Reverse logistics (RL) has turned into a key capability in current supply chain for treating the end-of-life products by all organizations (both public and private) to achieve sustainability. Organizing performance measures is a significant phase in adopting a coordinated and extensive approach to RL performance. This study aims to identify and prioritize the Reverse Logistics Performance Indicators (RLPI) according to their capability of measuring the success of RL implementation. A hybrid framework of Fuzzy Analytic Hierarchy Process (F-AHP) and modified Fuzzy Additive Ratio Assessment (F-ARAS) is proposed to fulfill the objective of this research. F-AHP in association with Extent Analysis utilized to get relative weights of RL enablers and modified F-ARAS is utilized to prioritize the RLPI due to the execution of RL enablers. An Indian electrical manufacturing company is selected for a case examination to validate the proposed framework’s pertinence. The prioritized list finds Environmental performance indicators (EP), Industrial Operations performance indicators (OP) and Customers performance indicators (CP) are of prime importance. Financial performance indicators (FP) and Social performance indicators (SP) are the next in the list. This is the most detailed, structured and systematic approach to study RLPI due to the implementation of RL enablers. The outcomes will provide knowledge to help the decision-makers to identify success and potential opportunities and to uncover the potency of organizational strategies.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Agrawal S, Singh RK, Murtaza Q (2016) Prioritizing critical success factors for reverse logistics implementation using fuzzy-TOPSIS methodology. J Ind Eng Int 12(1):15–27
Agrawal S, Singh RK, Murtaza Q (2018) Reverse supply chain issues in Indian electronics industry: a case study. Journal of Remanufacturing 8(3):115–129
Bouzon M, Spricigo R, Rodriguez CM, de Queiroz AA, Cauchick Miguel PA (2015) Reverse logistics drivers: empirical evidence from a case study in an emerging economy. Prod Plan Control 26(16):1368–1385
Brotherton B, Shaw J (1996) Towards an identification and classification of critical success factors in UK hotels Plc. Int J Hosp Manag 15(2):113–135
Chan FT, Chan HK, Jain V (2012) A framework of reverse logistics for the automobile industry. Int J Prod Res 50(5):1318–1331
Chang DY (1996) Applications of the extent analysis method on fuzzy AHP. Eur J Oper Res 95(3):649–655
Cheng YH, Lee F (2010) Outsourcing reverse logistics of high-tech manufacturing firms by using a systematic decision-making approach: TFT-LCD sector in Taiwan. Ind Mark Manag 39(7):1111–1119
Difrancesco RM, Huchzermeier A (2016) Closed-loop supply chains: a guide to theory and practice. Int J Log Res Appl 19(5):443–464
Dowlatshahi S (2012) A framework for the role of warehousing in reverse logistics. Int J Prod Res 50(5):1265–1277
Down to Earth (2018) Government notifies new solid waste management rules. [online] https://www.downtoearth.org.in/news/waste/solid-waste-management-rules-2016-53443/. Accessed 10 Dec 2018
Eltayeb TK, Zailani SHM (2011) Drivers on the reverse logistics: evidence from Malaysian certified companies. Int J Logist Syst Manag 10(4):375–397
Euchi J, Bouzidi D, Bouzid Z (2019) Interpretive structural modeling technique to analyze the interactions between the factors influencing the performance of the reverse logistics chain. Glob J Flex Syst Manag 20(1):43–55
Fawcett SE, Magnan GM, McCarter MW (2008) A three-stage implementation model for supply chain collaboration. J Bus Logist 29(1):93–112
Freires FG, Guedes AP (2008) Power and trust in reverse logistics systems for scraptires and its impact on performance. Journal of Operations and Supply Chain Management 1(1):57–65
Gardas BB, Raut RD, Narkhede B (2018) Reducing the exploration and production of oil: reverse logistics in the automobile service sector. Sustain Prod Consum 16:141–153
Geethan KAV, Jose S, Chandar CS (2011) Methodology for performance evaluation of reverse supply chain. Int J Eng Technol 3(3):213–224
Gharaei A, Naderi B, Mohammadi M (2015) Optimization of rewards in single machine scheduling in the rewards-driven systems. Manag Sci Lett 5(6):629–638
Ghezavati VR, Beigi M (2016) Solving a bi-objective mathematical model for location-routing problem with time windows in multi-echelon reverse logistics using metaheuristic procedure. J Ind Eng Int 12(4):469–483
Guo K, Zhang Q (2017) A discrete artificial bee Colony algorithm for the reverse logistics location and routing problem. Int J Inf Technol Decis Mak 16(05):1339–1357
Ho W, Ma X (2018) The state-of-the-art integrations and applications of the analytic hierarchy process. Eur J Oper Res 267(2):399–414
Hosseini MR, Chileshe N, Rameezdeen R, Lehmann S (2015) Integration of design for reverse logistics and harvesting of information: a research agenda. Int J Logist Syst Manag 20(4):480–515
Hsu CC, Tan KC, Mohamad Zailani SH (2016) Strategic orientations, sustainable supply chain initiatives, and reverse logistics: empirical evidence from an emerging market. Int J Oper Prod Manag 36(1):86–110
Huscroft JR, Hazen BT, Hall DJ, Hanna JB (2013) Task-technology fit for reverse logistics performance. Int J Logist Manag 24(2):230–246
John ST, Sridharan R, Kumar PR, Krishnamoorthy M (2018) Multi-period reverse logistics network design for used refrigerators. Appl Math Model 54:311–331
Kubler S, Robert J, Derigent W, Voisin A, Le Traon Y (2016) A state-of the-art survey & testbed of fuzzy AHP (FAHP) applications. Expert Syst Appl 65:398–422
Lambert S, Riopel D, Abdul-Kader W (2011) A reverse logistics decisions conceptual framework. Comput Ind Eng 61(3):561–581
Luthra S, Mangla SK, Kumar S, Garg D, Haleem A (2017) Identify and prioritise the critical factors in implementing the reverse logistics practices: a case of Indian auto component manufacturer. Int J Bus Syst Res 11(1–2):42–61
Mangla S, Madaan J, Chan FT (2012) Analysis of performance focused variables for multi-objective flexible decision modeling approach of product recovery systems. Glob J Flex Syst Manag 13(2):77–86
Mangla SK, Govindan K, Luthra S (2016) Critical success factors for reverse logistics in Indian industries: a structural model. J Clean Prod 129:608–621
Mathiyazhagan K, Govindan K, NoorulHaq A, Geng Y (2013) An ISM approach for the barrier analysis in implementing green supply chain management. J Clean Prod 47:283–297
Mavi RK (2015) Green supplier selection: a fuzzy AHP and fuzzy ARAS approach. Int J Serv Oper Manag 22(2):165–188
Mavi RK, Goh M, Zarbakhshnia N (2017) Sustainable third-party reverse logistic provider selection with fuzzy SWARA and fuzzy MOORA in plastic industry. Int J Adv Manuf Technol 91(5–8):2401–2418
Meng K, Lou P, Peng X, Prybutok V (2017) Quality-driven recovery decisions for used components in reverse logistics. Int J Prod Res 55(16):4712–4728
Min H, Galle WP (2001) Green purchasing practices of US firms. Int J Oper Prod Manag 21(9):1222–1238
Min H, Ko HJ (2008) The dynamic design of a reverse logistics network from the perspective of third-party logistics service providers. Int J Prod Econ 113(1):176–192
Nguyen HT, Dawal SZM, Nukman Y, Rifai AP, Aoyama H (2016) An integrated MCDM model for conveyor equipment evaluation and selection in an FMC based on a fuzzy AHP and fuzzy ARAS in the presence of vagueness. PLoS ONE 11(4):e0153222. https://doi.org/10.1371/journal.pone.0153222
Pandian GRS, Abdul-Kader W (2017) Performance evaluation of reverse logistics enterprise–an agent-based simulation approach. Int J Sustain Eng 10(6):384–398
Panjehfouladgaran H, Bahiraie N, Yusuff R (2018) Identification of critical success factors in reverse logistics; analysing interrelationships by interpretive structural modelling. Int J Serv Oper Manag 30(4):447–464
Prajapati H, Kant R, Shankar R (2018) Bequeath life to death: state-of-art review on reverse logistics. J Clean Prod 211:503–520
Prajapati H, Kant R, Shankar R (2019) Prioritizing the solutions of reverse logistics implementation to mitigate its barriers: A hybrid modified SWARA and WASPAS approach. J Clean Prod 240:118219
Prakash C, Barua MK (2016) A combined MCDM approach for evaluation and selection of third-party reverse logistics partner for Indian electronics industry. Sustain Prod Consum 7:66–78
Ravi V, Shankar R (2014) Reverse logistics: insights from sectoral analysis of Indian manufacturing industries. Int J Logist Syst Manag 17(2):234–259
Ravi V, Shankar R (2017) An ISM-based approach analyzing interactions among variables of reverse logistics in automobile industries. J Model Manag 12(1):36–52
Ravi V, Shankar R, Tiwari MK (2005a) Productivity improvement of a computer hardware supply chain. Int J Product Perform Manag 54(4):239–255
Ravi V, Shankar R, Tiwari MK (2005b) Analyzing alternatives in reverse logistics for end-of-life computers: ANP and balanced scorecard approach. Comput Ind Eng 48(2):327–356
Rubio S, Jiménez-Parra B (2014) Reverse logistics: overview and challenges for supply chain management. International Journal of Engineering Business Management 6(12):1–7
Saaty TL (1980) The analytical hierarchy process, planning, Priority. Resource Allocation. RWS Publications, USA
Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1(1):83–98
Shaik M, Abdul-Kader W (2012) Performance measurement of reverse logistics enterprise: a comprehensive and integrated approach. Meas Bus Excell 16(2):23–34
Shankar R, Ravi V, Tiwari MK (2008) Analysis of interaction among variables of reverse logistics: a system dynamics approach. Int J Logist Syst Manag 4(1):1–20
Škapa R, Klapalová A (2012) Reverse logistics in Czech companies: increasing interest in performance measurement. Manag Res Rev 35(8):676–692
Srivastava SK (2008) Network design for reverse logistics. Omega 36(4):535–548
Subramanian N, Gunasekaran A, Abdulrahman M, Liu C (2014) Factors for implementing end-of-life product reverse logistics in the Chinese manufacturing sector. Int J Sustain Dev World Ecol 21(3):235–245
Sundari PT, Vijayalakshmi C (2016) A comprehensive review of closed loop supply chain. Glob J Pure Appl Maths 12(4):2785–2792
Turskis Z, Zavadskas EK (2010) A new fuzzy additive ratio assessment method (ARAS-F). Case study: the analysis of fuzzy multiple criteria in order to select the logistic centers location. Transport 25(4):423–432
Van Laarhoven PJ, Pedrycz W (1983) A fuzzy extension of Saaty's priority theory. Fuzzy Sets Syst 11(1–3):229–241
Vaz CR, Grabot B, Maldonado MU, Selig PM (2012) Some reasons to implement reverse logistics in companies. Int J Environ Technol Manag 16(5/6):467–479
Wu C, Barnes D (2016) Partner selection for reverse logistics centres in green supply chains: a fuzzy artificial immune optimisation approach. Prod Plan Control 27(16):1356–1372
Yusuf YY, Olaberinjo AE, Papadopoulos T, Gunasekaran A, Subramanian N, Sharifi H (2017) Returnable transport packaging in developing countries: drivers, barriers and business performance. Prod Plan Control 28(6–8):629–658
Zavadskas EK, Turskis Z (2010) A new additive ratio assessment (ARAS) method in multicriteria decision-making. Technol Econ Dev Econ 16(2):159–172
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Scale of TFNs used in F-AHP is given in Table 7.
The TFNs scale used for rating criterion in F-ARAS is given in Table 8.
Appendix 2: Sample questionnaire
Opinion sheet of pairwise comparison matrix for main RL criteria’s (Table 9). Please use the following linguistic variables to fill up the matrix.
Equal importance (E); Low importance (L); Medium importance (M); High importance (H) and Very High importance (VH)
Opinion sheet of the impact of RL enablers’ on the RLPI (Table 10). Please use the following linguistic variables to fill up the opinion sheet.
Very low impact (VL); Low impact (L); Medium impact (M); High impact (H) and Very High impact (VH)
Appendix 3: Sample calculation for reverse logistics enablers main criteria using F-AHP as data received by expert group 1
1. Pairwise comparison matrix provided by expert group 1 for major criteria of RL enablers (Table 11).
2. Pairwise decision matrix converted to fuzzy matrix (Table 12)
3. Calculating fuzzy extent synthetic value Si
S(OE) = (9.2, 15, 24) ⊗(38, 66, 102)-1 = (0.0903, 0.2330, 0.6356)
Similarly other values were also calculated and are given below.
S(ER) = (0.1374, 0.3647, 0.9005); S(EE) = (0.0623, 0.1904, 0.5120); S(SC) = (0.0339, 0.0863, 0.2790); S(SE) = (0.0191, 0.0385, 0.1412); S(TE) = (0.0277, 0.0871, 0.2295)
4. The obtained fuzzy value are then used for fuzzy value comparison and to get priority weights
V (OE ≥ ER) = 0.791; V (OE ≥ EE) = 1; V (OE ≥ SC) = 1; V (OE ≥ SE) = 1; V (OE ≥ TE) = 1
V (ER ≥ OE) 1; V (ER ≥ EE) = 1; V (ER ≥ SC) = 1; V (ER ≥ SE) = 1; V (ER ≥ TE) = 1
V (EE ≥ OE) = 0.908; V (EE ≥ ER) = 0.683; V (EE ≥ SC) = 1; V (EE ≥ SE) = 1; V (EE ≥ TE) = 1
V (SC ≥ OE) = 0.562; V (SC ≥ ER) = 0.337; V (SC ≥ EE) = 0.675; V (SC ≥ SE) = 1; V (SC ≥ TE) = 0.9966
V (SE ≥ OE) = 0.208; V (SE ≥ ER) = 0.012; V (SE ≥ EE) = 0.342; V (SE ≥ SC) = 0.692; V (SE ≥ TE) = 0.700
V (TE ≥ OE) = 0.488; V (TE ≥ ER) = 0.249; V (TE ≥ EE) = 0.618; V (TE ≥ SC) = 1; V (TE ≥ SE) = 1
5. The minimum degree of possibility are determined as-
d′(OE) = min (0.791, 1, 1, 1, 1) = 0.791
d′(ER) = min (1, 1, 1, 1, 1) = 1
d′(EE) = min (0.908, 0.683, 1, 1, 1) = 0.683
d′(SC) = min (0.562, 0.337, 0.675, 1, 0.9966) = 0.337
d′(SE) = min (0.208, 0.012, 0.342, 0.692, 0.700) = 0.012
d′(TE) = min (0.488, 0.249, 0.618, 1, 1) = 0.0.249
6. Normalized weight vector is calculated as-
Same process was followed while calculating the weight vector for the sub-criteria.
The relative weights of major criteria and its mean are given in Table 13.
The relative weight of sub-criteria is multiplied with the relative weight of major criteria to obtain global weights for each sub-criterion. The global weights of sub-criteria and its mean are given in Table 14.
Appendix 4: Sample calculation for reverse logistics performance indicators using Modified F-ARAS as data received from expert groups E1, E2 and E3
1. Pairwise comparison provided by expert groups for major criteria of RLPI (Table 15).
2. Pairwise linguistic decision matrix converted to fuzzy matrix (Table 16).
3. The Optimal decision matrix is calculated from fuzzy comparison matrix (Table 17).
4. The Optimal decision matrix calculated in step 3 above is Normalized (Table 18).
6. The values of optimality function Pi and utility degree Ki is calculated from weighted normalized matrix (Table 20).
About this article
Cite this article
Prajapati, H., Kant, R. & Shankar, R. Devising the performance indicators due to the adoption of reverse logistics enablers. Jnl Remanufactur (2021). https://doi.org/10.1007/s13243-020-00098-4
- Reverse logistics
- Performance indicators
- Critical success factors
- Fuzzy analytic hierarchy process
- Fuzzy additive ratio assessment method