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نتیجه جستجو - analytics

تعداد مقالات یافته شده: 506
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1 Digital Livestock Farming
دامداری دیجیتال-2021
As the global human population increases, livestock agriculture must adapt to provide more livestock products and with improved efficiency while also addressing concerns about animal welfare, environmental sustainability, and public health. The purpose of this paper is to critically review the current state of the art in digitalizing animal agriculture with Precision Livestock Farming (PLF) technologies, specifically biometric sensors, big data, and blockchain technology. Biometric sensors include either noninvasive or invasive sensors that monitor an individual animal’s health and behavior in real time, allowing farmers to integrate this data for population-level analyses. Real-time information from biometric sensors is processed and integrated using big data analytics systems that rely on statistical algorithms to sort through large, complex data sets to provide farmers with relevant trending patterns and decision-making tools. Sensors enabled blockchain technology affords secure and guaranteed traceability of animal products from farm to table, a key advantage in monitoring disease outbreaks and preventing related economic losses and food-related health pandemics. Thanks to PLF technologies, livestock agriculture has the potential to address the abovementioned pressing concerns by becoming more transparent and fostering increased consumer trust. However, new PLF technologies are still evolving and core component technologies (such as blockchain) are still in their infancy and insufficiently validated at scale. The next generation of PLF technologies calls for preventive and predictive analytics platforms that can sort through massive amounts of data while accounting for specific variables accurately and accessibly. Issues with data privacy, security, and integration need to be addressed before the deployment of multi-farm shared PLF solutions be- comes commercially feasible. Implications Advanced digitalization technologies can help modern farms optimize economic contribution per animal, reduce the drudgery of repetitive farming tasks, and overcome less effective isolated solutions. There is now a strong cultural emphasis on reducing animal experiments and physical contact with animals in-order-to enhance animal welfare and avoid disease outbreaks. This trend has the potential to fuel more research on the use of novel biometric sensors, big data, and blockchain technology for the mutual benefit of livestock producers, consumers, and the farm animals themselves. Farmers’ autonomy and data-driven farming approaches compared to experience-driven animal manage- ment practices are just several of the multiple barriers that digitalization must overcome before it can become widely implemented.
Keywords: Precision Livestock Farming | digitalization | Digital Technologies in Livestock Systems | sensor technology | big data | blockchain | data models | livestock agriculture
مقاله انگلیسی
2 Data Driven Robust Optimization for Handling Uncertainty in Supply Chain Planning Models
بهینه سازی قوی مبتنی بر داده ها برای مدیریت عدم قطعیت در مدل های برنامه ریزی زنجیره تامین-2021
While addressing supply chain planning under uncertainty, Robust Optimization (RO) is regarded as an efficient and tractable method. As RO involves calculation of several statistical moments or maximum / minimum values involving the objective functions under realizations of these uncertain parameters, the accuracy of this method significantly depends on the efficient techniques to sample the uncertainty parameter space with limited amount of data. Conventional sampling techniques, e.g. box/budget/ellipsoidal, work by sampling the uncertain parameter space inefficiently, often leading to inaccuracies in such estimations. This paper proposes a methodology to amalgamate machine learning and data analytics with RO, thereby making it data-driven. A novel neuro fuzzy clustering mechanism is implemented to cluster the uncertain space such that the exact regions of uncertainty are optimally identified. Subsequently, local density based boundary point detection and Delaunay triangulation based boundary construction enable intelligent Sobol based sampling to sample the uncertain parameter space more accurately. The proposed technique is utilized to explore the merits of RO towards addressing the uncertainty issues of product demand, machine uptime and production cost associated with a multiproduct, and multisite supply chain planning model. The uncertainty in supply chain model is thoroughly analysed by carefully constructing examples and its case studies leading to large scale mixed integer linear and nonlinear programming problems which were efficiently solved in GAMS framework. Demonstration of efficacy of the proposed method over the box, budget and ellipsoidal sampling method through comprehensive analysis adds to other highlights of the current work.
Keywords: Uncertainty Modelling | Supply chain Management | Data driven Robust Optimization | Neuro Fuzzy Clustering | Multi-Layered Perceptron
مقاله انگلیسی
3 Facilitating artificial intelligence powered supply chain analytics through alliance management during the pandemic crises in the B2B context
تسهیل تجزیه و تحلیل های زنجیره تامین مجهز به هوش مصنوعی از طریق مدیریت اتحاد در طول بحران های همه گیر در زمینه B2B-2021
The COVID-19 pandemic has disrupted global supply chains and exposed weak links in the chains far beyond what most people have witnessed in their living memory. The scale of disruption affects every nation and industry, and the sudden and dramatic changes in demand and supply that have occurred during the pandemic crisis clearly differentiate its impact from other crises. Using the dynamic capabilities view, we studied alliance management capability (AMC) and artificial intelligence (AI) driven supply chain analytics capability (AI-SCAC) as dynamic capabilities, under the moderating effect of environmental dynamism. We tested our four research hypotheses using survey data collected from the Indian auto components manufacturing industry. For data analysis we used Warp PLS 7.0 (a variance-based structural equation modelling tool). We found that alliance management capability under the mediating effect of artificial intelligence-powered supply chain analytics capability enhances the operational and financial performance of the organization. Moreover, we also observed that the alliance management capability has a significant effect on artificial intelligence-powered supply chain analytics capability under the moderating effect of environmental dynamism. The results of our study provide a nuanced understanding of the dynamic capabilities and the relational view of organization. Finally, we noted the limitations of our study and provide numerous research directions that may help answer some of the questions that arise from our study.
Keywords: Artificial intelligence | Supply chain analytics | Alliance management | Environmental dynamism | Dynamic capability view
مقاله انگلیسی
4 A framework based on BWM for big data analytics (BDA) barriers in manufacturing supply chains
چارچوبی مبتنی بر BWM برای موانع تجزیه و تحلیل داده های بزرگ (BDA) در تولید زنجیره های تأمین-2021
Due to its potential utility, Big Data (BD) recently attracted researchers and practitioners in decision- making. Big Data analytics (BDA) becomes more common among manufacturing companies because it lets them gain insight and make decisions based on BD. Given the importance of both BD and BDA, this study aims to identify and analyse essential BDA adoption barriers in supply chains. This study explores the current knowledge base using a BWM (Best Worst Method) to discuss these barriers. Data were obtained from five Indian manufacturing companies. Research findings show that data-related barriers are most significant. The findings will help managers understand the exact nature of the challenges and possible advantages of the BDA and implement BDA policies for the growth and output of supply chain operations.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 3rd International e-Con- ference on Frontiers in Mechanical Engineering and nano Technology.
Keywords: Big data analytics | Barriers | Manufacturing supply chains | Best worst method (BWM)
مقاله انگلیسی
5 The convergence of big data and accounting: innovative research opportunities
همگرایی داده های بزرگ و حسابداری: فرصت های تحقیق نوآورانه-2021
This study aims to develop accounting standards, curriculums, and research to cope with the rapid development of big data. The study presents several potential convergence points between big data and different accounting techniques and theories. The study discusses how big data can overcome the data limitations of six accounting issues: financial reporting, performance measurement, audit evidence, risk management, corporate budgeting and activity-based techniques. It presents six exciting research questions for future research. Then, the study explains the potential convergence between big data and agency theory, stakeholders theory, and legitimacy theory. This theoretical study develops new convergence points between big data and accounting by reviewing the literature and proposing new ideas and research questions. The conclusion indicates a significant conver- gence between big data and accounting on the premise that data is the heart of accounting. Big data and advanced analytics have the potential to overcome the data limitations of accounting techniques that require estimations and predictions. A remarkable convergence is argued between big data and three accounting the- ories. Overall, the study presents helpful insights to members of the accounting and auditing community on the potential of big data.
keywords: اطلاعات بزرگ | تجزیه و تحلیل | حسابداری | علم داده | هوش تجاری | Big data | Analytics | Accounting | Data science | Business intelligence
مقاله انگلیسی
6 Propagation of online consumer perceived negativity: Quantifying the effect of supply chain underperformance on passenger car sales
انتشار مصرف منفی مصرف کننده آنلاین: کمی کردن تأثیر کم عملکرد زنجیره تامین بر فروش خودروهای سواری-2021
The paper presents a text analytics framework that analyses online reviews to explore how consumer-perceived negativity corresponding to the supply chain propagates over time and how it affects car sales. In particular, the framework integrates aspect-level sentiment analysis using SentiWordNet, time-series decomposition, and bias- corrected least square dummy variable (LSDVc) – a panel data estimator. The framework facilitates the business community by providing a list of consumers’ contemporary interests in the form of frequently discussed product attributes; quantifying consumer-perceived performance of supply chain (SC) partners and comparing the competitors; and a model assessing various firms’ sales performance. The proposed framework demonstrated to the automobile supply chain using a review dataset received from a renowned car-portal in India. Our findings suggest that consumer-voiced negativity is maximum for dealers and minimum for manufacturing and assembly related features. Firm age, GDP, and review volume significantly influence car sales whereas the sentiments corresponding to SC partners do not. The proposed research framework can help the manufacturers in inspecting their SC partners; realising consumer-cited critical car sales influencers; and accurately predicting the sales, which in turn can help them in better production planning, supply chain management, marketing, and consumer relationships.
Keywords: Supply chain management | Sentiment analysis | Panel data modelling | Online reviews | Natural language processing
مقاله انگلیسی
7 Computer vision approach to characterize size and shape phenotypes of horticultural crops using high-throughput imagery
رویکرد بینایی رایانه ای برای توصیف فنوتیپ های اندازه و شکل محصولات باغی با استفاده از تصاویر با توان بالا-2021
For many horticultural crops, variation in quality (e.g., shape and size) contributes significantly to the crop’s market value. Metrics characterizing less subjective harvest quantities (e.g., yield and total biomass) areroutinely monitored. In contrast, metrics quantifying more subjective crop quality characteristics such as ideal size and shape remain difficult to characterize objectively at the production-scale due to the lack of modular technologies for high-throughput sensing and computation. Several horticultural crops are sent to packing facilities after having been harvested, where they are sorted into boxes and containers using high-throughput scanners. These scanners capture images of each fruit or vegetable being sorted and packed, but the images are typically used solely for sorting purposes and promptly discarded. With further analysis, these images could offer unparalleled insight on how crop quality metrics vary at the industrial production-scale and provide further insight into how these characteristics translate to overall market value. At present, methods for extracting and quantifying quality characteristics of crops using images generated by existing industrial infrastructure have not been developed. Furthermore, prior studies that investigated horticultural crop quality metrics, specifically of size and shape, used a limited number of samples, did not incorporate deformed or non-marketable samples, and did not use images captured from high-throughput systems. In this work, using sweetpotato (SP) as a use case, we introduce a computer vision algorithm for quantifying shape and size characteristics in a high-throughput manner. This approach generates 3D model of SPs from two 2D images captured by an industrial sorter 90 degrees apart and extracts 3D shape features in a few hundred milliseconds. We applied the 3D reconstruction and feature extraction method to thousands of image samples to demonstrate how variations in shape features across SP cultivars can be quantified. We created a SP shape dataset containing SP images, extracted shape features, and qualitative shape types (U.S. No. 1 or Cull). We used this dataset to develop a neural network-based shape classifier that was able to predict Cull vs. U.S. No. 1 SPs with 84.59% accuracy. In addition, using univariate Chi-squared tests and random forest, we identified the most important features for determining qualitative shape type (U.S. No. 1 or Cull) of the SPs. Our study serves as a key step towards enabling big data analytics for industrial SP agriculture. The methodological framework is readily transferable to other horticultural crops, particularly those that are sorted using commercial imaging equipment.
Keywords: Crop phenotyping | Machine learning | Computer vision
مقاله انگلیسی
8 Data-driven detection and characterization of communities of accounts collaborating in MOOCs
شناسایی و توصیف مبتنی بر داده جوامع حساب‌هایی که در MOOC همکاری می‌کنند-2021
Collaboration is considered as one of the main drivers of learning and it has been broadly studied across numerous contexts, including Massive Open Online Courses (MOOCs). The research on MOOCs has risen exponentially during the last years and there have been a number of works focused on studying collaboration. However, these previous studies have been restricted to the analysis of collaboration based on the forum and social interactions, without taking into account other possibilities such as the synchronicity in the interactions with the platform. Therefore, in this work we performed a case study with the goal of implementing a data-driven approach to detect and characterize collaboration in MOOCs. We applied an algorithm to detect synchronicity links based on their submission times to quizzes as an indicator of collaboration, and applied it to data from two large Coursera MOOCs. We found three different profiles of user accounts, that were grouped in couples and larger communities exhibiting different types of associations between user accounts. The characterization of these user accounts suggested that some of them might represent genuine online learning collaborative associations, but that in other cases dishonest behaviors such as free-riding or multiple account cheating might be present. These findings call for additional research on the study of the kind of collaborations that can emerge in online settings.
keywords: تجزیه و تحلیل یادگیری | داده کاوی آموزشی | یادگیری مشارکتی | دوره های آنلاین گسترده باز | هوش مصنوعی | Learning analytics | Educational data mining | Collaborative learning | Massive open online courses | Artificial intelligence
مقاله انگلیسی
9 Future Generation Computer Systems 116 (2021) 209–219
سیستم های کامپیوتری نسل آینده 116 (2021) 209-219-2021
An organisation wishing to conduct data analytics to support day-to-day decision making often needs a system to help analysts represent and maintain knowledge about research variables, datasets or analytical models, and effectively determine the best combination to use when solving the problem at hand. Often, such knowledge is not explicitly captured by the organisation. To address this problem, this paper presents the design of an innovative Information Technology (IT) platform which enables data sharing between different analytics models and provides the ability to extend or customise models or data sources without necessarily involving the analysts who created them. It can make analytics knowledge readily available and modifiable for future use and problem-solving by analysts and other stakeholders. In the context of our work, we organise analytics knowledge around the concept of a research variable, which analysts often use when defining and proving a hypothesis. By focusing on such a concept, this platform is particularly suited to develop empirical data analytics applications in any domain. This paper presents the architecture of this platform, including the knowledge base and the Application Programming Interface (API) layer. Capabilities of this platform are illustrated through a software prototype and a use case on property price prediction across Sydney, Australia.
keywords: تجزیه و تحلیل پیش بینی | مدیریت دانش | دانش محور | مدل سازی معنایی | هستی شناسی | Predictive analytics | Knowledge management | Knowledge base | Semantic modelling | Ontologies
مقاله انگلیسی
10 Improving supply chain resilience through industry 4:0: A systematic literature review under the impressions of the COVID-19 pandemic
بهبود انعطاف پذیری زنجیره تأمین از طریق صنعت 4:0: بررسی ادبیات سیستماتیک تحت تأثیر همه گیری COVID-19-2021
The COVID-19 pandemic is one of the most severe supply chain disruptions in history and has challenged practitioners and scholars to improve the resilience of supply chains. Recent technological progress, especially industry 4.0, indicates promising possibilities to mitigate supply chain risks such as the COVID-19 pandemic. However, the literature lacks a comprehensive analysis of the link between industry 4.0 and supply chain resilience. To close this research gap, we present evidence from a systematic literature review, including 62 papers from high-quality journals. Based on a categorization of industry 4.0 enabler technologies and supply chain resilience antecedents, we introduce a holistic framework depicting the relationship between both areas while exploring the current state-of-the-art. To verify industry 4.0’s resilience opportunities in a severe supply chain disruption, we apply our framework to a use case, the COVID-19-affected automotive industry. Overall, our results reveal that big data analytics is particularly suitable for improving supply chain resilience, while other industry 4.0 enabler technologies, including additive manufacturing and cyber-physical systems, still lack proof of effectiveness. Moreover, we demonstrate that visibility and velocity are the resilience antecedents that benefit most from industry 4.0 implementation. We also establish that industry 4.0 holistically supports pre-disruption resilience measures, enabling more effective proactive risk management. Both research and practice can benefit from this study. While scholars may analyze resilience potentials of under-explored enabler technologies, practitioners can use our findings to guide industry 4.0 investment decisions.
Keywords: Industry 4.0 | Supply chain risk management | Supply chain resilience | Supply chain disruption | Digital supply chain | Literature review
مقاله انگلیسی
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