INTRODUCTION:

In the modern era, Information Technology (IT) has penetrated in almost all fields of science, commerce and management. Drastic change in the adoption of IT in service retail industry is reported in common^1.In an earth moving and construction equipment manufacturing company, the two main sectors of the retail industry are sales and after sales. In normal terms, the two divisions of the automotive retail sector are basically sales of the product and service that is provided by the retail for the same product that is sold^{1, 2.} This has led to introduction of the dealerships in the automotive industries in 1890s. In order to expand the business in different regions of the country rather than being restricted to a particular state or a region, these dealerships helped the companies to expand and increase the business in a positive way^3.Then there is a need for the management of these dealers who are located in different regions of the country. As the dealerships increased, the businesses started to spread across different countries as well as the brand names became famous^3. To manage and run the dealerships in a smooth and efficient way, Dealer Management System (DMS) started getting a research direction came. DMS played a major role in managing operation from high level to low level in an easy way. There is a need of data pertaining to handling vehicles, store and inventory management. These were the basic operations where DMS provided solutions for the companies which are in need of it^4. The functionality of DMS actually varied in a very large aspect. However, the basic function the DMS is used in a larger extent relates to follow up with customer after the purchase of the vehicle. The aftermarket activities are in huge numbers and the service operation performed on a vehicle are also drastically increasing. In order to manage all the operations and keep a track on all these service activities, DMS is used extensively^5. Then the definition for the service of the vehicles started to report in a different way. Service fulfilment always included mobility of field service (infield, outfield), and reverse logistics. These features started to play a major role in designing DMS than that of features related to handle operations in service divisions of dealerships^6. Most of the systems are now being used as a cloud server instead of the traditional servers, which are more error and damage bound. The system will always have more advantage in terms of easy to use and easy to access as well. The main advantage from the proposed DMS model deals with any type of data that can be accessed from anywhere without any location or geographical barrier. In particular, the Cloud based field service management is an effective tool to manage service because of which OEMs are drifting towards it⁷. This system helps in removing wastes in the process and reduces the service lead time in drastically. Service operations cost and customer service quality are the two main factors that are used to model the implementation of DMS. With the effective use of DMS and implementing it in service operations, there will be significant decrease in travel costs and communication costs in turn reducing the total costs in management of service operations.

LITERATURE REVIEW:

The concept of DMS came in to existence due to the involvement of IT management in the aftermarket operations of an Original Equipment Manufacturers (OEM). Initially, the need for management of dealerships is not only restricted to automotive industry^8. Service oriented softwares for earth moving and construction equipment manufacturing companies needed more of this type of IT involvement in their vehicle service management. This is because most of the services of the huge equipment is conducted on site. The need in this industry grew significantly because of this main reason. Thus, there is a huge requirement in software solutions for management of dealers and their service process in this part of automotive domain. The dealers needed systems to get a competitive business advantage in the market and to retaining customers^9.It is also applicable for each industry, which use to operate sales and service through retail chain. However, the use of IT in aftermarket is limited in India⁹. Since, the definition of customer satisfaction was not broadened to aftermarket operations. Initially, it is only restricted to buying experience of customers. However, satisfaction in terms of aftermarket operations and their experience with aftermarket solutions from the companiesstarted gaining momentum¹⁰. It is also found that Computer Aided Sales Process (CASP) had a huge impact on increasing the sales in the automotive sector^8.The major part of the automobile industry is defined to be the car manufacturing companies. It is one of the most beneficial sector of automotive industry, which provides people with safe, quick and easy way of transport solution. The car manufacturing industry is growing in a very rapid rate and produce as well as sell good volumes compared to other automotive industries. It is found that DMS has very good application in the automotive sector and will be an effective tool to manage the operations related to car sales as well as services given to the customer after the sale of the vehicle¹¹. This is applicable to the earth moving and construction equipment as well. The need and demand of customers, determine the fluctuation in the market. These changes alter the need in the system to develop the changes as well. These changes are entirely dependent upon the market need and customers drift towards certain type of a service. There shall be constant change in the design of the DMS to improve the efficiency in operation as well as improve customer satisfaction on a positive note^12.The basic working of the DMS is to provide efficient solution to the dealers in order to manage their day to day operation effectively. In order to implement DMS, a basic comprehension of the service operations is necessary. This involves too much of time and effort to understand the needs for which the DMS has to be designed for the client. In the modern era, customer has different demands. In order to fulfill those demands, dealers are considering cost to invest in a single platform. The customers are fine with different modules in the application for different operations^4. Even with the knowledge about the operations while designing a DMS, there should always be a foresight in the business. The possible developments in the industry has to be considered so that the lifecycle of the system in use can adapt easily to the changing needs. For that to happen, there must be a continuous development and updation of the system must be carried out. System must be designed in such a way that constant updates in regular intervals can be performed easily. At any point of time, an end user should not feel that the system they are using is outdated and difficult to carry on with the operations^5.

On the other hand, value added activities are those activities that absorb resources for the processes but creates more value from the goods produced or the service provided for the customer. Non-value added activities are those activities which absorbs resources for the processes but do not create any value from the goods produced or the service provided to the customer^13.

Statistical Package for Social Sciences (SPSS) software is a basic tool to conduct the data analysis of the data obtained from the empirical study that is conducted during the research. In the research, the reliability of the questionnaire is found out using SPSS software considering Cronbach’s Alpha¹⁶ as the basic benchmark to test the reliability^14.

Smart Partial Least Square (PLS) software is used to build Structural Equation Modelling (SEM) or in general, it is called as the conceptual modelling. The modelling is carried out according to the dependent and the independent variables that are considered in the empirical study. In addition, smart PLS software gives us an insight on how the proposed model’s validity varies in case each variable is removed and used again in the model creation. The smart PLS software is also used to conduct the data analysis in this research^15.

PROBLEM DEFINITION AND FORMULATION:

In order to analyze the factors that are responsible for the implementation of DMS in an earth moving and construction equipment manufacturing, the proposed research is based on the following identified gaps:

· Limited information about the overall effect in sales, service as well as spares management of DMS collectively²

· Lack of DMS related papers backed with proper explanation about its importance in Indian automotive market with respect to earth moving and construction equipment manufacturing company⁵

· Scarcity of complete paper on effect of DMS on operations carried out in a dealer end in an automobile sector⁴

· Papers lack in giving an insight about the field service management in Indian Automobile industry⁷

· There is a visible compromise in number of papers related to usage of DMS in earth moving and construction equipment manufacturing companies¹¹

AIM AND OBJECTIVES:

Implementation of Dealer Management System in an Earth moving and construction equipment manufacturing company in India from operations perspective

The above aim can be accomplished through the following objectives:

· To identify and analyze the existing practices of DMS

· To prepare a value stream mapping for capturing value added and non-value added activities

· To conduct an empirical study to identify critical factors for modelling DMS from operations perspective

· To prepare a conceptual model for Dealer Management System and to analyze the conceptual model in operations perspective

· To verify and validate the conceptual model for the DMS using hypothesis testing

SCOPE OF PRESENT INVESTIGATION:

The study is carried out in a DMS designing and implementation company. This is an organization where their product called as Quest-DMS who implement DMS to their clients based on their needs. The company has variety of modules in DMS which is implemented in different regions of India. Further, the company have major clients in an earth moving and construction equipment manufacturing companies. The secondary data is obtained for the research purpose to depict the working of the DMS and to conduct an empirical study for identifying factors for modelling DMS.

METHODOLOGY:

In order to study the existing system of DMS, the secondary data from a company that design and implement DMS in earth moving and constructions equipment manufacturing company and literature review are considered for research. The Value Stream Mapping (VSM) is prepared analyzing the secondary data information as well as primary data for the conventional field service process. Also by using lean principles, the VSM is analyzed to identify value added or non-value added activities. In order to identify critical factors and carryout empirical study, a questionnaire is formulated using the important variables from literature and interactions with industry experts that are needed to design and implement DMS by using Google forms and the basic analysis are performed through excel solver. In order to model the DMS, tools like SPSS and smart PLS are used. To verify and validate the model, hypothesis testing is conducted using the t-test from smart PLS software. Finally, the results obtained from the objectives are interpreted and concluded.

HYPOTHESIS FORMULATION:

Hypothesis 1-To find the effect of customer service quality on service operations

H₀-Service operations are not affected by customer service quality

H₁-Service operations are affected by customer service quality

Hypothesis 2- To find the effect of resource utilization on service operations

H₀-Service operations are not affected by resource utilization

H₁-Service operations are affected by resource utilization

Hypothesis 3- To find the effect of service related cost on service operations

H₀-Service operations are not affected by service related cost

H₁-Service operations are affected by service related cost

DATA ANALYSIS AND PROBLEM SOLVING:

DMS application is designed and developed to manage parts and service operations across various domains/sectors. Different sectors where the Quest DMS has its applications are listed below:

· Construction equipment

· Earth moving equipment

· Industrial machines

· Wind mills

· Trucks

· Tractors

· Escalators

· Generators

· Gauging machines

Quest DMS consists of various modules to provide end-to-end solutions to the customers. Their solution has a wide application as the whole process is considered. Customer centric solutions are also provided in a large way and customization for different clients with respect to the DMS and its features are being deployed. Different modules of the Quest DMS are as stated below:

· Help desk

· Parts module

· Service module

· Sales module

· Customer Portal

· Job card planning

· Time management

· Tools management

Analysis of the different modules in Quest DMS is carried out mainly based on:

· Usage

Fig. 1: Usage of Quest DMS in percentage

Figure 1 depicts the usage of the DMS as different modules. The major finding revealed that, 48% of the companies use service module as their major use of DMS.

· Customer base:

Fig. 2: Customer base per centage

The above figure depicts the customer base of DMS is higher in construction equipment and earth moving equipment manufacturing companies. 67% of the companies are from construction equipment and earth moving equipment manufacturing companies. Moreover, the advantages with Quest DMS Field Service Management module are as listed below:

· User friendly

· Bird’s Eye View

· Complimentary customer portal

· Mobile co ordination

· Customer Service

· Improved tracking

· Business rules flexibility

· Avoid double data entries

In order to explain the flow of customer enquiry regarding service of product in earth moving and construction equipment manufacturing industry, the VSM is prepared. VSM provides an insight to find out the non-operational time that is considered as waste in the process. In this study, VSM is carried out in the context of a typical dealer with a traditional ERP system in managing the service of the products. Moreover, Quest DMS has features that can actually reduce the time spent in non-value added service (waste) and convert it to value added service. This enhances the service efficiency and increase in customer satisfaction.

In the below VSM, the boxes that are indicated with green colour are defined as the operation in the field service and the boxes in the blue relate to the amount of operation time that is taken by each operation in the service process. The boxes in brown represents the time that is being wasted in the conventional service process that is followed without the use of Quest DMS. The conventional process may use an outdated ERP or simple tools like operation tracker using Microsoft excel or handwritten spreadsheets.

Fig. 3: Value stream mapping of a conventional service process

Based on results and discussion on VSM on the Conventional Service Process:

The total cycle time of the process is found to be:

Average time for the service work= (2+10)/2= 6hrs

Sum of operation times of each process (Cycle Time) = 12 hrs

Lead time for the process is found to be:

Average time of transition between parts inventory and service= (48+240)/2=144 hrs

Sum of transition times between operations (Waste) =153 hrs

Sum of transition times between operations (Waste) + sum of operation times of each process = 153+12 =165 hrs=6.8 days

Lead time = 165 hrs = 6.8 days

Features in DMS, which Reduces Waste in the Process:

The strategic importance of field service delivery as a driver of customer satisfaction and brand reputation are confirmed in the research report, ‘The Road Ahead - The Future of Field Service Delivery’⁷. Field-based employees, whose potential as brand ambassadors are unnoticed, now, being rightly recognized as the new frontline in customer service. Decision makers are suffering from data overload in their attempts to operate the most efficient workforces and fleets on the road. Moreover, decision makers need a high-level trends and benchmarking, not a mass of information⁶. But, analytical tools allow companies to not only extract rich, meaningful data from its various solutions, but also ensures that key stakeholders obtain that information in salient, relevant reports and snapshot. DMS field service module consists of list of features that have a huge amount of impact in reducing wastes among the service processes. Some of main features are listed down below:

· Job card wise activity updating by SMS or application

· Scheduling of engineers as well as tool

· Allocation of service engineers based on operations

· Easy tracking of the requested part status

· Reclaim and claim settlement options

· Options for credit note and debit note creation

· Life cycles of parts indication in the application directly

· Parts replacement recommendation for the next service

· If the process of conversion of enquiry to preparation of job card takes more than 6 hrs automatic mails to MD of the service centre will be sent

Considering these features in the service processes, scope for reduction in the non-value added activities that are present in the conventional practices for field service in a larger extent in looked. An empirical study to identify critical factors is attempted in the this study.

Different sections framed were based on the variables considered in the design of questionnaire as below:

Fig. 4: Variables identified for empirical study

Reliability of the Questionnaire:

Reliability is measured with Cronbach’s alpha factor. If the value of the Cronbach’s alpha is greater than 0.70, reliability of the questionnaire is deemed as better. If the value of Cronbach’s alpha is more than 0.80, reliability of the questionnaire is good and if the Cronbach’s alpha value is more in the 0.90, reliability of the questionnaire is best¹⁶. When the data obtained from the questionnaire is analyzed for reliability using SPSS software, it was found that the Cronbach’s alpha value is 0.928. Reliability of the questionnaire used for the research study is statistically significant.

Statistical data analysis gives an insight about the basic statistical data values such as mean, median, range, and first quartile value, third quartile value, minimum and maximum.

Table 1: Statistical descriptive data from questionnaire

	Service operation	Service related cost	Customer service quality	Resource utilization
Mean	11	10.2	10.2	10.4
Range	27	32	32	25
Minimum	0	0	0	1
Q1	5	5	4	6
Median	6	5	6	7
Q3	17	9	9	12
Maximum	27	32	32	26

The questionnaire is formulated using the Google forms and the data from the Google forms are interpreted with the below diagrams

Fig. 5: Results on service related tracking

The above figure shows that 74.50% of the respondents opined that an application to track the service related activities is warranted

Fig. 6: Results on service remainder initiation

Similarly, fig. 6 depicts that 74.50% of the respondents inferred that an application to initiate the service related remainders is appreciated

Fig. 7: Results on decrease in communication cost

The above figure explains that 60.80% of the respondents strongly agree that there is decrease in the communication costs using DMS to manage the service operations

Fig. 8: Results on decrease in travel cost

The above figure explains that 60.80% of the respondents strongly agree that there is decrease in Travel costs using DMS to manage the service operations. It is also found that deeper analysis of the questionnaire who have ERP to track service related activities have less hold control on resource utilization.

In order to prepare a Conceptual Model for DMS and analyze the variable, a relationship model to find out the effectiveness of each variables that are used to design and implement DMS in the industry are explored. The variables are however classified as independent variables and dependent variables. In this case, there is one dependent variable and three independent variables. The model is conceptualized in the PLS software using the dependency status.

Fig. 9: Conceptual model 1 considering 3 independent variables

There are two analysis that are run in PLS software on this model, which are listed below:

· PLS algorithm

· Bootstrapping

After running the two analysis respectively, the models are represented with certain elemental values and the model is shown below.

Fig. 10: Results on analysis of conceptual model 1 considering 3 independent variables

After the above analysis is run in the PLS software, it is found that the variable resource utilization has values of ‘p’ and ‘β’ that are actually contributing in an insignificant way having p value as 0.598 and β value is 0528. Therefore, there may be a possible positive development in the model if that variable is not considered in the model creation. Preparation of conceptual model by not considering the resource utilization variable for model construction may give a model which is more optimal for design and implementation of DMS.

Fig. 11: Conceptual model 2 considering 2 independent variables with highest contribution

The model behavior towards the loading of the questions under variable service related cost and customer service quality are analysed. The model after without considering the resource utilization variable is as shown below:

Fig. 12: Results on analysis of conceptual model 2 independent variables with highest contribution

It is observed that there is a huge drift in the p values as well as β values in the model and all the values adhere to the standard values of p and β. Standard value of p being 0.05 and standard value of β being the value greater than 1. By boot strapping analysis and comparison of R²values with and without resource utilization variable, it is found that there is a slight decrease in the value of R².

Results and Discussion on Analysis of Conceptual Model to Implement DMS:

The below figure shows the comparison of p values of service related costs variable, customer service quality variable and resource utilization variable. The resource utilization variable has a p value which is much above the standard value of 0.05

Fig. 13: Comparison of P values of independent variables

Fig. 14: Comparison of Beta values of independent variables

The above figure depicts the comparison of β values of the variables. It is observed that the β value of the resource utilization is quite less compared to other two variables. That indicatively states that the resource utilization has lesser impact on the dependent variable

Fig. 15: R square value shift in models with and without resource utilization variable

Figure 15 indicates the shift in the R² values that is plotted for the conceptual model with resource utilization and without resource utilization variable.

Verification and Validation of the proposed conceptual model:

To verify and validate the proposed conceptual model, the Hypothesis testing is conducted on the variables. Considering the ‘p’ and ‘β’ values of each variable that was observed after bootstrapping and PLS algorithm attained using smart PLS software, variables have lesser contribution to the construction of model warranting for in depth research. Figure16 indicates the dependency of individual variables on each other. In summary, the hypothesis test is conducted to validate the proposed model.

Fig. 16: Hypothesis formulation

The results from the hypothesis test are interpreted whether to consider null or alternate hypothesis. Hypothesis is framed against each independent variable as null hypothesis and alternate hypothesis. The ‘p’ and ‘β’ values are compared against their standard values to decide whether the conceptual model is validated through the impact of the independent variables on dependent variables.

From the above table, the results can be summarized as:

· Customer service quality: H₁ is accepted

Service operations are affected by customer service quality

· Resource utilization: H₀ is accepted

Service operations are not affected by Resource utilization

· Service related cost: H₁ is accepted

Service operations are affected by Service related cost

Considering values of β there is a lesser effect of resource utilization on service operations, other variables have a much significant effect on service operations

CONCLUSIONS:

The major outcomes and conclusions from the study include that Quest DMS is an effective tool in providing the aftermarket solutions for service operations. In addition, value stream mapping gives an insight on the amount of non-value added activities that are created in a conventional service process. The empirical study provides an industry opinion in terms of variables to be considered for implementation. Analysis of the questionnaire provides information on effectiveness of each Independent variables on Dependent variable. Finally the Hypothesis testing verifies and validates the relationship of variables derived from analysis. It is found that resource utilization had least impact in modelling implementation of DMS compared to service related cost and customer service quality variables. From this it is depicted that service related cost and customer service quality variables are important variables to be considered while modelling DMS.

Future Directions:

Identified point for the further study can be taken forward by considering from operation’s perspective. There is a huge gap on the effect of resource utilization in service operation to implement DMS. Overall DMS application by the selected company as a whole can be considered for further study. Also, business aspects in implementation can be a research topic to explore further. Finally, comparative study on the use of DMS and ERP can be done in a very broader sense and researched further.

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Received on 23.08.2019 Modified on 26.09.2019

Asian Journal of Management. 2019; 10(4):321-329.

DOI: 10.5958/2321-5763.2019.00048.9

Dependent construct	Hypothesis	p and β values	Inference
Customer service quality	H₀ = Service operations are not affected by customer quality H₁ =Service operations are affected by customer quality	P=0.038 β=2.078	H₁ is accepted, hence Customer Service Quality affects Service operations
Resource Utilization	H₀ = Service operations are not affected by Resource utilization H₁ = Service operations are affected by Resource utilization	P=0.598 β=0.528	H₀ is accepted, hence Resource utilization affects service operations
Service related cost	H₀ = Service operations are not affected by service related cost H₁ = Service operations are affected by service related cost	P=0.034 β=2.216	H₁ is accepted, hence Service related cost affects service operations