Advances in Interdisciplinary Research in Engineering and Business Management

The present research would measure the barriers faced by an individual in using the UPI-based apps. The barriers have been categorized into five distinct groups, viz., usage, value, risk, tradition and image barrier. An attempt has been made to measure the relationship of these various barriers with the behavioral intention of the individuals for using the UPI-based apps. The structured questionnaire was prepared using the standard scales. Step-wise regression was done to find out the relationship between the various barriers. It was found that usage barrier, risk barrier and tradition barrier have the most significant relationship with behavior intention of using UPI apps.

Existing research studies on outsourcing industry have not much discussed about the relationship between employees’ gender and their perception about organizational welfare programs. The present paper studies about the role of employees’ gender on their perception about welfare practices. The concept of present paper defended that common welfare programs might not be helpful for each gender and management should consider requirement of each gender while framing employee welfare policies. The objective of the study is to analyze and compare the impact of gender on the opinion of male and females’ employees about the effectiveness of common welfare policies offered by management to promote stress-free work environment to increase employee’s productivity. The research design of the study is descriptive, and quantitative research methodologies are used to process the primary data collected by using questionnaire through survey method. The data were statistically processed on computing tools—MS Excel and SPSS ver16.0. Chi-square test was deployed to reject the null hypothesis and for categorical comparison (gender-based) of respondents’ opinion. The results show variation in views across groups based on their gender. Male and female employees showed differences even in their whole-heartedly participation in such activities. The associations among dependent variable (perceptions) and independent variables (gender) have been outlined and possible suggestions about cost-effective gender-based stress management programs have been presented. Results are limited as quantitative methods provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of humans’ perception.

Augmented reality (AR) is a technology that syndicates virtual objects with actual world enabling real-time interaction with virtual objects. AR is an upcoming technology in India which is now widely used by the marketers to attract customers. This research paper will examine the present uses of AR technology as a digital marketing tool in India along with the future scope of this technology. Based on secondary data, this paper will also examine the challenges faced by digital marketers in India to use AR technology. The aim of this paper is also to provide awareness about uses of AR in marketing.

In the present highly cutthroat global business environment, to uphold in the inflationary market, many times the retailer orders and stores large stock to get the edge over his competitors. At times, the size of the order is even larger than his storage capacity. Under such a situation, the retailer opts for renting the warehouse as an alternative to capacity augmentation. Considering the importance of such a scenario, the present research work develops the optimal replenishment policy for deteriorating items under inflationary conditions over a finite planning horizon in a two-warehouse facility using a discounted cash flow (DCF) approach. A DCF approach is used to estimate the value of an investment based on its future cash flows and attempts to figure out the financial implication of the opportunity cost in inventory analysis. The present model jointly optimizes the frequency of replenishments by minimizing the retailer’s total cost. Conclusively, a numerical example along with sensitivity analysis is carried to demonstrate the model characteristics.

Mutual fund industry is turning to be the fastest growing and competitive industry, offering operational flexibility and attractive returns to investors. Growth of asset under management (AUM) has registered a compound annual growth rate of 25% over the last five years (2013–2018) as compared to aggregate growth of bank deposits of scheduled banks (11%). Regulatory changes have always been one of the prominent factors affecting the growth of financial services industry in general and mutual fund industry in particular. Starting from 1964 to 2019, there were alarming changes in the growth of mutual fund industry like in 1987 (entry of public sector banks), in 1993 (entry of private sector banks), in 1994 (UTI have no separate existence), in 1996 (imposition of SEBI regulations), in 2004 (mergers and acquisition), in 2017 (re-classification of schemes) and so on. The study examines the impact of re-classification of schemes on the AUM of mutual fund industry. The European fund and asset management association established European classification fund soon after 2008 financial crisis. This classification enhances investor protection and independence. Similar mechanism is adopted by Securities and Exchange Board of India for open-ended schemes in 2017. These regulatory changes have impacted the growth on investors, industry and economy. The paper will encompass the conceptual concept and factors that are important for growth and investment pattern of investors that will lead to mutual fund companies’ sustainability and progressive growth of Indian mutual fund industry.

Lean philosophy has become very vital for the survival of organizations. Lean construction, the potential solution for companies to address waste of all kinds has been gradually introduced. Limited studies exist on Lean implementation in construction SMEs. This paper sought to suggest Lean tools which can be implemented within the construction SME setup. A systematic review of empirical and theoretical studies published in Research Gate, International Group of Lean Construction Conference (IGLC), Science Direct (Elsevier), Emerald Insight, Taylor and Francis, Google Scholar and other internet sources were considered in this study. This paper was based on four hypotheses, which are related to the construction SMEs capacity to implement Lean as efficiently as large enterprises. The review found that Lean principles could be applicable in construction SMEs, but has to be contextualized within their peculiar characteristics such as their size, financial capabilities, organizational culture and human resource capacities. The paper concludes by suggesting Lean tools, such as 5S, A3’s, and 5 Why’s, which require less monetary investments to be implemented by construction SMEs. A change of mind-set is needed for Lean implementation as there is still a lower rate of adoption among SMEs.

To achieve the system’s reliability, an approach for the complex series-parallel system has been proposed. This approach is based on the universal generating function (UGF) technique. UGF provides the state performance and state probabilities, expected time, expected cost, MTTF, tail signature and signature of the proposed complex system. In this paper, complex series-parallel system has three subsystems, where subsystem A and subsystem B are connected with subsystem C in series. Subsystem A and subsystem B are connected in parallel while subsystem A has two components in series. To demonstrate the approach, a numerical example is taken for independent identical design.

Nowadays, cloud computing is rigorously used by academicians as well as by researchers due to its excellent applicability. Most significant feature of cloud computing is Live Migration of Virtual Machines (VMs). However, it demands large amount of migration time to transfer data from source to destination. Migration time of VMs can be mitigated by several techniques available in study. In migration, scheduling also plays a vital role which handles the processes, task, resources, etc. FCFS (first come, first serve), RR (round robin), and SJF (shortest job first) are among the several scheduling algorithms in CPU scheduling, utilized for minimization of makespan. In this paper, a more optimized scheduling algorithm known as MLFQ (multilevel feedback queue) is utilized. MLFQ helps in minimizing the makespan and overall workload with the help of feedback scheduler and by categorizing the load as under load and overload, resulting in effective migration.

The paper presents development of a data acquisition system designed for collecting data from a large number of Smart Multifunctional Meters (SMFMs) located in an electric sub-station of a university. For this purpose, the SMFMs are connected in an RS-485 PAN (personal area network) and the data read from meters is sent through a gateway module to the Ethernet LAN already available in the campus. The gateway has the capability of conversion between Modbus RTU/ASCII running on RS485 PAN and Modbus TCP/IP running on Ethernet LAN. The data can be accessed from the Ethernet LAN on any Windows compatible device like PC, using WinTech ModScan32 software. This system has been tested extensively. The results are entirely satisfactory and encouraging. The scope of the system can be expanded by adding suitable data processing and monitoring software on the PC.

The power distribution system of Sharda University is being upgraded and modernized into a smart microgrid. This microgrid will be centrally controlled using SCADA (supervisory control and data acquisition) concepts. The SCADA system will comprise a master terminal unit (MTU) located in a control room, RTUs (remote terminal units) located in four electric sub-stations in the campus, and a communication network linking all the RTUs with the MTU. The LAN (local area network) of the Sharda University will be used for MTU-RTU communication. Each RTU uses an Allen Bradley MicroLogix 1400 PLC (programmable logic controller). This paper presents the equipment details, single line diagram of electric sub-station-1, hardware design of the RTU being located in this sub-station, and ladder programs for the PLC of this RTU. The design also includes the RS-485 network used for communication between the RTU and SMFMs (smart multi-functional meters). Program for the PLC has been developed using ladder programming language on RS Logix 500 emulator.

Any organization, in any industry, is able to perform efficiently when the objectives of the organization and the resulting objectives of the roles in the organization are unambiguous, structured, and well communicated and understood. In the event of lack of such clarity, the organization often faces various complex inter-related problems, such as wasted employee expertise, unrealistic performance standards, lack of human resource planning, incorrect talent hiring, talent gaps, low employee motivation, and so on. This case study, therefore, tries to elaborate upon the important link of job analysis that serves as a strong foundation in an organization to prevent many human-resource-related problems.

In today’s scenario of cut throat competition, profitability is a major challenge for any industry. Survival of any industry and, in turn, survival of employee and employer depend on profit earned. Advanced technologies, skilled manpower, and quality raw materials can help to increase the production but at the same time it requires quite good amount of financial investment. Many industries are not in favor of extra investment due to their financial constraints. Industries are looking for the techniques to minimize rejection, rework, and optimum utilization of the available resources. In this paper, an attempt has been made to discuss such techniques.

Current business scenario is facing high volatility, uncertainty and rapid occurring changes. At the same time, organisations want to increase their productivity with lean manpower and more automation. It necessitates organisations to find out ways to tap full potential of their employees with better performances and efficiencies. Employees put their discretionary efforts on jobs, only if they feel happy and engaged at workplace. Employee engagement became a viable solution for organisations in their human resource practices to drive and empowering the changes. Based on the existing literature and available definitions, the aim of this study is to systematically understand the origin, evolution and various concepts of employee engagement in chronological order. Further, the study also tries to highlight the distinction of employee engagement with related concepts and makes a conscious effort to identify different variables, constructs and their relationship with employee engagement.

Powder Coating (PC) is an economical and widely preferred surface finishing process. Powder coating being a special process has its share of quality-related problems. In this work, an attempt is being made to integrate Genichi Taguchi’s Robust Design (RD) methodology and Dual Response Surface (DRS) methodology. The main purpose of this research is to determine the various parameters that govern the quality characteristics of PC process and to further optimize the process to achieve the products critical-to-quality specifications nearer to the target value by reducing the process variation. The process under study is an electrostatic PC process done using a corona gun. The response output is dry film thickness whereas the input variables are high voltage, current limitation, total air flow, and feed air. RD method was used to predict the significant effects of parametric levels on the PC process. Further the DRS methodology was utilized to optimize the PC process. Confirmation experiment revealed that with the adopted methodology, it is possible to maintain the CTQ parameter nearer to target value of 100 microns with an acceptable variation.

Data Mining (DM) is the extrication of inevitable, formerly unknown, and probably useful data from statistics. In the current scenario, data mining studies had been accomplished in many engineering disciplines. DM is the new dollar. It is the advance method of analyzing records from different parameters and abridgment into functional data. It allows users to investigate records from numerous parameters and categorizes it to summarize the relationships recognized. Technically, DM is the method of finding correlations or styles among multi-fields in big relational databases. Data Mining is doubtlessly beneficial record from records. It is the interpretation of big data in the required formats. It is the process through which different patterns are discovered from large data. New information is generated by the assessment of the pre-existing databases. This paper represents the significance and application of data mining tools and its techniques in different fields of civil engineering.

In recent times, the subject of imperfect quality items has received significant consideration from various researchers and supply chain members. There is not a single field of business that has not been affected by the imperfect quality items. In today’s competitive world, the management of imperfect items is the need of the hour for doing good business. In this paper, an extensive literature review is presented, based on the papers that considered the effect of imperfect quality items in inventory replenishment or production models. The analysis is based on 70 papers, which are sorted after a detailed study of 640 papers collected through the Scopus database. The findings and observations give state-of-the-art insights to scientists and business professionals. The present study discusses the major works in the field of inventory modeling driven by imperfect quality items starting from 2000 to 2020 (08 March 2020).

The advent of information technology and science has affected almost every sphere of human life and also businesses. While traditional quality management practices were relying upon data collection-based approach, the upcoming generation demands a predictive maintenance system for quality control to cater to the need for real-time digitally generated data. Unlike the traditional quality management approach, the focus of such predictive maintenance system will be more on defect prevention instead of defect reduction. Understanding the changing scenario of quality management practices is, therefore, relevant to examine in order to reflect upon the contemporary need of the market. Hence, this research paper is aimed to propose a continuous thread of discussion regarding the quality management environment from the initiation of quality management practices to the futuristic digital economy. Various dimensions of quality management practices over different generations are reviewed and discussed in this paper. In addition to this, the paper also proposes a possible future scenario of quality management practices with reference to different dimensions like changing customer need, economic conditions, business environment, technology, etc.

In the recent era, the manufacturing industries have been growing at a fast pace due to the increased demand of the present society. The government has also taken initiatives, and has lent a helping hand to the growth and development of these industries. Quality plays a vital role while not only dealing with the finished product but also while starting from procuring of quality raw materials, quality manpower, quality techniques, and quality process. Total Quality Management (TQM) programs initiated by different agencies are the medium for identifying the genuineness of implementing quality aspects in the above-mentioned factors. However, it is a vital issue to measure the assumption of the human resources towards the execution of TQM practices, adopted in the organizations. The view-point of the employee will certainly help to find out the effectiveness and the outcomes of the adopted TQM practices. The present research work was conducted at National Aluminum Company (NALCO), to examine the different outcomes of TQM practices by considering the responses of its employees. NALCO is one among the Navaratna Companies of our country. For research purpose, statistical tools like ANOVA, Regression test, etc. have been applied to evaluate the perception of the respondents to find out the contribution 0f TQM towards Organization Development.

Recent research presents Electro Magnetic Radiations (EMR) from the smartphone as a major risk contributor. The research study presents the critique of previous research work and professionals discussions exploring users’ perception and investigates the criteria impacting their inclination towards the usage of smartphones and services. Decision-making Trial and Evaluation Laboratory (DEMATEL) have been used to understand the cause-effect associations.

With the advent of computers and the rapidly increasing speed of technological advancements, data is now being collected at breakneck speeds. Accelerating increase in the amount and speed at which data is collected has been matched with advancements in data storage and data analysis technology. Crime data has been steadily collected for many decades now and can be used to analyze and predict novel and interesting patterns as they emerge. With this new technology, we can forecast crimes and crime rates to help law enforcements authorities. This paper uses predictive data analysis and forecasting to predict future crime rates.

In the area of software engineering, many principles for software development have been precisely defined. One of the most important principles is the principle of software readiness analysis. Software testing includes reliability measure in the design phase of new software and verification is the aim of keeping established reliability level in the phase of software use. In this paper software reliability from the point of software reliability parameters and readiness in open environment is considered. Entropy is simply the average (expected) amount of the information from the event. This manuscript introduces a new software readiness rating based on combination weighting methods. The determination of combination weights relies on experiments, expert judgments, and statistical calculations collectively. AHP technique is used according to judgment to verify the meaning of the degree of precedent. Entropy weighting method is associated with identifying objective weights to avoid weight subjectivity. The manuscript follows combination weighting means to obtain the assessment criterion combination weights. Next, the combination weights are exercised to get the trustworthiness degree of software. The result enriched decision-makers with more findings and results demonstrate the robustness of the offered assessment approach.

Machine learning is a subset of man-made brainpower (AI) that enables the framework to the system to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. This exploration is gone for seeing if age and blood cholesterol has any impact on the pulse. Along these lines, if there exist any changes in their body system, it can without quite a bit of a stretch be taken after and be supervised. Government at each level should hone their subjects on the need to decrease any sustenance that can manufacture the level of blood cholesterol in the body by methods for an oily sustenance. It is doubtlessly knowing from this examination that the level of circulatory strain scrutinizing in the body can be influenced by age and blood cholesterol, thusly it is fitting for every individual beyond 18 years old to go for restorative enlistment at any rate once in 3 months.

OSS development is remarkably different from paradigms of conventional software engineering. Although reliability modelling is an area of research since 1970s but reliability analysis for OSS is relatively recent. This paper tries to establish the reliability growth phenomenon for such software in terms of its usage in operational environment. This study further investigates the variation between number of faults detected and corrected as Fault Reduction Factor (FRF) and thus justifies its importance in reliability modelling under imperfect debugging. Three different trends of FRF are discussed in terms of number of users adopting the software with time. The proposed NHPP models for OSS are tested on two real-world fault datasets, namely, GNOME 2.0 and Firefox 3.0 and it is empirically deduced that the model precisely describes the failure process for OSS and thus can be adopted and further extended for reliability characterization.

In the present paper, non-Markovian queue with bulk arrival, server breakdown, and randomized vacation policy is studied. It is considered that the balking phenomenon may arise in the units while joining the system in distinct server’s states. The units who find the service unsatisfactory may rejoin the queue as feedback customer. There is a provision of essential/optional service to all arriving units joining the system. Moreover, the server may fail while providing essential or optional service. The broken-down server can be recovered with the aid of repair facility, but delay in repair may occur due to any unforeseen circumstance. The server is permitted to avail at most l vacations under randomized vacation policy. Supplementary variable technique is employed for evaluating the various performance characteristics of the system. Cost function is developed and numerical illustrations are presented for sensitivity analysis of concerned model.

Availability of smartphones at affordable prices and efficient data packs provide by telecom industry has popularized the concept of mobile commerce. This has forced the firms to incorporate mobile application in their business operations. The study proposes a conceptual model considering six independent variables, namely, perceived benefits, organizational compatibility, technical incompatibility, complexity, top management support, and external pressure; and assessing their impact on the timing of mobile application with respect to firms. A survey of 204 small and medium firms in India was used to validate the model through multivariate analysis of variance (MANOVA). The results show acceptance of all the hypotheses and the influence of these determinants are more on earlier adopters rather than late adopters. The paper discusses the findings for practitioners. Future studies may be extended to electronic commerce framework.

Estimation of the economy of a developed country is affected by the performance of share market of the country. Predicting stock prices has been a major research challenge. There is an influx of various approaches that attempt to address this problem. Stock markets generate massive data, almost as much as social media, which can be used to identify current trends, accordingly price of stocks can be predicted thereby yielding profits to those invested. Existing stock market models are complex. Stock prices are volatile in nature, the determination of which is difficult to estimate as it is influenced by numerous parameters even small news can prompt an increase or decrease in price. Clustering refers to grouping of similar objects in order to form a cluster. Many surveys have been done on clustering that shows the worthy enactment on datasets for cluster formation such as K-means and fuzzy c-means. Similarly, the use of machine learning algorithms can test and train the data to find out the best possible way to forecast the values of data and predict where the stock prices will move toward. We conducted a study and found that before applying machine learning algorithms we must apply clustering to the dataset to make it tailor ready for the predictive algorithms. This paper surveys different approaches of clustering and presents a better-formed way to apply clustering to stock prices.

With the increasing market pressure to incorporate sustainability, manufacturing organizations need to develop innovative strategies to achieve competitiveness. This has led to a number of manufacturing enterprises to explore the possibility of adoption of Lean, Green, and Resilient (LGR) practices into their operations. The major problems with these strategies are the incorporation and identification of major LGR practices which should be focused by the top management in enhancing the overall performance of the automotive supply chains. To address this issue, the present study proposes a framework for analyzing these practices by evaluating and prioritizing various LGR paradigms, using a new multi-criteria technique of best–worst method (BWM). The results of this study will aid the practitioners and decision-makers in deciding the major focus to achieve their target and stand ahead of their competitions. A case of the Indian automotive industry is used for validating the present study.

With an expected world population growth of over 6 billion by 2050, it is anticipated that 70% of people will live in cities which will exert pressure on demand for resources and infrastructure; hence, it has become imperative to maintain a balance between overall energy generation and consumption. Henceforth, for the same, society needs a greener future while the usage of non-renewable resources, raw material will be reduced with the help of ICT (Information and Communication Technology). This has surfaced way for Green Internet of Things (G-IoT) which will create our society a greener and more sustainable place to live in. This paper emphasizes how G-IoT can help in utilizing the resources and significantly making cities safer, smarter and sustainable.

The undertaking manages the amassing of the “movement exhaust cooling structure.” We comprehend that an I.C. motor has an effectiveness of around 35–40%, which deduces for all intents and purposes 33% of the centrality in the fuel changes over into significant work, and around 60–65% gets squandered in nature. In which around 28–30% gets perplexed by cooling dihydrogen monoxide and oil hardships, around 30–32% lose all competency to ken east from west a similar number of fume gases and staying by else (radiation and more). In this A.C. structure, a physicochemical procedure substitutes the mechanical technique of the vapor compression system by using the importance of warmth in lieu of interesting mechanical work. The shine required for working this imperceptibly cooling framework gets got from that which gets squandered into the air from an I.C. motor.

Medical industry, though various researches over decades, has figured out breast cancer to be one of the most common diseases in women. Studies have shown that every eighth woman is suffering from it. This research has been done with the intent to predict the occurrence of breast cancer with the help of various machine learning algorithms. For the analysis purpose, three different datasets were utilized, Wisconsin Breast Cancer (WBC) dataset, Wisconsin Prognosis Breast Cancer (WPBC), and Wisconsin Diagnosis Breast Cancer (WDBC) dataset. Also, the classification used for these datasets was done using Hierarchical Decision Tree (HIDER), PSO, and Genetic Algorithm for Neural Network (GANN). The results were compared based on the accuracy achieved was found that HIDER showed the best results with WBC Dataset, while GANN was the most accurate one with WDBC and WPBC datasets. This research would help organizations, working in the health sector, especially in cancer studies, to predict breast cancer accuracy with accuracy and help cure it in the early stages.

Early prediction of software maintainability is a potential solution to save the cost of software maintenance which is almost 60–70% of the software development cost. Various machine learning (ML) techniques have been investigated in literature to predict software maintainability. The performance of well-established ML techniques degrades considerably if the training dataset contains irregularities in the form of data skewness or class imbalance. This study empirically investigates the performance of two types of ensembles for class problem, namely Bagging-based ensembles and Boosting-based ensembles. The study also compares the predictive performance of proposed ensembles with classic ensembles: Bagging and AdaBoost. The results of the empirical analysis favor the use of proposed ensembles to develop effective Software Maintainability Prediction (SMP) models from imbalanced datasets.

Tuberculosis (TB) is caused by Mycobacterium tuberculosis (Mtb) bacteria. This disease is still a worldwide threat as it remains one of the leading causes of death in the world. Almost one-third of the world population is still infected by the Mtb bacteria either causing Latent TB or Active TB. In spite of huge progress in medical sciences with the latest prevention and treatment techniques, it is still a big threat to human beings. Possibly, there are several reasons behind the existence and survival of these Mtb bacteria depending upon the standard of living or the medical history of the person. Different people are treated in a different manner according to their conditions, which is strictly followed by the norms fixed by the government. Till date, there is limited research in the field of modeling clinical diagnosis path in TB; hence, it requires a full interplay between diagnosis and treatment. This motivated us to use a Discrete Event System (DES), Petri net, to model the diagnostic path as it helps to understand the structural and behavioral properties of the process. In this chapter, we are modeling the clinical diagnosis path of TB considering some important aspects within the process with the use of a graphical tool, Petri net. The modeling and analysis of the diagnostic path help to find and validate the longest and shortest methods to treat tuberculosis.

In steam turbine-based power plants, it is important, rather essential, to present an optimal cost-effective design for feedwater exchangers. The mostly faced problem that attracts the attention in this context is the worldwide issue of cost saving. For that, several techniques can hereby be resorted including the technique of dynamic programming. The relevant methodology of this work was thereby based on the concept of optimization and thus implied empirically setting optimal areas of feedwater exchangers as a viable pertinent means. The purpose of this work is to exhibit different application aspects of the concept of dynamic programming in order to optimize a specific solution in area distribution. This has practically involved distributing 1000m2 total area of heat exchange in a power plant with a four bleeding points steam turbine. The said area has therefore been empirically distributed as four subsequent parts to form four concurrent chambers of heat exchange; each chamber is coupled to an individual steam bleeding point of the turbine. Maximum saving of heat at the respective boiler has whereby been reached yielding to minimize the cost of steam used. Saving of 1.186 Cent/sec has thus been attained through distributing the 1000m2 total area of the four consecutive heaters as 200m2, 200m2, 300m2, and 300m2, respectively. This has led to an empirical formula for such case stating that the total area ‘A’ of the four consecutive heaters needs to be allocated as 0.2A, 0.2A, 0.3A, 0.3A, respectively. Because of the strong need generally arisen for optimal solutions, the technique used here seems to be quite applicable in power plants engineering design, in their underlying scientific experiments besides in the consequent business decision-making.

Clearing of forests and burning of oil-origin fuels have enhanced the Earth’s natural greenhouse effect on our planet. The motivation behind this work is to control such an effect that is widely deemed to be crucial in maintaining life on earth. This effect causes global warming besides increasing environmental pollution due to the harmful carbon dioxide (CO2) emission. Remarkable applications of geospatial technologies have thus been clearly noticed in the relevant field of remote sensing. Such application has been justifiably witnessed in the rapid spatiotemporal monitoring of forest resources. This in turn has participated in the formulation of substantial strategy outlines to set the sustainable management of these forests. Many countries of the world are now heavily working to protect trees and conserve forests. Satellite imaging can hereby be a very efficient way for this purpose. The objective of this paper is to propose a model that uses satellite imaging with Google Earth application to protect forests from being cleared. The pertinent methodology of this model is conceptually based on the method of edge detection. The paper discusses the issues of satellite image acquisition and processing besides the international REDD+ forest conservation program for such protection. The governmental role in providing resources to protect this natural resource has further been highlighted. Using satellite imagery for the conservation of forest and protecting trees from being cut, the anticipated outcome of the proposed model is envisaged to achieve better results that are much “faster” and more “accurate” than the other available approaches. In addition to forests’ conservation, the application field of this work encompasses diverse issues like overcoming environmental and climate changes, diminishing deforestation, and defeating desertification.

This paper is an effort to explore and identify the scope of improvement in the service sector by utilizing established principles of Theoria Resheneyva Isobretatelskehuh Zadach (TRIZ) combined with Lean Six Sigma methodology. Service sector being the leading sector in 201 economies/countries contributes 63 percent of total global wealth. Banking and financial sector being one of the fastest growing sub‐sectors within the services sector was considered for this research. Lean Six Sigma principles have proven their capability in the manufacturing sector by reducing cost and improving quality. Here we have attempted to apply these principles in the service sector to analyze customer needs and deliver quality solutions in less time. The use case of opening an account in a bank by a customer is considered to measure the impact of TRIZ methodology intertwined with LSS. Hence suggest a process framework to be later extended to other use cases of financial or other service sector industries. It is a potential framework that will help the service sector leaders to increase their service efficiency with reduced cost and increased customer satisfaction.

The applications of information and communication technologies (ICT) for sustainable supply chain is being studied for more than one decade by operations management and information systems scholars. Much investment was made to apply new ICT technologies for western industrial parks to recover their competitiveness, in initiatives like Smart Manufacturing and Industrie 4.0, and improvements in the sustainability of operations are expected by these programs developers and researchers. How the sustainability can be impacted by these technologies, however, is not clear yet. This research uses the systematic review to map the previous research about ICT for sustainable supply chain and analyze it in the Smart Manufacturing perspective. The ICT literature presented some technology applications that are being included in Smart Manufacturing programs and has already some empirical applications that can guide and evaluate the real impact of them. As a result, the proposals of both types of research are very similar, and Smart Manufacturing can be seen as an evolution of previous concepts.

Agile testing is not a single phase testing, commonly it is performed till the end of software development life cycle. An agile testing team is responsible for the error free quality product. So the testers in agile team should be dynamic in nature to adopt the abrupt changes in system; today’s testing industry is striving for rapid software delivery with keeping in view the changing customer requirements. Agile testing approach has evolved to address the necessities of dynamic environment in which traditional approaches were failing to cope with. It has the cutting edge like fast release and minimum documentation which results in maximizing speed and profit. However, the most difficult task is to make the decision such that the agile testing method should be chosen according to the given requirements of a particular project. In the absence of empirical work, an approach is proposed using the world widely accepted methods as Analytic Hierarchy Process (AHP). The calculated results from multi criteria decision making and the final result is evaluated using the rank aggregation methods. This work would prove to be a pivotal point in the field of agile testing as it includes these empirical methods which provide the much awaited authenticity and reliability, which sometimes is questioned in case of testing approach.