2012 Third International Conference on Innovations in Bio-Inspired Computing and Applications
Assessing Sustainability in Manufacturing using a Fuzzy Expert System Swee S. Kuik, Sev V. Nagalingam & Yousef Amer Barbara Hardy Institute, School of Adv. Manufacturing & Mechanical Engineering, University of South Australia
[email protected],
[email protected],
[email protected] have added value to the business performance in comparison with traditions business processes, if the relationship of returns handling system and current end-of-life planning is isolated. Therefore, this paper presents the development of performance evaluation using fuzzy logic approach to assess product returns and recovery operations. Firstly, the mathematical formulations are derived in relation to the performance attributes of cost, time, waste and quality. Secondly, an assessment guideline using fuzzy logic perceptions to predict an utilisation value for a manufactured product is proposed for the performance evaluation. Thirdly, an illustrative example is conducted to demonstrate the proposed fuzzy expert system for recovery operations. Finally, the contribution of this study is summarised.
Abstract One of the recent business challenges is evaluating performance measurement on product returns and recovery operations. Recovery strategy is important in minimising landfill waste. However, there is a lack of measureable criteria to examine the performance attributes of cost, time, waste and quality for a manufactured product with recovery operations in a supply chain. This paper proposes the development of a fuzzy expert system for performance measurement on product returns and recovery operations, which is one of the key supply chain management processes in achieving sustainability in manufacturing. Finally, contributions of this research study are provided.
1. Introduction
2. Product utilisation value
Product design strategy for sustainable development is a concept that integrates multiple product returns streams with recovery options to maximise economic returns and minimise environmental impacts [1,2]. This concept has become predominant of current market trends to meet the highly competitive economy. Much of the early literature [3,4] for the development reverse manufacturing processes focuses on technology and logistics networks of recovery for product management and end-of-life planning. However, global manufacturers are having difficulties to assess returns management process and monitor reverse supply chain due to the fact that an integrated approach for performance evaluation is still currently limited on assessing performance attributes of cost, time, waste and quality. In fact, critical parameters on recovery operation are classified critical-to-reprocess (CTR) operations that have some characteristic of variation [1,5]. These operations play significant roles for successful implementation of product returns and recovery operations in optimising economic returns and minimising environmental impacts [6]. A strategy of product returns and recovery operations may not 978-0-7695-4837-1/12 $26.00 © 2012 IEEE DOI 10.1109/IBICA.2012.36
Figure 1. PUV estimation through recovery operations
In this study, a product utilisation value (PUV), is an expected quantification measure of the recoverable content for a manufactured product by considering recovery options, such as utilising those parts assembly for reuse/remanufacture/recycle operations in which the performance measurement based on cost, time, waste and quality is assessed and derived as in Equation (1). The research study presented in this 162
paper proposes an approach of a performance evaluation analysis, which is categorised as model based research that adequately describes the causal relationships of product returns with recovery settings that may exist in reality, and aiming at understanding of a returns management process in a supply chain, which is illustrated in Figure 1. A discussion of PUV scope of measures is summarised in Table 1.
Poor quality of any received product from the return streams is one of the critical issues for implementing manufacturing processes with recovery settings. A manufactured product’s reliability at ‘postuse’ stage has to be examined, to which average usage, failure phase, and life span of a product/module/component from return stream is required.
Quality
Table. 1: PUV scope of measures [5] Attributes
Cost
Time
Waste
Applying Equation 1, the estimation of ሾܷܸܲሿ is defined as a function of the performance measures in terms of total recovery cost savings (TC), manufacturing lead time (MLT), recoverable content proportion (WM) and quality performance in terms of reliability characteristic (QR) of a manufactured product as shown in Equation (2).
PUV scope of measures In considering recovery costs for any returns stream, these costs can be generally divided into manufacturing related costs and collection related activity costs of after ‘use’ stage from consumers, retailers, suppliers, and distributors. Manufacturing related costs include new acquisition of any material/product/module/component for operational processes, assembly/disassembly operation, cleaning, repairing, refurbishing and recycling processes, disposal activities related costs etc. Collection related activity costs include activity cost of transportation, initial sorting activity, administrative work and authorisation, and returns financial/rebate incentives provided by manufacturers etc.
ሾܷܸܲሿ ൌ ݂ሺݐݏܥǡ ܶ݅݉݁ǡ ܳݕݐ݈݅ܽݑǡ ܹܽ݁ݐݏሻ
(1)
Hence, ሾܷܸܲሿ ൌ ݂ሺܶܥ௧ ǡ ܶܮܯ௧ ǡ ܴܳ௧ ǡ ܹܯ௧ ሻ
(2)
where the mathematical expressions for these ratios are given in Equations (3) to (15). The subscripts REC, VIR, and SET in these equations indicates Recovery, Virgin, and Set target by a manufacturer respectively.
In considering overall manufacturing lead time for product returns with recovery options, where lead-time involves three essential elements of setup related time including machine initial setup or preparation and adjustment of each operational process. The operational time includes assembly and / disassembly, disposing, cleaning, repairing or refurbishing related activities, and recycling processes. The non-operational related time involves transporting, inspecting, quality, testing, and sorting related activities.
ሾܶܥሿ௧ ൌ
ሾܶܥሿூோ െ ሾܶܥሿோா ሾܶܥሿூோ
ሾܶܮܯሿ௧ ൌ ሾܴܳሿ௧ ൌ
ሾܶܥሿோா ൌ
ሾܶܮܯሿூோ െ ሾܶܮܯሿோா ሾܶܮܯሿூோ
(4)
ሾܴܳሿோா ሾܴܳሿௌா்
ሾܹܯሿ௧ ൌ
A manufactured product with recovery settings need to satisfy a recovery rate, which is based on government environmental legislation or organisational procedures. To minimise landfill waste due to used product disposal, there is a need to design a product for environment where utilisation value or usage of recoverable proportions (e.g. product/module/component) is increased. A waste strategy has to account the recovery rate of product/part/module utilisation or usage for a manufactured product.
(3)
(5)
ሾܹܯሿோா ሾܹܯሿௌா்
(6)
ଷ
ܺଵǡǡ ୀଵ ୀଵ
(7)
൭଼ܥǡ ܥǡ ൱ ହ
ୀଵ
ோ௨௦ ܺଶǡǡ ൭ ܥǡ ൱ ୀଷ ோ ൭ ܥǡ ൱ ܺଷǡǡ ୀଷ ோ௬
ܺସǡǡ ସ
ܥǡ௧ ୀଵ
163
ହ
൭ܥǡ ܥǡ ൱൩ ୀଶ
ଷ
ሾܶܥሿூோ ൌ ܺଵǡǡ ൭଼ܥǡ ܥǡ ൱൩ ୀଵ ୀଵ
All relevant parameters in Equations (7) to (14) are defined in Tables (2) to (6).
(8)
ୀଵ
Table 2 Disposition decision variables
ሾܶܮܯሿோா ൌ
Notations ܺ
ଶ
ܺଵǡǡ ቌܶǡ ୀଵ ୀଵ
ܶǡ ቍ
(9)
ୀଵ
ܺଵǡǡ
ସ ோ௨௦ ܺଶǡǡ ቌ ܶǡ ቍ
ோ௨௦ ܺଶǡǡ
ୀଶ
ோ ܺଷǡǡ
ହ
ோ ܺଷǡǡ
ቌ ܶǡ ቍ
ோ௬
ܺସǡǡ
ୀଶ ସ ோ௬
ܺସǡǡ
i
ቌܶǡ ܶǡ ቍ ୀଵ
m
ଶ
ሾܶܮܯሿூோ ൌ ܺଵǡǡ ቌܶǡ ܶǡ ቍ ୀଵ
ୀଵ ୀଵ
ഃభǡ ್భǡ
ି൬ ൰ ሾܴܳሿோா ൌ ෑ ෑ ܺଵǡǡ ൭݁ ഇభǡ ୀଵ ோ௨௦ ܺଶǡǡ ൭݁
ି൬
ோ ൭݁ ܺଷǡǡ ோ௬
ܺସǡǡ
ሾܹܯሿோா ൌ
ሾܹሿ ்ை் ൌ
ି൬
൭݁
(11)
ൈഃమǡ ್మǡ ഇమǡ
൰
൱
ൈഃయǡ ್యǡ
ି൬
ഇయǡ
൰
್రǡ ഃర ൰ ഇరǡ
ܥଵǡ ܥଶǡ
൱൩
ܥଷǡ ܥସǡ
(12)
ܥହǡ ܥǡ
ோ௨௦ ൣܺଵǡǡ ൫ܼଵǡ ൯ ܺଶǡǡ ൫ܼଶǡ ൯ ୀଵ ோ ܺଷǡǡ ൫ܼଷǡ ൯ ோ௬ ܺସǡǡ ൫ܼସǡ ൯൧
(13) ܥǡ ଼ܥǡ ܥǡ௧
ୀଵ
Table 3 Cost related expressions Notations ܥǡ ൌ
൱
ோ௨௦ ோ ሾܹሿோா ൌ ൣܺଶǡǡ ൫ܼଶǡ ൯ ܺଷǡǡ ൫ܼଷǡ ൯
ܺଵǡǡ
ߙହǡ ǡ ߙǡ ǡ ߙǡ ߮ହǡ ǡ ߮ǡ ǡ ߮ǡ
൱
ሾܹሿோா ሾܹሿ ்ை்
ߛǡ ǡ ߛǡ ǡ ߛ଼ǡ ߚǡ ǡ ߚǡ ǡ ߚ଼ǡ
(10)
ோ௬
ܺସǡǡ
(14)
ோ௬
Cost parameters and expressions Cost associated with operational activity, o for component, i such that recovery operational activity of ൌ ሺͳǡ ʹǡ ǥ ǡͺሻ Acquisition of materials = ܥ௨ǡଵǡ ܥௗǡଵǡ Manufacturing = ܥ௨௧௨ǡଶǡ ܥ௦௧ǡଶǡ ܥ௧௦௧ǡଶǡ ܥௗǡଶǡ Joint & Assembly = ܥ௧ǡଷǡ ܥ௦௦ǡଷǡ Direct reuse = ܥǡସǡ ܥ௦௧ǡସǡ ܥ௧௦௧ǡସǡ ܥௗǡସǡ Disjoint & Disassembly = ܥௗ௦௧ǡହǡ ܥௗ௦௦ǡହǡ Remanufacturing = ߛǡ ܥǡǡ ߚǡ ܥǡǡ ܥ௦௧Ǥǡǡ ܥ௧௦௧ǡǡ ܥௗǡǡ Recylcling = ߛǡ ܥ௬̴௪௧௨௧ǡǡ ߚǡ ܥ௬̴௪௧ǡǡ ܥௗǡǡ Disposal = ߛ଼ǡ ܥௗ௦̴௭ǡ଼ǡ ߚ଼ǡ ܥௗ௦̴௭ǡ଼ǡ Cost associated with collection process, n for returned item such that collection process of ݊ ൌ ሺͳǡ ʹǡ ǥ ǡ Ͷሻ, e.g rebate, administration, sorting, and shipping
Note: Subscripts in use, such as ‘disassem’: disassembly,‘dis_haz’: Dispose hazardous item, ‘dis_nonhaz’: Dispose non-hazardous item
൫ܼସǡ ൯൧
ோ௨௦ ோ ܺଶǡǡ ܺଷǡǡ ܺସǡǡ
Decision parameters ோ௬ ோ௨௦ ோ ൌ ሺܺଵǡǡ ǡ ܺଶǡǡ ǡ ܺଷǡǡ ǡ ܺସǡǡ ሻ disposition decision variable associated with 3R processing operation ൌ ͳ if ith component of mth module, is virgin, otherwise it is 0 ൌ ͳ if ith component of mth module, is directly reused, otherwise it is 0 ൌ ͳ if ith component of mth module, i is directly remanufactured, otherwise it is 0 ൌ ͳ if ith component of mth module, i is directly recycled, otherwise it is 0 ൌ ሺͳǡʹǡ ǥ ǡ ݎሻ components for a product-modulecomponent based level or a product-component based level, where each component is comprised of a single material only ൌ ሺͳǡʹǡ ǥ ǡ ݆ሻ modules in a product-modulecomponent level ൌ ͳ for ܥǡ repairable/replaceable, ܥǡ recyclable without or with disassembly and ଼ܥǡ disposable hazardous or non-hazardous contents, otherwise it is 0 ൌ ͳ for ܶହǡ repairable/replaceable, ܶǡ recyclable without or with disassembly and ܶǡ disposable hazardous or non-hazardous contents, otherwise it is 0
ൌͳ
Table 4. Time related parameters
(15) Notations ܶǡ ൌ
Note: Superscript, Reman= Remanufacture ܶଵǡ
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Time parameters and expressions Lead-time involved with setup time, operation time and non-operation time for each operational process, ݃ ൌ ͳǡʹǡ ǥ for ith component Lead-time for manufacturing = ܶ௦௧௨ǡଵǡ
ܶଶǡ ܶଷǡ ܶସǡ ܶହǡ ܶǡ ܶǡ
ܶ௨௧௨ǡଵǡ ܶ௦௧ǡଵǡ ܶ௧௦௧ǡଵǡ Lead-time for assembly = ܶ௦௧௨ǡଶǡ ܶ௧ǡଶǡ ܶ௦௦ǡଶǡ ܶ௦௧ǡଶǡ ܶ௧௦௧ǡଶǡ Lead-time for direct reuse = ܶ௦௧௨ǡଷǡ ܶǡଷǡ ܶ௦௧ǡଷǡ ܶ௧௦௧ǡଷǡ Lead-time for disaasembly = ܶ௦௧௨ǡସǡ ܶௗ௦௧ǡସǡ ܶ௩ǡସǡ ܶ௦௧ǡସǡ ܶ௧௦௧ǡସǡ Lead-time for remanufacturing = ܶ௦௧௨ǡହǡ ߙହǡ ܶǡହǡ ߮ହǡ ܶǡହǡ ܶ௦௧ǡହǡ ܶ௧௦௧ǡହǡ Lead-time for recycling = ܶ௦௧௨ǡǡ ߙǡ ܶ௬̴௪௧௨௧ǡǡ ߮ǡ ܶ௬̴௪௧ǡǡ ܶ௦௧ǡǡ ܶ௧௦௧ǡǡ Lead-time for disposal processing =ܶ௦௧௨ǡǡ ߙǡ ܶ௭ௗ௨௦ǡǡ ߮ǡ ܶ௭ௗ௨௦ǡǡ ܶ௦௧ǡǡ ܶ௧௦௧ǡǡ
for performance evaluation on the efficiency of product returns with recovery operations for a manufactured product. A fuzzy expert system in Mamdani-style of fuzzy inference process [7] is developed in accordance to the four sequential steps. There are: (1) Fuzzification, (2) Rule evaluation (3) Aggregation of rule outputs and (4) Defuzzification.
3.1. Membership functions The first step in the development of a fuzzy expert system is to take crisp inputs, TC, MLT, QR, and WR for a manufactured product and determine the degree to which these input performance attributes belong to each of the appropriate fuzzy sets. These are two types of pertinence functions to be used in this research study such as triangular and Gaussian functions. Tables 7 to 9 show the pertinence functions and linguistics terms in the input and output variables. Then, as the next step, rules evaluation has to be established and fuzzy relations are to be formed.
Table 5. Quality related parameters Notations ܰ l ܾேǡ ߠேǡ ߜଵǡ ߜଷǡ ߜଷǡ ߜସǡ
Quality parameters N=1 (virgin); 2 (reuse); 3 (remanufacture) and 4 (recycle) Maximum allowable lifecycle before wear-out, which is applicable for either reused or remanufactured ith component where l=(1,2,…n) Weibull shape parameter for ith component Characteristic life for ith component Average operating hours before virgin ith component is taken back Average operating hours before reused ith component is taken back Average operating hours before remanufactured ith component is taken back Average operating hours before recycled ith component is taken back
Table 7. Pertinence functions (Triangular) and linguistics terms of the input variables Att. ሾܶܥሿ௧ %
ܼଷ ܼସ
L (5;0) (11.5;1) (17.5;0)
M (10;0) (18.7;1) (27.5;0)
H (20;0) (27.5;1) (35;0)
VH (32.5;0) (37.5;1)
ሾܶܮܯሿ௧ (2.5;1) (7.5;0) %
(5;0) (10;0) (20;0) (32.5;0) (11.5;1) (18.7;1) (27.5;1) (37.5;1) (17.5;0) (27.5;0) (35;0) Note: Notations in use: ‘VL’= very low, ‘L’= low, ‘M’= medium, ‘H’= high, ‘VH’= very high, and ‘Att’= Attribute
Table 6. Waste related parameters Notations ܼଵ ܼଶ
VL (2.5;1) (7.5;0)
Table 8. Pertinence functions (Gaussian) and linguistics terms of the input variables
Waste parameters “virgin” proportion with a unit system Recoverable content of “reuse” proportion with a unit system Recoverable content of “remanufacture” proportion with a unit system Recoverable content of “recycle” proportion with a unit system
Att. ሾܴܳሿ௧
%
P ([80,3];1)
G ([90,3],1)
E ([95,3],1)
([80,2.5];1) ([90,2.5];1) ([95,2.5];1) ሾܹܴሿ௧ % Note: Notations in use, ‘P’= Poor, ‘G’= Good, and ‘E’= Excellent
Table 9. Pertinence functions (Triangular) and linguistics terms of the output variables
3. A Fuzzy expert System
Att. Rej. ሾܷܸܲሿ (0.73;1) (1.5;0)
Pr. Acc. Satis. Opt. (1.5;0) (3.0;0) (6.0;0) (8.5;0) (2.25;1) (4.5;1) (7.0;1) (9.0;1) (3.0;0) (6.0;0) (8.0;0) Note: Notations in use: ‘Rej’= reject, ‘Pr’= poor, ‘Acc’= Acceptable, ‘Satis’= Satisfied, and ‘opt.’= optimum
A fuzzy expert system theory was initially proposed by Lotfi Zadeh [7]. Then, an extension of fuzzy set theory is developed and related to the response of human reasoning and judgment. This type of methodology has been successfully applied in many engineering and operation management problems, which may not able to be solved with any mathematical based approaches in system design. One of the significant advantages using fuzzy logic is to capture any uncertainties involved with human cognition and judgmental processes. Hence, this study provides decision makers with a fuzzy rule approach
3.2. Rules base, aggregation and defuzzification In constructing the rules base, the “if” statements are named as performance attributes for recovery operations, while “then” is used as performance status. For example, if ‘TC’ is low, ‘MLT’ is low, ‘QR’ is poor and ‘WM’ is poor, then ‘PUV’ is rejected (i.e. ‘PUV’ is
165
fuzzy region by determining the weighted mean of the output fuzzy region. In the following section, a simulation experiment is described to demonstrate this fuzzy expert system.
under the category of unsatisfied performance and improvement is required). The construction of fuzzy rules base can be analytically estimated based on derived equation of ‘PUV’ as discussed in Equation (1). Tables (10) to (12) illustrate the summary of 225 rules base, which are developed for the four attributes as input variables.
Table 12. Fixed WM = “Pr.” and its rules base ܶܥൗ ܶܮܯ
Table 10. Fixed WM = “E.” and its rules base ܶܥൗ ܶܮܯ
VL
VL L M H VH
Rej. Rej. Pr. Pr. Pr.
VL L M H VH
Pr. Pr. Pr. Pr. Pr.
VL L M H VH
Pr. Pr. Pr. Pr. Pr.
L
M
ܴܳ ൌ ̶̶ܲݎ Rej. Rej. Rej. Rej. Pr. Pr. Pr. Pr. Pr. Pr. ܴܳ ൌ ̶̶ܩ Pr. Pr. Pr. Pr. Pr. Acc. Pr. Acc. Pr. Acc. ܴܳ ൌ ̶̶ܧ Pr. Pr. Pr. Pr. Pr. Acc. Pr. Satis. Pr. Opt.
H
VH
Rej. Rej. Pr. Pr. Pr.
Rej. Rej. Pr. Pr. Pr.
Pr. Pr. Acc. Acc. Acc.
Pr. Pr. Acc. Acc. Satis.
Pr. Pr. Acc. Satis. Opt.
Pr. Acc. Satis. Opt. Opt.
VL
VL L M H VH
Rej. Rej. Pr. Pr. Pr.
VL L M H VH
Pr. Pr. Pr. Pr. Pr.
VL L M H VH
Pr. Pr. Pr. Pr. Pr.
L
M
ܴܳ ൌ ̶̶ܲݎ Rej. Rej. Rej. Rej. Pr. Pr. Pr. Pr. Pr. Pr. ܴܳ ൌ ̶̶ܩ Pr. Pr. Pr. Pr. Pr. Acc. Pr. Acc. Pr. Acc. ܴܳ ൌ ̶̶ܧ Pr. Pr. Pr. Pr. Pr. Acc. Pr. Satis. Pr. Opt.
H
Rej. Rej. Pr. Pr. Pr.
Pr. Pr. Acc. Acc. Acc.
Pr. Pr. Acc. Acc. Satis.
Pr. Pr. Acc. Satis. Opt.
Pr. Acc. Satis. Opt. Opt.
Rej. Rej. Pr. Pr. Pr.
VL L M H VH
Pr. Pr. Pr. Pr. Pr.
VL L M H VH
Pr. Pr. Pr. Pr. Pr.
M
H
VH
Rej. Rej. Pr. Pr. Pr.
Rej. Rej. Pr. Pr. Pr.
Pr. Pr. Pr. Pr. Pr.
Pr. Pr. Pr. Pr. Acc.
Pr. Pr. Pr. Acc. Acc.
Pr. Pr. Pr. Acc. Acc.
To illustrate the strengths and effectiveness of the proposed integrated method, a typical decision making problem, which compares two product configurations is demonstrated. In this method, four disposition decision variables are recommended for performance ܸ݅݊݅݃ݎ ோ௨௦ , ܺଶǡ , measurement analysi. There are: ܺͳǡ݅ ோ௬ ோ ܺଷǡ , and ܺସǡ . Any of the recoverable ith component based on recovery options is assumed to have its wear-out life, which is longer than its technology cycle and the depreciation values over a certain period remains constant. Finally, the decision makers would assess the performance measurement for a manufactured product of ‘A’ and ‘B’ with recovery configurations for a module of 20 components. Table 13 and 14 show a summary of the PUV estimation for product configurations using a fuzzy expert system to account the trade-off consideration.
VH
Rej. Rej. Pr. Pr. Pr.
VL L M H VH
L
ܴܳ ൌ ̶̶ܲݎ Rej. Rej. Rej. Rej. Pr. Pr. Pr. Pr. Pr. Pr. ܴܳ ൌ ̶̶ܩ Pr. Pr. Pr. Pr. Pr. Pr. Pr. Pr. Pr. Pr. ܴܳ ൌ ̶̶ܧ Pr. Pr. Pr. Pr. Pr. Pr. Pr. Pr. Pr. Pr.
3.3. An illustrative example
Table 11. Fixed WM = “G.” and its rules base ܶܥൗ ܶܮܯ
VL
Table 13. Summary of product configuration of “A” and “B”
When aggregating, the outputs of each rule are unified. However, this aggregation takes place only once for one fuzzy set, corresponding to each output variable, PUV. The last step of fuzzy inference process is named as defuzzification, where the input to this process is the aggregated output fuzzy sets. The technique used in this defuzzification process is named as centroid defuzzification method. This method is simply to assess the balance point of the aggregated
Configuration Virgin (ܺଵǡ ) Reuse (ܺଶǡ ) Remanufacture (ܺଷǡ ) Recycle (ܺସǡ ) Cost (TC) Time (MLT) Waste (WM) Quality (QR)
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Virgin 20 0 0 0 $676.80 1.78hr =landfill 0.9600
‘A’ 5 5 4 6 $525.44 1.51hr 0.7889 0.8917
‘B’ 3 5 8 4 $514.97 1.54hr 0.7193 0.8906
summarised as follows: (1) Proposed PUV estimation to optimise trade-off considerations, (2) Developed a fuzzy logic approach for the performance measurement system design to evaluate all four performance attributes simultaneously for the supply chain design in returns management process with recovery operations. If there exits lack of accurate and reliable input data as a potential benefit, a fuzzy expert system helps decision makers standardising obtained results based on performance assessment category of reject, poor, average, good and excellent by establishing appropriate evaluation scales for all performance attributes under performance evaluation study. The integrated evaluation based on all four performance attributes in this paper is to target on resolving problems of oversimplification issue in the past research, such as the performance measurement on optimising total supply chain costs, recovery costs, logistics networks, manufacturing lead-time etc [3,4]. Due to the page limit on this article, the comparison between the proposed methods and industrial case study applications will be reported in forthcoming journal articles.
Table 14. A summary of calculated recovery ratings Configuration Cost ൫ܶܥ௦௩௦ ൯ Time ൫ܶܮܯ௦௩௦ ൯ Waste ሺܹܯ௩ௗ ሻ Qualityሺܴܳ௩ௗ ሻ ሾࡼࢁࢂሿࡾࡱ
Product ‘A’ 22.36% 15.17% 92.81% 93.86% 0.561
Product ‘B’ 23.39% 13.48% 84.62% 93.74% 0.509
The estimation of PUV for configuration ‘A’ and ‘B’ is approximately 0.561 and 0.509 respectively, which is within the acceptable range of recovery improvement (Table 9). However, the result obtained is by no means perfect as this illustrative example is to demonstrate the performance evaluation analysis using a fuzzy logic approach, which is a feasible method for designing effective returns workflow with recovery operations and to implement continuous improvement throughout organisations. Subsequently, the performance evaluation analysis for product returns and recovery settings can provide some insights to improve the overall performance level of a manufactured product. Fig. 2 illustrate the rules base and a surface plot for the estimation of PUV, which based on the trade-off scenario of quality and total recovery cost savings of a manufactured product. This surface plot highlights the relationship between TC and QR and their effect on PUV. To achieve high PUV score, these performance attributes of recovery cost savings and quality need to be maximised, which means an ideal recovery case of a manufactured product is achieved at TC ratio, which is above 40, and QR ratio, at 100.
5. References [1] S.S. Kuik, S.V. Nagalingam, and Y. Amer, Sustainable supply chain for collaborative manufacturing. Journal of Manufacturing Technology Management, Emerald, 22 (2012) 984-1001. [2] H.W. Lin, S.V. Nagalingam, S.S. Kuik, and T. Murata, Design of a global decision support system for a manufacturing SME: Towards participating in collaborative manfuacturing. International Journal of Production Economics, Elsevier, 136 (2012) 1-12. [3] A. Gungor and S. Gupta, Issues in environmentally conscious manufacturing and product recovery: a survey. Computers & Industrial Engineering, Elsevier, 36 (1999) 811-853. [4] M.A. Ilgin and S.M. Gupta, Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art. Journal of Environmental Management, Elsevier, 91 (2010) 563-591. [5] S.S. Kuik, S.V. Nagalingam, and Y. Amer, A framework of product recovery to improve sustainability in manufacturing. Advances in Mechanical Engineering, IERI, 2 (2012) 41-47. [6] R. Hischier, P. Wager, and J. Gauglhofer, Does WEEE recycling make sense from an environmental perspective? The environmental impacts of the Swiss take-back and recycling systems for waste electrical and electronic equipment (WEEE). Envionmental Impact Assessment Review, Elsevier, 25 (2005) 525-539. [7] L.A. Zadeh, The role of fuzzy logic in the management of uncertainity in expert systems. Fuzzy sets and Systems, Elsevier, 11 (1983) 199-227.
Figure 2. PUV estimation for recovery operations
4. Conclusion The research highlights include suggesting a fuzzy expert system for assessing performance measurement of returns management process with recovery and exploiting trade-off considerations of cost, time, waste and quality attributes for achieving higher performance level. The main contribution of this study is
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