Simulation of purchase or rental decisionmaking based on product service system

Transcription

1 Simulation of purchase or rental decisionmaking based on product service system The International Journal of Advanced Manufacturing Technology ISSN Volume 52 Combined 9-12 Int J Adv Manuf Technol (2010) 52: DOI / s

2 Your article is protected by copyright and all rights are held exclusively by Springer-Verlag London Limited. This e-offprint is for personal use only and shall not be self-archived in electronic repositories. If you wish to selfarchive your work, please use the accepted author s version for posting to your own website or your institution s repository. You may further deposit the accepted author s version on a funder s repository at a funder s request, provided it is not made publicly available until 12 months after publication. 1 23

3 Int J Adv Manuf Technol (2011) 52: DOI /s ORIGINAL ARTICLE Simulation of purchase or rental decision-making based on product service system Tsai Chi Kuo Received: 16 March 2009 /Accepted: 4 January 2010 /Published online: 30 June 2010 # Springer-Verlag London Limited 2010 Abstract In recent years, global demand for and dependency on resources has increased with the advancement of technology, development of a global economy, societal progress, and rapid population growth. The concept of a product service system (PSS) has been actively promoted by some companies in the US and Europe to mitigate this resource strain and has resulted in significant effects. The objective of this study is to construct a renting system in a reverse logistics environment on the basis of the PSS theory: this system includes the complete management of product examination, maintenance, upgrading, products recycling, and final waste disposal. Due to the different procurement processes of PSS, the following two product operations are analyzed: (1) procurement of new products and (2) rental of products. The simulation model is analyzed by using three different scheduling and dispatching rules (shortest processing time, first come first served, and earliest due date). A home/office electronic case study is conducted to validate its implementation. Keywords Renting. Product service system. Simulation. Decision making 1 Introduction In recent years, global demand for and dependency on resources has increased with the advancement of technology, development of a global economy, societal progress, T. C. Kuo (*) Department of Industrial and Systems Engineering, Chung Yuan Christian University, Chung-Li, Taiwan 32023, Republic of China tckuo@cycu.edu.tw and rapid population growth. In the beginning of 1990, the European Union pushed forward the environmental protection concepts of integrated product policy (IPP) and extended producer responsibility (EPR) to encourage companies to produce environmentally friendly products for the reduction of resource waste. IPP emphasizes optimal combinations in product design to improve the environmental performance of products during their life cycles. EPR aims to reduce the total environmental impact of a product. Thus, a producer must be responsible for the environmental impact of its product during the entire product life cycle. Furthermore, some studies [3, 11] have suggested that the transformation from the past business model, which is based on selling tangible products, into a model that emphasizes product function, the usage efficiency and service upgrading cannot only increase the additional value of the products but also reduce the impact of the products on the environment. This concept is known as a product service system (PSS). The PSS involves tangible products and intangible services. The key to sustainable product-service systems is that they are designed and marketed to provide customers with a particular result or function clean clothes, mobility, warmth, etc. without them necessarily having to own or buy a physical product [13]. In other words, the product functions are provided to consumers by sharing, cooperation, and/or rental. By reusing and remanufacturing the products, materials, or components, PSS enhances the usage efficiency of the resources, reduces the resource consumption and wastes, and allows for the environmental design of products [11, 13]. In functional sales, a service-providing company decides how to fulfill the function that the customer is buying, such as leasing a physical product with a function known or specified by the customer. Renting a product

4 1240 Int J Adv Manuf Technol (2011) 52: provides even more of a link to a specific physical product. In the past, the PSS concept has been actively promoted by some companies in the US and Europe and has resulted in significant effects. However, most companies still refuse to promote the system since they do not understand it. The reason is because when PSS is introduced, most companies not only need to change their original business operational model but also have to consider the sale types, product maintenance services, and reverse logistic plans. Brezet [3] described five PSS services: (1) products/ services/combinations/substitutes, (2) service at point of sale, (3) different concepts of product use, (4) maintenance service, and (5) revalorization services (Fig. 1). The different concepts of product use are all on display in PSS rental. The car industry has been implementing the rental or leasing program for a long time. The critical issue is how to design and convert the product (car) from a traditional program to the leasing program. Therefore, the objective of this study is to construct a simulation model exploring the feasibility of a product renting system based on the PSS concept. The reverse logistics, maintenance, and remanufacturing/recycling processes are also considered and simulated in the system simultaneously. The performance indices for the system are analyzed by controlling the inventory level, maintenance time, dispatching rules, and economic cost/ profit of the system. The dispatching rules include the shortest processing time (SPT), first come first served (FCFS), and earliest due date (EDD). A procurement model is also constructed for comparison. The data for the simulation model are surveyed from a case study: an international office copy machine company that includes procurement and renting sales. The results of the case study are applicable to other electronic or electric equipment. 2 Literature review of PSS The original ideas for reducing the lifecycle impacts of products and services through product servicing, remanufacturing, and recycling were proposed by Stahel and Reday [14]. They advocated the need to distinguish between industrial and service-oriented economies; in the latter, products and technology are models of providing services that satisfy customers. PSS emphasizes the sales of product functions or services instead of the products [6, 9]. By this definition, a network and supporting infrastructure are needed to facilitate a PSS. Further, a product service must not only reduce the environmental burden but also meet customer needs and be competitive in the market. A great deal of creativity and innovation is required to meet these constraints. To simultaneously be economically competitive and environmentally sound is not easy, but putting effort into doing so for the sake of sustainability is worth it. A number of examples of PSS initiatives, from take back/remanufacturing to car sharing, havebeenproposed in the literature. Baines et al. [2] summarizedand highlighted examples of successful product service systems (Table 1). Among the above examples, the rental industry is of especial note. The rental process starts with a check-out at a station, where a customer signs a contract and ends with a check-in at the same or different station, which is when the product is returned. Check-out data includes the planned check-in station and rental length. Examples in the automobile industry include Autoplus and Liselec, which are initiatives established in La Rochelle by PSA Peugeot-Citroen in September Fink and Reiners [5] analyzed the logistics process in the short-term car rental industry. They described the major system components and core processes of car rental operations. In the leasing Fig. 1 PSS types [3] Sales techniques Life cycle information Customer education Leasing Sharing Pooling Renting Cleaning Repairing On-line monitoring Reusing Refurbishing Recycling

5 Int J Adv Manuf Technol (2011) 52: Table 1 Examples of successful product service systems [2] Organization Xerox International Parkersell (UK) Castrol Inc. (USA) Eastern Energy (UK) Electrolux (Sweden) Mobility (Switzerland) contracts, most products are bought from a producer and leased to a customer, and the producer holds responsibility for some damages or malfunctions of the product. At the end of the leasing contract, products are usually sold to a customer or the second-hand market [10].Thepurchaseof cars through leasing arrangements is increasingly popular; for example, in Germany, one in four cars is purchased in this way, and leasing has become the most important means of financing purchases [15]. Because of the rental strategy, producers are still responsible for the damage or breakdown of products; thus, they should enhance maintenance services for the companies to reduce the product impact on the environment [14]. For long-term rental, BT industries provide functional sales and long-term product rentals to their customers. This leads to closer connections between the end-users and the manufacturing company (if the manufacturer provides the function) as well as better knowledge on how the company products perform during use. Recently, HP and HP Financial Services offered a pay-per-use program; this is a unique offering that provides the needed capacity in real time and allows for pay according to the level of usage. The HP products in this program include notebooks, desktops, servers, printers, and monitors. As part of HP s utility pricing solutions, pay-per-use helps the customers align their costs with their usage, better manage and allocate their IT resources, and pay less when the customers use less. Acrobat company provides the Acrobat Connect Pro Meeting Online program to allow customers to conduct collaborative online meetings. 3 Research method Link In this study, a simulation model is constructed to determine the feasibility of a product renting system based on the PSS concept. In addition, the reverse logistics, maintenance, and remanufacturing/recycling processes are considered and simulated in the system simultaneously. The simulation system involves reverse logistics, i.e., it involves (1) customers, (2) retailers, and (3) a customer service/remanufacturing center, as shown in Fig Customers. A customer finds that the product is defective and sends it back to the retailer for remanufacturing. 2. Retailers. A retailer informs the customer service/remanufacturing center to remanufacture the defective product. 3. Customer service/remanufacturing center. Once the defective product is received, it is inspected. The product is diagnosed by the center to evaluate the malfunction level and to evaluate the maintenance or remanufacturing time. The product is then disassembled and remanufactured on the basis of different dispatching rules: FCFS, EDD, or SPT. Here, the processing time is referred to as the maintenance or remanufacturing time. 4. Waste processing center. Products with serious damages that cannot be repaired are recycled or disposed. 3.1 Construction of system variables In order to compare different types of business models, two product sale types procurement (model I) and rental (model II) are constructed in this study (Fig. 3). In order to maintain a high customer service level and reduce product waste, the defective product is collected and sent to the customer service center for remanufacturing. There are many factors that have been considered in simulation studies of job remanufacturing shop behavior [4, 12]. Guide et al. [7] cited the following most commonly used system variables in remanufacturing: the arrival time distribution, work flow characteristics, processing time distributions, shop configuration, shop rules, and performance measurements. In this study, three system variables are selected, namely, product damage rate, successful maintenance product rate, and maintenance time. In addition, different shop rules are applied to the simulation models. The system variables are determined on the basis of the work by Mont et al. [10] and Kara et al. [8] on remanufacturing problems. The variables are described below. 1. Product damage rate. Since the internal damage of products cannot be effectively controlled, it significantly affects maintenance. This study considered low, middle, and high levels of damage to products. 2. Successful maintenance product rate. Because of unknown situations after the product is damaged, product repair and component replacements cannot be controlled. This study considered low, middle, and high levels of maintenance success rates. 3. Maintenance time. When the customers products break down, the uncertainty of damage or maintenance success leads to the technical personnel having differ-

6 1242 Int J Adv Manuf Technol (2011) 52: Fig. 2 Rental/procurement simulation model in the PSS Customer 1 Customer 2 Retailer 1 Retailer 2 Customer service/remanufacturing center (service dispatch rules: FCFS, EDD, SPT) Inspection Disassembly Remanufacturing Waste processing Customer n Retailer n Remanufactured products ent levels of control over the maintenance time. This study considered low, middle, and high levels of maintenance time. 4. Shop dispatching rules. Scheduling and dispatching are critical for repairing products within the time demanded by the customers. Thus, this study considered three dispatching rules: FCFS, EDD, and SPT. 3.2 Analysis of performance indices Based on a literature review, six performance indices are included in this system [1, 7], as described below: 1. Maintenance cycle time for customers. This is the time from receipt of the customer s demand to reporting the maintenance completion to customer service center. The time can be shortened. In other words, the maintenance service should satisfy the customers demands and create more business opportunities. 2. Total maintenance time. The total maintenance time should be as short as possible. 3. Total maintenance cost. This refers to the maintenance finished in the planned simulation time and the total maintenance cost of maintenance units and maintenance. In general, the total maintenance time and the total maintenance cost should be less. 4. Total component cost. This refers to the components and costs needed by maintenance personnel. The average component cost should be low. 5. Total waste amount. This refers to the amount of waste stored. The amount of waste stored for an average unit should be small. 6. System cost. This includes the costs for maintenance personnel, maintenance service, components, and transportation. 4 Case study and analysis To evaluate the usefulness of the analysis results, we conducted a case study using the Taiwan subsidiary of an international company. The company engages in the development, manufacture, marketing, servicing, and financing of document equipment, software, solutions, and services. Its name has been omitted for confidentiality reasons. In addition to equipment sales, the Taiwan subsidiary provides mixed copy machine leasing and payper-copy service to consumers. Approximately 55% of the revenue comes from equipment sales; the remaining 45% comes from service activities that include lease, maintenance, and financing. Currently, 40% of the company employees are maintenance personnel. They must respond Fig. 3 Research structure for home/office electronics

7 Int J Adv Manuf Technol (2011) 52: Fig. 4 Part of the simulation structure to customer problems within 48 h and solve the problems within 5 days. Customer problems include maintenance, repair, toner cartridge orders, and customer complaints. Currently, the percentage of remanufactured components ranges from 20% to 95%. The quality of the remanufactured components is monitored and controlled in a timely manner. The simulation model (Fig. 4) was constructed using the simulation tool Extend. 4.1 Design of the system variables on the basis of the case study The three system variables product damage rate, successful maintenance product rate, and product maintenance time are included and applied to the simulation models. The three variables are numeral variables with three levels: low (L), middle (M), and high (H) (Table1). In addition, the dispatching rule is applied to the two models (Table 2). In order to explore all the relationships between the system variables for their effect on the simulation model, a full factorial design was used. Therefore, 33 experimental designs were analyzed on the basis of the three system variables and dispatching rules. The three variables in this study, which used the information provided by case company, are designated as the parameters for adjusting system variables. The parameters designed in this study for the two different models remained the same from the customer to the customer service center, material supply, and maintenance. These parameters are based on the Table 2 Three levels of the system variables No Variables Procurement (model I) Rental (model II) L M H L M H Source: Case company 1 Product damage rate (%) Success rate of product maintenance (%) Product maintenance time (in minute)

8 1244 Int J Adv Manuf Technol (2011) 52: Table 3 Parameters of maintenance service (Case company) No. Name of parameter Number 1 Arrival time of maintenance product 6.5 piece/min 2 Customers demand for maintenance 300 pieces/unit 3 Online test time of customer service 5.8 min/unit 4 Customer service dispatching and 20 min/unit informing time of material supply 5 Component unit cost Scraping cutter NT 3,000 5,000 Roller NT 20,000 30,000 Imaging NT 7,000 8,000 6 Maintenance station 20 7 Maintenance time 30 min/unit or 300 pieces/day 8 Maintenance unit cost NT$1200/unit 9 Unit cost of maintenance personnel NT$2250/person 10 Component transportation unit cost NT$150/unit 11 Maintenance Transportation unit cost NT$800/unit 12 Waste saving unit cost NT$12,000/unit 13 Waste saving 8,000 pieces/month information provided by the case company. The design of the related parameters is given below (see Table 3): 4.2 Analysis and validation of system simulation design Before the simulation test, this study needed to confirm the steady state analysis, validate the system simulation model, and calculate the simulation frequency, as detailed below: 1. Steady state analysis In order to validate the steady state of the system in the simulation, one performance index was selected, and all variables were set at low level (L) for the test. The test was based on the average processing time of the system; a simulation period (test time) of 525,600 min and machine warming time of 43,200 min were used. The steady state validation results (Fig. 5) demonstrated the steady state of the two models. 2. Validation of system simulation model A simulation period (time) of 1,000 min and a machine warming time of 80 min were considered for this test. This test resets the parameters listed in Table 4. For maintenance, the daily output and demand were set at 400 for each. The maintenance time was set as 1 min, the cost of each material was NT$1, and the supply was infinite. The maintenance demand was 100%, the online maintenance was 0%, the maintenance success rate was 0.5, and the repetitive maintenance ratio was 0.5 (Table 5). Thus, during the simulation period, the system could produce and finish 400 pieces of maintenance. The total maintenance time was 783 min, the component cost was NT$400, the total basic maintenance expense was NT$400, and the average processing time was 8.48 min. 3. Calculation of simulation frequency Based on the formula for the simulation experiment frequency suggested by Law and Kleton [8], a simulation experiment of the model was conducted ten times (n=10), and one observation or performance value was selected to further calculate the mean and variance of the related simulation results ten times. Finally, the mean and variance were substituted in the formula to calculate the frequency of the simulation experiment. ( n» g ðgþ ¼ min i n : t pffiffiffiffiffiffiffiffiffiffiffiffiffiffi ) i 1;1 a=2 s 2 ðnþ=i g 0 X ðnþ where g 0 ¼ g= ð1 þ gþ refers to the adjusted relative error, n» gð g Þ is an approximation of the simulation frequency, α is the significance level, (n) is the simulation frequency, X is the sample mean, t i 1;1 a=2 is the T test, s 2 is the sample (a) Model I (b) Model II Fig. 5 Validation for the steady states of Models I and II. a Model I, b Model II

9 Int J Adv Manuf Technol (2011) 52: Table 4 Calculation of simulation frequency Simulation frequency variance, and i refers to the simulation frequency needed. Based on the above, with regard to procurement and rental models, the average processing time of one performance index was selected as the calculation index, and all variables were set at a high level (H): the simulation period (time) was 525,600 min, the machine warming time was 43,200 min, the confidence level was 95% (α=0.05), the relative error g was 0.1, and the adjusted relative error was g 0 ¼ g= ð1 þ gþ ¼ 0:09. After substituting the values for the simulation frequency in the above equation, the formulas for the procurement and rental models were obtained. 1. Experiment frequency formula of procurement model: ( n g» ð0:1þ ¼ min i 10 : t pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ) i 1;1 a=2 1:220962=i 0:09 122:2593 ¼ 10 ðtimesþ Average processing time Model I Model II Sample average X Sample variance (s 2 ) Experiment frequency formula of rental model: ( n g» ð0:1þ ¼ min i 10 : t pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ) i 1;1 a=2 0:996858=i 0:09 135:7424 ¼ 10 ðtimesþ 4.3 Results and analysis The analysis can be categorized by the main effect on the variables and interactions between variables Main effect analysis for the variables This study probed into the influence of system variables on performance indices in the procurement and rental models and analyzed the results statistically. The results showed that the product damage rate was the most significant factor for an enterprise when implementing the business model. Other factors such as the maintenance success rate and maintenance time had less effect on the business model. 1. Analysis of the influence of the product damage rate For the procurement or rental models, when the P value was less than the significance level of 0.05, different malfunction rates significantly influenced the total maintenance and total costs for the maintenance, components, waste saving, and system. 2. Analysis of the influence of the product maintenance success rate For the procurement or rental models, when the P value was less than the significance level of 0.05, different product maintenance success rates significantly influenced the maintenance cycle for customers. Moreover, in rental models, they also significantly influenced the total waste saving cost as compared to the other costs. Table 5 Influences of system variables on performance indices (principal factor effect) System variables Damage product rate Product maintenance success rate Product maintenance time Scheduling and dispatching Performance indices I II I II I II I II Maintenance cycle time for customers - a - a - a - a - a - a Total maintenance time - a - a Total maintenance cost - a - a Total component cost - a - a Total waste saving cost - a - a - a System cost - a - a a For the significance level 0.05, the variables revealed a significant influence on procurement and rental models.

10 1246 Int J Adv Manuf Technol (2011) 52: Table 6 Influences of performance indices with system variables System variables Malfunction rate (1) Product maintenance success rate (2) Product maintenance time(3) Scheduling and dispatching (4) Performance indices Procurement Rental Procurement Rental Procurement Rental Procurement Rental Total maintenance (2) X - X X X X X Maintenance cycle time for customers (2)(3) (4) (2)(3) (4) (3)(4) (3)(4) (4) (4) - - Total maintenance time (2)(3) (2)(3) (3) (3) - - X X Total maintenance cost X (2) X X X X X X Total component cost X X X X X X X X Total waste saving cost X (2) X X X X X X System cost X X X X X X X X Variables with numbers are those that reveal interaction with certain variables X system variables do not reveal interaction, - the interaction has already appeared with previous variables The reason is that higher product maintenance success rates result in successful product maintenance. The reduction in related maintenance reduces the overall cycle of maintenance for the customers. When the success rate increases and the waste reduces, the related waste saving cost decreases. 3. Analysis of the influence of product maintenance time For procurement or rental models, when the P value was less than the significance level of 0.05, different product maintenance times significantly influenced the maintenance cycle for customers and the total maintenance time as Fig. 6 Performance index comparison between the two models Average quantity (piece) Model I Model II Maintenance quantity Minutes Model I Model II Cycle time of maintenance for customers Minutes Model I Model II Total maintenance time Total maintenance cost Model I Model II NT dollars NT dollar NT dollars Model I Model II Component cost Model I Model II Total waste cost

11 Int J Adv Manuf Technol (2011) 52: Table 7 Comparison between procurement and rental models (T test of pair samples) Rental/procurement Difference between the pair variables T test Significant (two-tailed) Mean Standard deviation Standard deviation of mean Lower bound Upper bound Total maintenance * Total maintenance time * Total maintenance cost * Total component cost * Total waste saving cost * System cost * Upon P value less than significance level (0.05), procurement and rental models reveal significant difference compared to the other costs. When the product maintenance time increased, the maintenance cycle for customers and the total maintenance time increased gradually. 4. Analysis of the influence of scheduling and dispatching For procurement or rental models, when the P value was less than the significance level (0.05), different scheduling and dispatching significantly influenced the maintenance cycle for customers. SPT was superior in this study; the reason for this was because maintenance service can be managed in a short time. Thus, the system treats the customer with less maintenance time as the priority to save unnecessary time and satisfy the customers in the shortest time possible. 4.4 Interactive results between variables Since there was a correlation among system variables, this was explored during the study, and the results are presented here (Table 6). 1. Total maintenance For the procurement model, the product damage and product maintenance success rates significantly influenced the total maintenance. For the interaction between the malfunction and product maintenance success rates, since the malfunction rate in the procurement model was lower than that of the rental model, the product maintenance success rate was higher than in the rental model. Thus, when the malfunction rate increases, the product maintenance success rate increases as well. 2. Cycle time of maintenance for customers In the procurement and rental models, this variable significantly influenced the performance indices. When the malfunction rate increased, the product maintenance success rate is reduced and the demand for maintenance increases. Thus, repetitive maintenance increases. The overall maintenance cycle for customers then increases; when the malfunction rate is reduced, the product maintenance success rate increases, which enhances the possibility of success and reduces the overall maintenance cycle for customers. 3. Total maintenance time In the procurement and rental models, this variable significantly influenced the performance indices. When the malfunction rate increases, the product maintenance success rate increases. Thus, it does not considerably affect the total maintenance time. When the malfunction rate increases, the product maintenance success rate is reduced, which increases the demand for maintenance as well as repetitive maintenance. This increases the total maintenance time. 4. Total maintenance cost For the variables that significantly influenced the performance indices, only the interaction between the malfunction rate and product maintenance success rate did so in the rental model. The malfunction rate controls the demand for maintenance. When the product fault rate is higher, there are more pieces demanded. Thus, when the malfunction rate increases, the product maintenance success rate increases. The influence is not significant. When the malfunction rate is higher, the product maintenance success NT dollars FCFS EDD SPT Dispatching rules Model I Model II Fig. 7 The results of different dispatching rules for the two models

12 1248 Int J Adv Manuf Technol (2011) 52: rate is reduced, and demand for maintenance increases; this results in more repetitive maintenance and a higher total maintenance cost. When the malfunction rate is reduced, the product maintenance success rate and the possibility of success increase. The total maintenance cost becomes lower. 5. Total waste saving cost In the rental model, only the interaction between the malfunction and product maintenance success rates affected the performance indices. The reason is because the malfunction rate was higher and the product maintenance success rate was lower in the rental model than in procurement model. Moreover, the malfunction rate controls the pieces of demand for maintenance. When the malfunction rate is higher, there are more pieces (Fig. 6). 4.5 T test correlation analysis This section compares and analyzes the two models (procurement and rental) with regard to their different system variables that affect the total maintenance, maintenance cycle for customers, total maintenance time, and the total costs for maintenance, components, waste saving, and system. According to the statistical analysis results (Table 4), when the P value was less than the significance level of 0.05, the two models revealed significant differences in the total maintenance, maintenance cycle for customers, total maintenance, and the total costs for the maintenance, components, waste saving, and system (Table 7). 5 Conclusions and future studies This study used the office copy machine as an example to propose a maintenance service based on PSS; the service includes maintenance, recycling, reverse logistics, and final waste disposal. Therefore, the results can be applied to home/office electronics in general. The results of different procurements were analyzed by system simulation. The total system cost was found to be better for the procurement model than for the rental model. The reason for this was because the customer uses the product more carefully since they think the product is their own property. The other findings are detailed below: 1. In the analysis on the principal factor effect in procurement and rental models, when the malfunction rate increases, it significantly affects the total maintenance and total costs for maintenance, components, waste saving, and the system. The rest of the variables significantly influence the maintenance cycle for customers. 2. In procurement and rental models, the interaction among the variables influences the maintenance cycle for customers and slightly affects the total maintenance time. For the rest of the variables, the malfunction and product maintenance success rates significantly influence the total maintenance of the procurement model and the total maintenance and waste saving costs of the rental model. 3. In the procurement and rental models, in order to optimize all performance indices, the malfunction rate should be at a low level, the product success rate should be at a high level, and product maintenance should be at a low level. In order to optimize scheduling and dispatching, SPT, EDD, and FCFS, respectively, should be used for the above rates (Fig. 7). In addition, if an enterprise wants to transform the business model from procurement to rental, it should change the product design to be more environmental. The product should also allow for easier maintenance for the customers. Since maintenance service and reverse logistics are complicated, further work can be done to review other literatures for the purpose of obtaining diverse variables and parameters to clarify the overall framework. Future intended research involves probing into transportation cost, transportation time, and recycling expenses for complete reverse logistics and maintenance. In addition to the procurement and rental models, follow-up studies on overall prices and profits in different models will specify corporate revenues and future development of the firms. Acknowledgments The authors would like to thank the National Science Council of the Republic of China, Taiwan for financially supporting this research under contract NSC Z MY3. References 1. Amini MM, Donna R-R, Bienstock CC (2005) Designing a reverse logistics operation for short cycle time repair services. Int J Prod Econ 96(3): Baines TS, Lightfoot HW, Evans S, Neely A, Greenough R, Peppard J, Roy R, Shehab E, Braganza A, Tiwari A, Alcock JR, Angus JP, Bastl M, Cousens A, Irving P, Johnson M, Kingston J, Lockett H, Martinez V, Michele P, Tranfield D, Walton IM, Wilson H (2007) State-of-the-art in product-service systems. Proc Inst Mech Eng B J Eng Manuf V221:10 3. Brezet H (2000) Product-service substitution: examples and cases from the Netherlands, ''Funktionsförsäljning''-product-service systems, Stockholm, Swedish EPA, AFR-report Dar-El EM, Wysk RA (1982) Job shop scheduling a systematic approach. J Manuf Syst 1: Fink A, Reiners T (2006) Modeling and solving the short-term car rental logistics problem. Transp Res Part E Logist Trans Rev 42(4): Fishbein B, McGarry LS, Dillon PS (2001) Leasing: a step toward producer responsibility. J Ind Ecol 5(4): Guide VDR Jr, Srivastava R, Kraus ME (1997) Product structure complexity and scheduling of operations in recoverable manufacturing. Int J Prod Res 35(11):

13 Int J Adv Manuf Technol (2011) 52: Kara S, Rugrungruang F, Kaebernick H (2007) Simulation modelling of reverse logistics networks. Int J Prod Econ 106: Makower J (2001) The clean revolution: technologies from the leading edge, Presented at the Global Business Network Worldview Meeting, May 14-16, San Francisco Bay Area 10. Mont O, Dalhammar C, Jacobsson N (2006) A new business model for baby prams based on leasing and product remanufacturing. J Clean Prod 14: Mont O (2002) Clarifying the concept of product service system. J Clean Prod 10: Philipoom PR, Russel RS, Fry TD (1991) A Preliminary investigation of multi-attribute based sequencing rules for assembly shops. Int J Prod Res 29(no. 4): Roy R (2000) Sustainable product service systems. Futures 32: Stahel W, Reday G (1976) Jobs for tomorrow, the potential for substituting manpower for energy. Vantage Press, Brussels 15. Williams A (2006) Product-service systems in the automotive industry: the case of micro-factory retailing. J Clean Prod 14: