Predictive Analytics Capabilities Research Articles

Evaluating Predictive Financial Analysis Techniques, Impacts, and Challenges in Nigerian Banking Institutions

This study examined the techniques, impacts, and challenges of predictive financial analysis in Nigeria Banking Institutions. The Nigerian Banking Institutions is the life wire of the nation's economy. They play a vital and massive role in the nation's economic development and financial system. The role of these institutions remains a catalyst for a nation's growth and development. Despite the importance of predictive financial analysis in improving financial performance, accurately forecasting financial outcomes, mitigating the impact of financial crises, and making informed decisions, many Nigerian banks still need help to adopt and use this technique effectively. As a result, this study aims to examine the predictive financial analysis, evaluate the techniques, impact, and challenges, and recommend possible solutions that can help address the challenges faced by the banking sector. Methodology: The study adopted a descriptive survey research design. A simple random sampling technique was used to select 7 respondents representing five banks (Access Bank, Fidelity Bank, First Bank, Wema Bank, and Zenith Bank). A self-designed online questionnaire (SDOQ) was used for data collection. The data collected were analyzed using descriptive statistics (percentage). Results: Banks: 2 of the respondents, representing 28.6%, work at Access Bank; 1 of the respondents, representing 14.3%, works at Fidelity Bank; 2 of the respondents representing 28.6%, works at First Bank,1 respondent representing 14.3%, works at Wema Bank and the remaining 1 representing 14.3% works at Zenith bank. Role of Respondents: AHOP, Compliance Officer, Data Administrator, Data Analyst/Analytics, Relationship Manager and Team Lead. Analysis techniques: 3 banks representing 42.9% rely on the time series analysis technique, 2 banks representing 28.6% rely on the Regression analysis technique and 2 banks representing 28.6% rely on the machine learning algorithms technique. Satisfaction Level: 3 respondents representing 42.9% are very satisfied with current predictive analytics capabilities, while 4 respondents representing 57.1% are moderately satisfied with current predictive analytics capabilities. Discussion: The study's findings revealed that the primary challenges faced by banks in implementing predictive financial analysis techniques include data quality issues, regulatory constraints, lack of expertise, and integration with existing systems. Similarly, most banks rely on time series analysis to forecast future financial trends. Conclusion and Recommendations: Based on the findings in this study, it was concluded that the primary challenges faced by banks in implementing predictive financial analysis techniques include data quality issues, regulatory constraints, lack of expertise, integration with existing systems, and others. Similarly, most banks rely on the time series analysis technique to forecast future financial trends. The findings in this study also revealed that respondents perceive the impact of technological advancements to improve accuracy significantly. It was therefore recommended that a robust data management system practice be established to ensure the accuracy and consistency of data. Also, beyond time series analysis, banks should explore other predictive analytics techniques to enhance forecasting accuracy.

Data-Driven Management: The Impact of Big Data Analytics on Organizational Performance

Inevitably, in such a fast-moving landscape of big data analytics lies the transformative opportunity for any organization to unlock new avenues of growth, operational efficiency, and strategic decision-making. This paper contributes a comprehensive methodology that will help businesses take advantage of big data analytics to secure a continued competitive advantage. At the core of this methodology is to have a robust data governance framework that will establish the security, integrity, and accessibility of the enterprise data assets by specifying relevant policies, processes, and technologies. This, in turn, within such a framework, allows state-of-the-art AI-driven anomaly detection mechanisms for encryption and access control to implement protection measures around sensitive information while enabling secure yet efficient data utilization. The methodology also continues its approach of putting in place an integrated data ecosystem that brings together different pockets or sources of data, such as real-time operation data, customer interaction, and unstructured information. A critical component of this methodology is putting in place the advanced predictive analytics capabilities that could be run based on tapping into the power of machine learning algorithms; by doing so, the organization will be predicting market trends, customer behavior, and risks highly accurately. This will be a very proactive way of making decisions that would let the business efficiently use the available resources, innovate products and services ahead of time, and create a distinctly competitive advantage. The transformative ability of this methodology for big data analytics opened new channels toward growth and innovation and firmly established the organization as an industry leader with long-lasting competitive advantage. The place of data-driven insight, data culture, and responsible data practice has been the key to success in this organization.

Exploring the Role of Artificial Intelligence in Mental Healthcare: Current Trends and Future Directions - A Narrative Review for a Comprehensive Insight.

Mental health is an essential component of the health and well-being of a person and community, and it is critical for the individual, society, and socio-economic development of any country. Mental healthcare is currently in the health sector transformation era, with emerging technologies such as artificial intelligence (AI) reshaping the screening, diagnosis, and treatment modalities of psychiatric illnesses. The present narrative review is aimed at discussing the current landscape and the role of AI in mental healthcare, including screening, diagnosis, and treatment. Furthermore, this review attempted to highlight the key challenges, limitations, and prospects of AI in providing mental healthcare based on existing works of literature. The literature search for this narrative review was obtained from PubMed, Saudi Digital Library (SDL), Google Scholar, Web of Science, and IEEE Xplore, and we included only English-language articles published in the last five years. Keywords used in combination with Boolean operators ("AND" and "OR") were the following: "Artificial intelligence", "Machine learning", Deep learning", "Early diagnosis", "Treatment", "interventions", "ethical consideration", and "mental Healthcare". Our literature review revealed that, equipped with predictive analytics capabilities, AI can improve treatment planning by predicting an individual's response to various interventions. Predictive analytics, which uses historical data to formulate preventative interventions, aligns with the move toward individualized and preventive mental healthcare. In the screening and diagnostic domains, a subset of AI, such as machine learning and deep learning, has been proven to analyze various mental health data sets and predict the patterns associated with various mental health problems. However, limited studies have evaluated the collaboration between healthcare professionals and AI in delivering mental healthcare, as these sensitive problems require empathy, human connections, and holistic, personalized, and multidisciplinary approaches. Ethical issues, cybersecurity, a lack of data analytics diversity, cultural sensitivity, and language barriers remain concerns for implementing this futuristic approach in mental healthcare. Considering these sensitive problems require empathy, human connections, and holistic, personalized, and multidisciplinary approaches, it is imperative to explore these aspects. Therefore, future comparative trials with larger sample sizes and data sets are warranted to evaluate different AI models used in mental healthcare across regions to fill the existing knowledge gaps.

DEVELOPMENT OF DATA ANALYSIS METHOD IN BIG DATA ANALYTICS

The goal of developing data analysis methods in the field of Big Data Analytics is multifaceted and revolves around enabling organizations to effectively harness the vast amounts of data at their disposal to derive actionable insights and drive informed decision-making. The goal encompasses facilitating real-time and predictive analytics capabilities to enable proactive decision-making and rapid response to dynamic business conditions. Keywords: development, data analysis method, big data analytics, challenges, techniques, algorithms,

DEVELOPMENT OF DATA ANALYSIS METHOD IN BIG DATA ANALYTICS

The goal of developing data analysis methods in the field of Big Data Analytics is multifaceted and revolves around enabling organizations to effectively harness the vast amounts of data at their disposal to derive actionable insights and drive informed decision-making. The goal encompasses facilitating real-time and predictive analytics capabilities to enable proactive decision-making and rapid response to dynamic business conditions. Keywords: development, data analysis method, big data analytics, challenges, techniques, algorithms,

IoT based Women Safety Smart System Development

Designing an effective system for women's safety is essential for reducing crimes against women and prosecuting the guilty. Women harassment at workplace, public transport etc., has been a rising concern all over the world. An early action and proper mechanism could reduce the number of crimes against women. The development of a Women Safety Smart System within the Internet of Things (IoT) domain represents a significant advancement in leveraging technology to address the pressing issue of women's safety. This paper outlines the design, implementation, and potential impact of such a system, which integrates IoT devices, sensors, and data analytics to create a proactive safety infrastructure. Key components of the system include IoT-enabled wearables, smart surveillance and monitoring systems, a dedicated mobile application, and predictive analytics capabilities. These components work synergistically to provide real-time monitoring, timely intervention, and community engagement in ensuring the safety and well-being of women. By harnessing IoT technology, this system offers a scalable and adaptable solution to mitigate risks, empower women, and foster societal change. The paper discusses the benefits, challenges, and future directions of Women Safety Smart Systems in the IoT domain, highlighting their potential to transform safety paradigms and promote inclusivity and equality in urban environments.

THE ROLE OF PREDICTIVE ANALYTICS IN OPTIMIZING SUPPLY CHAIN RESILIENCE: A REVIEW OF TECHNIQUES AND CASE STUDIES

This study investigates the transformative impact of predictive analytics on enhancing supply chain resilience (SCR). Employing a systematic literature review and content analysis, the research aims to explore the integration, challenges, and strategic implications of predictive analytics within the supply chain ecosystem. Focusing on literature from 2014 to 2023, the study synthesizes insights from peer-reviewed articles and conference papers, adhering to stringent inclusion and exclusion criteria to ensure relevance and recency. The findings reveal that predictive analytics significantly contributes to supply chain agility, flexibility, and responsiveness, thereby bolstering SCR against disruptions. Key challenges identified include data privacy concerns, the need for skilled personnel, and the integration of predictive analytics into existing supply chain frameworks. Despite these challenges, the future outlook for predictive analytics in SCM is promising, with potential for unprecedented efficiency, sustainability, and competitive advantage. Strategic recommendations for practitioners emphasize the importance of developing predictive analytics capabilities, prioritizing data governance, and continuous staff training. For policymakers, the study suggests the need for standards and regulations that encourage innovation while ensuring the ethical use of predictive analytics. Finally, predictive analytics is pivotal in revolutionizing SCM, offering pathways for innovation, efficiency, and enhanced resilience. Future research should focus on the integration of emerging technologies, ethical data use frameworks, and overcoming adoption barriers, particularly in SMEs and developing economies. This study underscores the critical role of predictive analytics in the future of SCM, driving forward the agenda for research and practice in this evolving field.
 Keywords: Predictive Analytics, Supply Chain Resilience, Systematic Literature Review, Strategic Implications.

الدور الفعال للبيانات الضخمة في دعم قطاع الاتصالات: دراسة تحليلية

ملخص في عصر التطور التقني الذي نعيشه اليوم، تعمل تقنيات الحوسبة السحابية ووسائل التواصل الاجتماعي وتقنيات الاتصالات المتنقلة والحوسبة الرقمية إلى جانب عدد متزايد من المعاملات على تعزيز نمو البيانات. وتم تصميم تقنيات البيانات الضخمة لاستخلاص قيمة اقتصادية من أحجام ضخمة من البيانات المختلفة والمتنوعة، وذلك من خلال تمكين الاستحواذ على البيانات بسرعة عالية واكتشافها و/أو تحليلها، ويتم تطبيق هذه التقنيات في كافة القطاعات. فالدراسة الحالية تهدف إلى إلقاء الضوء على تقنية البيانات الضخمة والتعرف عليها، ومن ثم التعرف على كيفية تطبيقها في قطاع الاتصالات. حيث اعتمدت الدراسة الحالية على المنهج الوصفي التحليلي وذلك باستخدام أداة التحليل الرباعي (Strengths, Weaknesses, Opportunities and Threats) SWOT، والتي تمكن من تحديد نقاط القوة ونقاط الضعف لتقنية البيانات الضخمة في قطاع الأعمال المختلفة، وتشخيص الفرص والتهديدات الخاصة بتلك التقنية. وعليه فإن من النتائج البارزة التي خرجت بها الدراسة هي أن تقنية البيانات الضخمة ساهمت بشكل كبير في تطوير العمليات والوظائف المختلفة لقطاعات الاتصالات، مثل تحسين الرؤية واتخاذ قرارات أفضل وأكثر دقة في الوقت المناسب، تحليل قطاعات السوق، التعرف على الفرص التجارية الجديدة، وتحسين العمليات وتبسيطها، وتوفير قدرات تحليلية تنبؤية لاكتشاف اتجاهات العملاء. كما خرجت الدراسة الحالية بمجموعة من التوصيات كان من أبرزها أنه يجب على المهنيين في مجال الأعمال التجارية وتكنولوجيا المعلومات العمل معا في جميع أنحاء العالم في تطوير البيانات الضخمة وجعلها أكثر فعالية، والاهتمام بالناحية القانونية والأمنية لتقنية البيانات الضخمة. Cloud computing, social media, mobile communication technologies, digital computing, along with an increasing number of transactions are fueling the growth of data. Big data technologies are designed to extract economic value from huge volumes of different and diverse data by enabling high-speed data acquisition, discovery, and/or analysis. These technologies are being applied in all sectors. The current study aims to shed light on the technology of big data and to identify how to apply it in the telecommunications sector. To achieve these objectives, the study utilized the analytical descriptive approach, using the SWOT analysis technique (Strengths, Weaknesses, Opportunities and Threats). SWOT analysis enables identifying the strengths and weaknesses of big data technology in the different business sectors and diagnosing the opportunities and threats of this technology. One of the prominent findings of this study is that big data technology has contributed significantly to the development of various operations and functions of the telecommunications sectors, such as improving companies' vision, making better and accurate decisions in a timely manner, analyzing market sectors, identifying new business opportunities, and improving and simplifying operations. In addition, big data assists in providing predictive analytics capabilities to discover customer trends which can provide a competitive advantage today. The current study came up with a set of recommendations, the most prominent of them was that business and information technology professionals should work together all over the world to develop big data technologies and make them more effective, along with paying more attention to the legal and security aspects of the big data technologies.

The Role of Knowledge Extraction from Big Data for Enhancing Healthcare Services

All knowledge management methods and activities are meaningless unless knowledge is generated for effective various applications. Based on this fact, the aim of this article is to shed the light on the function of knowledge extraction from big data in improving healthcare services. The research derives various results after conducting this survey and examining several studies based on the descriptive analytical approach that is based on examining several scholarly papers from 2011 to 2022. The main achieved results revealed that most key benefits of using big data in healthcare include traceability, analytical capability, speeds to decisions capability, predictive analytics capability, interoperability capability, quicker clinical research progress, a more personalized approach to patient care, and earlier illness identification. On the other hand, high dimensionality, inconsistency, sparseness, distortion, and bias are some characteristics of healthcare big data that make their management and processing difficult. In addition, barriers to adopting big data technology include skepticism about its benefits, the necessity for a substantial initial investment, and difficulty reengineering healthcare systems and gaining approval for disruptive technologies. Finally concluded, it was shown that knowledge extraction from big data had altered healthcare practice and revolutionized the link between sickness and health.

Utilizing data and artificial intelligence to optimize treatment room scheduling and staffing.

436 Background: Tennessee Oncology is a large community oncology practice with over 30 clinics providing 89,000 treatments per year across Tennessee and northern Georgia. Tennessee Oncology’s scheduling application was unable to optimally schedule treatment appointments. This scheduling gap was causing frequent patient delays and employee extended hours. Tennessee Oncology partnered with Smirta, Inc., to develop a data and artificial intelligence (AI) driven scheduling overlay platform that would optimize and simplify cancer treatment scheduling as well as predict scheduling patterns and resource needs. Methods: Named OncoSmart, the scheduling optimization platform ingests historic scheduling data, detailed clinic configuration data including provider and nursing schedules, and available resource data such as treatment room chairs. Utilizing AI, the platform generates optimal scheduling recommendations matching the specific set of services that need to be scheduled. The platform overlays the current scheduling app and provides dynamic, real-time recommendations based on current resource (treatment room, provider, etc.) schedule availabilities and bookings. Tennessee Oncology piloted the scheduling optimization platform at 1 clinic and has currently expanded the pilot to 12 additional clinics. Results: After various ranges of clinic pilot times (6 months to 2 years), Tennessee Oncology treatment volumes have increased by 7%. In parallel to this increase, the optimization platform has helped decrease extended hours by over 32%. The original pilot site has shown major improvement in all 4 primary key performance indicators (KPI): treatment volume +12%; Chair utilization +12%; treatment delay -9%; extended hours -82%. Additionally, using the platform’s predictive analytics capabilities, analyses have been completed to generate optimal treatment scheduling patterns as well as optimal treatment nursing staffing models. Conclusions: Within a short period after deployment, Smirta Inc’s OncoSmart has helped Tennessee Oncology identify better treatment scheduling options for these 13sites. The scheduling optimization platform has proven to be very effective in identifying optimal treatment scheduling strategies and in identifying critical resource bottlenecks. The platform’s clinic management, optimization, nurse assignment, business intelligence, and resource management modules has empowered Tennessee Oncology to better manage critical clinical resources and reduce staff overtime during a period of growth.

Responsible governance mechanisms and the role of suppliers’ ambidexterity and big data predictive analytics capabilities in circular economy practices improvements

Recent years have seen a growing interest in the separate areas of responsible governance mechanisms and circular economy practices. However, the literature lacks evidence on supplier capabilities as a responsible governance mechanism pathway for buyer firms to improve their circular economy practices. Drawing on the natural resource-based view, and the dynamic capabilities perspective, this study examines suppliers’ supply chain ambidexterity and big data predictive analytics capabilities as enabling factors in support of buyer firms’ responsible governance mechanism for circular economy practices improvements. Using data from surveys conducted among 404 firms (202 focal firm-supplier dyads), the results suggest that the focal firm’s supplier-selection mechanism significantly and positively influences its circular economy practices while the supplier-development mechanism has no such effect. We find further that the governance mechanisms vary in their effects on suppliers’ supply chain ambidexterity and subsequent big data analytics capabilities and circular economy practices improvements outcomes. In sum, our findings provide unique insights into the role of suppliers’ capabilities for responsible governance mechanisms to circular economy practices improvements from the perspective of the dyad.

Intrinsic value of food chain data

Intrinsic value of food chain data

Predictive asset availability optimization for underground trucks and loaders in the mining industry

With an increased focus on operational optimization, cost rationalization and increased advances in sensors, data storage and machine learning, mining Original Equipment Manufacturers are in need of intelligent machines to create an unassailable competitive advantage. To address this, Sandvik Mining and Rock Technology (Sandvik) and IBM have developed a customized service for fleet management and predictive maintenance for Sandvik’s mining equipment for the improvement of Overall Equipment Efficiency. In this endeavour, this paper aims to discuss the application of analytics to reduce machine downtime by predicting equipment failure in advance thus improving asset utilization of the fleet used in the mining industry. It details the journey of the analysis using the IoT data, data exploration, statistical approximations employed, various machine learning algorithms considered, and final selection of the techniques based on the industry recommended criteria. Predictive models for component failures (engine, brakes and transmission) and predictive models for equipment time to failure were built for underground mining trucks and loaders. With increased availability of additional sensor data and the need to interpret the outcomes that are actionable, supervised machine learning algorithms like decision trees were considered. Our work highlights various challenges encountered, the workarounds and solutions used to overcome them. The resulting models (built with IBM’s predictive analytics capability) of this work are augmented with Sandvik’s analytical offering, OptiMine® Analytics. This paper also highlights as to how our work has made a significant impact in financial terms and the client testimonials received.

Utilizing of Big Data and Predictive Analytics Capability in Crisis Management

This study investigates the impact of Big Data and Predictive Analytics (BDPA) capability on crisis management in the Greater Amman Municipality (GAM), Jordan. Effective utilization of BDPA is considered in the study to depend upon the five dimensions: Tangible resources, technical skills, management skills, organizational learning and data driven culture. Crisis management is classified into the three stages of crisis preparedness, crisis response and crisis recovery. A32 item questionnaire was developed for the purpose of the study, which was completed by 140 participants; with128 responses deemed suitable for research analysis. PLS2 software was used to analyze the data. The study results confirm that the Greater Amman Municipality has medium levels of big data availability and predictive analytics capability overall. The study’s findings further demonstrate that big data and predictive analytics capability has a significant impact on crisis management. The main recommendation of the study is that organizations enhance their ability to utilize big data through expanding their tangible resources infrastructure, including machine learning algorithms, python software, simulation and regression analysis for understanding huge volumes of data. In addition, the study recommends that organizations improve the technical skills of their staff by providing the necessary training for performing big data analysis.

Machine Learning Approaches for EV Charging Behavior: A Review

As the smart city applications are moving from conceptual models to development phase, smart transportation is one of smart cities applications and it is gaining ground nowadays. Electric Vehicles (EVs) are considered one of the major pillars of smart transportation applications. EVs are ever growing in popularity due to their potential contribution in reducing dependency on fossil fuels and greenhouse gas emissions. However, large-scale deployment of EV charging stations poses multiple challenges to the power grid and public infrastructure. To overcome the issue of prolonged charging time, the simple solution of deploying more charging stations to increase charging capacity does not work due to the strain on power grids and physical space limitations. Therefore, researchers have focused on developing smart scheduling algorithms to manage the demand for public charging using modeling and optimization. More recently, there has been a growing interest in data-driven approaches in modeling EV charging. Consequently, researchers are looking to identify consumer charging behavior pattern that can provide insights and predictive analytics capability. The purpose of this article is to provide a comprehensive review for the use of supervised and unsupervised Machine Learning as well as Deep Neural Networks for charging behavior analysis and prediction. Recommendations and future research directions are also discussed.

Factor-based big data and predictive analytics capability assessment tool for the construction industry

Big data and predictive analytics have huge potential to create value to the construction industry. However, there is a lack of benchmarking system to evaluate organizations' competency to adopt big data and predictive analytics. Hence, this study aims to develop a big data and predictive analytics capability assessment tool that can measure construction organizations' capability in big data and predictive analytics implementation and that also highlights strengths and weaknesses of the organization to provide a benchmark in the process of big data and predictive analytics implementation. 21 determinants were identified and assessed in sense of their impacts on an organization's capability to implement big data and predictive analytics. These determinants were categorized into five determinant groups and assigned weights, to form the basis for the big data and predictive analytics capability assessment tool. The developed tool was then validated with four construction organizations to reflect their big data and predictive analytic capability levels, strengths and weaknesses. The findings of this study contribute to knowledge and practice by identifying the determinants impacting construction organizations' capability to adopt big data and predictive analytics and in the development of a computerized assessment tool which also serves as a benchmarking tool for construction organizations in the implementation of big data and predictive analytics.

Big Data and Predictive Analytics Capabilities: A Review of Literature on Its Impact on Firm’s Financial Performance

In the era of the fourth industrial revolution, big data and predictive analytics (BDPA) capabilities considered as one of the significant resources that enable a firm to gain competitiveness. Nowadays, the advancement of technology information increases the difficulty of the firm to manage vast amounts of structured and unstructured data. The excessive growth between data captured and the firm’s capabilities to manage, process, analyze, and transfer the big data to actionable knowledge and value still challenges many firms in the competitive market. Besides, the lack of tangible resources, technical skills, management skills, organizational learning, and data-driven culture are some of the challenges for firms to apply analytics approach to support the data processing process. These situations led to poor decision making by the firm and result in high operation cost and lower profitability. However, the situation will be different if the firm able to manage BDPA capabilities in the right way. Based on the identified problems, this study aims to review the impact of BDPA capabilities on a firm’s financial performance. This study is expected to enhance the body of knowledge on BDPA capabilities and the firm’s financial performance. This study also provides information regarding the importance of BDPA capabilities on the firm’s financial performance to industrial practitioners. An empirical study on this subject matter is suggested for future researchers, especially in Malaysia manufacturing industry.

Examining the role of big data and predictive analytics on collaborative performance in context to sustainable consumption and production behaviour

Abstract The organizations engaged in sustainable development programmes are increasingly paying serious attention towards synergetic relationships between focal firms and their partners to achieve the goal of sustainable consumption and production (SCP) via big data and predictive analytics (BDPA). The study examines the role of BDPA in collaborative performance (CP) among the partners engaged in sustainable development programme to achieve the goal of SCP. The study further investigates the contingent effect of organization fit on the impact of BDPA on CP. We used variance based structural equation modelling (PLS SEM) to test research hypotheses using a sample of 190 respondents working in auto-components manufacturing organizations in India drawn from the ACMA and Dun & Bradstreet databases. The results indicate that BDPA has a significant positive impact on the CP among partners and the organizational compatibility and resource complementarity have positive moderating effects on the path joining BDPA and CP. The study contributes to the understanding of BDPA and collaboration literature in the context of sustainable development. These findings extend the dynamic capability view (DCV) to create a better understanding of contemporary applications of big data and predictive analytics capability, while also providing theoretically grounded directions to managers who seek to use information processing technologies to continuously improve the collaboration in supply chain networks. We have also noted some of the limitations of our study and identified numerous further research directions.

Multi-criteria evaluation of real-time key performance indicators of supply chain with consideration of big data architecture

One of the major issues a designer of Big Data Architecture has to trade with is incorporating real-time predictive analytics capability using offline synergistic approaches like simulation, fuzzy analytic network process, and Technique for Order Preference. Further, under this setting, which involves re-engineering of operational units, the present study proposes a simple, yet practical heuristic to quickly handle the unstructured relational key-performance-indicators (KPIs) data of a supply chain that are obtained from the results of the simulation. Within the big data framework, the proposed model can be used as a decision support tool by the companies to evaluate their KPIs in a real-time dynamic system.

Impact of big data and predictive analytics capability on supply chain sustainability

PurposeThe purpose of this paper is to develop a theoretical model to explain the impact of big data and predictive analytics (BDPA) on sustainable business development goal of the organization.Design/methodology/approachThe authors have developed the theoretical model using resource-based view logic and contingency theory. The model was further tested using partial least squares-structural equation modeling (PLS-SEM) following Peng and Lai (2012) arguments. The authors gathered 205 responses using survey-based instrument for PLS-SEM.FindingsThe statistical results suggest that out of four research hypotheses, the authors found support for three hypotheses (H1-H3) and the authors did not find support for H4. Although the authors did not find support for H4 (moderating role of supply base complexity (SBC)), however, in future the relationship between BDPA, SBC and sustainable supply chain performance measures remain interesting research questions for further studies.Originality/valueThis study makes some original contribution to the operations and supply chain management literature. The authors provide theory-driven and empirically proven results which extend previous studies which have focused on single performance measures (i.e. economic or environmental). Hence, by studying the impact of BDPA on three performance measures the authors have attempted to answer some of the unresolved questions. The authors also offer numerous guidance to the practitioners and policy makers, based on empirical results.