scholarly journals Next Generation Industrial IoT Digitalization for Traceability in Metal Manufacturing Industry: A Case Study of Industry 4.0

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 628
Author(s):  
Michail J. Beliatis ◽  
Kasper Jensen ◽  
Lars Ellegaard ◽  
Annabeth Aagaard ◽  
Mirko Presser
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Careful Consideration ◽  
Added Value ◽  
Shop Floor ◽  
Circuit Boards ◽  
Next Generation ◽  
Road Map ◽  
Industrial Iot ◽  
Production Cycles

This paper investigates digital traceability technologies taking careful consideration of the company’s needs to improve the traceability of products at the production of GPV Group as well as the efficiency and added value in their production cycles. GPV is primarily an electronics manufacturing service company (EMS) that manufactures electronic circuit boards, in addition to big metal products at their mechanics manufacturing sites. The company aims to embrace the next generation IoT technologies such as digital traceability in their internal supply chain at manufacturing sites in order to stay compatible with the Industry 4.0 requirements. In this paper, the capabilities of suitable digital traceability technologies are screened together with the actual GPV needs to determine if deployment of such technologies would benefit GPV shop floor operations and can solve the issues they face due to a lack of traceability. The traceability term refers to tracking the geolocation of products throughout the manufacturing steps and how that functionality can foster further optimization of the manufacturing processes. The paper focuses on comparing different IoT technologies and analyze their positive and negative attributes to identify a suitable technological solution for product traceability in the metal manufacturing industry. Finally, the paper proposes a suitable implementation road map for GPV, which can also be adopted from other metal manufacturing industries to deploy Industry 4.0 traceability at shop floor level.

scholarly journals An Artificial Intelligence-Based Collaboration Approach in Industrial IoT Manufacturing: Key Concepts, Architectural Extensions and Potential Applications

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5480 ◽  
Author(s):  
Panagiotis Trakadas ◽  
Pieter Simoens ◽  
Panagiotis Gkonis ◽  
Lambros Sarakis ◽  
Angelos Angelopoulos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Business Intelligence ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Shop Floor ◽  
Efficient Production ◽  
Human In The Loop ◽  
Industrial Iot ◽  
Potential Applications

The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented.

scholarly journals FASTEN IIoT: An Open Real-Time Platform for Vertical, Horizontal and End-To-End Integration

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5499
Author(s):  
Felipe S. Costa ◽  
Silvia M. Nassar ◽  
Sergio Gusmeroli ◽  
Ralph Schultz ◽  
André G. S. Conceição ◽  
...  
Keyword(s):  
Real Time ◽  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Business Models ◽  
Production Systems ◽  
Transformation Process ◽  
Digital Transformation ◽  
Shop Floor ◽  
Decentralized Decision Making ◽  
End To End

The Industry 4.0 paradigm, since its initial conception in Germany in 2011, has extended its scope and adoption to a broader set of technologies. It is being considered as the most vital mechanism in the production systems lifecycle. It is the key element in the digital transformation of manufacturing industry all over the world. This scenario imposes a set of major unprecedented challenges which require to be overcome. In order to enable integration in horizontal, vertical, and end-to-end formats, one of the most critical aspects of this digital transformation process consists of effectively coupling digital integrated service/products business models with additive manufacturing processes. This integration is based upon advanced AI-based tools for decentralized decision-making and for secure and trusted data sharing in the global value. This paper presents the FASTEN IIoT Platform, which targets to provide a flexible, configurable, and open solution. The platform acts as an interface between the shop floor and the industry 4.0 advanced applications and solutions. Examples of these efforts comprise management, forecasting, optimization, and simulation, by harmonizing the heterogeneous characteristics of the data sources involved while meeting real-time requirements.

scholarly journals Performance Evaluation Framework under the Influence of Industry 4.0: The Case of the Czech Manufacturing Industry

2021 ◽  
Vol 24 (1) ◽  
pp. 118-134
Author(s):  
Martina Hedvičáková ◽  
Martin Král
Keyword(s):  
Performance Evaluation ◽  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Industrial Revolution ◽  
Performance Indicator ◽  
Negative Impact ◽  
Evaluation Framework ◽  
Economic Situation ◽  
Added Value ◽  
Long Run

The current economic situation creates general pressure to increase performance. Any inefficient use of production factors will lead to problems and long-term economic unsustainability in many industries. The effects of the Covid-19 pandemic will also have a negative impact on all sectors of the economy and the faster onset of the fourth industrial revolution. The article, therefore, proposes a new framework for the performance evaluation of the manufacturing industry, which is based on the composite performance indicator. This indicator is obtained by a cross-sectoral comparison of all sub-key performance indicators. Using cluster analysis and analysis of variance, a total of 6 indicators to evaluate performance in the manufacturing industry were selected as statistically significant. The added value of the whole concept is its direct independence on the economic situation, which eliminates short-term economic oscillations that would be reflected in classical methods of performance evaluation otherwise. The results show that some industries are more efficient in the long run due to their effective investments in the capital, which replaces the labour factor and creates room for the realization of relatively higher profits. By contrast, some sectors, despite high investments, do not achieve the desired level of performance – these investments are not efficient or they are complementary to the labour factor, thus denying the principles of Industry 4.0. It thus creates preconditions for increasing dependence on external factors and, at the same time, makes the given sectors in a freely competitive environment economically unsustainable in the long run.

scholarly journals Production Innovation and Effective Dissemination of Information for Operator 4.0

2020 ◽  
Author(s):  
Dan Li ◽  
Åsa Fast-Berglund ◽  
Dan Paulin
Keyword(s):  
Mass Production ◽  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Shop Floor ◽  
Process Innovations ◽  
Product Innovations ◽  
Effective Dissemination ◽  
Everyday Work ◽  
Cognitive Automation ◽  
Organizational Innovations

The manufacturing industry is becoming increasingly more complex as the paradigm of mass-production moves, via mass-customization, towards personalized production and Industry 4.0. This increased complexity in the production system also makes everyday work for shop-floor operators more complex. To take advantage of this complexity, shop-floor operators need to be properly supported in order to perform their important work. The shop-floor operators in this future complex manufacturing industry, the Operator 4.0, need to be supported with the implementation of new cognitive automation solutions. These automation solutions, together with the innovativeness of new processes and organizations will increase the competitiveness of the manufacturing industry. This paper discusses three different aspects of production innovation in the context of the needs and preferences of information for Operator 4.0. Conclusively, product innovations can be applied in the manufacturing processes, and thus becoming process innovations, but the implementation of such innovations require organizational innovations.

A Highly Efficient and Optimised Simulation Based Multi objective Decision-Making for FMS Control

2018 ◽  
Vol 6 (6) ◽  
pp. 98
Author(s):  
Shreyanshu Parhi ◽  
S. C. Srivastava
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Manufacturing Industry ◽  
Simulation Software ◽  
Shop Floor ◽  
Simulation Tool ◽  
Routing Flexibility ◽  
Decision System ◽  
Multi Objective ◽  
Arena Simulation

Optimized and efficient decision-making systems is the burning topic of research in modern manufacturing industry. The aforesaid statement is validated by the fact that the limitations of traditional decision-making system compresses the length and breadth of multi-objective decision-system application in FMS.  The bright area of FMS with more complexity in control and reduced simpler configuration plays a vital role in decision-making domain. The decision-making process consists of various activities such as collection of data from shop floor; appealing the decision-making activity; evaluation of alternatives and finally execution of best decisions. While studying and identifying a suitable decision-making approach the key critical factors such as decision automation levels, routing flexibility levels and control strategies are also considered. This paper investigates the cordial relation between the system ideality and process response time with various prospective of decision-making approaches responsible for shop-floor control of FMS. These cases are implemented to a real-time FMS problem and it is solved using ARENA simulation tool. ARENA is a simulation software that is used to calculate the industrial problems by creating a virtual shop floor environment. This proposed topology is being validated in real time solution of FMS problems with and without implementation of decision system in ARENA simulation tool. The real-time FMS problem is considered under the case of full routing flexibility. Finally, the comparative analysis of the results is done graphically and conclusion is drawn.

scholarly journals Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4836
Author(s):  
Liping Zhang ◽  
Yifan Hu ◽  
Qiuhua Tang ◽  
Jie Li ◽  
Zhixiong Li
Keyword(s):  
Decision Making ◽  
Data Base ◽  
Real Time ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Data Driven ◽  
Dispatching Rules ◽  
Shop Floor ◽  
Production Data ◽  
Online Decision Making

In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers’ expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness.

scholarly journals Revitalization Road Map of Vocational Higher Education in Industry 4.0 (a study in Politeknik Negeri Balikpapan)

2021 ◽  
Vol 1807 (1) ◽  
pp. 012021
Author(s):  
Gozali ◽  
Elisabeth Milaningrum ◽  
Bambang Jati Kusuma ◽  
Lilik Damayanti
Keyword(s):  
Higher Education ◽  
Industry 4.0 ◽  
Road Map ◽  
Vocational Higher Education

scholarly journals TO SUPPORT IOT COLLABORATIVE EXPRESSIVENESS ON THE SHOP FLOOR

2021 ◽  
Vol 1 ◽  
pp. 3149-3158
Author(s):  
Álvaro Aranda Muñoz ◽  
Yvonne Eriksson ◽  
Yuji Yamamoto ◽  
Ulrika Florin ◽  
Kristian Sandström
Keyword(s):  
Industry 4.0 ◽  
Shop Floor ◽  
Manufacturing Companies ◽  
Technical Aspects ◽  
Industrial Landscape ◽  
The Future ◽  
New Research ◽  
Empirical Material

AbstractThe availability of new research for IoT support and the human-centric perspective of industry 4.0 opens a gap to support operators in unleashing their creativity so they can provide improvements opportunities with IoT technology. This paper presents a case-study carried out in four Swedish manufacturing companies, where four different workshops were facilitated to support operators in the conceptualization of manufacturing improvements with IoT technologies. The empirical material gathered during these workshops has been analyzed in five different reflective sessions and discussed in light of previous research from industry 4.0, operators, and IoT support. Results indicate that operators can collaboratively create conceptual IoT solutions and that expressiveness in communicating their ideas and needs using IoT technology is more relevant than technical aspects and details of their proposed IoT solutions. This technological expressiveness is identified as a necessary skill to be cultivated on the shop floor and can potentially contribute to making a more effective and socially sustainable industrial landscape in the future.

scholarly journals Factors that Inhibit Sustainable Adoption of Industry 4.0 in the South African Manufacturing Industry

2021 ◽  
Vol 13 (3) ◽  
pp. 1013
Author(s):  
Whisper Maisiri ◽  
Liezl van Dyk ◽  
Rojanette Coeztee
Keyword(s):  
South African ◽  
Education System ◽  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Youth Unemployment ◽  
Small Scale ◽  
The South ◽  
Qualitative Descriptive ◽  
Cultural Construct ◽  
Sub Saharan

Industry 4.0 (I4.0) adoption in the manufacturing industry is on the rise across the world, resulting in increased empirical research on barriers and drivers to I4.0 adoption in specific country contexts. However, no similar studies are available that focus on the South African manufacturing industry. Our small-scale interview-based qualitative descriptive study aimed at identifying factors that may inhibit sustainable adoption of I4.0 in the country’s manufacturing industry. The study probed the views and opinions of 16 managers and specialists in the industry, as well as others in supportive roles. Two themes emerged from the thematic analysis: factors that inhibit sustainable adoption of I4.0 and strategies that promote I4.0 adoption in the South African manufacturing industry. The interviews highlighted cultural construct, structural inequalities, noticeable youth unemployment, fragmented task environment, and deficiencies in the education system as key inhibitors. Key strategies identified to promote sustainable adoption of I4.0 include understanding context and applying relevant technologies, strengthening policy and regulatory space, overhauling the education system, and focusing on primary manufacturing. The study offers direction for broader investigations of the specific inhibitors to sustainable I4.0 adoption in the sub-Saharan African developing countries and the strategies for overcoming them.

scholarly journals Filling the void of family leadership: institutional support to business model changes in the Italian Industry 4.0 experience

2021 ◽  
Author(s):  
Marco Cucculelli ◽  
Ivano Dileo ◽  
Marco Pini
Keyword(s):  
Business Model ◽  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Business Models ◽  
Institutional Support ◽  
Negative Influence ◽  
Positive Influence ◽  
External Support ◽  
Family Management ◽  
Family Leadership

AbstractWe examine whether the probability of innovating a company’s business model towards the Industry 4.0 paradigm is affected by external institutional support and family leadership. Industry 4.0 is the information-intensive transformation of global manufacturing enabled by Internet technologies aimed at reinventing products and services from design and engineering to manufacturing. Using a sample of 3000 firms from a corporate survey on the manufacturing industry in Italy, our results showed that family leadership has a significant positive influence on the adoption of Industry 4.0 business models, but only in terms of family ownership. By contrast, family management has a negative influence on the probability of adopting a new business model. However, this negative influence is almost totally offset by the presence of the Triple Helix, i.e. the external support by public institutions and universities, which counterbalances the lower propensity of family managers to adopt Industry 4.0 business models. This supporting role only occurs when institutions and universities act together.

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