presentation reverse final

Reverse Logistics: Strategy to achieve total customer satisfaction and enhancing competitive performance.

Project Guide: Prof. Rajesh KumarRupesh Kumar TiwariM.Tech. 4th SemesterIndustrial Engineering & Management

ContentsIntroduction to Concept development and importance

of reverse logistics.Literature Review (Salient points)Problem statementResearch question & objectiveSample design & data collectionMeasurement scale and questionnaire developmentData analysis & InterpretationConclusionLimitations

Concept Development and Importance of Reverse LogisticsResources are scarce in nature and we got

limited disposal capacities. Some times it is economical to reuse the

product rather going for its disposal. Growing environmental concern and

population emphasizes the reuse of products and materials.

It is only in the recent past need to investigate logistics aspects of product recovery and unsold merchandise have been acknowledged.

Concept Development and Importance of Reverse Logistics Continued---

To remain competitive and differentiated, organizations across the world are showing speed and reliability in offering services such as:

Replacing defective productRepairing of used productRefurbishing the return productCalling back sub standard or harmful productDisposing off product waste All these services add to competitiveness of the

organization by increasing the value of the products and providing clean environment to society. This led to a development of concept Reverse Logistics.

Evolution and definition of Reverse LogisticsLike any other concept the reverse logistics is also kept

continuously evolving over the period of time.The new concept of reverse logistics is different from

traditional concept as process of recycling product. The new definition is comprehensive and wider in scope.

Reverse logistics is defined as “The process of planning, implementing, and controlling the efficient, cost effective flow of raw materials, in-process inventory, finished goods and related information from the point of consumption to the point of origin for the purpose of recapturing value or proper disposal (Dale & Ronald, 1999)”

STORES PRODUCTION WAREHOUSE

SUPPLIERS

RETURNS

REVERSE LOGISTICS FLOW

CUSTOMERSDISPOSAL

REPAIRS

Literature ReviewCompany’s management needs to keep close eye over

some thing which take away the potential profit, dissatisfy the customers and put a drain over the firm’s scarce resources (Carter and Ellram 1998; Blumberg 1999;

2000; - 2001; Dowlatshahi Rogers and Tibben Lembke Mason2002).

Increased competition in today’s globalized world and shrinking technological life cycle is the prime driving force making companies to think about some differentiating factors such as reverse logistics as an performance enhancing strategy with regards to customer satisfaction in particular ( , . 2005)Richey Chen et al

Some of the organizations are gone to the extent of using reverse logistics process as a tool to ensure total customer

’ satisfaction by customer s needs and wants are listen and ( 2002). cared for its entire life cycle Mason

Benefits of Reverse LogisticsImproved customer contentment and retention.Enhance revenue and profitability of

organization. Improved effectiveness of the organization, which led to significant improvement in the productivity of the organization.

Improved time bound sensitivity towards listening to the voice of the customers.

Results in anticipation of needs and wants of the customer much ahead before actually they could actually realize it .

Drivers of Reverse Logistics Drivers of Reverse Logistics: Some of the companies are going for reverse

logistics by recognizing the great value of it, some of them doing it because they have to and some are socially motivated to do so. By and large the main driving force behind Reverse logistics can be categorized as follows----

Economic ProfitEnvironmental rules and regulationsCorporate social responsibility

Dimensions of Reverse Logistics The field of reverse logistics is so vast that cut

across several functions of the organization. Researchers are focussing on holistic view of the

reverse logistics and their research work wrap . entire process of reverse flow

There can not be any universal definition of reverse logistics. Definition of reverse logistics vary company to company and largely dependent upon how the company is going to exploit process of reverse logistics for value addition.

Frame Work of Reverse Logistics The nature of reverse logistics is quite different from

conventional logistics. The following are the prime characteristics of reverse logistics (Daniel Zavala, 2001).

We do not know and predict accurately when the product is likely to be returned or disposed.

Random and wide variety of return product with uncertain quality.

Return flow will depend upon the end users.Because of uncertainty of returned mercantile, its planning,

routing, scheduling and processing become difficult.Potential value of returned mercantile is uncertain and we

need to maximize the value of returned asset.Returned assets need to be sold in the secondary market

but, its demand is uncertain and pricing become difficult.

Problem StatementIn a competitive open market situation, the

meaning of customer satisfaction is to exceed the customer expectations.

In the era of cut throat competition where the differentiation is fast becoming a rare commodity, Reverse Logistics can be a big differentiation factor to stay ahead in the business and achieve total customer satisfaction.

How the reverse logistics can be instrumental in achieving total customer satisfaction.

Research Question & ObjectiveStatement of Research Problem/Opportunity: To find out relationship between the reverse

chain performance and customer satisfaction and testing its validity.

Research Objective: Whether reverse chain performance is

instrumental in achieving total customer satisfaction or not?

Research Hypothesis: Reverse chain performance leads to total

customer satisfaction.

Sample DesignConvenient SampleSample frame consist of members of

Automotive After Market Industry Association of India, third party reverse logistics providers in India and the end users of the product

My sample consists of diverse elements of targeted population so that the quantum of biasness is mitigated to some extent.

Data CollectionIn order to understand the underlying

construct of proposed concept, it’s beneficial to rely more on primary data and make use of it.

Questionnaire administered survey seems to me a better choice because of its versatility.

Survey provides you with sufficient information to understand outlook, behaviour and expectations of the respondent.

Questionnaire Development Quality of research is by and large dependent upon the

quality of questionnaire design. Questionnaire for the purpose of research will be developed after carefully reviewing the literature available to me.

Online self administered questionnaire will be sent to all the respondents chosen from the sample selected .

All the closed ended questions are developed using simple english and minimum of fuss with almost same wording in order to capture maximum response and information from the respondents.

In order to understand underlying construct of the concept undertaken, I found 31 important variables. Based on these 31 variables questionnaire will be developed.

Measurement ScaleFive points likert scale ( Where 1= Totally

disagree and 5= Totally agree) will be used to gather and measure information collected from the respondents.

Five point likert scale is well balanced and easy to administered. Respondents need to express his or her degree of agree ness and disagree ness from the options given in the questions

Content ValidityAccording to Blumberg et al. (2005), content validity

of a measuring instrument is the extent to which it provides adequate coverage of the investigative questions guiding the study.

Generally speaking, if the items contained in the measurement scale are representative enough to the study goal we are interested in.

Constructs and measurement scales in this study are almost entirely based on the previous literature.

Because of the extensive study, these items have been proved to be representative to the constructs, thus the overall content validity is ensured.

Data Analysis Data collected during the course of research will

be entered, edited and analyse using SPSS-19.Factor Analysis is used for data reduction and

finding the latent variables.Development of a model which explain the

relationship between reverse chain performance and customer satisfaction.

Testing the signification and statistical validity at 95% confidence interval or 5% level of significance.

Sr. No. Description of the variables

V1 We are prepared to devote people and resources to the reverse logistics activities we conduct with this customer.

V2 Consistency of return procedure

V3 It is convenient to contact and reach return service personnel.

V4 We tell the truth to our supplier

V5 Filling out return form is easy for me

V6 We appoint contact person to process information for product recovery

V7 Offer meaningful guarantee to our customer

V8 We trust that this customer will understand any problems we have that we share with him/her

V9 My company aims to preserve the relationship. We have with the customers indefinitely.

V10 There are direct computer-to-computer links with key suppliers

Sr. No Description of the variables

V11 We can count on this customer taking into consideration the way his/her

decisions and actions affect us

V12 We incur lower compliance costs with environmental regulations due to

our returns handling method

V13 Variety of options available to me for returning product.

V14 We respond quickly towards the customer’s return needs

V15 Availability of collection centers

V16 We compensate our customer if new product sent to replace the returned

product does not arrive on time.

V17 We use advanced information systems to track and/or expedite

shipments

V18 We recognize our returns policies to be liberal

V19 Inter-organizational coordination is achieved using electronic links

V20 Our strategy for dealing with returned merchandise improves our cost

position relative to our closest competitors

V21 We use information technology-enabled transaction processing

Sr. No. Description of the variables

V22 Real-time information

V23 Duties, authority, and accountability for reverse logistics are

documented in policies and procedures

V24 We address reverse logistics issues mainly with technologies we have

developed

V25 We are realizing cost savings because of our reverse logistics activities

V26 Reverse logistics program evaluations in our firm are based on written

standards

V27 Preparing the product for return is easy

V28 Handle the return without customer intervention

V29 Written procedures and guidelines are available for most reverse

logistics related work situations

V30 Relationship with the supplier

V31 Overall Satisfaction

Result of the survey(Summary)Sr. No. 1% 2% 3% 4% 5%

V1 9 23 30 18 20

V2 0 23 32 34 11

V3 0 13 44 29 14

V4 3 19 43 26 12

V5 0 9 32 46 13

V6 27 20 4 29 20

V7 7 22 31 27 13

V8 9 25 25 33 8

V9 11 23 31 34 11

V10 25 28 2 36 9

V11 11 20 27 36 19

V12 0 18 27 36 19

Result of the survey continued----Sr. No. 1% 2% 3% 4% 5%

V13 0 7 30 44 19

V14 9 25 27 24 15

V15 4 21 32 28 15

V16 7 24 20 33 16

V17 10 37 12 28 13

V18 2 11 31 51 05

V19 14 21 30 25 10

V20 0 15 33 45 7

V21 18 27 9 26 20

V22 18 21 10 20 31

V23 8 11 41 31 9

V24 0 23 36 14 25

Result of the survey continued----Sr.No. 1% 2% 3% 4% 5%

V25 0 19 29 45 7

V26 7 19 37 22 15

V27 0 10 51 36 3

V28 0 39 21 26 14

V29 4 12 45 28 11

V30 32 15 6 10 37

V31 11 23 24 27 15

1. Least Agree2. Somewhat Agree3. Agree Moderately4. Agree to a Great Extent5. Strongly Agree

Factor AnalysisTotal variance explained Extraction Method: Principle component

analysisComponent Extraction Sums of Squared Loadings

Total

% of

Variance

Cumulative

%

1 16.348 54.494 54.494

2 6.059 20.195 74.689

3 2.982 9.941 84.630

4 2.060 6.867 91.497

Factor Analysis continued---


Rotated component matrix

Factor Analysis continued---The null hypothesis that the population correlation matrix is an

identity matrix is rejected by Bartlett’s test of sphericity. Thus the factor analysis may be considered as an appropriate technique for analyzing the correlation matrix.

Several considerations are involved in determining the number of factors that should be used in the analysis. In this study eigen value is used as a basis for determining the number of factors. The factors having Eigen value greater than 1 are retained. The other factors are not included in the model.

If percentage of total variance is taken into consideration these 4 factor extracted to gather explain 91% of total variance.It is recommended that the factors extracted should account for at least 60% of the variance. These four extracted factors are also having eigen value more than one. Hence the extracted factors assumed to be stable.


Factor 1 accounts for 54.494% of total variance.Factor 2 accounts for 20.195% of total variance.Factor 3 accounts for 9.941% of total variance.Factor 4 accounts for 4.874 % of total variance Interpretation of the solution is often enhanced by a

rotation of the factors. Rotated component matrix often achieves simplicity and enhances interpretability. It also helps in determining clear factor and variables correlation.

Factor Analysis continued--- Factor 1 has high coefficient of correlation for variables V1: (We are prepared to devote people and resources to the reverse logistics

activities we conduct with this customer) V8: (We trust that this customer will understand any problems we have that we

share with him/her) V11: (We can count on this customer taking into consideration the way his/her

decisions and actions affect us) V28: (Handle the return without customer intervention) V7: (Offer meaningful guarantee to our customer ) V15: (Availability of collection centres) V14: (We respond quickly towards the customer’s return needs) V9: (we have with the customers indefinitely.) V16: (We compensate our customer if new product sent to replace the returned

product does not arrive on time.)If these variables are observed they are centered on the category of performance based on service quality and service recovery issues hence this factor can be safely labeled as “service quality & recovery” factor. This factor explains 54.494 % of total variance. This suggests that while measuring performance of reverse logistics service quality and recovery issues could be one of the prime determinants of reverse logistics’ performance.

Factor Analysis continued--- Factor 2 has high coefficient of correlation for variablesV26 : (Reverse logistics program evaluations in our firm are

based on written standards).V23 : (Duties, authority, and accountability for reverse

logistics are documented in policies and procedures)V29: (Written procedures and guidelines are available for most

reverse logistics related work situations)V2 : (Consistency of return procedure)

These variables when observed they can be centered on the category of performance related to importance of standardization of reverse logistics process. This factor contributes in explaining 20.195 % of variance, which makes this factor as second most important issues in measuring reverse logistics' performance . This factor can be labeled as “Standardization of reverse logistics process”.

Factor Analysis continued--- Factor 3 has high coefficient of correlation for

variablesV5 : (Filling out return form is easy for me)V13 : (Variety of options available to me for returning

product.)V3 : (It is convenient to contact and reach return service

personnel.)V27 : (Preparing the product for return is easy)V4 : (We tell the truth to our supplier) These variables on observation can be club together to indicate that customer's involvement analysis is considered important before measuring reverse logistics' performance. So this factor can be labeled as "customer's involvement". It explains 9.941% of total variance, and it forms third most important factor in our study.

Factor Analysis continued--- Factor 4 has high coefficient of correlation for

variablesV24: (We address reverse logistics issues mainly with

technologies we have developed)V12: (We incur lower compliance costs with environmental

regulations due to our returns handling method)V18: (We recognize our returns policies to be liberal)V25: (We are realizing cost savings because of our reverse

logistics activities)V20: (Our strategy for dealing with returned merchandise

improves our cost position relative to our closest competitors.)These variables together explain reverse logistics strategy formulation,

while measuring reverse logistics' performance. This factor can be termed as “reverse logistics' strategy”. This accounts for 6.867% of variance.

Proposed conceptual model--

Service Quality & Recovery

Standardization of reverse logistics

process

Customer's involvement

Reverse logistics' strategy

Customer Satisfaction

Customers’ Loyalty

Multiple Regression Analysis---

R R Square Adjusted R Square

.966 .934 .931

Model Summary

Multiple Regression Analysis continued---

Model Summary

Model Sum of Squares df

Mean Square F Sig.

1 Regression 21.537 4 5.384 333.917 .000(a) Residual 1.532 95 .016 Total 23.069 99

a Predictors: (Constant), X4, X1, X3, X2b Dependent Variable: Y


Model Summary

Model

Unstandardized Coefficients

Standardized Coefficients t Sig.

B Std.

Error Beta 1 (Constant) .074 .106 .698 .487 X1 .528 .020 .749 26.907 .000 X2 .217 .022 .277 9.792 .000 X3 .179 .019 .256 9.507 .000 X4 .097 .020 .135 4.952 .000

a Predictors: (Constant), X4, X1, X3, X2b Dependent Variable: Y

Coefficient


•To understand the relationship between customer's satisfaction and performance of the reverse logistics process and its statistical significance, regression analysis has been done. In regression analysis the four factors extracted are taken as independent variables and customer's satisfaction as dependent variable. Statistical veracity of the model has been tested using 5% level of significance.•It can be observed that p value =.000, which is less than .05. Hence the proposed regression model is highly significant. The value of adjusted R-square is 93%, means regression model explain 93% variation and only 7% is unexplained. Since the value of adjusted R-square and R-square is almost same, hence the phenomenon of multicollinearity is ruled out and no interaction amongst the independent variables.


The proposed regression model can be framed as- Y=.074+.528*X1+.217*X2+.179*X3+.097*X4Y= Overall customer satisfactionX1= Service quality and recoveryX2= Standardization of reverse logistics processX3= Customer's involvementX4= Reverse logistics strategy

ConclusionWith the help of factor analysis 30 variables is reduced to 4

factors. The four extracted factors are service quality and recovery followed by Standardization of reverse logistics process, Customer's involvement and Reverse logistics strategy.

It is evident from the model, service quality and recovery is the most important factor in explaining overall customer satisfaction followed by Standardization of reverse logistics process, Customer's involvement and Reverse logistics strategy.

Regression model developed to explain customer satisfaction is found to be highly significant with p value of 0.000. Regression model is developed taking customer satisfaction as dependent variable and 4 extracted factors as independent variable.

The explanatory power of the regression model is also very high with almost negligible interdependency and interaction amongst the independent variable.

Hence the proposed hypothesis reverse chain performance leads to total customer satisfaction is valid and hold true.

Limitations & scope of future workSince the research is based on qualitative data and it is

quantified using suitable measurement scale. Measuring qualitative data has always been a difficult task for majority of researchers worldwide. At the same time the majority decision variable in industrial engineering and management are qualitative in nature. The conversion of qualitative data into quantitative can be questioned.

The sample size chosen is 100 because of the resource and time constraints; hence it is difficult to generalize the findings of the research.

The linear relationship amongst the explained and explanatory variables can be questioned and need some further analysis.

Apply multi objective optimization taking two conflicting objectives of cost minimization and maximization of customer satisfaction using some evolutionary algorithm.

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