCR). The certainty of the results was statistically defined using a confidence interval of 95%. The framework of the system simulation is shown in Figure 2. Note that since remanufactured items were assumed to be of the same quality as the manufactured items and to be sold for the same price on the same market, the research did not distinguish between the two.

Figure 2 Framework of simulation system.

In this research, the use of the single product closed-loop supply chain was the focus. It included the following distinct operations: (1) supply, (2) production, (3) distribution, (4) use, (5) collection (and inspection), (6) remanufacturing and (7) disposal. Moreover, it was assumed that the used products in the reverse channel had already been collected, cleaned and remanufactured in advance. In order to complete the closed-loop supply chain system framework, the model included the following three components:

1.	Supply chain relationship analysis. This refers to the relationships between the functions of the suppliers, manufacturers, recycling centres, logistics centres, waste disposal centres and consumers in the system.
2.	System parameters and variables setting. The setting contains the information of all parameters and variables in the system and observes the changes by adjusting the system variables.
3.	Indicator assessment and analysis. It mainly collects the assessment indicators used to assess the performance of the system.

2.1 Supply chain relationship analysis

Through this system, the research seeks to explore the relationships between the functions of the suppliers (S), recycling centres (R), manufacturers (M), waste disposal centres (W) and consumers (C; Figure 3).

Figure 3 The remanufacturing processes within the supply chain.

1.	Suppliers (S). The function of suppliers is mainly to provide the new materials/parts that complied with the environmental protection standards.
2.	Recycling centres (R). The recycling centres disassemble and clean the recycled products and then provide the reused materials/parts (including master parts and secondary parts) to the manufacturers.
3.	Manufacturers/remanufacturers (M/RM). The manufacturers play the role as remanufacturing producers. When the manufacturers supervise the remanufacturing production process, they have to consider the amount of reused materials/parts inventory. If the reused materials/parts (encompassing master parts and secondary parts) inventory is insufficient in terms of meeting the demands of the orders, they have to order new materials/parts or new products (original products) from the suppliers.
4.	Waste disposal centres (W). End-of-life (EOL) products, parts and materials are recycled here. Since it is not easy to acquire relevant information, the manufacturing process of the waste disposal is ignored.
5.	Consumers (C). After the end-users no longer use the products, they deliver the used products to the reverse logistics centre, which in turn delivers the products to the recycling centre for used product recycling.

2.2 System parameters and variables setting

System parameters already included data on suppliers, consumers and final waste disposal centres, so they were not investigated separately. Meanwhile, manufacturers, recycling centres and logistics centres were studied and analysed simultaneously. The system variables selected for this research focused mainly on several critical characteristics and potential strategic issues in the remanufacturing production system and supply chain system. The system variables are defined as follows:

1.	RCR. This refers to the rate of EOL products being recycled and processed.
2.	DR. This refers to how much time it takes to receive an order from the consumers.
3.	RUR. This refers to the rate of EOL products being recycled and reused.

2.3 Indicator assessment and analysis

According to the supply chain performance index proposed by Beamon (1998), the performance indices used are described as follows:

1.	CT of customer's order. This refers to the time needed from the orders of logistics centre to the manufacturers. This research had assumed that the order flow was through information flow; thus, it did not consider the order delivery time and thereby only dealt with the remanufacturing time for the manufacturer and the delivery time from supplying new products (original products) to the customers. Therefore, the objective of this research is to minimise the CT.
2.	PRR. This refers to the ratio of the total number of remanufactured (TNR) products to the total number of demand (TND) of the actual orders. When the demand of remanufacturing products increases, the environmental impacts tend to weaken. Thus, the PRR objective function is
3.	AW. After distributing the quantity of wastes, the researcher could clearly determine in terms of a unit number, the quantity of total wastes (TWs) from one unit of remanufactured products in the production line. AW should gradually decrease with the increasing number of remanufacturing products so that it would fulfil the purpose of the resource recovery operations. Hence, the AW objective function is
4.	TCR. This refers to the costs of the recycling centre to remanufacture the EOL products. The cost is defined as

3. Case study

In this simulation system, a recycled product's closed-loop supply chain process was implemented and presented in Figure 4. Its name was omitted for the purpose of confidentiality. In this case, manufacturing constituted 70% of the market while remanufacturing consisted 30% of the market. The data were collected and analysed based on the company's raw data in Table 1. A simulation tool, ExtendSim, was used to construct the model.

Figure 4 The remanufactured process for the case study.

Table 1 Definition of the parameters and variables in the model.

	Demand number of an order, 3/order
	Assembly time
	Assembly cost ($100/unit)
	Total assembly cost
	Test time (5 min/unit)
	Test cost ($10/h)
	Total test cost
	Yield rate of remanufactured product (5%)
	Maintenance time (5 min/unit)
	Maintenance unit cost ($50/unit)
	Total maintenance cost
	Packaging time (3 min/unit)
	Packaging unit cost ($50)
	Total packaging cost
	Unit cost of new product ($120/unit)
	Total cost of new product
	Unit inventory cost of new materials/parts
	Inventory cost of new products ($0.2857/day)
	Total cost of new products
	Total transportation cost ($100/order)
	Reorder point of new products inventory (100)
	Order number of new products (200)
	Order lead time of new products (1.5 days)
	Reorder point of new materials/parts inventory (100)
	Order number of new materials/parts (200)
	Order lead time of new materials/parts
	Disassembly time
	Disassembly unit cost ($15/min)
	Total disassembly cost
	Cleaning unit cost ($15/number)
	Total cleaning cost
	Unit inventory cost of reused materials/parts ($0.2857/day)
	Total inventory cost of reused materials/parts
	Unit cost of waste disposal ($20/batch)
	TW disposal cost
	Reassembly–disassembly point of reused materials/ parts inventory (50)
	Disassembly number of recycling products (100)
	Unit cost of recycling old products ($100/number)
	Total recycling cost
	Unit inventory cost of recycling products ($0.2857/day)

Three inventory management strategies were used and three multiple types of policies were adopted in this system. These policies were RMTS, RATO and RMTO respectively. In addition, three system variables (RCR, DR and RUR) were interpreted in terms of three levels: low (L), medium (M) and high (H). This was a 3³ factorial design and there were total of kinds of experimental situations. In analysing the experimental figures, the researcher referred to a system simulation proposed by Law and Kelton (1991). In terms of changing certain system strategies or comparing two different systems, the researcher applied independent t-test or zone estimation.

The probability of model error or α error in statistics (when was real, it was rejected) increased with the increase in the number of times tested. For example, RCR had low, medium and high levels. In order to test the influence of different RCR levels in the model, the test was to be carried out times. Under the condition of α = 0.05, the probability of model error would increase to . Therefore, the α value was changed from the original 0.05 to 0.017 ( = α/3).

Based on the simulation system depicted above, a remanufactured cartridge model was simulated and then analysed. It focused on evaluating the manufacturing process at different levels to ensure that the influence of production constraints on the performance of different remanufacturing strategies could be detected. The definitions of the assigned levels are described below. The used cartridges were collected from three distribution centres. All of the collected used cartridges were then sent and remanufactured in the recycling centre. During the remanufacturing processes, the main components were cleaned and replaced. Table 2 depicts the different levels for the three distribution centres. For each variable, the medium was set as the standard. For the other two, L  =  M/2 and H = 2M. By taking RCR in R1 as an example, it was calculated as , and .

Table 2 The data of the case study.

Variables		Simulation level
RCR	R1: 205/week	R1: (L, M, H) = (0.0171, 0.0341, 0.0682)	Exponential distribution (day/unit). How long can one recycle a used product?
	R2: 68/week	R2: (L, M, H) = (0.0515, 0.1029, 0.2058)
	R3: 120/week	R3: (L, M, H) = (0.0292, 0.0583, 0.1166)
DR	C1: 15/day	C1: (L, M, H) = (0.0333, 0.0666, 0.1332)	Exponential distribution (order/day). How long can one get an order?
	C2: 8/day	C2: (L, M, H) = (0.0625, 0.1250, 0.2500)
	C3: 10/day	C3: (L, M, H) = (0.0050, 0.1000, 0.0200)
RUR	40∼80%	(L, M, H) = (0.3, 0.5, 0.7)	The usage rate of remanufactured rate.

4. Analysis of simulated results

The researcher first explored the influences of variable factors shown in Table 1. He then explored the influences on CT, PRR, AW and TCR during different processes.

4.1 The significance level

According to Table 2, at α = 0.017 significance level, the increase in RCR has a significant influence on CT, PRR and TCR instead of AW. Also, the increase in RUR has a great impact on PRR, AW and TCR at α = 0.017 significance level. The results on influences can be found in Table 2.

1.	When RCR increases (L → M → H), CT and PRR also increase.
2.	When DR increases (L → M → H), CT and TCR also increase, yet PRR decrease.
3.	When RUR increases (L → M → H), AW and TCR reduce.

After the simulation model was constructed, the simulation was conducted. Table 3 shows the results resulted from changes of system variables.

Table 3 Comparison for different resource recovery operations.

	Level test of equal variables		t-Test of equal averages
Performance index	F-test	Significance	t	Freedom	Significance (two tailed)
CT	1.092	0.297*	− 2.667	160	0.008**
PRR	17.310	0.000*	− 13.111	135.855	0.000**
AW	0.009	0.926*	0.036	160	0.971**
TCR	32.296	0.000*	4.396	134.741	0.000**
*Mean at α = 0.05 significant level, there was significant difference among the variables (the variables were not assumed as equal).
**Mean at α = 0.017 significant level, there was significant difference between different resource recovery operations.

4.2 Interpretation of findings

Based on the data of the case study, the findings could be listed as:

1.	H1: CT is significant to the different resource recovery plan At α = 0.017 significance level, different RCRs have a significant influence on the CT and PRR. When the RCR increases (), the CT and PRR will also increase. The reason lies in that increases in RCR will help to achieve meeting the demand order. This will lead to increase in the remanufacturing time. In other words, the CT will also increase. Since the recycled quantities increase, the requirement of brand new products will decrease. Then, the TCR will decrease. If the enterprise wants to reduce the CT for the remanufactured products, the inventory of the remanufactured will, in turn, decrease in amount.
2.	H1: DR exerts great impacts on different RCRs At α = 0.017 significance level, different DRs have a significant influence on the CT, the PRR and the TCR. In the beginning of the simulation, the CT did not respond to the increasing DR (). However, later on, it did increase. Recycled main components are to be remanufactured and then reassembled into a final product. It takes time to undergo the whole remanufacturing process and then to meet customer demands. This explains the delayed response mentioned above. Furthermore, when the DR increases, there are not enough recycled components to meet the demands, neither is there sufficient time to remanufacture enough products. In place of recycled products, new products are used. This will cause the inventory of recycled components and the TCR to increase.
3.	H1: RCR is significant to the CT and also significant to TCR For the RUR, at α = 0.017 significance level, different RCRs have significant influences on the CT, the PRR and the TCR. When the RCR increases (), the CT, the PRR and the total remanufacturing cost will increase. The reason for this is that increases in recycled quantities will be sufficient to supply the demand order. Therefore, the remanufacturing time, as well as the CT, will increase. As a result of increases in the recycled quantities, the requirement of raw material will decrease. The TCR will then decrease.

5. Conclusions and suggestions

A closed-loop supply chain system constructed for this research did not only bear the characteristics of the traditional supply chain, but it also involved the application of recycling and remanufacturing processes. Firms took into account the environmental issues during the entire supply chain process: from the suppliers' material supply (green suppliers selection, green purchase and materials/parts meeting environmental protection), the manufacturers' manufacturing/remanufacturing (using recoverable materials/parts or the materials with low pollution and energy), delivery/sales (green marketing and green products), consumers and recycling firms (purchasing green products and having clear recycling access) to final waste disposal companies (having final waste disposal). Such a method can alleviate environmental problems and at the same time comply with the environmental regulations set by various countries. Green trading obstacles could be overcome. The competitiveness of industry can be raised leading to better and more sustainable corporate and national operations. Different inventory management policies in this simulation were used in this research than those used in the research of Goggin and Browne (2000). The remanufacturing infrastructure model was constructed and analysed in detail in the research. In spite of the holistic remanufacture model, some of the parameters are still yet to be considered, including dispatching rule, profit sharing, etc. Also, it is suggested that the lean principle, material required planning in remanufacturing, forecasting in remanufacturing be used to revise this research model. The satisfaction between customer and remanufacturer could also be studied for further research (Östlin 2008).