Evaluation of logistics providers for sustainable service quality: Analytics based decision making framework

Abstract

In the present era of the circular economy, sustainable service quality has become an order winning criteria for all logistics provider across the world. The selection of appropriate logistics service providers (LSPs) greatly influences the performance of supply chains in terms of sustainability indicators. Most of the previous studies have not considered sustainability measures in the evaluation of logistics service providers. Therefore, the uniqueness of this study is that we are proposing a framework for selecting the best logistics provider based on sustainable service quality. Total seventeen attributes related to sustainable service quality are finalized based on literature review and subsequent focused group discussions. Through a questionnaire-based survey, data from 150 customers of LSPs are collected. Data is analysed through factor analysis and seventeen attributes of sustainable service quality are categorized into five factors namely Commitment, Competence, Communication, Creativity & Customization, and Coordination and Collaboration. It is named as a 5C framework. This framework is further used to illustrate the selection of best LSP based on sustainable service quality. Listed attributes are evaluated through hybrid Fuzzy Analytic Hierarchy Process (AHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) techniques. This decision-making framework is illustrated with the help of a real-life case study. Sensitivity analysis is also done to validate the robustness of the proposed framework. From this study, it has been observed that the development of competencies for the adoption of sustainable practices should be the thrust area for logistics service providers. Findings imply that the logistics providers should give more focus on sustainable network optimization, response time reduction, reliable green services, flexibility in green processes, and development of mutual trust with all stakeholders to become the first choice of their customers. Insights from the study will help LSPs to develop their strategies for ensuring sustainable service quality to customers.

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Appendices

Appendix 1

Kindly rate each attribute on the scale of 1 to 5 as per their importance for evaluating sustainable service quality of logistics service providers. This activity is a part of vetting the proposed service quality model for identifying and measuring all important attributes which impacts the sustainable service quality of Indian logistics service providers. Your inputs are highly valuable and will help me in understanding the practical insights on the same. Kindly suggest if you find need to add any new attribute or remove any attribute due to duplicity.

S. No Attributes Meaning/Definition Corresponding statements in questionnaire Kindly rate attributes on 1-5 scale
     1 Very Unimportant
     2. Unimportant
     3 Can be considered
     4 Important
     5. Very Important
1 Reliability for green services (REL) Ability to perform the promised green services without failure Delivers the promised services without failure through sustainable means  
2 Responsiveness towards green practices (RES) Ability to respond faster to customers by making use of green practices Gives prompt service  
3 Accuracy in delivering goods through green operations Ability to deliver right product to the right customer at right time through sustainable means Delivers the right product to the right place  
4 Assurance for green operations (ASS) Ability to deliver goods through sustainable means Assures the delivery of goods by using green practices  
5 On-time Delivery Delivery of goods on time through sustainable means Delivers goods on scheduled date/time  
6 Safety in handling shipments Handling documents safely Handles documents safely  
7 Green infrastructure (INF) Adequate number of CNG or eco-friendly fleet and green warehouses available to the logistics provider Availability of eco-friendly fleet and green warehouses  
8 Manpower for implementing green operations(MAN) Equipped with an adequate number of trained personnel for adoption and implementation of green practices Availability of skilled and trained Workforce on sustainability  
9 Sustainable network optimization(NTW) Ability to optimize distribution network for sustainability Geographical reach and network expansion by encouraging green practices  
10 Capability for sustainable capacity optimization (CAP) Ability to handle volume business of customers effectively Sufficient capacity to optimize inventory controls  
11 Optimizing Inventory Controls Maintain and control inventory of customers Adequate importance to your inventory controls  
12 Managing Global Sustainable Operations Manage operations globally for implementing sustainability Manages global operations  
13 Product Returns Managing the return of product either in the form of used or empty bin   
14 Cost optimization(COST) Optimize the cost to be paid to LSPs for their green services   
15 IT support for green practices (IT) Equipped with the adequate IT support for promoting use of green initiatives   
16 Optimization of information quality (IQ) Frequency, quality, and accuracy of the content provided to the customer   
17 Access by customers Ability to reach and approach easily by customers   
18 Response time optimization (RT) Ability to respond to customer order/queries/complaints efficiently   
19 Efficient Data Handling Collection, maintenance of all transactions and retrieval of data by customers through digitalization   
20 Integrated Sustainable Logistics Management (ILM) Coordinating and integrating sustainable practices among all supply chain partners Integration and collaboration with other supply chain partners for sustainable implementation  
21 Mutual Trust and Relationship (MTR) Understanding and mutual trust among supply chain partners for successful operations Mutual trust and confidence for adopting green practices  
22 Tracking and Tracing of shipments Keeping track and trace all vehicles through GPS technology Keeps Tracking and tracing of shipments  
23 Effective Shipment Planning Effective route plans of all shipments Effective route planning of shipments  
24 Response time optimization (RT) Ability to respond to customer order/queries/complaints efficiently Optimal allocation of resources and use of renewable resources  
25 Understanding customer sustainable needs Understanding needs of customers for green Makes effort to understand your requirements  
26 Green and flexible processes (FLEX) Ability to make sustainable changes in processes as per customer requirements Accommodate your changing/urgent requirements  
27 Innovation capability (INN) Serving customers with creative and customized services in a sustainable manner Innovates towards green supply chain management and environmental sustainability  
28 Attitude towards customer green requirements Attitude of LSPs towards customers sustainable needs Shows positive attitude/Maintains honesty in all operations with you/willingness to help customers  
29 Courtesy towards customers Respect, comfort level, politeness and friendliness shown to customers Maintains courteous behavior in all transactions  
30 Maintaining Confidentiality in customers information Ability to secure information Maintains/values confidentiality in all operations  
31 Empathy towards customer Ability to understand problem as own issue and take appropriate steps to resolve Understands your problems and solve it  
32 Concern towards environment Adopting sustainable practices to make environment safe Shows concern towards environment sustainability  
33 Green and flexible processes (FLEX) Ability to make sustainable changes in processes as per customer requirements Allows flexibility in greening of logistics operations  
34 Technology adoption for sustainable operations (TECH) Adoption of technological options for encouraging digital processes (paper-less) Adopts latest technology including EDI,RFID,VMI, GPS, WMS etc. for optimizing resources  
35 Use of Warehouse Management Software Usage of IT and software for warehouse management Uses software for warehouse management  

Any suggestions related to addition/deletion of any attribute: ……………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………

Thanks for your time, support and suggestions.

Appendix-2

figurea
figureb

Appendix-3

A3(a). Steps for Fuzzy AHP (Chang 1996)

Let X = {x1, x2… xn} be an object set, and U = {u1, u2… um} be a goal set. According to the method of extent analysis given by Chang (1996), each object is taken and extent analysis for each goal gi is performed, respectively. Therefore, M-extent analysis values for each object can be obtained and are represented as follows:

$$ M_{{g_{i} }}^{1} ,M_{{g_{i} }}^{2} , \ldots ,M_{{g_{i} }}^{m} ,i = 1,2, \ldots ,n, $$
(1)

where all the \( M_{{g_{i} }}^{j} \) (\( j = 1,2, \ldots \ldots m \) are triangular fuzzy numbers (TFN) represented by (l, m, u), l,m and u is the least possible, most likely and largest possible. From literature review, we have observed that number of authors have used TFN as well as trapezoidal. The other forms can be also used but most of the authors using fuzzy AHP and Fuzzy TOPSIS methodology with TFN have observed that it is very powerful combination of MCDM (Liu and Wang 2009; Onut et al. 2010; Mangla et al. 2017; Singh et al. 2018; Sirisawat and Kiatcharoenpol 2018; Wang et al. 2019).

Step 1 The value of fuzzy synthetic extent with respect to the \( i^{th} \) object is defined as

$$ S_{i} = \mathop \sum \limits_{j = 1}^{m} M_{{g_{i} }}^{j} \left[ {\mathop \sum \limits_{i = 1}^{n} \mathop \sum \limits_{j = 1}^{m} M_{{g_{i} }}^{j} } \right]^{ - 1} $$
(2)

To obtain \( \sum\limits_{j = 1}^{{m^{{}} }} {\mathop M\nolimits_{gi}^{j} } \) perform the fuzzy addition operation of M-extent analysis values for a particular matrix such that

$$ \mathop \sum \limits_{j = i}^{m} M_{{g_{i} }}^{j} = \left( {\mathop \sum \limits_{j = 1}^{m} l_{j} ,\mathop \sum \limits_{j = 1}^{m} m_{j} ,\mathop \sum \limits_{j = 1}^{m} u_{j} } \right) $$
(3)

and to obtain \( \left[ {\sum\limits_{i = 1}^{n} {\sum\limits_{j = 1}^{{m^{{}} }} {\mathop M\nolimits_{gi}^{j} } } } \right]^{ - 1} \), perform the fuzzy addition operation of \( M_{{g_{i} }}^{j} \) (\( j = 1,2, \ldots ,m) \) values such that

$$ \mathop \sum \limits_{i = 1}^{n} \mathop \sum \limits_{j = 1}^{m} M_{{g_{i} }}^{j} = \left( {\mathop \sum \limits_{i = 1}^{n} l_{i} , \mathop \sum \limits_{i = 1}^{n} m_{i} , \mathop \sum \limits_{i = 1}^{n} u_{i} } \right) $$
(4)

The inverse of the vector in “Eq. (2)” can be computed as,

$$ \left[ {\sum\limits_{i = 1}^{n} {\sum\limits_{j = 1}^{{m^{{}} }} {\mathop M\nolimits_{gi}^{j} } } } \right]^{ - 1} = \left( {\frac{1}{{\sum\limits_{i = 1}^{n} {u_{i} } }},\frac{1}{{\sum\limits_{i = 1}^{n} {m_{i} } }}.\frac{1}{{\sum\limits_{i = 1}^{n} {l_{i} } }}} \right) $$
(5)

Step 2 The degree of possibility of M2 = (12, m2, u2) \( \ge \) M1 = (l1, ml, u l) is defined as

$$ V\left( {M_{2} \ge M_{1} } \right) = \mathop {\sup }\limits_{y \ge x} \left[ {\hbox{min} \left( {\mu_{{{\text{M}}_{ 1} }} \left( x \right),\mu_{{{\text{M}}_{ 2} }} \left( y \right)} \right)} \right] $$
(6)

and can be equivalently expressed as follows:

$$ \begin{aligned} {\text{V }}\left( {{\text{M}}_{2} \ge {\text{M}}_{1} } \right) & = {\text{hgt }}\left( {{\text{M}}1 \cap {\text{M}}2} \right) = {{\mu }}_{{{\text{M}}_{2} }} \left( {\text{d}} \right) \\ & = \left\{ {\begin{array}{*{20}l} {1,} \hfill & {{\text{if}}\; {\text{m}}_{2} \ge {\text{m}}_{1} ,} \hfill \\ {0,} \hfill & {{\text{if}}\; {\text{l}}_{1} \ge {\text{u}}_{2} ,} \hfill \\ {\frac{{{\text{l}}_{1} - {\text{u}}_{2} }}{{\left( {{\text{m}}_{2} - {\text{u}}_{2} } \right) - \left( {{\text{m}}_{1} - {\text{l}}_{1} } \right)}}} \hfill & {{\text{otherwise}},} \hfill \\ \end{array} } \right. \\ \end{aligned} $$
(7)

where d is the ordinate of the highest intersection point D between \( \mu_{{M_{1} }} \) and \( \mu_{M2} \) shown in Fig. 5.

Fig. 5
figure5

The Interaction Between Triangular Fuzzy Numbers, M1 and M2

To compare M1 and M2, we need both the values of \( V\left( {M_{2} \ge M_{1} } \right) \) and \( V\left( {M_{1} \ge M_{2} } \right) \).

Step 3 The degree possibility for a convex fuzzy number to be greater than k, convex fuzzy numbers Mi (i = 1, 2,…, k) can be defined as

$$ \begin{aligned} & V\left( {M \ge M_{1} , M_{2} , \ldots , M_{k} } \right) \\ & = V\left[ {\left( {M \ge M_{1} } \right)\;{\text{and}}\;\left( {M \ge M_{2} } \right)\;{\text{and}} \ldots {\text{and}}\; \left( {M \ge M_{k} } \right)} \right] \\ & = \hbox{min} V \left( {M \ge M_{i} } \right), \quad i = 1,2,3, \ldots ,k \\ \end{aligned} $$
(8)
$$ {\text{Assume that}},\;d^{\prime}\left( {A_{i} } \right) = \hbox{min} V \left( {S \ge S_{k} } \right) $$
(9)

for, k = 1,2,…..,n and k ≠ 1. Now the weight vector can be given by the following formulae,

$$ W^{\prime} = \left\{ {d^{\prime}\left( {A_{1} } \right), d^{\prime}\left( {A_{2} } \right), \ldots , d^{\prime}\left( {A_{n} } \right)} \right\}^{T} , $$
(10)

Where Ai (i = 1, 2, 3,…, n) are n elements.

Step 4 Via normalization, the normalized weight vectors are given as,

$$ W^{\prime} = \left\{ {d\left( {A_{1} } \right),d\left( {A_{2} } \right), \ldots ,d\left( {A_{n} } \right)} \right\}^{T} , $$

Where “W” is a non- fuzzy number

Step 5 Integrate the opinions of decision-makers and apply geometric average to combine the fuzzy weights of decision-makers.

A3(b). Steps for fuzzy TOPSIS are as follows (Chen et al. 2006)

Step 1 Choose the linguistic rating values for the alternatives with respect to criteria.Let us assume that there are m possible alternatives called S = {S1, S2, S3…… Sm} which are evaluated against the criteria, B = {B1, B2, B3……..Bn). The criteria weights are represented by wj (j = 1,2,3…n).

The performance rating of each expert Ek (k = 1,2,3,…k) for each criteria Bj (j = 1,2,3,…n) with respect to alternative Si (i = 1,2,3,…….m) are denoted by R̃k = Ãijk(i = 1,2,3,…m; j = 1,2,3,…n; k = 1,2,3,….k) membership function μR̃k(x).

Step 2 Find out aggregate fuzzy ratings for alternatives.

All the experts gives fuzzy rating in triangular fuzzy number (TFN) R̃k = (lk, mk, nk), k = 1,2,3,…,k. Then convert fuzzy rating of all experts into aggregate fuzzy rating R̃ = (l,m,n) k = 1,2,3,…,k where \( 1 = \min_{k} \left\{ {lk} \right\},m = \frac{1}{k}\sum\limits_{k = 1}^{k} {m_{k} } ,n = \min_{k} \left\{ {nk} \right\} \). Fuzzy rating of Kth decision maker are X̃ijk = (lijk, mijk, nijk), i = 1,2,3,…m, j = 1,2,3…n, then aggregate fuzzy rating X̃ij(lij, mij, nij) where

$$ 1{\text{ij}} = \min_{k} \left\{ {lijk} \right\},{\text{mij}} = \frac{1}{k}\sum\limits_{k = 1}^{k} {m_{ijk} } ,{\text{nij}} = \min_{k} \left\{ {nijk} \right\} $$
(11)

Step 3 Construct the fuzzy decision matrix.

The fuzzy decision matrix for the alternatives (D̃) is constructed as follows:

Step 4 Construct the Normalized Fuzzy Decision Matrix

We normalized the raw data with the help of linear scale transformation to convert into comparable scale. The normalized fuzzy decision matrix Ñ is given by:Ñ = [rij] m × n,i = 1,2,3,…m; j = 1,2,3,…n,

$$ {\text{Where}}\; \tilde{r}_{\text{ij}} = \left( {\frac{{l_{ij} }}{{n_{j}^{*} }},\frac{{m_{ij} }}{{n_{j}^{*} }},\frac{{n_{ij} }}{{n_{j}^{*} }}} \right)\;{\text{and}}\;n_{j}^{*} = \max_{i} \left\{ {{\text{n}}_{\text{ij}} } \right\}\;\left( {\text{benefit criteria}} \right) $$
(12)
$$ \tilde{r}_{\text{ij}} = \left( {\frac{{l_{j}^{ - } }}{{n_{ij} }},\frac{{l_{j}^{ - } }}{{m_{ij} }},\frac{{l_{j}^{ - } }}{{l_{ij} }}} \right)\;{\text{and}}\;l_{j}^{ - } = \max_{i} \left\{ {l_{ij} } \right\}\;\left( {\text{cost criteria}} \right) $$
(13)

Step 5 Construct the Weighted Normalized Matrix

We multiply the weight (aj) of evaluated criteria with normalized fuzzy decision matrix (r̃ij) to get weighted normalized matrix (w̃).

$$ \tilde{W} \, = \, \left[ {\tilde{w}_{ij} } \right]_{m \times n} \quad i \, = \, 1,2,3, \ldots ,m; \quad j \, = \, 1,2,3, \ldots ,n $$
(14)

Where w̃ij = (r̃ij) × (aj) w̃ij is a triangular fuzzy number which is represented by (l̃ijk, m̃ijk, ñijk)

Step 6 Find out Fuzzy Positive Ideal Solution (FPIS) and Fuzzy Negative Ideal Solution (FNIS).

The FPIS and FNIS of the alternatives find out by:

$$ {\text{FPIS }}: {\text{A}}^{*} = ({\tilde{\text{p}}}_{ 1}^{*} ,{\tilde{\text{p}}}_{2}^{*} ,{\tilde{\text{p}}}_{3}^{*} \ldots {\tilde{\text{p}}}_{\text{n}}^{*} ) $$
(15)

Where \( {\tilde{\text{p}}}_{j}^{*} = \left( {{\tilde{\text{n}}}_{j}^{*} ,{\tilde{\text{n}}}_{j}^{*} ,{\tilde{\text{n}}}_{j}^{*} } \right) \) and \( {\tilde{\text{n}}}_{j}^{*} = {\tilde{\text{n}}}_{\text{i}} \max_{i} \left\{ {\text{j}} \right\} \)

$$ {\text{FPIS }}: {\text{A}}^{ - } = ({\tilde{\text{p}}}_{ 1}^{ - } ,{\tilde{\text{p}}}_{2}^{ - } ,{\tilde{\text{p}}}_{3}^{ - } \ldots {\tilde{\text{p}}}_{\text{n}}^{ - } ) $$
(16)

where \( {\tilde{\text{p}}}_{j}^{ - } = \, ({\tilde{\text{l}}}_{j}^{*} ,{\tilde{\text{l}}}_{j}^{*} ,{\tilde{\text{l}}}_{j}^{*} ) \;{\text{and}}\;{\tilde{\text{l}}}_{j}^{*} = \min_{i} \left\{ {{\tilde{\text{l}}}_{{}} } \right\}_{ij} ; \) i = 1,2,3,…,m; j = 1,2,3,…,n

Step 7 Find out the distance of each alternative from Fuzzy Positive Ideal Solution (FPIS) and Fuzzy Negative Ideal Solution (FNIS)

We find out the distance (\( {\text{d}}_{i}^{ + } ,{\text{d}}_{i}^{ - } \)) of each alternative i = 1,2,3,…m from FPIS and FNIS is computed as follows:

$$ {\text{d}}_{\text{i}}^{ + } = \sum\limits_{j = 1}^{n} {dp({\tilde{\text{w}}}_{\text{ij}} ,{\tilde{\text{p}}}_{j}^{*} ),\quad {\text{i}} = 1,2,3, \ldots ,{\text{m}}} $$
(17)
$$ {\text{d}}_{\text{i}}^{ - } = \sum\limits_{j = 1}^{n} {dp({\tilde{\text{w}}}_{\text{ij}} ,{\tilde{\text{p}}}_{j}^{ - } ),\quad {\text{i}} = 1,2,3, \ldots ,{\text{m}}} $$
(18)

Step 8 Find out the Closeness Coefficient (CCi) of each alternative

The closeness coefficient shows the distance to the fuzzy ideal solution and fuzzy negative ideal solution simultaneously.

$$ {\text{CCi = }}\frac{{{\text{d}}_{\text{i}}^{ - } }}{{{\text{d}}_{\text{i}}^{ + } + {\text{d}}_{\text{i}}^{ - } }} $$
(19)

Step 9 Give rank to the alternatives according to the decreasing order of closeness coefficient (CCi).

Appendix 4

See Table 11

Table 11 Inputs of Experts on 5C Framework for Different LSPs

Appendix 5 Data Analysis using TOPSIS

See Tables 12, 13 and 14.

Table 12 Aggregated Normalized and Weighted Fuzzy Matrix for ABC
Table 13 Aggregated Normalized and Weighted fuzzy matrix for PQR
Table 14 Aggregated Normalized and Weighted fuzzy matrix for XYZ

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Gupta, A., Singh, R.K. & Mangla, S.K. Evaluation of logistics providers for sustainable service quality: Analytics based decision making framework. Ann Oper Res (2021). https://doi.org/10.1007/s10479-020-03913-0

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Keywords

  • Logistics providers
  • Sustainable development
  • Service quality
  • Factor analysis
  • Fuzzy AHP -TOPSIS
  • Sensitivity analysis