Devising the performance indicators due to the adoption of reverse logistics enablers

Abstract

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.

Appendices

Appendix 1

Scale of TFNs used in F-AHP is given in Table 7.

Table 7 TFNs scale

The TFNs scale used for rating criterion in F-ARAS is given in Table 8.

Table 8 TFNs preference used for rating criterion

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)

Table 9 Comparison matrix for main reverse logistics criteria’s

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)

Table 10 Opinion sheet of the impact of Reverse Logistics enablers’ on the reverse logistics performance indicators

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).

Table 11 Linguistic decision matrix provided by expert group 1

2. Pairwise decision matrix converted to fuzzy matrix (Table 12)

Table 12 Fuzzy pairwise comparison matrix

3. Calculating fuzzy extent synthetic value S_i

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-

$$ {W}^{\prime }={\left(0.258,0.326,0.222,0.110,0.004,0.081\right)}^T $$

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.

Table 13 Relative weight of the major criteria

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.

Table 14 Global weights of the sub-criteria

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).

Table 15 Linguistic decision matrix

2. Pairwise linguistic decision matrix converted to fuzzy matrix (Table 16).

Table 16 Fuzzy comparison matrix

3. The Optimal decision matrix is calculated from fuzzy comparison matrix (Table 17).

Table 17 Optimal decision matrix

4. The Optimal decision matrix calculated in step 3 above is Normalized (Table 18).

Table 18 The Normalized matrix

5. The normalized matrix is converted to weighted normalized matrix (weights are obtained from fuzzy group weight criteria, Table 9) (Table 19).

Table 19 Weighted normalized matrix

6. The values of optimality function Pi and utility degree Ki is calculated from weighted normalized matrix (Table 20).

Table 20 Optimality function P_i and utility degree K_i