A Generic Methodology to Assess Quality and Reliability in the Reuse Strategy

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1 A Generic Methodology to Assess Quality and in the Reuse Strategy Maria Anityasari, Hartmut Kaebernick Life Cycle Engineering and Management Research Group School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney Australia Abstract Quality and reliability are critical to ensure market acceptance and economic feasibility of reused products. Therefore, a proper assessment of quality and reliability is necessary to realize the reuse strategy. This paper proposes a generic methodology to assess quality and reliability including reliability analysis, threshold determination, and reliability prediction. Threshold determination is critical as it sets a standard value against which the reliability of a used item can be compared at the end of its first life. The methodology can be integrated in some current available models for assessing the technical reusability potential of used products. A case study is used to demonstrate the applicability of the methodology for reusability evaluation. Keywords: Reuse; Quality; ; Assessment Methodology; Threshold Value 1 INTRODUCTION It is acknowledged that reuse of components, subassemblies, or entire products is highly recommended from an environmental perspective. However, its execution greatly depends on its technical and economic feasibility. The technical aspect is the most challenging and doubtful factor in the reuse strategy. This factor has been proven to be the main reason why many companies hesitate to implement the reuse strategy as it rules the profitability [1] [2] [3]. Figure 1 demonstrates four factors forming the technical aspect of reuse. Design for Reuse Technological Change Technical Aspect Quality & The Availability of Reuse Technology Figure 1: Factors forming the technical aspect of reuse. For technologically stable products, quality and reliability are the key factors that need to be addressed. Quality in this case refers to the functionality of a product. It represents how well a product can perform its intended functions at the beginning of its life. For reused products, it represents the condition of the product at the beginning of the second life. On the other hand, reliability represents the ability of a product to perform its function in an acceptable way during a given period of time, which is a normal usage period. In the reuse strategy, reliability of a used item must be assessed based on the probability of its survival during the second life. Some studies [2] [4] [5] [6] reveal that reliability or functioning without failure becomes one of the top quality features expected by customers. Therefore reliability is strongly recommended to be used for assessing the quality of used items [7] [8] [9]. Ultimately, in a reuse context, reused products should deliver the same quality as new products in the sense that they should be functioning properly and working reliably during a normal usage period. Up to now, several works have been undertaken to investigate the quality of used products for reuse purposes. However, somehow the results are still scattered, thus a generic methodology needs to be established as a framework for the assessment process. This paper will describe a generic methodology to assess the reusability of used products at their end-of-life. The methodology is established to facilitate different approaches in reuse evaluation, thus it can accommodate different companies interests. 2 THE METHODOLOGY 2.1 The concepts Reuse and remanufacturing aim to provide as-good-as-new or equivalent-to-new (ETN) products. This can be achieved when reused products have constant random failure as in the first life [8]. In addition, as shown in Figure 2, as long as the reliability of reused products is still above the desired performance, the reused products could be considered as ETN products. Normally the quality assessment of used products can only be done after collection and disassembly. However, for economic reasons it would be more effective to assess the reusability potential before collection to avoid unnecessary costs. Graphically, the ideal flow of assessment is shown in Figure 3. Used products that have no potential for reusability or remanufacturability do not need to be collected and disassembled. 2.2 The structure There are three steps prior to the reliability assessment, i.e. reliability analysis, threshold determination, and reliability prediction (Figure 4). analysis aims to determine the reliability distribution and the reliability parameters of the product s life. Based on this analysis, the reliability status of a 15th CIRP International Conference on Life Cycle Engineering, 2008

2 product at a certain time can be determined and the evaluation can be carried out. The second step is threshold determination, at which a standard will be selected. The standard will be used as a basis to justify the reusability potential of a used product. The next step is reliability prediction, in which the reliability of a used product at its endof-life will be predicted for further assessment. PERFORMANCE 1 st Life Reusability Evaluation Analysis 2 nd Life. Time Figure 2: The concept of as-good-as-new. Reusable N Leave it Y Take-back Figure 3: The ideal flow of assessment. Threshold Determination Prediction INITIAL CAPABILITY Margin for deterioration DESIRED PERFORMANCE Disassembly Functionality Check Assessment Figure 4: The steps in the reliability assessment. 2.3 analysis One of the most popular practices to manage reliability is the use of life data to predict reliability parameters and reliability distribution of a product [9]. There are three generic prediction methods in the reliability analysis, i.e. physics of failure and extrapolation, collection and analysis of failure data from reliability testing (in-house data), also known as accelerated life testing, and empirical prediction models based on field data. These methods have their pros and cons, thus they are used at their specific strengths and when the expenses can be justified [10]. The first method is rarely available for consumer products, especially for the purpose of remaining lifetime prediction [11] [12], hence the second and the third methods are more popular and recommended. In-house data is obtained from life testing in the company or at the laboratory, while field data is collected from warranty claims or failure-records in the field use. Most of the reported studies utilize laboratory life test data. However, literature claims that in many situations field data is superior to laboratory data because it captures actual usage profiles and the combined environmental exposures that are difficult to be simulated in the laboratory environment [15]. Thus field data is more likely to reveal the real time-to-failures. Despite its downsides [16], this method is the least expensive method, and it offers immense benefits. In this paper, field data collected from maintenance and after sale service will be utilized in the analysis. This type of data is available in most companies, thus it facilitates the development of a generic assessment model. The required data for the analysis is shown in Figure 5. The basic input for the reliability analysis is failure and suspension data. data or time-to-failure (TTF) data are recorded when components actually fail, while suspended or censored data are logged when components still work properly until the end of the observation time. For field data analysis, suspended data refers to the functioning products in the market. Product Type Part Number Purchasing Mode Operation Distribution Analysis Characteristics Suspension Parameters Sales Registered Products Figure 5: Required data for the field data analysis. As failure only happens to a few units sold, the number of surviving units has to be included to determine the correct reliability parameters. The number of unfailing units can be obtained from sales data minus the number of failed units. However, since not all failures are reported to the registered maintenance centre, but to private or unauthorized service centres, further data breakdown is needed. Moreover, the distribution of sold products in the market also needs to be considered to get a more accurate figure of suspension data. For this purpose, the field situation as shown in Figure 6 can be used to determine the number of failed units and surviving units. a% units stay in the coverage area (100 - b)% units not registered with the Maintenance Centre (MC) Units fail - reported as failure data ( F) X units sold (100 - a)% units go outside the coverage area b% units registered with the Maintenance Centre (MC) Units survive - classified as suspended data ( S) Figure 6: A typical market situation. The failure and suspended data then will be plotted to find the best fitted reliability distribution. The life of a product can be represented by various life distributions. In the reuse strategy, the Weibull distribution is prominent as it is very

3 flexible and powerful to provide a pragmatic solution. This paper focuses on the use of the Weibull distribution for empirical data analysis. Graphically, the steps in the reliability analysis are illustrated in Figure 7. 1R(t) Life threshold. For example, if manufacturers only allow 10 products out of 100 products to fail during an average usage period, R* will be equal to 90%. Accordingly, T* will be equal to B10. (F) Suspended (S) Plotting Maximum Likelihood Estimation (MLE) R* = 0.9 Distribution, Parameters, Life Characteristics Figure 7: The steps in the reliability analysis. 2.4 Threshold determination After knowing the life characteristics of a product, a reliability threshold needs to be selected. This threshold will be used to evaluate whether a used product is good enough to be reused or not. This step is critical since it will determine the success of the reuse strategy and its economic viability. In the literature on reuse evaluation, reliability of used products is usually evaluated based on a qualitative standard, named an acceptable level or a reasonable level. However, a quantitative standard is required to better justify the potential reusability of used products. A selected reliability threshold is denoted by R*, while the maximum time of concern related to the selected reliability level is denoted by T*. The reliability assessment will be based on this value, against which the reliability of a used item can be compared at the end of its first life. A detailed explanation on how to develop the threshold can be found in [17]. There are three quantitative standards commonly used in the evaluation, i.e. Maintenance Free Operating Period (MFOP), a particular reliability level and its associated BX value, and Mean Time to (MTTF). BX is defined as the time at which X% of the units in a population will have failed. A typical curve for each standard can be seen in Figures 8 to 10 for MFOP, BX, and MTTF respectively. 1 R(t) R* = 1 MFOP T* = MFOP Time ( t) Figure 8: A typical reliability curve with MFOP. Manufacturers use MFOP if no failure is allowed during the life of a product. This represents a very strict standard, which is usually related to safety issues. The use of MFOP will set R* equal to 100% and T* equal to MFOP. Alternatively, manufacturers can set a maximum allowable number of failed products during the average life as their 1 R (t) R* T* = B10 Figure 9: A typical reliability curve with BX. T* = MTTF Time ( t) Time ( t) Figure 10: A typical reliability curve with MTTF. Furthermore, if manufacturers allow their products to be used up to MTTF, the value of T* will be equal to MTTF. In this situation, the value of R* will be varied depending on the reliability distribution of the product. In general, at MTTF the associated R* will be approximately 50%, however, this is not always the case. For example, for the Exponential distribution, MTTF always relates to 36.78% of reliability. As reused products have to perform as-good-as-new products, irrespective of the standard selected by manufacturers, this has to be reflected in the threshold. For instance, if MFOP is selected as the threshold, both new and reused products must have no failure during the average usage life. If the selected threshold is R* = 90%, the maximum allowable failure during the second life is only 10% of all reused products. For consumer products, MTTF and BX are the most popular standards. However, any reliability level chosen by manufacturers in order to evaluate the reusability of used products can be accommodated in this methodology. As previously mentioned, the threshold determination is critical to evaluate the reusability of used products. It has not only technical consequences but also economic implications. The setting is subjective, depending on the product s characteristics and the marketing strategy of the manufacturer, including factors such as company image,

4 market competitiveness, and targeted customers. The steps of threshold determination are illustrated in Figure 11. Product's Characteristics Cost Information Market Information Company's Preferences Selected Level Threshold Parameters (R*, T* ) Figure 11: The steps of threshold determination. 2.5 prediction This step aims to predict the age and the associated reliability of used products and their parts at the end of the first life. There are many factors affecting the reliability prediction, such as the length of the first life, the usage type, the intensity of use, the consumer s behaviour toward products, and the availability of accurate techniques. A thorough study reported in [14] has reviewed possible methods to predict the remaining life of used products at the end of their life. The review suggested the use of life data and condition monitoring data recorded by an electronic device mounted in the product. Nevertheless, until now the use of recording devices in household products is not common and considered only for future application. Therefore a more general and structured approach is needed for current application on reuse practices. In this paper, statistical data analysis is used to predict the age of used products at their end-of-life. The required data for the estimation are purchasing date, returning or failure date, and operation data such as the intensity of use. The age is derived from the difference between the purchasing date and the returning date. For non-continuous operating products, operation data is needed to convert the date into operating hours or cycles. If life is indexed as a, the age and the reliability at the end of the first life will be denoted as t a = t 1 and R(t a) = R(t 1). Furthermore, the reliability prediction will follow the steps shown in Figure 12. Life Purchasing Returning/ Operation Age prediction - t 1 prediction - R(t 1 ) Figure 12: The steps of the reliability prediction. 2.6 assessment To assess the potential for reuse, the reliability of the item at the end of the second life has to be estimated. This can be done by adding up the average second life, t 02, to the first life, t 1. In general, the average second life is usually shorter than the average first life, t 02< t 01. Based on the result of reliability prediction and the threshold values (R* and T*), there will be three different scenarios reflecting situations at which the reuse decision has to be made. The first scenario, called Scenario A, may occur when the reliability of a used item at the end of its second life is estimated to be greater than the threshold value (Equation 1). In this scenario, the used item can be reused without remanufacturing or additional reprocessing. * ( t t ) R > R (1) Where: t 1 t 02 R(t 1) = the first life of a product = estimated average second life of a product = the reliability of a product at end-of-life R* = the threshold value The second scenario, Scenario B, occurs when R(t 1)>R* but R(t 1+t 02)<R*. In this situation, there are two possible options to be considered. The first option is to remanufacture the used product to as-good-as-new standard. However, the remanufacturing option is not always available since current technologies do not offer remanufacturing processes for all types of products. If the required technology is available, the additional remanufacturing cost should be added later in the economic analysis. The second option is for manufacturers to provide a lifetime warranty to ensure that customers receive a guaranteed product function during the second life. A lifetime warranty covers the risk of having a bad image as a result of a product s failure. That means the reserved budget for warranty cost will be higher than what is usually reserved for a normal situation. This additional warranty cost will add to the life cycle cost of the product and will affect the economic viability of this option. Furthermore, the third scenario occurs when R(t 1)<R* and R(t 1 + t 02)<R*.This is a very undesirable scenario. For this reason, a general recommendation would be not to reuse but to recycle the used products. Only if the remanufacturing technology is able to bring back the used item to as-good-asnew standard, the remanufacturing option can be considered but at a higher price. In summary, the reliability assessment will deliver three possible results, which are Scenario A, B, and C as illustrated in Figure 13. In general, collection needs to be carried out only if the used products satisfy Scenario A and B. 2.7 Discussions As clearly explained in the previous sections, the generic methodology presented in Figure 4 can be fitted to any method available in current practices. For instance, if accelerated life testing is performed by a manufacturer, the results can be fitted into the reliability analysis to establish the reliability behaviour of the product under investigation. If for a particular product an Electronic Logger (EDL) is available, the life data retrieved from the EDL is suitable for predicting the reliability of the first life.

5 Yes R(t 1 +t 02 ) > R* No Yes Assessment R(t 1 ) > R* No Furthermore, the market situation can be explained as follows: from X units sold, only 97% will stay in the Maintenance Centre (MC) coverage area while the other 3% will leave the coverage area to other regions. Among those that remain in the coverage area, only around 80% are registered with the authorized MC. This percentage indicates the potential of reporting. From the registered products, the maintenance database reveals the reported failed components over the observation period. The number of surviving units that form the suspension data is then calculated by using the number of sales, the percentages of market distribution, and the number of units that fail. Scenario A Scenario B Scenario C Figure 13: Three possible results of the reliability assessment. 3 A CASE STUDY 3.1 Description The compressor of a domestic refrigerator has been selected to demonstrate the steps of the proposed methodology. The useful life of refrigerators ranges from 10 to 15 years, while the compressor is expected to have a longer life. For the reuse analysis, the average usage period of a compressor is set to be 10 years. This will apply to the first and the second life, thus t 01 = t 02 = 10 years. This case study utilizes real field data collected from a leading manufacturer of home appliances. The nature of the data includes time-to-failure data recorded by the maintenance department, the number of surviving products from sales data, and the distribution of the product in the market region. 3.2 analysis Around 660 failure data sets were collected over a three-year period for the selected refrigerator. Among those data, only 50 failures were due to compressor problems. The failure data sheet is shown in Table 1. Most of the data are selfexplanatory. The time-to-failure (TTF), in days, is simply the difference between the purchasing date and the failure date. Since a refrigerator is used continuously after its installation, the value of TTF does not need to be converted into hours or other measurement units because it immediately reflects the age of the product. Purchasing Diagnosis TTF (days) 23/7/03 23/9/03 No Cold 62 06/4/03 19/6/03 Doesn t Work 74 06/4/03 19/6/03 No Cold 74 The data were then analysed by using the Weibull distribution to reveal the main parameters as summarized in Table 2. Based on these reliability parameters, the reuse evaluation was conducted. Shape Parameter (β) Scale Parameter (η) years Table 2: The Weibull parameters for the compressor. 3.3 Threshold determination Two threshold values were selected to evaluate the potential reusability of used compressors, i.e. 90% and 50%. The first is selected to represent a strict performance policy, while the second represents a common practice related to the use of median life as the basis for remaining life estimation. With regard to the selected values of R *, the associated T * has been calculated and the results are presented in Table 3. BX R* T* (days) T* (years) B B Table 3: Threshold values for the evaluation. 3.4 Reusability assessment Based on the selected threshold value, the reusability evaluation for used compressors is then carried out by following the methodology outlined in Section 2.6. As an input, the age of used compressors is required. In this case, the age of a compressor is estimated simply by subtracting the refrigerator s failure reporting date by the purchasing date. Several values of t 1 have been selected to demonstrate the applicability of the proposed methodology, and the outcomes of the reliability assessment are shown in Table 4. It can be seen that under the strictest threshold, there is no chance of having reusable compressors when the collected products age is more than 4 years. As expected, the reuse strategy will have a higher possibility if the reliability threshold is lowered. Eventually, since the result of the reliability assessment will determine whether collection and the subsequent processes will be undertaken or not, the selection of the reliability threshold must be done carefully. Table 1: data sheet for the selected refrigerator. As maintenance data naturally only provides failure data for some of the sold products, the number of surviving units has to be calculated to determine accurate reliability parameters. For this purpose, monthly sales data over a three-year period have been collected.

6 t 02 = 10 years t 1 R * = 0.9 T * = years R * = 0.5 T * = years 2 Scenario A Scenario A 4 Scenario A Scenario A 6 Scenario B Scenario A 8 Scenario B Scenario A 10 Scenario B Scenario A 12 Scenario B Scenario A 14 Scenario B Scenario A 16 Scenario C Scenario A 18 Scenario C Scenario B 20 Scenario C Scenario B Table 4: Some examples of the technical assessment. 4 CONCLUSIONS In this paper, a generic methodology to assess the reliability of used products for reuse purposes has been proposed. The methodology provides a framework into which different models and techniques available in the literature or in the current industrial practices can be fitted. The methodology suggests that the selection of a threshold value is critical for determining the reusability potential of used products. Therefore, many aspects need to be considered in the selection including manufacturers and customers interests. By utilizing some existing data recorded by most companies, a case study on compressors has confirmed the applicability of the methodology. Conclusively, the proposed methodology is suitable to be implemented in an ordinary industrial environment. 5 REFERENCES [1] Kaebernick, H., Anityasari, M., Kara, S., 2001, Reuse or Recycling of Industrial Products: A Technical and Economic Model for Decision Support, Proceedings of the 8 th CIRP International Seminar on Life Cycle Engineering, Varna, Bulgaria, pp [2] Anityasari, M., 2002, Reuse or Recycling of Industrial Products - A Technical and Economic Model for Decision Support, School of Mechanical and Manufacturing Engineering UNSW, Sydney, Australia. [3] Takata, S., Kimura, T., 2003, Life Cycle Simulation System for Life Cycle Process Planning, Annals of the CIRP, 52/1: [4] Anityasari, A., Kaebernick, H., Kara, S., Hanafi, J., 2004, A - Centered Model to Forecast the Flow of Used Products, Proceedings of the Pacific Congress on Manufacturing and Management (PCMM), Queensland, Australia, CD Rom. [5] Dhillon, B.S., 1999, Design : Fundamentals and Applications, Electronic resource ed. Boca Raton Fla: CRC Press. [6] Dowlatshahi, S., 2005, A Strategic Framework for the Design and Implementation of Remanufacturing Operations in Reverse Logistics, International Journal of Production Research, 43/16: [7] Shu, L. H., Flowers, W. C., 1998, Modeling in Design for Remanufacture, Journal of Mechanical Design: Transaction of the ASME, 120: [8] Shu, L. H., Flowers, W. C., 1999, Application of a Design-for-Remanufacture Framework to the Selection of Product Life-Cycle Fastening and Joining Methods, Robotics and Computer-Integrated Manufacturing, 15/3: [9] Murayama, T., Yamamoto, S., Oba, F., 2004, Mathematical Model of Reusability, Proceedings of the 2004 IEEE International Symposium on Electronics and the Environment, pp [10] Mazhar, M. I., Kara, S., Kaebernick, H., 2004, Reuse Potential of Used Parts in Consumer Products: Assessment with Weibull Analysis, Proceedings of the 11 th CIRP International Seminar on Life Cycle Engineering, Belgrade, Serbia, CD Rom. [11] Held, M., Sennhauser, U., 2004, rate prediction models used for reliability monitoring of electronics systems, Proceedings of the 2004 Electronics Goes Green, Berlin Germany, pp [12] Foucher, B., Boullie, J., Meslet, B., Das, D., 2002, A Review of Prediction Methods for Electronic Devices, Microelectronics and, 42/8: [13] Jensen, F., 1995, Electronic Component : Fundamentals, Modeling, Evaluation, and Assurance. West Sussex, England: John Wiley & Sons. [14] Kara, S., Mazhar, M. I., Kaebernick, H., 2004, Lifetime Prediction of Components for Reuse: An Overview, International Journal of Environmental Technology and Management, 4/4: [15] Oh, Y. S., Bai, D. S., 2001, Field Analyses with Additional After-Warranty, Engineering & System Safety, 72/1:1-8. [16] Fitzgibbon, K., Barker, R., Clayton, T., Wilson, N., 2002, A -Forecast Method based on Weibull and Statistical-Pattern Analysis, Proceedings of IEEE Symposium on and Maintainability, pp [17] Anityasari, M., Kaebernick, H., 2008, A Concept of Evaluation for Reuse and Remanufacturing, International Journal of Sustainable Manufacturing, 1/1 (forthcoming).