scholarly journals X-Reality System Architecture for Industry 4.0 Processes

2018 ◽  
Vol 2 (4) ◽  
pp. 72 ◽  
Author(s):  
Bruno Simões ◽  
Raffaele De Amicis ◽  
Iñigo Barandiaran ◽  
Jorge Posada
Keyword(s):  
Manufacturing Industry ◽  
Shop Floor ◽  
Test Case ◽  
Multiple Sources ◽  
Data Format ◽  
Hardware Configuration ◽  
Access To Data ◽  
Configuration Changes ◽  
Fast Access ◽  
User Intervention

Information visualization has been widely adopted to represent and visualize data patterns as it offers users fast access to data facts and can highlight specific points beyond plain figures and words. As data comes from multiple sources, in all types of formats, and in unprecedented volumes, the need intensifies for more powerful and effective data visualization tools. In the manufacturing industry, immersive technology can enhance the way users artificially perceive and interact with data linked to the shop floor. However, showcases of prototypes of such technology have shown limited results. The low level of digitalization, the complexity of the required infrastructure, the lack of knowledge about Augmented Reality (AR), and the calibration processes that are required whenever the shop floor configuration changes hinders the adoption of the technology. In this paper, we investigate the design of middleware that can automate the configuration of X-Reality (XR) systems and create tangible in-site visualizations and interactions with industrial assets. The main contribution of this paper is a middleware architecture that enables communication and interaction across different technologies without manual configuration or calibration. This has the potential to turn shop floors into seamless interaction spaces that empower users with pervasive forms of data sharing, analysis and presentation that are not restricted to a specific hardware configuration. The novelty of our work is due to its autonomous approach for finding and communicating calibrations and data format transformations between devices, which does not require user intervention. Our prototype middleware has been validated with a test case in a controlled digital-physical scenario composed of a robot and industrial equipment.

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 Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1432
Author(s):  
Xwégnon Ghislain Agoua ◽  
Robin Girard ◽  
Georges Kariniotakis
Keyword(s):  
Satellite Images ◽  
Forecast Accuracy ◽  
Absolute Error ◽  
Data Sources ◽  
Test Case ◽  
Temporal Data ◽  
Multiple Sources ◽  
Forecasting Error ◽  
Spatio Temporal ◽  
The Impact

The efficient integration of photovoltaic (PV) production in energy systems is conditioned by the capacity to anticipate its variability, that is, the capacity to provide accurate forecasts. From the classical forecasting methods in the state of the art dealing with a single power plant, the focus has moved in recent years to spatio-temporal approaches, where geographically dispersed data are used as input to improve forecasts of a site for the horizons up to 6 h ahead. These spatio-temporal approaches provide different performances according to the data sources available but the question of the impact of each source on the actual forecasting performance is still not evaluated. In this paper, we propose a flexible spatio-temporal model to generate PV production forecasts for horizons up to 6 h ahead and we use this model to evaluate the effect of different spatial and temporal data sources on the accuracy of the forecasts. The sources considered are measurements from neighboring PV plants, local meteorological stations, Numerical Weather Predictions, and satellite images. The evaluation of the performance is carried out using a real-world test case featuring a high number of 136 PV plants. The forecasting error has been evaluated for each data source using the Mean Absolute Error and Root Mean Square Error. The results show that neighboring PV plants help to achieve around 10% reduction in forecasting error for the first three hours, followed by satellite images which help to gain an additional 3% all over the horizons up to 6 h ahead. The NWP data show no improvement for horizons up to 6 h but is essential for greater horizons.

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 A Web GIS-Based Platform to Harvest Georeferenced Data from Social Networks: Examples of Data Collection Regarding Disaster Events

2018 ◽  
Vol 14 (02) ◽  
pp. 165 ◽  
Author(s):  
Cidália Costa Fonte ◽  
Diogo Fontes ◽  
Alberto Cardoso
Keyword(s):  
Social Networks ◽  
Data Collection ◽  
Emergency Response ◽  
Web Gis ◽  
Geographical Information ◽  
Civil Protection ◽  
Physical Sensors ◽  
Access To Data ◽  
Programming Interface ◽  
Fast Access

Whenever disaster situations occur the civil protection authorities need to have fast access to data that may help to plan emergency response. To contribute to the collection and integration of all available data a platform that aims to harvest Volunteered Geographical Information (VGI) from social networks and collaborative projects was created. This enables the integration of VGI with data coming from other sources, such as data collected by physical sensors in real time and made available through Applications Programming Interface (APIs), as well as, for example, official maps. The architecture of the created platform is described and its first prototype presented. Some example queries are performed and the results are analyzed.

scholarly journals Industrial Case Study on Lean Manufacturing Implemented on Manufacturing Polypropylene Ropes Industry Amravati

2021 ◽  
Vol 9 (12) ◽  
pp. 1318-1323
Author(s):  
Vipul Deshpande
Keyword(s):  
Lean Manufacturing ◽  
Manufacturing Industry ◽  
Service Industry ◽  
Developing Economies ◽  
Labour Productivity ◽  
Shop Floor ◽  
Industrial Case Study ◽  
Lean Principles ◽  
Manufacturing Techniques

Abstract: Lean manufacturing has been one of the most standard method in the manufacturing and service industry for elimination of waste. Every manufacturing industry has to put in continuous effort for its survival in the current impulsive and competitive economy. The purpose of this paper is to investigate the adoption of lean manufacturing tools and techniques in the manufacturing industries. This paper is based on actual implementation of lean manufacturing techniques. It focuses on the execution of flow from the start until the end of the implementation, types of analysis and tools applied, evaluation methods and how the industry benefited from the implementation. In this case study we particularly focused on Shop floor management, Quality Management (QM), Supplier and Customer Management (SCM) and Workforce Management (WM). After going through various testing on implementation of Lean Manufacturing principles in Micro Small medium Enterprise (MSME), researcher studied thoughts of some author where they discussed pragmatic problems they overcome while implementing lean principles in developing economies MSME. At the end, the result shows that there is monthly increment in capital productivity and labour productivity. And decrement in inhouse rejection, breakdown hours and customer complaint from the implementation of lean.

Big Data and Privacy State of the Art

2019 ◽  
pp. 104-158
Author(s):  
Amine Rahmani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Exponential Growth ◽  
Scientific Community ◽  
Ethical Issues ◽  
Massive Data ◽  
Medium Term ◽  
Access To Data ◽  
Fast Access ◽  
The Web

The phenomenon of big data (massive data mining) refers to the exponential growth of the volume of data available on the web. This new concept has become widely used in recent years, enabling scalable, efficient, and fast access to data anytime, anywhere, helping the scientific community and companies identify the most subtle behaviors of users. However, big data has its share of the limits of ethical issues and risks that cannot be ignored. Indeed, new risks in terms of privacy are just beginning to be perceived. Sometimes simply annoying, these risks can be really harmful. In the medium term, the issue of privacy could become one of the biggest obstacles to the growth of big data solutions. It is in this context that a great deal of research is under way to enhance security and develop mechanisms for the protection of privacy of users. Although this area is still in its infancy, the list of possibilities continues to grow.

Big Data and Privacy State of the Art

2021 ◽  
pp. 947-991
Author(s):  
Amine Rahmani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Exponential Growth ◽  
Scientific Community ◽  
Ethical Issues ◽  
Massive Data ◽  
Medium Term ◽  
Access To Data ◽  
Fast Access ◽  
The Web

The phenomenon of big data (massive data mining) refers to the exponential growth of the volume of data available on the web. This new concept has become widely used in recent years, enabling scalable, efficient, and fast access to data anytime, anywhere, helping the scientific community and companies identify the most subtle behaviors of users. However, big data has its share of the limits of ethical issues and risks that cannot be ignored. Indeed, new risks in terms of privacy are just beginning to be perceived. Sometimes simply annoying, these risks can be really harmful. In the medium term, the issue of privacy could become one of the biggest obstacles to the growth of big data solutions. It is in this context that a great deal of research is under way to enhance security and develop mechanisms for the protection of privacy of users. Although this area is still in its infancy, the list of possibilities continues to grow.

Classification and relationships between risks that affect lean production implementation

2015 ◽  
Vol 26 (1) ◽  
pp. 57-79 ◽  
Author(s):  
Giuliano Almeida Marodin ◽  
Tarcísio Abreu Saurin
Keyword(s):  
Factor Analysis ◽  
Lean Production ◽  
Shop Floor ◽  
Management Support ◽  
The South ◽  
Multiple Sources ◽  
Content Type ◽  
Confirmatory Factor ◽  
South Of Brazil

Purpose – The purpose of this paper is twofold: to classify the risks that affect the lean production implementation (LPI) process, and to demonstrate how that classification can help to identify the relationships between the risks. Design/methodology/approach – Initially, a survey was conducted to identify the probability and impact of 14 risks in LPI, which had been identified based on a literature review. The sample comprised 57 respondents, from companies in the south of Brazil. An exploratory factor analysis was carried out to analyze the results of the survey, allowing the identification of three groups of risks in LPI. Then, a case study was conducted in one of the companies represented in the survey, in order to identify examples of relationships between the risks. Multiple sources of evidence were used in the case study, such as interviews, observations and documents analysis. Findings – The risks that affect LPI were grouped into three categories: management of the process of LPI, top and middle management support and shop floor involvement. A number of examples of relationships between the risks were identified. Research limitations/implications – The survey was limited to companies from the south of Brazil and therefore its results cannot be completelly generalized to other companies. Moreover, the results of the survey were not subjected to a confirmatory factor analysis. Originality/value – This study helps to improve the understanding of LPI, as: it re-interprets the factors, barriers and difficulties for LPI from the perspective of risk management, which had not been used for that purpose so far; it presents a classification of the risks that affect LPI, which can support the understanding of the relationships between the risks and, as a result, it can support the development of more effective methods for LPI.

An empirical study for implementation of lean principles in Indian manufacturing industry

2016 ◽  
Vol 23 (1) ◽  
pp. 183-207 ◽  
Author(s):  
Naga Vamsi Krishna Jasti ◽  
Rambabu Kodali
Keyword(s):  
Customer Satisfaction ◽  
Manufacturing Industry ◽  
Manufacturing Industries ◽  
Shop Floor ◽  
Survey Questionnaire ◽  
Content Type ◽  
Empirical Survey ◽  
Manufacturing Organizations ◽  
The Status ◽  
Research Studies

Purpose – Lean manufacturing (LM) principles are one of the alternatives to improve manufacturing productivity, quality and customer satisfaction in Indian manufacturing industry. The purpose of this paper is to find the implementation status of LM principles across Indian manufacturing organizations through the empirical survey methodology. Design/methodology/approach – The survey questionnaire was developed based upon literature review conducted on LM and also considered experts suggestion in the field of LM. The survey questionnaire was sent to 753 manufacturing organization located in India. The respondent organization details have gathered from the list of Confederation of Indian Industries directory for the year 2011.The selected respondents were production managers, quality managers, sales managers, maintenance managers, CEOs of the organization. The empirical survey collected 180 filled survey questionnaires from Indian manufacturing industries. Findings – The study clearly identified that many manufacturing organizations were in initial transition stage and concentrating mostly in-plant operations instead of collaboration in all levels of business with suppliers and customers. The present study found that drivers for implementation of LM were customer satisfaction and organizational continuous improvement program. The present study also found that barriers to implement LM principles were employee resistance, implementing few elements of LM principles instead of the complete package of LM framework, budget constraints and lack of understanding of LM principles to shop floor managers. Finally the study concluded that Indian manufacturing organizations have to conduct continuous learning programmed to improve understanding of LM principles as well as to maintain their motivation level in apex point. The study also suggested that a systematic LM framework is needs to Indian manufacturing organizations, which will act as clear cut guiding torch to the organization managers to implement LM principles across organization. Research limitations/implications – The sample size of the present study was moderate number than previous studies. However the study only concentrated on manufacturing organizations across India. The results of the present study cannot generalize across all the sectors of Indian organizations. Originality/value – The concept of LM was very popular among developed and developing countries in the world. Many research studies were performed across world to find the status of LM implementation in their countries. Very few research studies reported the status of LM implementation in Indian manufacturing industries and those studies also with limited focus of the status of LM implementation. Hence the study presented details status of LM principles implementation in Indian manufacturing industries.

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