Advanced predictive-analysis-based decision support for collaborative logistics networks

Purpose – The purpose of this paper is to examine challenges and potential of big data in heterogeneous business networks and relate these to an implemented logistics solution. Design/methodology/approach – The paper establishes an overview of challenges and opportunities of current significance in the area of big data, specifically in the context of transparency and processes in heterogeneous enterprise networks. Within this context, the paper presents how existing components and purpose-driven research were combined for a solution implemented in a nationwide network for less-than-truckload consignments. Findings – Aside from providing an extended overview of today’s big data situation, the findings have shown that technical means and methods available today can comprise a feasible process transparency solution in a large heterogeneous network where legacy practices, reporting lags and incomplete data exist, yet processes are sensitive to inadequate policy changes. Practical implications – The means introduced in the paper were found to be of utility value in improving process efficiency, transparency and planning in logistics networks. The particular system design choices in the presented solution allow an incremental introduction or evolution of resource handling practices, incorporating existing fragmentary, unstructured or tacit knowledge of experienced personnel into the theoretically founded overall concept. Originality/value – The paper extends previous high-level view on the potential of big data, and presents new applied research and development results in a logistics application

Changes and Countermeasures of Investigation Activities Under the Background of Big Data

In recent years, the ideas and methods of big data have caused a substantial change in the field of social and economic life and brought new opportunities and challenges to the public, prosecutors, and judicial organs. However, academic research on big data investigation is still at an early stage, and no theory and method of using big data systems have been formed. Therefore, this study focuses on examining the effect of big data on modern investigation activities and discusses how to use big data to improve the efficiency of investigation activities. The study also proposes the author’s views and opinions on how to maintain high efficiency in big data investigations. This study investigates the application of big data in investigative activities. First, the background of big data and the significance of studying big data investigation are introduced. On this basis, the origin and development of big data and the informatization of investigation are systematically classified. The connotation and changes of the investigation and the informatization of investigation under the background of big data are further examined. This study suggests that big data influences investigative thinking, investigative methods, and techniques. Moreover, traditional investigation methods should be reformed to understand big data investigation. Last, considering the risks that big data may bring to investigation activities, how to use big data to enhance investigation activities from the perspectives of privacy protection, platform establishment, and talent training is analyzed. Keywords: big data, investigation activities, investigation reform and response DOI: 10.7176/DCS10-9-06 Publication date:September 30th 202

Blockchain in Agriculture: A PESTELS Analysis JAVIER

Blockchain (BC) represents a disruptive technology that has been extensively used to ensure immutability of digital transactions. Starting as an underlying mechanism in the digital currency sector, it has been applicable in a wide range of sectors and application domains. Agriculture represents a sector of significance for overall sustainability challenges that is benefiting from digitalisation and technological evolution and the enforcement of Industry 4.0 paradigm shift towards precision agriculture. Introduction of Internet of Things, and Cyber-Physical Systems increase overall complexity, with Big Data analysis and Machine Learning technologies paving the way for innovative applications. BC appears to be a promising technology for agriculture introducing new mechanisms for tracing of products and overall agricultural Supply Chain management from the farm to the fork. The authors perform a review of 152 scientific works, providing a concise summary for each and extracting current challenges and open issues for the application of BC in agriculture. By synthesizing their findings, they perform a state of the art analysis along the PESTELS framework. A large number of challenges including technological ones, create big research potential for the evolution of the area.SUSTAINABLE Project, funded by the European Union’s Horizon 2020 Research and Innovation Program, through the Marie Skłodowska-Curie-Research and Innovation Staff Exchange (RISE) under Grant 10100770

Empirical Study on Big Data Analysis for Supply Chain Management

with globalization, outsourcing is reaching beyond continents. Design is done in one part of the world, manufacturing in another low-cost country and distribution to other countries in the world. Procurement is illuming as a central focus that requires to be synchronized with all other business functions. As a matter of course, a sizeable amount of a firm’s revenue goes for its supply chain that interprets the significance of the supply chain lays in a firm’s bottom-line. So, the supply chain has a tremendous opportunity to get used of data. Nowadays, the supply chain is attracting much and more attention because in terms of analytics it is behind other functions of a firm.  Specifically, this paper will (1) redefine, by research on scientific work, what BDA means in the context of Supply Chain Management, and how it differs and has evolved from analytics technologies; (2) evolve taxonomy of Big Data within SCM that identifies and classifies the different sources and types of data arising in modern supply chains and (3) suggest some applications of BDA and show the potential high value of this technology offers to solve intricate SCM challenges.  This research tries to explore how the behavior of Big Data can succor procurement and SCM in greater decision making. Big data can be a lightening of a resilient environment while managing suppliers in global SCM is a challenging task. Another studied aspect is having access to a greater pool of data and what kind of potential data can render benefit SCM. SCM professionals were interviewed to understand what they expect from their logistics, procurement and marketing systems and how Big Data can contribute to that. What type of transparency is needed? What requires to be automated? What delineation of data is useful? Furthermore, how Big Data can help with SCM risk management

Development on advanced technologies – design and development of cloud computing model

Big Data has been created from virtually everything around us at all times. Every digital media interaction generates data, from computer browsing and online retail to iTunes shopping and Facebook likes. This data is captured from multiple sources, with terrifying speed, volume and variety. But in order to extract substantial value from them, one must possess the optimal processing power, the appropriate analysis tools and, of course, the corresponding skills. The range of data collected by businesses today is almost unreal. According to IBM, more than 2.5 times four million data bytes generated per year, while the amount of data generated increases at such an astonishing rate that 90 % of it has been generated in just the last two years. Big Data have recently attracted substantial interest from both academics and practitioners. Big Data Analytics (BDA) is increasingly becoming a trending practice that many organizations are adopting with the purpose of constructing valuable information from BD. The analytics process, including the deployment and use of BDA tools, is seen by organizations as a tool to improve operational efficiency though it has strategic potential, drive new revenue streams and gain competitive advantages over business rivals. However, there are different types of analytic applications to consider. This paper presents a view of the BD challenges and methods to help to understand the significance of using the Big Data Technologies. This article based on a bibliographic review, on texts published in scientific journals, on relevant research dealing with the big data that have exploded in recent years, as they are increasingly linked to technolog

HUBUNGAN DUKUNGAN ORANG TUA DENGAN KARAKTER KEJUJURAN SISWA KELAS III A

The involvement of parents and teachers will have a big influence on children's education. The role of teachers and parents is very important in guiding and educating children. Children who have good character will have a big impact in facing challenges outside. The aim of this research is to determine the relationship between parental support and the honest character of class III A Mi An Nuur Cahaya Umat students. This research was carried out by 13 class III A MI An Nuur Cahaya Umat students. This type of research is correlation research. The method used in this research is data collection methods using questionnaires, checklist sheets, and distributing a Likert scale. The data analysis technique uses the Pearson product moment correlation analysis technique from Karl Person with the help of IBM SPSS-26. Data analysis was carried out to see the relationship between two variables, namely parametric techniques with paired sample tests with a significance result of 0.563 and a person correlation score of -0.138 with the non-destructive category, so the results of the study showed that there was no significant relationship between parental support and honest character. student. So it can be concluded that the higher the parental support given, the lower the honesty character possessed by the students. Vice versa, the lower the parental support, the higher the student's honesty character

An Efficient Intelligent Semi-Automated Warehouse Inventory Stocktaking System

In the context of evolving supply chain management, the significance of efficient inventory management has grown substantially for businesses. However, conventional manual and experience-based approaches often struggle to meet the complexities of modern market demands. This research introduces an intelligent inventory management system to address challenges related to inaccurate data, delayed monitoring, and overreliance on subjective experience in forecasting. The proposed system integrates bar code and distributed flutter application technologies for intelligent perception, alongside comprehensive big data analytics to enable data-driven decision-making. Through meticulous analysis, system design, critical technology exploration, and simulation validation, the effectiveness of the proposed system is successfully demonstrated. The intelligent system facilitates second-level monitoring, high-frequency checks, and artificial intelligence-driven forecasting, consequently enhancing the automation, precision, and intelligence of inventory management. This system contributes to cost reduction and optimized inventory sizes through accurate predictions and informed decisions, ultimately achieving a mutually beneficial scenario. The outcomes of this research offe

Impact of innovative technologies on highway operators: Tolling organizations' perspective

Highways play a vivacious role in a country's economic growth, by facilitating movement of both goods and people from one place to another. Over a short period of time, innovation in automobile and information technology has seen an unprecedented growth and this exploratory research highlights the impact of advent of innovative technologies like Autonomous and Connected Vehicles, Internet of Things applications and Big Data analytics on highway operators, as reflected in the opinions of organizations around the world (highway operators, toll agencies, suppliers, consultants and associations). The opinions were collected on a Likert scale type online survey, which was later tested for its empirical significance with non-parametric Binomial and Wilcoxon signed rank tests, supported by descriptive analysis. The research results clearly indicate that these technologies and products are not far from realization and while on one hand they would facilitate highway operations on the other hand they may pose some serious challenges for operators

Big data and wellbeing in the Arab world

© Springer Nature Switzerland AG 2019. All rights reserved. The rapid and widespread usage of social media platforms, such asTwitter, Facebook and Instagram has given rise to unprecedented amounts of user-generated data. This data contains expressions reflecting users thoughts, opinions and affective states. Systematic explorations of this type of data have begun to yield valuable information about a variety of psychological and cultural variables. To date however, very little of this research has been undertaken in the Arab world. It is important to extend this type of macro-level big data analysis across cultures and languages as each situation is likely to present different methodological challenges and to reveal findings particular to the sociocultural context. This chapter examines research-much of it our own-exploring subjective wellbeing in the United Arab Emirates (UAE) using data from Twitter and explores the findings from cross-linguistic analysis of happiness (positive-negative affective patterns of language use) and other variables associated with subjectivewellbeing in the region. Additionally, we explore temporal patterns of happiness observed in relation to Ramadan and other events of sociopolitical and religio-cultural significance. The UAE focus is discussed with reference to broader trends in data science, sentiment analysis and hedonometry