Industrial analytics – An overview

2022 ◽  
Vol 0 (0) ◽  
Author(s):  
Christoph Gröger

Abstract The digital transformation generates huge amounts of heterogeneous data across the industrial value chain, from simulation data in engineering, over sensor data in manufacturing to telemetry data on product use. Extracting insights from these data constitutes a critical success factor for industrial enterprises, e. g., to optimize processes and enhance product features. This is referred to as industrial analytics, i. e., data analytics for industrial value creation. Industrial analytics is an interdisciplinary subject area between data science and industrial engineering and is at the core of Industry 4.0. Yet, existing literature on industrial analytics is fragmented and specialized. To address this issue, this paper presents a holistic overview of the field of industrial analytics integrating both current research as well as industry experiences on real-world industrial analytics projects. We define key terms, describe typical use cases and discuss characteristics of industrial analytics. Moreover, we present a conceptual framework for industrial analytics that structures essential elements, e. g., data platforms and data roles. Finally, we conclude and highlight future research directions.

2012 ◽  
Vol 1 (2) ◽  
pp. 193-207
Author(s):  
Sajal Kabiraj ◽  
Dwarika Prasad Uniyal

The High-technology industries are highly capital-intensive enterprises dealing with short life cycle products, offering potential to simultaneously examine different perspectives of collaborative relationships. Although the extant literature on collaborative activities in business enterprises has progressed along the different stages in the industrial value chain, there is lack of cohesion in the current state of the art. Conceptual clarity and contextual description in particular are dispersed and disintegrated in the current state. In order to integrate the literature and highlight its contributions, it is essential to provide a more technical description and a discussion on supply chain collaboration from various perspectives of the High-technology industry. In this article, the issues pertaining to responsiveness, collaborative development practices, strategic procurement and information technology (IT), and control-oriented approaches are presented. By effectively doing so, we provide a comprehensive literature review to determine the scope for future research in the High-technology industries and other similar manufacturing environments.


Author(s):  
Natalya Yur'evna Titova

In Russian Federation the questions of transformation of the current linear model of economy into the low-carbon model having the least negative impact on the environment are very urgent. Among the ways to realize the required transition scientists recognize the introduction of the concept of circular economy, as well as the organization of a system of interaction between industrial enterprises on the basis of the principles of industrial symbiosis. The scope of application of the industrial symbiosis model in the circular economy remains insufficiently studied in the scientific environment, which actualizes its solution. There has been defined the hierarchy of conceptual and categorical apparatus of industrial symbiosis in the circular economy. Integration of the companies on the basis of using the model of industrial symbiosis takes place due to the combination of environmental and economic interests of its participants by forming the industrial value chain. Symbiotic links involve the rational use of resources, which leads to the less intensive production processes and, consequently, to the reduced carbon emissions. Obtaining a synergistic effect is an incentive that encourages industrial structures to form symbiotic links. There has been stated the growth of transaction costs, which can be designated as a negative effect of the industrial symbiosis model. The directions for reducing this influence are proposed: improving the pricing model for industrial waste, taking into account territorial proximity and the need for industry associations and unions to participate in finding partners. The concept of industrial symbiosis is considered and clarified, which includes the principles, purpose and effects to characterize this term. The conclusion about the role of industrial symbiosis in the circular economy has been made.


2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Claus Kordes ◽  
Hans H. Bock ◽  
Doreen Reichert ◽  
Petra May ◽  
Dieter Häussinger

Abstract This review article summarizes 20 years of our research on hepatic stellate cells within the framework of two collaborative research centers CRC575 and CRC974 at the Heinrich Heine University. Over this period, stellate cells were identified for the first time as mesenchymal stem cells of the liver, and important functions of these cells in the context of liver regeneration were discovered. Furthermore, it was determined that the space of Disse – bounded by the sinusoidal endothelium and hepatocytes – functions as a stem cell niche for stellate cells. Essential elements of this niche that control the maintenance of hepatic stellate cells have been identified alongside their impairment with age. This article aims to highlight previous studies on stellate cells and critically examine and identify open questions and future research directions.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrew P. Creagh ◽  
Florian Lipsmeier ◽  
Michael Lindemann ◽  
Maarten De Vos

AbstractThe emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and out-of-clinic. Deep Convolutional Neural Networks (DCNN) may capture a richer representation of healthy and MS-related ambulatory characteristics from the raw smartphone-based inertial sensor data than standard feature-based methodologies. To overcome the typical limitations associated with remotely generated health data, such as low subject numbers, sparsity, and heterogeneous data, a transfer learning (TL) model from similar large open-source datasets was proposed. Our TL framework leveraged the ambulatory information learned on human activity recognition (HAR) tasks collected from wearable smartphone sensor data. It was demonstrated that fine-tuning TL DCNN HAR models towards MS disease recognition tasks outperformed previous Support Vector Machine (SVM) feature-based methods, as well as DCNN models trained end-to-end, by upwards of 8–15%. A lack of transparency of “black-box” deep networks remains one of the largest stumbling blocks to the wider acceptance of deep learning for clinical applications. Ensuing work therefore aimed to visualise DCNN decisions attributed by relevance heatmaps using Layer-Wise Relevance Propagation (LRP). Through the LRP framework, the patterns captured from smartphone-based inertial sensor data that were reflective of those who are healthy versus people with MS (PwMS) could begin to be established and understood. Interpretations suggested that cadence-based measures, gait speed, and ambulation-related signal perturbations were distinct characteristics that distinguished MS disability from healthy participants. Robust and interpretable outcomes, generated from high-frequency out-of-clinic assessments, could greatly augment the current in-clinic assessment picture for PwMS, to inform better disease management techniques, and enable the development of better therapeutic interventions.


2021 ◽  
Vol 15 (1) ◽  
pp. 177-207
Author(s):  
Paolo Gaiardelli ◽  
Giuditta Pezzotta ◽  
Alice Rondini ◽  
David Romero ◽  
Farnaz Jarrahi ◽  
...  

AbstractRecent economic transformations have forced companies to redefine their value propositions, increasing traditional product offerings with supplementary services—the so-called Product-Service System (PSS). Among them, the adoption of Industry 4.0 technologies is very common. However, the directions that companies are undertaking to offer new value to their customers in the Industry 4.0 have not yet been investigated in detail. Based on a focus group, this paper contributes to this understanding by identifying the main trajectories that would shape a future scenario in which PSS and Industry 4.0 would merge. In addition, future research directions addressing (a) the transformation of the PSS value chain into a PSS ecosystem, (b) the transformation inside a single company towards becoming a PSS provider, and (c) the digital transformation of the traditional PSS business model are identified.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mario Zanfardino ◽  
Rossana Castaldo ◽  
Katia Pane ◽  
Ornella Affinito ◽  
Marco Aiello ◽  
...  

AbstractAnalysis of large-scale omics data along with biomedical images has gaining a huge interest in predicting phenotypic conditions towards personalized medicine. Multiple layers of investigations such as genomics, transcriptomics and proteomics, have led to high dimensionality and heterogeneity of data. Multi-omics data integration can provide meaningful contribution to early diagnosis and an accurate estimate of prognosis and treatment in cancer. Some multi-layer data structures have been developed to integrate multi-omics biological information, but none of these has been developed and evaluated to include radiomic data. We proposed to use MultiAssayExperiment (MAE) as an integrated data structure to combine multi-omics data facilitating the exploration of heterogeneous data. We improved the usability of the MAE, developing a Multi-omics Statistical Approaches (MuSA) tool that uses a Shiny graphical user interface, able to simplify the management and the analysis of radiogenomic datasets. The capabilities of MuSA were shown using public breast cancer datasets from TCGA-TCIA databases. MuSA architecture is modular and can be divided in Pre-processing and Downstream analysis. The pre-processing section allows data filtering and normalization. The downstream analysis section contains modules for data science such as correlation, clustering (i.e., heatmap) and feature selection methods. The results are dynamically shown in MuSA. MuSA tool provides an easy-to-use way to create, manage and analyze radiogenomic data. The application is specifically designed to guide no-programmer researchers through different computational steps. Integration analysis is implemented in a modular structure, making MuSA an easily expansible open-source software.


Author(s):  
Sarah N. Douglas ◽  
Yan Shi ◽  
Saptarshi Das ◽  
Subir Biswas

Children with autism spectrum disorders (ASD) struggle to develop appropriate social skills, which can lead to later social rejection, isolation, and mental health concerns. Educators play an important role in supporting and monitoring social skill development for children with ASD, but the tools used by educators are often tedious, lack suitable sensitivity, provide limited information to plan interventions, and are time-consuming. Therefore, we conducted a study to evaluate the use of a sensor system to measure social proximity between three children with ASD and their peers in an inclusive preschool setting. We compared video-coded data with sensor data using point-by-point agreement to measure the accuracy of the sensor system. Results suggest that the sensor system can adequately measure social proximity between children with ASD and their peers. The next steps for sensor system validation are discussed along with clinical and educational implications, limitations, and future research directions.


2021 ◽  
pp. 1-16
Author(s):  
Esther Laryea ◽  
Mawunyo Avetsi ◽  
Herman Duse

Study level/applicability The case is targeted at undergraduate students in international finance, international business, entrepreneurship and strategic marketing classes. Subject area At the broadest level, the case represents an opportunity for students to discuss internationalisation of local firms. It focusses on getting students to analyse the costs and benefits associated with the foreign entry decision as well as the strategies for foreign entry. Case overview The Exploring International Markets: Unique Quality Heads to Kenya case study provides a chronological report of how Unique Quality, a cereal production company, grew locally up until the point when it considers internationalisation. It details the key considerations the firm makes as it considers its foreign entry decision. Unique Quality is a cereal production company in Ghana, which operates within the agriculture industry. The industry operates at almost all the points along the value chain including coordinating the growing of the cereal until it is harvested, packaged and marketed for sale. The company which started operations in 2013 has made great gains in penetrating the Ghanaian market. Salma, who is currently at the helm of affair at the company, together with the board is considering entering into Kenya. This decision is one that must not be taken lightly and has left Salma in a dilemma. Expected learning outcomes The expected learning outcomes of the case are:To enable students:a) identify the reasons why firms go international;b) identify opportunities for cost-cutting benefits or revenue maximisation opportunities for Unique Quality in Kenya;c) understand and identify the various sources of country risk that Unique Quality could face in its attempt to enter the Kenyan market; andd) identify and analyse the various foreign entry strategy options available to Unique Quality. Supplementary materials Teaching Notes are available for educators only. Please contact your library to gain login details or email [email protected]_to_request_teaching_notes Subject code CSS 1: Accounting and finance.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sanghee Kim ◽  
Hongjoo Woo

Purpose According to the perspective of evolutionary economic theory, the marketplace continuously evolves over time, following the changing needs of both customers and firms. In accordance with the theory, the second-hand apparel market has been rapidly expanding by meeting consumers’ diverse preferences and promoting sustainability since 2014. To understand what changes in consumers’ consumption behaviors regarding used apparel have driven this growth, the purpose of this study is to examine how the second-hand apparel market product types, distribution channels and consumers’ motives have changed over the past five years. Design/methodology/approach This study collected big data from Google through Textom software by extracting all Web-exposed text in 2014, and again in 2019, that contained the keyword “second-hand apparel,” and used the Node XL program to visualize the network patterns of these words through the semantic network analysis. Findings The results indicate that the second-hand apparel market has evolved with various changes over the past five years in terms of consumer motives, product types and distribution channels. Originality/value This study provides a comprehensive understanding of the changing demands of consumers toward used apparel over the past five years, providing insights for retailers as well as future research in this subject area.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yusheng Lu ◽  
Jiantong Zhang

PurposeThe digital revolution and the use of big data (BD) in particular has important applications in the construction industry. In construction, massive amounts of heterogeneous data need to be analyzed to improve onsite efficiency. This article presents a systematic review and identifies future research directions, presenting valuable conclusions derived from rigorous bibliometric tools. The results of this study may provide guidelines for construction engineering and global policymaking to change the current low-efficiency of construction sites.Design/methodology/approachThis study identifies research trends from 1,253 peer-reviewed papers, using general statistics, keyword co-occurrence analysis, critical review, and qualitative-bibliometric techniques in two rounds of search.FindingsThe number of studies in this area rapidly increased from 2012 to 2020. A significant number of publications originated in the UK, China, the US, and Australia, and the smallest number from one of these countries is more than twice the largest number in the remaining countries. Keyword co-occurrence is divided into three clusters: BD application scenarios, emerging technology in BD, and BD management. Currently developing approaches in BD analytics include machine learning, data mining, and heuristic-optimization algorithms such as graph convolutional, recurrent neural networks and natural language processes (NLP). Studies have focused on safety management, energy reduction, and cost prediction. Blockchain integrated with BD is a promising means of managing construction contracts.Research limitations/implicationsThe study of BD is in a stage of rapid development, and this bibliometric analysis is only a part of the necessary practical analysis.Practical implicationsNational policies, temporal and spatial distribution, BD flow are interpreted, and the results of this may provide guidelines for policymakers. Overall, this work may develop the body of knowledge, producing a reference point and identifying future development.Originality/valueTo our knowledge, this is the first bibliometric review of BD in the construction industry. This study can also benefit construction practitioners by providing them a focused perspective of BD for emerging practices in the construction industry.


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