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class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 3407</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: business analytics</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3407</span> An Empirical Investigation of Big Data Analytics: The Financial Performance of Users versus Vendors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Evisa%20Mitrou">Evisa Mitrou</a>, <a href="https://publications.waset.org/abstracts/search?q=Nicholas%20Tsitsianis"> Nicholas Tsitsianis</a>, <a href="https://publications.waset.org/abstracts/search?q=Supriya%20Shinde"> Supriya Shinde</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the age of digitisation and globalisation, businesses have shifted online and are investing in big data analytics (BDA) to respond to changing market conditions and sustain their performance. Our study shifts the focus from the adoption of BDA to the impact of BDA on financial performance. We explore the financial performance of both BDA-vendors (business-to-business) and BDA-clients (business-to-customer). We distinguish between the five BDA-technologies (big-data-as-a-service (BDaaS), descriptive, diagnostic, predictive, and prescriptive analytics) and discuss them individually. Further, we use four perspectives (internal business process, learning and growth, customer, and finance) and discuss the significance of how each of the five BDA-technologies affects the performance measures of these four perspectives. We also present the analysis of employee engagement, average turnover, average net income, and average net assets for BDA-clients and BDA-vendors. Our study also explores the effect of the COVID-19 pandemic on business continuity for both BDA-vendors and BDA-clients. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BDA-clients" title="BDA-clients">BDA-clients</a>, <a href="https://publications.waset.org/abstracts/search?q=BDA-vendors" title=" BDA-vendors"> BDA-vendors</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20analytics" title=" big data analytics"> big data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20performance" title=" financial performance"> financial performance</a> </p> <a href="https://publications.waset.org/abstracts/152976/an-empirical-investigation-of-big-data-analytics-the-financial-performance-of-users-versus-vendors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152976.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">129</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3406</span> Integrating Service Learning into a Business Analytics Course: A Comparative Investigation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gokhan%20Egilmez">Gokhan Egilmez</a>, <a href="https://publications.waset.org/abstracts/search?q=Erika%20Hatfield"> Erika Hatfield</a>, <a href="https://publications.waset.org/abstracts/search?q=Julie%20Turner"> Julie Turner</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, we investigated the impacts of service-learning integration on an undergraduate level business analytics course from multiple perspectives, including academic proficiency, community awareness, engagement, social responsibility, and reflection. We assessed the impact of the service-learning experience by using a survey developed primarily based on the literature review and secondarily on an ad hoc group of researchers. Then, we implemented the survey in two sections, where one of the sections was a control group. We compared the results of the empirical survey visually and statistically. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20analytics" title="business analytics">business analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=service%20learning" title=" service learning"> service learning</a>, <a href="https://publications.waset.org/abstracts/search?q=experiential%20education" title=" experiential education"> experiential education</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20analysis" title=" statistical analysis"> statistical analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=survey%20research" title=" survey research"> survey research</a> </p> <a href="https://publications.waset.org/abstracts/151733/integrating-service-learning-into-a-business-analytics-course-a-comparative-investigation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151733.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">118</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3405</span> A Case Study of Business Analytic Use in European Football: Analysis and Implications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20C.%20Schloesser">M. C. Schloesser</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this paper is to explore the use and impact of business analytics in European football. Despite good evidence from other major sports leagues, research on this topic in Europe is currently very scarce. This research relies on expert interviews on the use and objective of business analytics. Along with revenue data over 16 seasons spanning from 2004/05 to 2019/20 from Manchester City FC, we conducted a time series analysis to detect a structural breakpoint on the different revenue streams, i.e., sponsorship and ticketing, after analytical tools have been implemented. We not only find that business analytics have indeed been applied at Manchester City FC and revenue increase is the main objective of their utilization but also that business analytics is indeed a good means to increase revenues if applied sufficiently. We can thereby support findings from other sports leagues. Consequently, professional sports organizations are advised to apply business analytics if they aim to increase revenues. This research has shown that analytical practices do, in fact, support revenue growth and help to work more efficiently. As the knowledge of analytical practices is very confidential and not publicly available, we had to select one club as a case study which can be considered a research limitation. Other practitioners should explore other clubs or leagues. Further, there are other factors that can lead to increased revenues that need to be considered. Additionally, sports organizations need resources to be able to apply and utilize business analytics. Consequently, findings might only apply to the top teams of the European football leagues. Nonetheless, this paper combines insights and results on usage, objectives, and impact of business analytics in European professional football and thereby fills a current research gap. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20analytics" title="business analytics">business analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=expert%20interviews" title=" expert interviews"> expert interviews</a>, <a href="https://publications.waset.org/abstracts/search?q=revenue%20management" title=" revenue management"> revenue management</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20analysis" title=" time series analysis"> time series analysis</a> </p> <a href="https://publications.waset.org/abstracts/167548/a-case-study-of-business-analytic-use-in-european-football-analysis-and-implications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167548.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">82</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3404</span> A Study on Big Data Analytics, Applications and Challenges</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chhavi%20Rana">Chhavi Rana</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, Healthcare, and business intelligence contain voluminous and incremental data, which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organization's decision-making strategy can be enhanced using big data analytics and applying different machine learning techniques and statistical tools on such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates on various frameworks in the process of Analysis using different machine-learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20analytics" title=" big data analytics"> big data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=review" title=" review"> review</a> </p> <a href="https://publications.waset.org/abstracts/162947/a-study-on-big-data-analytics-applications-and-challenges" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162947.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">91</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3403</span> A Study on Big Data Analytics, Applications, and Challenges</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chhavi%20Rana">Chhavi Rana</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, healthcare, and business intelligence contain voluminous and incremental data which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organisation decision-making strategy can be enhanced by using big data analytics and applying different machine learning techniques and statistical tools to such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates various frameworks in the process of analysis using different machine learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20analytics" title=" big data analytics"> big data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=review" title=" review"> review</a> </p> <a href="https://publications.waset.org/abstracts/150593/a-study-on-big-data-analytics-applications-and-challenges" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150593.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">101</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3402</span> Reference Architecture for Intelligent Enterprise Solutions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shankar%20Kambhampaty">Shankar Kambhampaty</a>, <a href="https://publications.waset.org/abstracts/search?q=Harish%20Rohan%20Kambhampaty"> Harish Rohan Kambhampaty</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data in IT systems in enterprises has been growing at a phenomenal pace. This has provided opportunities to run analytics to gather intelligence on key business parameters that enable them to provide better products and services to customers. While there are several artificial intelligence (AI/ML) and business intelligence (BI) tools and technologies available in the marketplace to run analytics, there is a need for an integrated view when developing intelligent solutions in enterprises. This paper progressively elaborates a reference model for enterprise solutions, builds an integrated view of data, information, and intelligence components, and presents a reference architecture for intelligent enterprise solutions. Finally, it applies the reference architecture to an insurance organization. The reference architecture is the outcome of experience and insights gathered from developing intelligent solutions for several organizations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=architecture" title="architecture">architecture</a>, <a href="https://publications.waset.org/abstracts/search?q=model" title=" model"> model</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligence" title=" intelligence"> intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20intelligence" title=" business intelligence"> business intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=AI" title=" AI"> AI</a>, <a href="https://publications.waset.org/abstracts/search?q=BI" title=" BI"> BI</a>, <a href="https://publications.waset.org/abstracts/search?q=ML" title=" ML"> ML</a>, <a href="https://publications.waset.org/abstracts/search?q=analytics" title=" analytics"> analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=enterprise" title=" enterprise"> enterprise</a> </p> <a href="https://publications.waset.org/abstracts/132436/reference-architecture-for-intelligent-enterprise-solutions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132436.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">147</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3401</span> Leveraging Learning Analytics to Inform Learning Design in Higher Education</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mingming%20Jiang">Mingming Jiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This literature review aims to offer an overview of existing research on learning analytics and learning design, the alignment between the two, and how learning analytics has been leveraged to inform learning design in higher education. Current research suggests a need to create more alignment and integration between learning analytics and learning design in order to not only ground learning analytics on learning sciences but also enable data-driven decisions in learning design to improve learning outcomes. In addition, multiple conceptual frameworks have been proposed to enhance the synergy and alignment between learning analytics and learning design. Future research should explore this synergy further in the unique context of higher education, identifying learning analytics metrics in higher education that can offer insight into learning processes, evaluating the effect of learning analytics outcomes on learning design decision-making in higher education, and designing learning environments in higher education that make the capturing and deployment of learning analytics outcomes more efficient. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=learning%20analytics" title="learning analytics">learning analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20design" title=" learning design"> learning design</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20in%20higher%20education" title=" big data in higher education"> big data in higher education</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20learning%20environments" title=" online learning environments"> online learning environments</a> </p> <a href="https://publications.waset.org/abstracts/149822/leveraging-learning-analytics-to-inform-learning-design-in-higher-education" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149822.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">184</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3400</span> Decoding Generational Shifts through Marketing Analytics and Big Data: Insights, Challenges, and Strategic Pathways</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khansa%20Zaman">Khansa Zaman</a>, <a href="https://publications.waset.org/abstracts/search?q=Amer%20Riaz%20Qureshi"> Amer Riaz Qureshi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of marketing analytics and data-driven tools to decode emerging generations’ preferences and value shifts is key to success. Increased sense of customer empowerment, consumer awareness, switch from traditional values and advanced technologies are some of the key factors that have played instrumental roles in this transformation. Decoding these generational preferences is essential for companies to achieve competitive advantage and sustainability. Recent research has paid attention to the use of marketing analytics to improve business performance, product success, and agility. However, understanding the role of marketing analytics in generational shifts needs further investigation. Thus, this article aims to explore the role of marketing analytics and big data in identifying, interpreting and responding to these generational shifts by highlighting the challenges and providing strategic solutions. This paper also provides a conceptual framework to understand the factors behind these shifts and outcomes of employing marketing analytics coupled with big data. Further, the outcomes for the marketers, researchers and policymakers have also been discussed that provide a strategic pathway to strike a balance between leveraging the power of data and the ethical concerns of stakeholders. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=marketing%20analytics" title=" marketing analytics"> marketing analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=generational%20shifts" title=" generational shifts"> generational shifts</a>, <a href="https://publications.waset.org/abstracts/search?q=generational%20values" title=" generational values"> generational values</a>, <a href="https://publications.waset.org/abstracts/search?q=strategies" title=" strategies"> strategies</a>, <a href="https://publications.waset.org/abstracts/search?q=marketing" title=" marketing"> marketing</a> </p> <a href="https://publications.waset.org/abstracts/198227/decoding-generational-shifts-through-marketing-analytics-and-big-data-insights-challenges-and-strategic-pathways" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/198227.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">14</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3399</span> Data Analytics in Energy Management</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sanjivrao%20Katakam">Sanjivrao Katakam</a>, <a href="https://publications.waset.org/abstracts/search?q=Thanumoorthi%20I."> Thanumoorthi I.</a>, <a href="https://publications.waset.org/abstracts/search?q=Antony%20Gerald"> Antony Gerald</a>, <a href="https://publications.waset.org/abstracts/search?q=Ratan%20Kulkarni"> Ratan Kulkarni</a>, <a href="https://publications.waset.org/abstracts/search?q=Shaju%20Nair"> Shaju Nair</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With increasing energy costs and its impact on the business, sustainability today has evolved from a social expectation to an economic imperative. Therefore, finding methods to reduce cost has become a critical directive for Industry leaders. Effective energy management is the only way to cut costs. However, Energy Management has been a challenge because it requires a change in old habits and legacy systems followed for decades. Today exorbitant levels of energy and operational data is being captured and stored by Industries, but they are unable to convert these structured and unstructured data sets into meaningful business intelligence. It must be noted that for quick decisions, organizations must learn to cope with large volumes of operational data in different formats. Energy analytics not only helps in extracting inferences from these data sets, but also is instrumental in transformation from old approaches of energy management to new. This in turn assists in effective decision making for implementation. It is the requirement of organizations to have an established corporate strategy for reducing operational costs through visibility and optimization of energy usage. Energy analytics play a key role in optimization of operations. The paper describes how today energy data analytics is extensively used in different scenarios like reducing operational costs, predicting energy demands, optimizing network efficiency, asset maintenance, improving customer insights and device data insights. The paper also highlights how analytics helps transform insights obtained from energy data into sustainable solutions. The paper utilizes data from an array of segments such as retail, transportation, and water sectors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20analytics" title="energy analytics">energy analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20management" title=" energy management"> energy management</a>, <a href="https://publications.waset.org/abstracts/search?q=operational%20data" title=" operational data"> operational data</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20intelligence" title=" business intelligence"> business intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/8716/data-analytics-in-energy-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8716.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">370</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3398</span> Analytics Capabilities and Employee Role Stressors: Implications for Organizational Performance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Divine%20Agozie">Divine Agozie</a>, <a href="https://publications.waset.org/abstracts/search?q=Muesser%20Nat"> Muesser Nat</a>, <a href="https://publications.waset.org/abstracts/search?q=Eric%20Afful-Dadzie"> Eric Afful-Dadzie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This examination attempts an analysis of the effect of business intelligence and analytics (BI&A) capabilities on organizational role stressors and the implications of such an effect on performance. Two hundred twenty-eight responses gathered from seventy-six firms across Ghana were analyzed using the Partial Least Squares Structural Equation Modelling (PLS-SEM) approach to validate the hypothesized relationships identified in the research model. Findings suggest both endogenous and exogenous dependencies of the sensing capability on the multiple role requirements of personnel. Further, transforming capability increases role conflict, whereas driving capability of BI&A systems impacts role conflict and role ambiguity. This study poses many practical insights to firms seeking to acquire analytics capabilities to drive performance and data-driven decision-making. It is important for firms to consider balancing role changes and task requirements before implementing and post-implementation stages of BI&A innovations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20intelligence%20and%20analytics" title="business intelligence and analytics">business intelligence and analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20capabilities%20view" title=" dynamic capabilities view"> dynamic capabilities view</a>, <a href="https://publications.waset.org/abstracts/search?q=organizational%20stressors" title=" organizational stressors"> organizational stressors</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20equation%20modelling" title=" structural equation modelling"> structural equation modelling</a> </p> <a href="https://publications.waset.org/abstracts/160565/analytics-capabilities-and-employee-role-stressors-implications-for-organizational-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160565.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">116</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3397</span> Organizational Culture, Support, and Competencies for Business Analytics: A Mixed-Methods Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arkhe%20M.%20Pacis">Arkhe M. Pacis</a>, <a href="https://publications.waset.org/abstracts/search?q=Tsung-Yu%20Tsai"> Tsung-Yu Tsai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research investigates the essential elements that facilitate the effective execution and assimilation of Business Analytics (BA), with a particular emphasis on organizational culture, support structures, and the requisite skills and tools for roles associated with BA. In a mixed-methods approach, the study amalgamates a Systematic Literature Review (SLR) with a content analysis of job postings derived from web scraping. The methodological framework is being developed, focusing the SLR on peer-reviewed articles and industry reports published in the last ten years and addressing themes related to BA adoption and organizational preparedness. The web scraping process is actively taking place, sourcing data from platforms like LinkedIn and Indeed to evaluate job postings for positions like Business Analyst and Data Scientist. The data extracted encompasses essential skills, desirable tools, and qualifications, which will be subject to both quantitative and qualitative analysis. Python libraries extract and preprocess data, including Beautiful Soup, Scrapy, and Pandas. Initial findings are anticipated to synthesize thematic insights from the SLR with industry patterns discerned through web scraping, organized within the Technology, Organization, and Environment (TOE) framework. The results aim to furnish actionable recommendations for academia and industry practitioners, underscoring the cultural foundations, organizational strategies, and skill sets paramount for BA initiatives' success. This study enhances academic curricula and informs industry practices, ensuring alignment with BA adoption and implementation requirements. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20analytics" title="business analytics">business analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20analytics%20implementation" title=" business analytics implementation"> business analytics implementation</a>, <a href="https://publications.waset.org/abstracts/search?q=systematic%20literature%20review" title=" systematic literature review"> systematic literature review</a>, <a href="https://publications.waset.org/abstracts/search?q=TOE" title=" TOE"> TOE</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20scraping%20methodology" title=" web scraping methodology"> web scraping methodology</a> </p> <a href="https://publications.waset.org/abstracts/198329/organizational-culture-support-and-competencies-for-business-analytics-a-mixed-methods-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/198329.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">11</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3396</span> Data-driven Decision-Making in Digital Entrepreneurship</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abeba%20Nigussie%20Turi">Abeba Nigussie Turi</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiangming%20Samuel%20Li"> Xiangming Samuel Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data-driven business models are more typical for established businesses than early-stage startups that strive to penetrate a market. This paper provided an extensive discussion on the principles of data analytics for early-stage digital entrepreneurial businesses. Here, we developed data-driven decision-making (DDDM) framework that applies to startups prone to multifaceted barriers in the form of poor data access, technical and financial constraints, to state some. The startup DDDM framework proposed in this paper is novel in its form encompassing startup data analytics enablers and metrics aligning with startups' business models ranging from customer-centric product development to servitization which is the future of modern digital entrepreneurship. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=startup%20data%20analytics" title="startup data analytics">startup data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=data-driven%20decision-making" title=" data-driven decision-making"> data-driven decision-making</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20acquisition" title=" data acquisition"> data acquisition</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20generation" title=" data generation"> data generation</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20entrepreneurship" title=" digital entrepreneurship"> digital entrepreneurship</a> </p> <a href="https://publications.waset.org/abstracts/145802/data-driven-decision-making-in-digital-entrepreneurship" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145802.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">334</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3395</span> Exploring the Intersection of Accounting, Business, and Economics: Bridging Theory and Practice for Sustainable Growth</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Stephen%20Acheampong%20Amoafoh">Stephen Acheampong Amoafoh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In today's dynamic economic landscape, businesses face multifaceted challenges that demand strategic foresight and informed decision-making. This abstract explores the pivotal role of financial analytics in driving business performance amidst evolving market conditions. By integrating accounting principles with economic insights, organizations can harness the power of data-driven strategies to optimize resource allocation, mitigate risks, and capitalize on emerging opportunities. This presentation will delve into the practical applications of financial analytics across various sectors, highlighting case studies and empirical evidence to underscore its efficacy in enhancing operational efficiency and fostering sustainable growth. From predictive modeling to performance benchmarking, attendees will gain invaluable insights into leveraging advanced analytics tools to drive profitability, streamline processes, and adapt to changing market dynamics. Moreover, this abstract will address the ethical considerations inherent in financial analytics, emphasizing the importance of transparency, integrity, and accountability in data-driven decision-making. By fostering a culture of ethical conduct and responsible stewardship, organizations can build trust with stakeholders and safeguard their long-term viability in an increasingly interconnected global economy. Ultimately, this abstract aims to stimulate dialogue and collaboration among scholars, practitioners, and policymakers, fostering knowledge exchange and innovation in the realms of accounting, business, and economics. Through interdisciplinary insights and actionable recommendations, participants will be equipped to navigate the complexities of today's business environment and seize opportunities for sustainable success. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=financial%20analytics" title="financial analytics">financial analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20performance" title=" business performance"> business performance</a>, <a href="https://publications.waset.org/abstracts/search?q=data-driven%20strategies" title=" data-driven strategies"> data-driven strategies</a>, <a href="https://publications.waset.org/abstracts/search?q=sustainable%20growth" title=" sustainable growth"> sustainable growth</a> </p> <a href="https://publications.waset.org/abstracts/185080/exploring-the-intersection-of-accounting-business-and-economics-bridging-theory-and-practice-for-sustainable-growth" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185080.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">60</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3394</span> A Machine Learning Decision Support Framework for Industrial Engineering Purposes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anli%20Du%20Preez">Anli Du Preez</a>, <a href="https://publications.waset.org/abstracts/search?q=James%20Bekker"> James Bekker</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data is currently one of the most critical and influential emerging technologies. However, the true potential of data is yet to be exploited since, currently, about 1% of generated data are ever actually analyzed for value creation. There is a data gap where data is not explored due to the lack of data analytics infrastructure and the required data analytics skills. This study developed a decision support framework for data analytics by following Jabareen’s framework development methodology. The study focused on machine learning algorithms, which is a subset of data analytics. The developed framework is designed to assist data analysts with little experience, in choosing the appropriate machine learning algorithm given the purpose of their application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Data%20analytics" title="Data analytics">Data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=Industrial%20engineering" title=" Industrial engineering"> Industrial engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=Machine%20learning" title=" Machine learning"> Machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=Value%20creation" title=" Value creation"> Value creation</a> </p> <a href="https://publications.waset.org/abstracts/116912/a-machine-learning-decision-support-framework-for-industrial-engineering-purposes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116912.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">180</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3393</span> Applications of Big Data in Education</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Faisal%20Kalota">Faisal Kalota</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Big Data and analytics have gained a huge momentum in recent years. Big Data feeds into the field of Learning Analytics (LA) that may allow academic institutions to better understand the learners’ needs and proactively address them. Hence, it is important to have an understanding of Big Data and its applications. The purpose of this descriptive paper is to provide an overview of Big Data, the technologies used in Big Data, and some of the applications of Big Data in education. Additionally, it discusses some of the concerns related to Big Data and current research trends. While Big Data can provide big benefits, it is important that institutions understand their own needs, infrastructure, resources, and limitation before jumping on the Big Data bandwagon. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20analytics" title=" learning analytics"> learning analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=analytics" title=" analytics"> analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data%20in%20education" title=" big data in education"> big data in education</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadoop" title=" Hadoop "> Hadoop </a> </p> <a href="https://publications.waset.org/abstracts/27525/applications-of-big-data-in-education" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27525.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">435</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3392</span> Analysing Competitive Advantage of IoT and Data Analytics in Smart City Context</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Petra%20Hofmann">Petra Hofmann</a>, <a href="https://publications.waset.org/abstracts/search?q=Dana%20Koniel"> Dana Koniel</a>, <a href="https://publications.waset.org/abstracts/search?q=Jussi%20Luukkanen"> Jussi Luukkanen</a>, <a href="https://publications.waset.org/abstracts/search?q=Walter%20Nieminen"> Walter Nieminen</a>, <a href="https://publications.waset.org/abstracts/search?q=Lea%20Hannola"> Lea Hannola</a>, <a href="https://publications.waset.org/abstracts/search?q=Ilkka%20Donoghue"> Ilkka Donoghue</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Covid-19 pandemic forced people to isolate and become physically less connected. The pandemic has not only reshaped people’s behaviours and needs but also accelerated digital transformation (DT). DT of cities has become an imperative with the outlook of converting them into smart cities in the future. Embedding digital infrastructure and smart city initiatives as part of normal design, construction, and operation of cities provides a unique opportunity to improve the connection between people. The Internet of Things (IoT) is an emerging technology and one of the drivers in DT. It has disrupted many industries by introducing different services and business models, and IoT solutions are being applied in multiple fields, including smart cities. As IoT and data are fundamentally linked together, IoT solutions can only create value if the data generated by the IoT devices is analysed properly. Extracting relevant conclusions and actionable insights by using established techniques, data analytics contributes significantly to the growth and success of IoT applications and investments. Companies must grasp DT and be prepared to redesign their offerings and business models to remain competitive in today’s marketplace. As there are many IoT solutions available today, the amount of data is tremendous. The challenge for companies is to understand what solutions to focus on and how to prioritise and which data to differentiate from the competition. This paper explains how IoT and data analytics can impact competitive advantage and how companies should approach IoT and data analytics to translate them into concrete offerings and solutions in the smart city context. The study was carried out as a qualitative, literature-based research. A case study is provided to validate the preservation of company’s competitive advantage through smart city solutions. The results of the research contribution provide insights into the different factors and considerations related to creating competitive advantage through IoT and data analytics deployment in the smart city context. Furthermore, this paper proposes a framework that merges the factors and considerations with examples of offerings and solutions in smart cities. The data collected through IoT devices, and the intelligent use of it, can create competitive advantage to companies operating in smart city business. Companies should take into consideration the five forces of competition that shape industries and pay attention to the technological, organisational, and external contexts which define factors for consideration of competitive advantages in the field of IoT and data analytics. Companies that can utilise these key assets in their businesses will most likely conquer the markets and have a strong foothold in the smart city business. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20analytics" title="data analytics">data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20cities" title=" smart cities"> smart cities</a>, <a href="https://publications.waset.org/abstracts/search?q=competitive%20advantage" title=" competitive advantage"> competitive advantage</a>, <a href="https://publications.waset.org/abstracts/search?q=internet%20of%20things" title=" internet of things"> internet of things</a> </p> <a href="https://publications.waset.org/abstracts/160237/analysing-competitive-advantage-of-iot-and-data-analytics-in-smart-city-context" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160237.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">140</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3391</span> Analyzing Competitive Advantage of Internet of Things and Data Analytics in Smart City Context</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Petra%20Hofmann">Petra Hofmann</a>, <a href="https://publications.waset.org/abstracts/search?q=Dana%20Koniel"> Dana Koniel</a>, <a href="https://publications.waset.org/abstracts/search?q=Jussi%20Luukkanen"> Jussi Luukkanen</a>, <a href="https://publications.waset.org/abstracts/search?q=Walter%20Nieminen"> Walter Nieminen</a>, <a href="https://publications.waset.org/abstracts/search?q=Lea%20Hannola"> Lea Hannola</a>, <a href="https://publications.waset.org/abstracts/search?q=Ilkka%20Donoghue"> Ilkka Donoghue</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Covid-19 pandemic forced people to isolate and become physically less connected. The pandemic hasnot only reshaped people’s behaviours and needs but also accelerated digital transformation (DT). DT of cities has become an imperative with the outlook of converting them into smart cities in the future. Embedding digital infrastructure and smart city initiatives as part of the normal design, construction, and operation of cities provides a unique opportunity to improve connection between people. Internet of Things (IoT) is an emerging technology and one of the drivers in DT. It has disrupted many industries by introducing different services and business models, and IoT solutions are being applied in multiple fields, including smart cities. As IoT and data are fundamentally linked together, IoT solutions can only create value if the data generated by the IoT devices is analysed properly. Extracting relevant conclusions and actionable insights by using established techniques, data analytics contributes significantly to the growth and success of IoT applications and investments. Companies must grasp DT and be prepared to redesign their offerings and business models to remain competitive in today’s marketplace. As there are many IoT solutions available today, the amount of data is tremendous. The challenge for companies is to understand what solutions to focus on and how to prioritise and which data to differentiate from the competition. This paper explains how IoT and data analytics can impact competitive advantage and how companies should approach IoT and data analytics to translate them into concrete offerings and solutions in the smart city context. The study was carried out as a qualitative, literature-based research. A case study is provided to validate the preservation of company’s competitive advantage through smart city solutions. The results of the researchcontribution provide insights into the different factors and considerations related to creating competitive advantage through IoT and data analytics deployment in the smart city context. Furthermore, this paper proposes a framework that merges the factors and considerations with examples of offerings and solutions in smart cities. The data collected through IoT devices, and the intelligent use of it, can create a competitive advantage to companies operating in smart city business. Companies should take into consideration the five forces of competition that shape industries and pay attention to the technological, organisational, and external contexts which define factors for consideration of competitive advantages in the field of IoT and data analytics. Companies that can utilise these key assets in their businesses will most likely conquer the markets and have a strong foothold in the smart city business. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=internet%20of%20things" title="internet of things">internet of things</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analytics" title=" data analytics"> data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20cities" title=" smart cities"> smart cities</a>, <a href="https://publications.waset.org/abstracts/search?q=competitive%20advantage" title=" competitive advantage"> competitive advantage</a> </p> <a href="https://publications.waset.org/abstracts/150793/analyzing-competitive-advantage-of-internet-of-things-and-data-analytics-in-smart-city-context" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150793.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">98</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3390</span> The Digital Desert in Global Business: Digital Analytics as an Oasis of Hope for Sub-Saharan Africa</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=David%20Amoah%20Oduro">David Amoah Oduro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the ever-evolving terrain of international business, a profound revolution is underway, guided by the swift integration and advancement of disruptive technologies like digital analytics. In today's international business landscape, where competition is fierce, and decisions are data-driven, the essence of this paper lies in offering a tangible roadmap for practitioners. It is a guide that bridges the chasm between theory and actionable insights, helping businesses, investors, and entrepreneurs navigate the complexities of international expansion into sub-Saharan Africa. This practitioner paper distils essential insights, methodologies, and actionable recommendations for businesses seeking to leverage digital analytics in their pursuit of market entry and expansion across the African continent. What sets this paper apart is its unwavering focus on a region ripe with potential: sub-Saharan Africa. The adoption and adaptation of digital analytics are not mere luxuries but essential strategic tools for evaluating countries and entering markets within this dynamic region. With the spotlight firmly fixed on sub-Saharan Africa, the aim is to provide a compelling resource to guide practitioners in their quest to unearth the vast opportunities hidden within sub-Saharan Africa's digital desert. The paper illuminates the pivotal role of digital analytics in providing a data-driven foundation for market entry decisions. It highlights the ability to uncover market trends, consumer behavior, and competitive landscapes. By understanding Africa's incredible diversity, the paper underscores the importance of tailoring market entry strategies to account for unique cultural, economic, and regulatory factors. For practitioners, this paper offers a set of actionable recommendations, including the creation of cross-functional teams, the integration of local expertise, and the cultivation of long-term partnerships to ensure sustainable market entry success. It advocates for a commitment to continuous learning and flexibility in adapting strategies as the African market evolves. This paper represents an invaluable resource for businesses, investors, and entrepreneurs who are keen on unlocking the potential of digital analytics for informed market entry in Africa. It serves as a guiding light, equipping practitioners with the essential tools and insights needed to thrive in this dynamic and diverse continent. With these key insights, methodologies, and recommendations, this paper is a roadmap to prosperous and sustainable market entry in Africa. It is vital for anyone looking to harness the transformational potential of digital analytics to create prosperous and sustainable ventures in a region brimming with promise. In the ever-advancing digital age, this practitioner paper becomes a lodestar, guiding businesses and visionaries toward success amidst the unique challenges and rewards of sub-Saharan Africa's international business landscape. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=global%20analytics" title="global analytics">global analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20analytics" title=" digital analytics"> digital analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=sub-Saharan%20Africa" title=" sub-Saharan Africa"> sub-Saharan Africa</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analytics" title=" data analytics"> data analytics</a> </p> <a href="https://publications.waset.org/abstracts/174450/the-digital-desert-in-global-business-digital-analytics-as-an-oasis-of-hope-for-sub-saharan-africa" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/174450.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">79</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3389</span> Evolution of Approaches to Cost Calculation in the Conditions of the Modern Russian Economy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elena%20Tkachenko">Elena Tkachenko</a>, <a href="https://publications.waset.org/abstracts/search?q=Vladimir%20Kokh"> Vladimir Kokh</a>, <a href="https://publications.waset.org/abstracts/search?q=Alina%20Osipenko"> Alina Osipenko</a>, <a href="https://publications.waset.org/abstracts/search?q=Vladislav%20Surkov"> Vladislav Surkov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The modern period of development of Russian economy is fraught with a number of problems related to limitations in the use of traditional planning and financial management tools. Restrictions in the use of foreign software when performing an order of the Russian Government, on the one hand, and sanctions limiting the support of the major ERP and MRP II systems in the Russian Federation, on the other hand, entail the necessity to appeal to the basics of developing budgeting and analysis systems for industrial enterprises. Thus, cost calculation theory becomes the theoretical foundation for the development of industrial cost management systems. Based on the foregoing, it would be fair to make an assumption that the development of a working managerial accounting model on an industrial enterprise using an automated enterprise resource management system should rest upon the concept of the inevitability of alterations of business processes. On the other hand, optimized business processes make the architecture of financial analytics more transparent and permit the use of all the benefits of data cubes. The metrics and indicator slices provide online assessment of the state of key business processes at a given moment of time, which improves the quality of managerial decisions considerably. Therefore, the bilateral sanctions situation boosted the development of corporate business analytics and took industrial companies to the next level of understanding of business processes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cost%20culculation" title="cost culculation">cost culculation</a>, <a href="https://publications.waset.org/abstracts/search?q=ERP" title=" ERP"> ERP</a>, <a href="https://publications.waset.org/abstracts/search?q=OLAP" title=" OLAP"> OLAP</a>, <a href="https://publications.waset.org/abstracts/search?q=modern%20Russian%20economy" title=" modern Russian economy"> modern Russian economy</a> </p> <a href="https://publications.waset.org/abstracts/106821/evolution-of-approaches-to-cost-calculation-in-the-conditions-of-the-modern-russian-economy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/106821.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">227</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3388</span> Estimation of Service Quality and Its Impact on Market Share Using Business Analytics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Haritha%20Saranga">Haritha Saranga</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Service quality has become an important driver of competition in manufacturing industries of late, as many products are being sold in conjunction with service offerings. With increase in computational power and data capture capabilities, it has become possible to analyze and estimate various aspects of service quality at the granular level and determine their impact on business performance. In the current study context, dealer level, model-wise warranty data from one of the top two-wheeler manufacturers in India is used to estimate service quality of individual dealers and its impact on warranty related costs and sales performance. We collected primary data on warranty costs, number of complaints, monthly sales, type of quality upgrades, etc. from the two-wheeler automaker. In addition, we gathered secondary data on various regions in India, such as petrol and diesel prices, geographic and climatic conditions of various regions where the dealers are located, to control for customer usage patterns. We analyze this primary and secondary data with the help of a variety of analytics tools such as Auto-Regressive Integrated Moving Average (ARIMA), Seasonal ARIMA and ARIMAX. Study results, after controlling for a variety of factors, such as size, age, region of the dealership, and customer usage pattern, show that service quality does influence sales of the products in a significant manner. A more nuanced analysis reveals the dynamics between product quality and service quality, and how their interaction affects sales performance in the Indian two-wheeler industry context. We also provide various managerial insights using descriptive analytics and build a model that can provide sales projections using a variety of forecasting techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=service%20quality" title="service quality">service quality</a>, <a href="https://publications.waset.org/abstracts/search?q=product%20quality" title=" product quality"> product quality</a>, <a href="https://publications.waset.org/abstracts/search?q=automobile%20industry" title=" automobile industry"> automobile industry</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20analytics" title=" business analytics"> business analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=auto-regressive%20integrated%20moving%20average" title=" auto-regressive integrated moving average"> auto-regressive integrated moving average</a> </p> <a href="https://publications.waset.org/abstracts/99732/estimation-of-service-quality-and-its-impact-on-market-share-using-business-analytics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99732.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">124</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3387</span> Adaptability of Analytics Capacities and Supply Chain Resiliency in Manufacturing Organizations: A Systematic Literature Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saira">Saira</a>, <a href="https://publications.waset.org/abstracts/search?q=Sheeraz%20Ali"> Sheeraz Ali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As the corporate world evolves swiftly, firms are discovering that their data must adapt to make supply lines more flexible and robust. This research shows the overall studies that have been done from 2015 to 2024 on analytics capability and supply chain resilience. The study uses a PRISMA model in order to identify the relevant articles included in the study. This bibliometric analysis examines academic literature on how mobility impacts analytics skills and how that affects organization flexibility and supply chain resilience. This study examines relevant publications to identify key findings, trends, and research gaps. The review highlights key publications, authors, and journals that have illuminated flexible analytics strategy discussions. It also examines geographical distribution and citations to determine academic importance and fame. This bibliometric review provides important information about existing research on analytics adaptability. They also suggest further research on this area. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptability" title="adaptability">adaptability</a>, <a href="https://publications.waset.org/abstracts/search?q=analytics%20capacities" title=" analytics capacities"> analytics capacities</a>, <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20resiliency" title=" supply chain resiliency"> supply chain resiliency</a>, <a href="https://publications.waset.org/abstracts/search?q=manufacturing%20organizations" title=" manufacturing organizations"> manufacturing organizations</a>, <a href="https://publications.waset.org/abstracts/search?q=systematic%20literature%20review" title=" systematic literature review"> systematic literature review</a> </p> <a href="https://publications.waset.org/abstracts/198543/adaptability-of-analytics-capacities-and-supply-chain-resiliency-in-manufacturing-organizations-a-systematic-literature-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/198543.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3386</span> High Performance Computing and Big Data Analytics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Branci%20Sarra">Branci Sarra</a>, <a href="https://publications.waset.org/abstracts/search?q=Branci%20Saadia"> Branci Saadia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Because of the multiplied data growth, many computer science tools have been developed to process and analyze these Big Data. High-performance computing architectures have been designed to meet the treatment needs of Big Data (view transaction processing standpoint, strategic, and tactical analytics). The purpose of this article is to provide a historical and global perspective on the recent trend of high-performance computing architectures especially what has a relation with Analytics and Data Mining. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=high%20performance%20computing" title="high performance computing">high performance computing</a>, <a href="https://publications.waset.org/abstracts/search?q=HPC" title=" HPC"> HPC</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analysis" title=" data analysis"> data analysis</a> </p> <a href="https://publications.waset.org/abstracts/15079/high-performance-computing-and-big-data-analytics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15079.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">525</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3385</span> Emerging Technology for Business Intelligence Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hsien-Tsen%20Wang">Hsien-Tsen Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Business Intelligence (BI) has long helped organizations make informed decisions based on data-driven insights and gain competitive advantages in the marketplace. In the past two decades, businesses witnessed not only the dramatically increasing volume and heterogeneity of business data but also the emergence of new technologies, such as Artificial Intelligence (AI), Semantic Web (SW), Cloud Computing, and Big Data. It is plausible that the convergence of these technologies would bring more value out of business data by establishing linked data frameworks and connecting in ways that enable advanced analytics and improved data utilization. In this paper, we first review and summarize current BI applications and methodology. Emerging technologies that can be integrated into BI applications are then discussed. Finally, we conclude with a proposed synergy framework that aims at achieving a more flexible, scalable, and intelligent BI solution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20intelligence" title="business intelligence">business intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20web" title=" semantic web"> semantic web</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title=" cloud computing"> cloud computing</a> </p> <a href="https://publications.waset.org/abstracts/162726/emerging-technology-for-business-intelligence-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162726.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">101</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3384</span> Energy Efficiency and Sustainability Analytics for Reducing Carbon Emissions in Oil Refineries</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaurav%20Kumar%20Sinha">Gaurav Kumar Sinha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The oil refining industry, significant in its energy consumption and carbon emissions, faces increasing pressure to reduce its environmental footprint. This article explores the application of energy efficiency and sustainability analytics as crucial tools for reducing carbon emissions in oil refineries. Through a comprehensive review of current practices and technologies, this study highlights innovative analytical approaches that can significantly enhance energy efficiency. We focus on the integration of advanced data analytics, including machine learning and predictive modeling, to optimize process controls and energy use. These technologies are examined for their potential to not only lower energy consumption but also reduce greenhouse gas emissions. Additionally, the article discusses the implementation of sustainability analytics to monitor and improve environmental performance across various operational facets of oil refineries. We explore case studies where predictive analytics have successfully identified opportunities for reducing energy use and emissions, providing a template for industry-wide application. The challenges associated with deploying these analytics, such as data integration and the need for skilled personnel, are also addressed. The paper concludes with strategic recommendations for oil refineries aiming to enhance their sustainability practices through the adoption of targeted analytics. By implementing these measures, refineries can achieve significant reductions in carbon emissions, aligning with global environmental goals and regulatory requirements. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20efficiency" title="energy efficiency">energy efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=sustainability%20analytics" title=" sustainability analytics"> sustainability analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=carbon%20emissions" title=" carbon emissions"> carbon emissions</a>, <a href="https://publications.waset.org/abstracts/search?q=oil%20refineries" title=" oil refineries"> oil refineries</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analytics" title=" data analytics"> data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20modeling" title=" predictive modeling"> predictive modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20optimization" title=" process optimization"> process optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=greenhouse%20gas%20reduction" title=" greenhouse gas reduction"> greenhouse gas reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=environmental%20performance" title=" environmental performance"> environmental performance</a> </p> <a href="https://publications.waset.org/abstracts/187014/energy-efficiency-and-sustainability-analytics-for-reducing-carbon-emissions-in-oil-refineries" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/187014.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">35</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3383</span> Social Semantic Web-Based Analytics Approach to Support Lifelong Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Halimi">Khaled Halimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hassina%20Seridi-Bouchelaghem"> Hassina Seridi-Bouchelaghem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this paper is to describe how learning analytics approaches based on social semantic web techniques can be applied to enhance the lifelong learning experiences in a connectivist perspective. For this reason, a prototype of a system called <em>SoLearn</em> (Social Learning Environment) that supports this approach. We observed and studied literature related to lifelong learning systems, social semantic web and ontologies, connectivism theory, learning analytics approaches and reviewed implemented systems based on these fields to extract and draw conclusions about necessary features for enhancing the lifelong learning process. The semantic analytics of learning can be used for viewing, studying and analysing the massive data generated by learners, which helps them to understand through recommendations, charts and figures their learning and behaviour, and to detect where they have weaknesses or limitations. This paper emphasises that implementing a learning analytics approach based on social semantic web representations can enhance the learning process. From one hand, the analysis process leverages the meaning expressed by semantics presented in the ontology (relationships between concepts). From the other hand, the analysis process exploits the discovery of new knowledge by means of inferring mechanism of the semantic web. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=connectivism" title="connectivism">connectivism</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20analytics" title=" learning analytics"> learning analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=lifelong%20learning" title=" lifelong learning"> lifelong learning</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20semantic%20web" title=" social semantic web"> social semantic web</a> </p> <a href="https://publications.waset.org/abstracts/100850/social-semantic-web-based-analytics-approach-to-support-lifelong-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/100850.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">220</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3382</span> Big Data: Impacts, Challenges, and Ethical Considerations Across Sectors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hassan%20Mistareehi">Hassan Mistareehi</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdulrahman%20Yarali"> Abdulrahman Yarali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Big Data era has revolutionized industries like healthcare, finance, government, and small business by enabling smarter decision-making and enhanced services. Leveraging cloud computing and advanced data science, organizations can process massive datasets in real time, improving customer experiences and streamlining operations. Big Data as a Service (BDaaS) further democratizes access to analytics, allowing even smaller businesses to benefit from powerful tools without large infrastructure costs. However, Big Data management presents challenges in data quality, security, and compliance. To address these, industry best practices and data governance standards are essential. In this paper, we presented an in-depth analysis of Big Data models, infrastructure, challenges, and applications across sectors, exploring how these innovations are transforming economies and society. As Big Data continues to evolve, it drives innovation and lays the groundwork for long-term impact on economic growth and social progress. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title=" cloud computing"> cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20security" title=" data security"> data security</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analytics" title=" data analytics"> data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20optimization" title=" business optimization"> business optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20governance" title=" data governance"> data governance</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a> </p> <a href="https://publications.waset.org/abstracts/198563/big-data-impacts-challenges-and-ethical-considerations-across-sectors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/198563.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3381</span> Visual Analytics in K 12 Education: Emerging Dimensions of Complexity </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Linnea%20Stenliden">Linnea Stenliden</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this paper is to understand emerging learning conditions, when a visual analytics is implemented and used in K 12 (education). To date, little attention has been paid to the role visual analytics (digital media and technology that highlight visual data communication in order to support analytical tasks) can play in education, and to the extent to which these tools can process actionable data for young students. This study was conducted in three public K 12 schools, in four social science classes with students aged 10 to 13 years, over a period of two to four weeks at each school. Empirical data were generated using video observations and analyzed with help of metaphors by Latour. The learning conditions are found to be distinguished by broad complexity characterized by four dimensions. These emerge from the actors’ deeply intertwined relations in the activities. The paper argues in relation to the found dimensions that novel approaches to teaching and learning could benefit students’ knowledge building as they work with visual analytics, analyzing visualized data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analytical%20reasoning" title="analytical reasoning">analytical reasoning</a>, <a href="https://publications.waset.org/abstracts/search?q=complexity" title=" complexity"> complexity</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20use" title=" data use"> data use</a>, <a href="https://publications.waset.org/abstracts/search?q=problem%20space" title=" problem space"> problem space</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20analytics" title=" visual analytics"> visual analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20storytelling" title=" visual storytelling"> visual storytelling</a>, <a href="https://publications.waset.org/abstracts/search?q=translation" title=" translation"> translation</a> </p> <a href="https://publications.waset.org/abstracts/17440/visual-analytics-in-k-12-education-emerging-dimensions-of-complexity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17440.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">382</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3380</span> Delivery Service and Online-and-Offline Purchasing for Collaborative Recommendations on Retail Cross-Channels</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20H.%20Liao">S. H. Liao</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20M.%20Huang"> J. M. Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The delivery service business model is the final link in logistics for both online-and-offline businesses. The online-and-offline business model focuses on the entire customer purchasing process online and offline, placing greater emphasis on the importance of data to optimize overall retail operations. For the retail industry, it is an important task of information and management to strengthen the collection and investigation of consumers' online and offline purchasing data to better understand customers and then recommend products. This study implements two-stage data mining analytics for clustering and association rules analysis to investigate Taiwanese consumers' (n=2,209) preferences for delivery service. This process clarifies online-and-offline purchasing behaviors and preferences to find knowledge profiles/patterns/rules for cross-channel collaborative recommendations. Finally, theoretical and practical implications for methodology and enterprise are presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=delivery%20service" title="delivery service">delivery service</a>, <a href="https://publications.waset.org/abstracts/search?q=online-and-offline%20purchasing" title=" online-and-offline purchasing"> online-and-offline purchasing</a>, <a href="https://publications.waset.org/abstracts/search?q=retail%20cross-channel" title=" retail cross-channel"> retail cross-channel</a>, <a href="https://publications.waset.org/abstracts/search?q=collaborative%20recommendations" title=" collaborative recommendations"> collaborative recommendations</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining%20analytics" title=" data mining analytics"> data mining analytics</a> </p> <a href="https://publications.waset.org/abstracts/189150/delivery-service-and-online-and-offline-purchasing-for-collaborative-recommendations-on-retail-cross-channels" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/189150.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">43</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3379</span> A Predictive Analytics Approach to Project Management: Reducing Project Failures in Web and Software Development Projects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tazeen%20Fatima">Tazeen Fatima</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Use of project management in web & software development projects is very significant. It has been observed that even with the application of effective project management, projects usually do not complete their lifecycle and fail. To minimize these failures, key performance indicators have been introduced in previous studies to counter project failures. However, there are always gaps and problems in the KPIs identified. Despite of incessant efforts at technical and managerial levels, projects still fail. There is no substantial approach to identify and avoid these failures in the very beginning of the project lifecycle. In this study, we aim to answer these research problems by analyzing the concept of predictive analytics which is a specialized technology and is very easy to use in this era of computation. Project organizations can use data gathering, compute power, and modern tools to render efficient Predictions. The research aims to identify such a predictive analytics approach. The core objective of the study was to reduce failures and introduce effective implementation of project management principles. Existing predictive analytics methodologies, tools and solution providers were also analyzed. Relevant data was gathered from projects and was analyzed via predictive techniques to make predictions well advance in time to render effective project management in web & software development industry. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=project%20management" title="project management">project management</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20analytics" title=" predictive analytics"> predictive analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20analytics%20methodology" title=" predictive analytics methodology"> predictive analytics methodology</a>, <a href="https://publications.waset.org/abstracts/search?q=project%20failures" title=" project failures"> project failures</a> </p> <a href="https://publications.waset.org/abstracts/69625/a-predictive-analytics-approach-to-project-management-reducing-project-failures-in-web-and-software-development-projects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69625.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">356</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3378</span> Food Supply Chain Optimization: Achieving Cost Effectiveness Using Predictive Analytics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jayant%20Kumar">Jayant Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Aarcha%20Jayachandran%20Sasikala"> Aarcha Jayachandran Sasikala</a>, <a href="https://publications.waset.org/abstracts/search?q=Barry%20Adrian%20Shepherd"> Barry Adrian Shepherd</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Public Distribution System is a flagship welfare programme of the Government of India with both historical and political significance. Targeted at lower sections of society,it is one of the largest supply chain networks in the world. There has been several studies by academics and planning commission about the effectiveness of the system. Our study focuses on applying predictive analytics to aid the central body to keep track of the problem of breach of service level agreement between the two echelons of food supply chain. Each shop breach is leading to a potential additional inventory carrying cost. Thus, through this study, we aim to show that aided with such analytics, the network can be made more cost effective. The methods we illustrate in this study are applicable to other commercial supply chains as well. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=PDS" title="PDS">PDS</a>, <a href="https://publications.waset.org/abstracts/search?q=analytics" title=" analytics"> analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=cost%20effectiveness" title=" cost effectiveness"> cost effectiveness</a>, <a href="https://publications.waset.org/abstracts/search?q=Karnataka" title=" Karnataka"> Karnataka</a>, <a href="https://publications.waset.org/abstracts/search?q=inventory%20cost" title=" inventory cost"> inventory cost</a>, <a href="https://publications.waset.org/abstracts/search?q=service%20level%20JEL%20classification%3A%20C53" title=" service level JEL classification: C53"> service level JEL classification: C53</a> </p> <a href="https://publications.waset.org/abstracts/21047/food-supply-chain-optimization-achieving-cost-effectiveness-using-predictive-analytics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21047.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">538</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=business%20analytics&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=business%20analytics&page=3">3</a></li> <li class="page-item"><a class="page-link" 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