Determinants of Industrial Internet of Things Adoption in German Manufacturing Companies

2019 ◽  
Vol 16 (06) ◽  
pp. 1950038 ◽  
Author(s):  
Christian Arnold ◽  
Kai-Ingo Voigt
Keyword(s):  
Logistic Regression ◽  
Internet Of Things ◽  
Technology Management ◽  
Significant Influence ◽  
Environmental Uncertainty ◽  
Relative Advantage ◽  
Survey Questionnaire ◽  
Manufacturing Companies ◽  
Industrial Internet Of Things ◽  
Industrial Internet

This study aims at examining factors that determine the adoption of the Industrial Internet of Things (IIoT) by manufacturing companies applying the technology–organization–environment framework. Data of 197 German manufacturers are gathered by means of a survey questionnaire and tested using a logistic regression. This paper contributes to academic discussion by revealing determinants, which have a significant influence on the adoption of the IIoT: relative advantage associated with the IIoT, support by a company’s top management, high levels of competition, and environmental uncertainty. The study provides important implications, both for research and practitioners, related to technology management in the context of the IIoT.

HOW THE INDUSTRIAL INTERNET OF THINGS CHANGES BUSINESS MODELS IN DIFFERENT MANUFACTURING INDUSTRIES

2016 ◽  
Vol 20 (08) ◽  
pp. 1640015 ◽  
Author(s):  
CHRISTIAN ARNOLD ◽  
DANIEL KIEL ◽  
KAI-INGO VOIGT
Keyword(s):  
Internet Of Things ◽  
Business Models ◽  
Manufacturing Companies ◽  
Industrial Internet Of Things ◽  
Study Approach ◽  
Current State ◽  
Industrial Internet ◽  
Information And Communication ◽  
Multiple Case ◽  
Industry Sectors

The Industrial Internet of Things (IIoT) poses large impacts on business models (BM) of established manufacturing companies within several industries. Thus, this paper aims at analyzing the influence of the IIoT on these BMs with particular respect to differences and similarities dependent on varying industry sectors. For this purpose, we employ an exploratory multiple case study approach based on semi-structured expert interviews in 69 manufacturing companies from the five most important German industries. Owing the lack of previous research, our study contributes to the current state of management literature by revealing the following valuable insights with regard to industry-specific BM changes: The machine and plant engineering companies are mainly facing changing workforce qualifications, the electrical engineering and information and communication technology companies are particularly concerned with the importance of novel key partner networks, and automotive suppliers predominantly exploit IIoT-inherent benefits in terms of an increasing cost efficiency.

scholarly journals Smart Anomaly Detection and Prediction for Assembly Process Maintenance in Compliance with Industry 4.0

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2376
Author(s):  
Pavol Tanuska ◽  
Lukas Spendla ◽  
Michal Kebisek ◽  
Rastislav Duris ◽  
Maximilian Stremy
Keyword(s):  
Neural Networks ◽  
Internet Of Things ◽  
Industry 4.0 ◽  
Assembly Line ◽  
Assembly Process ◽  
Manufacturing Companies ◽  
Industrial Internet Of Things ◽  
Smart System ◽  
Industrial Internet ◽  
Unique Approach

One of the big problems of today’s manufacturing companies is the risks of the assembly line unexpected cessation. Although planned and well-performed maintenance will significantly reduce many of these risks, there are still anomalies that cannot be resolved within standard maintenance approaches. In our paper, we aim to solve the problem of accidental carrier bearings damage on an assembly conveyor. Sometimes the bearing of one of the carrier wheels is seized, causing the conveyor, and of course the whole assembly process, to halt. Applying standard approaches in this case does not bring any visible improvement. Therefore, it is necessary to propose and implement a unique approach that incorporates Industrial Internet of Things (IIoT) devices, neural networks, and sound analysis, for the purpose of predicting anomalies. This proposal uses the mentioned approaches in such a way that the gradual integration eliminates the disadvantages of individual approaches while highlighting and preserving the benefits of our solution. As a result, we have created and deployed a smart system that is able to detect and predict arising anomalies and achieve significant reduction in unexpected production cessation.

Order Fulfilment Progress Estimation for Collaborative Manufacturing Enabled by Industrial Internet of Things

2020 ◽  
Author(s):  
Chen Peng ◽  
Zheng Zhang ◽  
Tao Peng ◽  
Renzhong Tang ◽  
Xiaoliang Zhao
Keyword(s):  
Internet Of Things ◽  
Order Fulfillment ◽  
Manufacturing Companies ◽  
Industrial Internet Of Things ◽  
Production Chain ◽  
Collaborative Manufacturing ◽  
Make To Order ◽  
Industrial Internet ◽  
Order Fulfilment

Abstract It has been recognized by manufacturing companies that working collaboratively is the way to advance their competiveness. Order fulfillment estimation addresses the issue of uncertainty from vendors. It is significant for collaborative manufacturing, which enhances companies’ responsiveness to market dynamics. In a data-rich scenario, order fulfillment estimation can be performed based on information extracted from data acquisition devices, such as smart sensors. The analysis result should serve the decisions-making of the production planning, and an indicator should be passed along the production chain even to its end customer for collaborative purpose. In the meanwhile, the manufacturer’s sensitive or confidential information is excluded to avoid risks. This article studies a method to effectively evaluate the order fulfillment process in an Industrial Internet of Things (IIoT) facilitated make-to-order production system. An order fulfilment progress (OFP) indicator is proposed to dynamically represent the fulfillment progress, and its estimation mathematical models are proposed. To improve the practicability of the OFP indicator in production, the influence of abnormal event scenarios are discussed to modify the OFP. A case study presented in this research demonstrates the proposed indicator with consideration of job in process (JIP) is promising comparing to conventional indicators that are represented by the proportion of finished over total products.

An Integrated Legacy of Modbus Devices to Industrial Internet of Things: Approach for Smart Industries

2020 ◽  
Author(s):  
Karthik Muthineni
Keyword(s):  
Internet Of Things ◽  
Industrial Revolution ◽  
Digital Technologies ◽  
Industrial Internet Of Things ◽  
Intelligent Decision Making ◽  
Internet Of Everything ◽  
Industrial Internet ◽  
Intelligent Decision ◽  
Network Platform ◽  
Smart Factories

The new industrial revolution Industry 4.0, connecting manufacturing process with digital technologies that can communicate, analyze, and use information for intelligent decision making includes Industrial Internet of Things (IIoT) to help manufactures and consumers for efficient controlling and monitoring. This work presents the design and implementation of an IIoT ecosystem for smart factories. The design is based on Siemens Simatic IoT2040, an intelligent industrial gateway that is connected to modbus sensors publishing data onto Network Platform for Internet of Everything (NETPIE). The design demonstrates the capabilities of Simatic IoT2040 by taking Python, Node-Red, and Mosca into account that works simultaneously on the device.

MONITORING AND PREDICTIVE ANALYTICS OF TECHNOLOGICAL EQUIPMENT ON THE BASED OF INDUSTRIAL INTERNET OF THINGS

2020 ◽  
pp. 7-12
Author(s):  
С.Л. Добрынин ◽  
В.Л. Бурковский
Keyword(s):  
Internet Of Things ◽  
Data Acquisition ◽  
Predictive Analytics ◽  
Actual State ◽  
Monitoring And Control ◽  
Industrial Internet Of Things ◽  
Machining Time ◽  
Industrial Internet ◽  
Acquisition Device ◽  
Data Acquisition Device

Произведен обзор технологий в рамках концепции четвертой промышленной революции, рассмотрены примеры реализации новых моделей управления технологическими процессами на базе промышленного интернета вещей. Описано техническое устройство основных подсистем системы мониторинга и контроля, служащей для повышения осведомленности о фактическом состоянии производственных ресурсов в особенности станков и аддитивного оборудования в режиме реального времени. Архитектура предлагаемой системы состоит из устройства сбора данных (УСД), реализующего быстрый и эффективный сбор данных от станков и шлюза, передающего ликвидную часть информации в облачное хранилище для дальнейшей обработки и анализа. Передача данных выполняется на двух уровнях: локально в цехе, с использованием беспроводной сенсорной сети (WSN) на базе стека протоколов ZigBee от устройства сбора данных к шлюзам и от шлюзов в облако с использованием интернет-протоколов. Разработан алгоритм инициализации протоколов связи между устройством сбора данных и шлюзом, а также алгоритм выявления неисправностей в сети. Расчет фактического времени обработки станочных подсистем позволяет более эффективно планировать профилактическое обслуживание вместо того, чтобы выполнять задачи обслуживания в фиксированные интервалы без учета времени использования оборудования We carried out a review of technologies within the framework of the concept of the fourth industrial revolution; we considered examples of the implementation of new models of process control based on the industrial Internet of things. We described the technical structure of the main subsystems of the monitoring and control system to increase awareness of the actual state of production resources in particular machine tools and additive equipment in real time. The architecture of the proposed system consists of a data acquisition device (DAD) that implements fast and efficient data collection from machines and a gateway that transfers the liquid part of information to the cloud storage for further processing and analysis. We carried out the data transmission at two levels, locally in the workshop, using a wireless sensor network (WSN) based on ZigBee protocol stack from the data acquisition device to the gateways and from the gateways to the cloud using Internet protocols. An algorithm was developed for initializing communication protocols between a data acquisition device and a gateway, as well as an algorithm for detecting network malfunctions. Calculating the actual machining time of machine subsystems allows us to more efficiently scheduling preventive maintenance rather than performing maintenance tasks at fixed intervals without considering equipment usage

Multi-source fusion for weak target images in the Industrial Internet of Things

2021 ◽  
Vol 173 ◽  
pp. 150-159
Author(s):  
Keming Mao ◽  
Gautam Srivastava ◽  
Reza M. Parizi ◽  
Mohammad S. Khan
Keyword(s):  
Internet Of Things ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Weak Target
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