Proactive event-driven computing refers to the use of event-driven information systems having the ability to eliminate or mitigate the impact of future undesired events, or to exploit future opportunities, on the basis of real-time sensor data and decision making technologies. Maintenance management can benefit from these advancements in order to tackle with the increasing challenges in today’s dynamic and complex manufacturing environment in the context of Industry 4.0.
To this end, the current thesis combines and brings together the research fields of Industry 4.0, Maintenance Management and Proactive Computing in order to frame maintenance management and information systems in the context of Industry 4.0. Therefore, it paves the way for the next generation of maintenance manage-ment in the frame of Industry 4.0, i.e. Proactive Maintenance. The focus of the cur-rent thesis is on proactive decision making. Consequently, it proposes proactive de-cision methods, capable of handling uncertainty, applicable to maintenance man-agement and its interrelationships with other manufacturing operations, algorithms for continuous improvement of proactive decision making through the proposed Sensor-Enabled Feedback (SEF) approach and algorithms for context-awareness in proactive decision making. To do this, it utilizes methods and techniques for opera-tional research, data analytics and machine learning.
The aforementioned algorithms have been embedded in a proactive information system for decision making which was integrated with other tools in order to imple-ment all the steps of the Proactive Maintenance framework. The system has been deployed and evaluated in real industrial environment, while further evaluation was conducted with extensive simulation experiments. Finally, the lessons learned and the managerial implications of the proposed approaches are discussed.
Dubuque, Iowa partnered with IBM to become a smarter city through cloud computing and distributed sensors. The document describes Dubuque's smarter water program which reduced water usage by 6.6% and increased leak detection eightfold through a portal providing households real-time water usage data. It also discusses how smarter electricity and travel programs could integrate all usage data to suggest community planning solutions improving quality of life.
Changes in community structure affect and are influenced by organisms. The document discusses various topics related to communities and ecosystems, including trophic levels in food webs, energy conversion rates, stable ecosystem emergence based on climate, and the influence of disturbance on ecosystem structure and change rates. It also provides guidance on understandings, applications, and skills related to these concepts.
The document discusses the development of a digital twin model for cutting tools used in manufacturing. It proposes a framework for collecting and analyzing data throughout a cutting tool's lifecycle, from design to use and maintenance, to create a digital representation that mirrors the physical tool. This digital twin would allow tool manufacturers to better monitor tool wear, predict remaining life, make selection decisions, and provide services to users. It describes previous limitations in modeling tools over their full lifecycle and discusses how a digital twin approach could help address issues like synchronization between physical and virtual models. The document also outlines two potential service modes the digital twin could enable manufacturers to provide and describes a virtual tool testing platform that fuses physical and simulated wear data.
One datum and many values for sustainable Industry 4.0: a prognostic and hea...IJECEIAES
Industrial context of today, driven by the Industry 4.0 paradigm, is overwhelmed by data. Decreasing cost of innovative technologies, and recent market dynamics have pushed and pulled respectively for those architectures and practices in which data are the masters. While advancing, we have to take care of waste, even though intangibility of data makes them hardly connected to waste. In this paper we are going to reflect on data intensive context of today, focusing on the industrial sector. A smart approach for fully exploiting data collecting infrastructures is proposed, and its declination in a prognostic and health management (PHM) use case set inside an automatic painting system is presented. The contributions of this papers are mainly two: first of all, the general conceptual take-away of "data re-use" is presented and discussed. Moreover, a PHM solution for painting system's number plates, based on optical character recognition (OCR), is proposed and tested as a proof-of-concept for the "data re-use" concept. Summarizing, the already-in-use data sharing principle for achieving transparency and integration inside Industry 4.0, is presented as complementary with the proposed "data re-use", in order to develop a really sustainable shift toward the future.
An expert system was designed to assist manufacturing industries in making make-or-buy decisions for parts and products. The system considers factors like available technology, production capacity, and the costs of in-house manufacturing versus outsourcing. It evaluates parts lists to determine which parts can be manufactured internally based on these constraints. The parts are sorted by potential cost savings, and capacity is allocated to the parts that provide the greatest savings. Parts that exceed remaining capacity are designated to be outsourced. The system was tested on a valve manufacturing industry and was able to make make-or-buy decisions in a timely manner based on the defined criteria.
AN EXPERT SYSTEM FOR MAKE OR BUY DECISION IN MANUFACTURING INDUSTRYIAEME Publication
A computer-based system is designed to assist manufacturing industries in the make or buy decision, which is arguably the most fundamental component of manufacturing strategy. A model of make or buy decision was developed through review of literature and discussion with industry people. The system employs both case-based reasoning (CBR) and decision support system components. As part of the development process, interviews were conducted with managers in valve manufacturing industry in order to determine current make or buy practice and elicit opinions on how the decision-making process would be enhanced. The model consists of various checks as technology, capacity, sorting of parts by cost in descending and allocating capacity for parts which give maximum cost saving as first. A Knowledge Based System (KBS) was developed which incorporates these checks into the make buy decision. This system is used for analysis of capacity of machine used and idle capacity remaining for better performance of the industry. Expert system developed is also used to take decision about a new part /product to be manufactured inhouse or bought outside and save the time in decision making
DIFFERENCES OF CLOUD-BASED SERVICES AND THEIR SAFETY RENEWAL IN THE HEALTH CA...IRJET Journal
The document discusses the benefits and risks of cloud-based services for the healthcare system. It begins by introducing how cloud computing has impacted various sectors including healthcare by enabling storage of large amounts of patient data and easy access. It then categorizes existing cloud applications and services used in healthcare. The document also analyzes security and privacy risks of cloud-based healthcare services and compares the risks of secure vs insecure cloud systems. It proposes that adopting cloud services in healthcare requires addressing security issues.
DIFFERENCES OF CLOUD-BASED SERVICES AND THEIR SAFETY RENEWAL IN THE HEALTH CA...IRJET Journal
The document discusses the benefits and risks of cloud-based services for healthcare systems. It begins by outlining how cloud computing has enabled new diagnostic technologies and easy access to patient data. However, it also notes security and privacy risks, such as data breaches and unauthorized access. The document then reviews existing literature on revolutionary impacts of cloud solutions, predictive threat analysis using big data, and risk analysis of cloud models. It proposes a methodology for categorizing cloud benefits and risks to help healthcare workers and IT professionals. The methodology aims to securely manage data exchange while addressing challenges like cyberattacks and lack of technical knowledge.
Dubuque, Iowa partnered with IBM to become a smarter city through cloud computing and distributed sensors. The document describes Dubuque's smarter water program which reduced water usage by 6.6% and increased leak detection eightfold through a portal providing households real-time water usage data. It also discusses how smarter electricity and travel programs could integrate all usage data to suggest community planning solutions improving quality of life.
Changes in community structure affect and are influenced by organisms. The document discusses various topics related to communities and ecosystems, including trophic levels in food webs, energy conversion rates, stable ecosystem emergence based on climate, and the influence of disturbance on ecosystem structure and change rates. It also provides guidance on understandings, applications, and skills related to these concepts.
The document discusses the development of a digital twin model for cutting tools used in manufacturing. It proposes a framework for collecting and analyzing data throughout a cutting tool's lifecycle, from design to use and maintenance, to create a digital representation that mirrors the physical tool. This digital twin would allow tool manufacturers to better monitor tool wear, predict remaining life, make selection decisions, and provide services to users. It describes previous limitations in modeling tools over their full lifecycle and discusses how a digital twin approach could help address issues like synchronization between physical and virtual models. The document also outlines two potential service modes the digital twin could enable manufacturers to provide and describes a virtual tool testing platform that fuses physical and simulated wear data.
One datum and many values for sustainable Industry 4.0: a prognostic and hea...IJECEIAES
Industrial context of today, driven by the Industry 4.0 paradigm, is overwhelmed by data. Decreasing cost of innovative technologies, and recent market dynamics have pushed and pulled respectively for those architectures and practices in which data are the masters. While advancing, we have to take care of waste, even though intangibility of data makes them hardly connected to waste. In this paper we are going to reflect on data intensive context of today, focusing on the industrial sector. A smart approach for fully exploiting data collecting infrastructures is proposed, and its declination in a prognostic and health management (PHM) use case set inside an automatic painting system is presented. The contributions of this papers are mainly two: first of all, the general conceptual take-away of "data re-use" is presented and discussed. Moreover, a PHM solution for painting system's number plates, based on optical character recognition (OCR), is proposed and tested as a proof-of-concept for the "data re-use" concept. Summarizing, the already-in-use data sharing principle for achieving transparency and integration inside Industry 4.0, is presented as complementary with the proposed "data re-use", in order to develop a really sustainable shift toward the future.
An expert system was designed to assist manufacturing industries in making make-or-buy decisions for parts and products. The system considers factors like available technology, production capacity, and the costs of in-house manufacturing versus outsourcing. It evaluates parts lists to determine which parts can be manufactured internally based on these constraints. The parts are sorted by potential cost savings, and capacity is allocated to the parts that provide the greatest savings. Parts that exceed remaining capacity are designated to be outsourced. The system was tested on a valve manufacturing industry and was able to make make-or-buy decisions in a timely manner based on the defined criteria.
AN EXPERT SYSTEM FOR MAKE OR BUY DECISION IN MANUFACTURING INDUSTRYIAEME Publication
A computer-based system is designed to assist manufacturing industries in the make or buy decision, which is arguably the most fundamental component of manufacturing strategy. A model of make or buy decision was developed through review of literature and discussion with industry people. The system employs both case-based reasoning (CBR) and decision support system components. As part of the development process, interviews were conducted with managers in valve manufacturing industry in order to determine current make or buy practice and elicit opinions on how the decision-making process would be enhanced. The model consists of various checks as technology, capacity, sorting of parts by cost in descending and allocating capacity for parts which give maximum cost saving as first. A Knowledge Based System (KBS) was developed which incorporates these checks into the make buy decision. This system is used for analysis of capacity of machine used and idle capacity remaining for better performance of the industry. Expert system developed is also used to take decision about a new part /product to be manufactured inhouse or bought outside and save the time in decision making
DIFFERENCES OF CLOUD-BASED SERVICES AND THEIR SAFETY RENEWAL IN THE HEALTH CA...IRJET Journal
The document discusses the benefits and risks of cloud-based services for the healthcare system. It begins by introducing how cloud computing has impacted various sectors including healthcare by enabling storage of large amounts of patient data and easy access. It then categorizes existing cloud applications and services used in healthcare. The document also analyzes security and privacy risks of cloud-based healthcare services and compares the risks of secure vs insecure cloud systems. It proposes that adopting cloud services in healthcare requires addressing security issues.
DIFFERENCES OF CLOUD-BASED SERVICES AND THEIR SAFETY RENEWAL IN THE HEALTH CA...IRJET Journal
The document discusses the benefits and risks of cloud-based services for healthcare systems. It begins by outlining how cloud computing has enabled new diagnostic technologies and easy access to patient data. However, it also notes security and privacy risks, such as data breaches and unauthorized access. The document then reviews existing literature on revolutionary impacts of cloud solutions, predictive threat analysis using big data, and risk analysis of cloud models. It proposes a methodology for categorizing cloud benefits and risks to help healthcare workers and IT professionals. The methodology aims to securely manage data exchange while addressing challenges like cyberattacks and lack of technical knowledge.
Let’s read more on What Are Digital Twins in Manufacturing & How Do Digital Twins Work?
1. Data Collection
2. Data Integration
3. Modeling and Simulation
4. Real-Time Monitoring
This document discusses the importance of having a robust technical support strategy to mitigate the risks and costs of downtime. It begins by outlining how downtime can negatively impact organizations through a "ripple effect" as business processes have become increasingly dependent on integrated IT systems. It then presents IBM's framework for a comprehensive technical support strategy covering people, processes, and technology. The document advocates conducting an assessment of an organization's current support maturity level and developing a roadmap to prioritize improvements. Finally, it argues that a managed support solution through a third party can help optimize support more cost-effectively across an organization's entire IT environment.
This document discusses the importance of having a robust IT technical support strategy. As businesses become more reliant on integrated IT systems, downtime can have far-reaching impacts across an organization. The costs of downtime have increased significantly in recent years. The document recommends taking a holistic view of technical support using a framework that considers people, processes, and technology. It also advises conducting an assessment of the current support structure to identify areas for improvement and prioritization. The overall message is that proactively managing technical support can help businesses optimize costs and mitigate risks from downtime in today's complex IT environments.
Manufacturers have long battled the menace of sudden equipment breakdown, leading to unplanned
downtime that hinders productivity, timely deliveries, and painful losses. The ecosystem has consistently
experimented with multiple approaches that can help them foresee possibilities of equipment
breakdown and prevent them in a timely manner. The initial reactive approach to breakdowns was
inefficient, leading to extensive losses and adding unimaginable stress to all the stakeholders. Preventive
maintenance came as a precious solution,
Digital twin based services for decision support over the product lifecycleShaun West
This presentation is based on an Innosuisse funded project with ten partners to demonstrate how the digital twin can support decision making over the product life cycle.
Industrie 2025, F&E Konferenz zur Industrie 4.0 5 February 2020
Mitigating Climate Change using Artificial IntelligenceIRJET Journal
The document discusses how artificial intelligence can help mitigate climate change. It outlines how machine learning can help reduce greenhouse gas emissions and assist with climate change adaptation in areas like energy, land use, and disaster response. While AI has benefits, there are challenges that have prevented it from being widely used in climate research. The document recommends ways to ensure AI is applied responsibly and effectively to address climate change issues. It provides examples of countries using AI to tackle problems related to deforestation, infrastructure, natural disasters, agriculture and more.
In the era of digital transformation, the concept of Digital Twins has emerged as a revolutionary approach to managing and optimizing the lifecycle of physical assets, systems, and processes. This talk delves into the transformative potential of Digital Maintenance in the Digital Twin Era, highlighting the seamless integration of digital replicas with real-world operations to foster unprecedented levels of efficiency, predictability, and sustainability in maintenance practices. We will explore how Digital Twins serve as dynamic, real-time reflections of physical assets, allowing for meticulous monitoring, analysis, and simulation. Through vivid examples, we'll demonstrate the benefits of this paradigm, such as predictive maintenance, which leverages data analytics and machine learning to anticipate failures and optimize maintenance schedules, thereby reducing downtime and extending asset lifespan. Further, the talk will showcase the role of Digital Twins in facilitating remote maintenance operations. By providing a comprehensive, virtual view of assets, maintenance professionals can perform diagnostics and identify issues without being physically present, enhancing safety and reducing response times. We'll also explore the environmental benefits of Digital Maintenance within the Digital Twin framework. By optimizing maintenance schedules and operations, organizations can significantly reduce their carbon footprint and resource consumption, contributing to more sustainable industrial practices. Finally, the presentation will highlight case studies from various industries, including manufacturing, energy, and transportation, where the adoption of Digital Twins has led to substantial cost savings, improved operational efficiency, and enhanced decision-making processes. These examples will illustrate the tangible value and competitive advantage that Digital Maintenance in the Digital Twin Era offers to forward-thinking organizations.
Simulation for predictive maintenance using weighted training algorithms in ...IJECEIAES
In the production, the efficient employment of machines is realized as a source of industry competition and strategic planning. In the manufacturing industries, data silos are harvested, which is needful to be monitored and deployed as an operational tool, which will associate with a right decisionmaking for minimizing maintenance cost. However, it is complex to prioritize and decide between several results. This article utilizes a synthetic data from a factory, mines the data to filter for an insight and performs machine learning (ML) tool in artificial intelligence (AI) to strategize a decision support and schedule a plan for maintenance. Data includes machinery, category, machinery, usage statistics, acquisition, owner’s unit, location, classification, and downtime. An open-source ML software tool is used to replace the short of maintenance planning and schedule. Upon data mining three promising training algorithms for the insightful data are employed as a result their accuracy figures are obtained. Then the accuracy as weighted factors to forecast the priority in maintenance schedule is proposed. The analysis helps monitor the anticipation of new machines in order to minimize mean time between failures (MTBF), promote the continuous manufacturing and achieve production’s safety.
The document proposes a project to implement embedded systems in a manufacturing process to automate controls and improve quality. It outlines objectives to integrate 10 embedded systems to standardize processes, reduce human errors, and lower costs. A cost-benefit analysis estimates the total costs of hardware, software, assembly, programming and maintenance to be $14,440. The proposal claims embedded systems can increase productivity while maintaining quality standards and that the costs will be offset by improved efficiency over time.
Organizational Factors Affecting Propensity to Adopt Cloud ComputingNiki Kyriakou
Cloud computing (CC) is emerging as a new paradigm of ICT resources acquisition and management by firms. This paper empirically investigates and compares the effects of a set of organizational factors on the propensity to adopt CC, based on data from 676 European firms from the glass, ceramics and cement industries, collected through the e-Business Watch Survey of the European Commission. Our results do not confirm the initial expectations that CC would be adopted primarily by the SMEs, as they indicate that the size has a positive effect on the propensity to adopt CC. Furthermore, we have found that the latter is associated with ICT investment reduction strategy (quite usual today due to the existing economic crisis), and only to a lower extent with innovation oriented strategy. Our results also indicate that previous experience of ICT outsourcing and employment of ICT specialized personnel have positive effects on the propensity to adopt CC. Finally, we have found that firms with higher ICT infrastructure sophistication have higher CC adoption propensity.
Design and Analysis of Runout Measuring Machine using Feaijtsrd
Industrial engineering is a branch of engineering which deals with the optimization of complex processes or systems. It is concerned with the development, improvement, implementation and evaluation of integrated systems of people, money, knowledge, information, equipment, energy, materials, analysis and synthesis, as well as the mathematical, physical and social sciences together with the principles and methods of engineering design to specify, predict, and evaluate the results to be obtained from such systems or processes. While industrial engineering is a traditional and longstanding engineering discipline subject to and eligible for professional engineering licensure in most jurisdictions, its underlying concepts overlap considerably with certain business oriented disciplines such as operations management. Depending on the subspecialties involved, industrial engineering may also be known as, or overlap with, operations management, management science, operations research, systems engineering, management engineering, manufacturing engineering, ergonomics or human factors engineering, safety engineering, or others, depending on the viewpoint or motives of the user. For example, in health care, the engineers known as health management engineers or health systems engineers are, in essence, industrial engineers by another name. Mr. Sandip Subhash Narkhede | Mr. Vijay Liladhar Firke | Mr. Dhruvakumar B. Sharma "Design and Analysis of Runout Measuring Machine using Fea" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd28028.pdf Paper URL: https://www.ijtsrd.com/engineering/mechanical-engineering/28028/design-and-analysis-of-runout-measuring-machine-using-fea/mr-sandip-subhash-narkhede
Industrial IoT has many applications in manufacturing including:
1. Connecting machinery to remotely manage factory units and identify key performance areas.
2. Using sensors to monitor equipment condition and trigger maintenance alerts to reduce downtime.
3. Real-time monitoring of production lines to eliminate waste and optimize costs.
4. Tracking inventory globally to improve supply chain visibility and reduce costs.
A Review on Smart Manufacturing, Technologies and ChallengesIRJET Journal
This document discusses the key components and technologies of smart manufacturing. It identifies six main components: manufacturing processes/technologies, materials, data, predictive engineering, sustainability, and sharing/networking. Additive manufacturing, artificial intelligence, virtual reality, augmented reality, big data analysis, and cyber physical systems are some of the important technologies discussed in enabling smart manufacturing. The document also discusses challenges in implementing these technologies for smart manufacturing.
Monitoring and Visualisation Approach for Collaboration Production Line Envir...Waqas Tariq
In this paper, a tool, called SPMonitor, to monitor and visualize of run-time execution productive processes is proposed. SPMonitor enables dynamically visualizing and monitoring workflows running in a system. It displays versatile information about currently executed workflows providing better understanding about processes and the general functionality of the domain. Moreover, SPMonitor enhances cooperation between different stakeholders by offering extensive communication and problem solving features that allow actors concerned to react more efficiently to different anomalies that may occur during a workflow execution. The ideas discussed are validated through the study of real case related to airbus assembly lines.
IRJET- Scope of Big Data Analytics in Industrial DomainIRJET Journal
This document discusses the scope of big data analytics in industrial domains. It begins by defining big data and its key characteristics, known as the "7 V's" - volume, velocity, variety, variability, veracity, value, and volatility. It then discusses how big data is generated in various fields like social media, search engines, healthcare, online shopping, and stock exchanges. The document focuses on how big data analytics can be applied in industrial Internet of Things (IoT) to extract meaningful information from large and continuous data streams generated by IoT devices using machine learning techniques.
Generalized Overview of Go-to-Market Concept for Smart ManufacturingIRJET Journal
The document discusses the application of artificial intelligence (AI) and machine learning (ML) in smart manufacturing. It first provides an overview of how AI and ML can optimize manufacturing processes through cost savings, increased productivity, quality control, and automation. It then discusses two specific applications: 1) using computer vision and ML for object detection to improve quality checks and real-time supervision, and 2) developing a go-to-market strategy and business model to introduce AI/ML solutions to manufacturers. The document also outlines a methodology for reviewing literature on AI/ML applications and impacts in manufacturing.
IRJET- Contradicting the Hypothesis of Data Analytics with the Help of a Use-...IRJET Journal
This document discusses how data analytics techniques can be used to analyze manufacturing industry data and help with decision making. It presents a case study analyzing expenses data from a manufacturing company from 1950 to 2020. Descriptive analytics on the data show trends in number of employees, working hours, costs of raw materials, machinery, overhead, labor, and profit over time. Diagnostic analytics provide reasons for these trends, such as increases in employees and costs correlating with new technologies and production increases. Predictive analytics are not discussed in the summary. The document suggests prescriptive analytics using advanced Industry 4.0 technologies like ultrafast 3D printing could help maximize profit and minimize employees and costs.
Optimized green it approach in it infrastructure (designing for next generati...eSAT Journals
Abstract
IT Infrastructure & Data Centers are backbone and key sources of Information technology. It runs as background process but
they are very first pillar for each and every event happening at user end and at application level. This paper derives to seek the
appropriate Technology with the help of Software's as well as Hardware's to enhance the performance and simplify the complex
architecture along with Hybridization of Green effect(Eco-friendly) Infrastructure. IT/ICT and Data Centers included the
services of Administration of Servers, infrastructure, backup safety, data-bases, web services, security and network advocates for
ICT- based solutions that enable the benefit by leveraging ICT innovations as well as less pollution emission. These High end
equipments are still a unaware topic which essentially be used to improve IT services at performance, robustness, stability,
security and management and should be bridge the digital divide with advance, innovative technologies and solutions as one
combined unit. According to requirement of Organizations, distinct solution and associated technologies are being taken to
perform multiple tasks simultaneously. As well as it help to get cost cutting and easy to governing through minimal components,
because One small Data Centre or huge Centre are getting on your nerves to manage and control efficiently and inexpensively.
Hopefully this paper would help to get consideration for the dilemma.
Since, In the IT field. Designed in conjunction with industry, which require equipping computer science (IT, and networking)
professional with the deep technical and organizational skills needed to manage complex IT systems and time consuming even for
successful IT teams. Going beyond the ad-hoc approach often used in IT management, this research enables layman or non-IT
person to take a more formal and forward looking approach to managing IT systems in today's rapidly changing environment.
Without solid foundation in the technical and management, depth skills needed for IT management. This learning is put into
practice in our server and networking labs, Where even non IT person have the opportunity to design, create, manage and update
IT systems based on real world scenarios without have depth level expertise of industry standard for hardware and software.
Green IT is one the very logical and important aspect to establish IT infrastructure as per eco-friendly concept norms to move one
step forward towards Next generation oriented Information technology. It will help to clear upcoming vision for future of advance
computing technology.
Key Words: Computer Engineering, IT, Data Centers, and Technology etc…
IRJET-Analysis of Losses Due to Breakdown of Equipments in Construction.IRJET Journal
This document summarizes research on losses due to equipment breakdown in construction projects. It discusses how proper maintenance of construction equipment is important for productivity and reducing costs. The document reviews several research papers on topics like maintenance management systems, criteria for sustainable equipment selection, and multi-criteria decision making for equipment selection. It also provides details on construction equipment planning, selection of appropriate equipment based on tasks, site constraints, and economic factors. The importance of maintenance planning and developing maintenance plans for individual equipment is highlighted.
Let’s read more on What Are Digital Twins in Manufacturing & How Do Digital Twins Work?
1. Data Collection
2. Data Integration
3. Modeling and Simulation
4. Real-Time Monitoring
This document discusses the importance of having a robust technical support strategy to mitigate the risks and costs of downtime. It begins by outlining how downtime can negatively impact organizations through a "ripple effect" as business processes have become increasingly dependent on integrated IT systems. It then presents IBM's framework for a comprehensive technical support strategy covering people, processes, and technology. The document advocates conducting an assessment of an organization's current support maturity level and developing a roadmap to prioritize improvements. Finally, it argues that a managed support solution through a third party can help optimize support more cost-effectively across an organization's entire IT environment.
This document discusses the importance of having a robust IT technical support strategy. As businesses become more reliant on integrated IT systems, downtime can have far-reaching impacts across an organization. The costs of downtime have increased significantly in recent years. The document recommends taking a holistic view of technical support using a framework that considers people, processes, and technology. It also advises conducting an assessment of the current support structure to identify areas for improvement and prioritization. The overall message is that proactively managing technical support can help businesses optimize costs and mitigate risks from downtime in today's complex IT environments.
Manufacturers have long battled the menace of sudden equipment breakdown, leading to unplanned
downtime that hinders productivity, timely deliveries, and painful losses. The ecosystem has consistently
experimented with multiple approaches that can help them foresee possibilities of equipment
breakdown and prevent them in a timely manner. The initial reactive approach to breakdowns was
inefficient, leading to extensive losses and adding unimaginable stress to all the stakeholders. Preventive
maintenance came as a precious solution,
Digital twin based services for decision support over the product lifecycleShaun West
This presentation is based on an Innosuisse funded project with ten partners to demonstrate how the digital twin can support decision making over the product life cycle.
Industrie 2025, F&E Konferenz zur Industrie 4.0 5 February 2020
Mitigating Climate Change using Artificial IntelligenceIRJET Journal
The document discusses how artificial intelligence can help mitigate climate change. It outlines how machine learning can help reduce greenhouse gas emissions and assist with climate change adaptation in areas like energy, land use, and disaster response. While AI has benefits, there are challenges that have prevented it from being widely used in climate research. The document recommends ways to ensure AI is applied responsibly and effectively to address climate change issues. It provides examples of countries using AI to tackle problems related to deforestation, infrastructure, natural disasters, agriculture and more.
In the era of digital transformation, the concept of Digital Twins has emerged as a revolutionary approach to managing and optimizing the lifecycle of physical assets, systems, and processes. This talk delves into the transformative potential of Digital Maintenance in the Digital Twin Era, highlighting the seamless integration of digital replicas with real-world operations to foster unprecedented levels of efficiency, predictability, and sustainability in maintenance practices. We will explore how Digital Twins serve as dynamic, real-time reflections of physical assets, allowing for meticulous monitoring, analysis, and simulation. Through vivid examples, we'll demonstrate the benefits of this paradigm, such as predictive maintenance, which leverages data analytics and machine learning to anticipate failures and optimize maintenance schedules, thereby reducing downtime and extending asset lifespan. Further, the talk will showcase the role of Digital Twins in facilitating remote maintenance operations. By providing a comprehensive, virtual view of assets, maintenance professionals can perform diagnostics and identify issues without being physically present, enhancing safety and reducing response times. We'll also explore the environmental benefits of Digital Maintenance within the Digital Twin framework. By optimizing maintenance schedules and operations, organizations can significantly reduce their carbon footprint and resource consumption, contributing to more sustainable industrial practices. Finally, the presentation will highlight case studies from various industries, including manufacturing, energy, and transportation, where the adoption of Digital Twins has led to substantial cost savings, improved operational efficiency, and enhanced decision-making processes. These examples will illustrate the tangible value and competitive advantage that Digital Maintenance in the Digital Twin Era offers to forward-thinking organizations.
Simulation for predictive maintenance using weighted training algorithms in ...IJECEIAES
In the production, the efficient employment of machines is realized as a source of industry competition and strategic planning. In the manufacturing industries, data silos are harvested, which is needful to be monitored and deployed as an operational tool, which will associate with a right decisionmaking for minimizing maintenance cost. However, it is complex to prioritize and decide between several results. This article utilizes a synthetic data from a factory, mines the data to filter for an insight and performs machine learning (ML) tool in artificial intelligence (AI) to strategize a decision support and schedule a plan for maintenance. Data includes machinery, category, machinery, usage statistics, acquisition, owner’s unit, location, classification, and downtime. An open-source ML software tool is used to replace the short of maintenance planning and schedule. Upon data mining three promising training algorithms for the insightful data are employed as a result their accuracy figures are obtained. Then the accuracy as weighted factors to forecast the priority in maintenance schedule is proposed. The analysis helps monitor the anticipation of new machines in order to minimize mean time between failures (MTBF), promote the continuous manufacturing and achieve production’s safety.
The document proposes a project to implement embedded systems in a manufacturing process to automate controls and improve quality. It outlines objectives to integrate 10 embedded systems to standardize processes, reduce human errors, and lower costs. A cost-benefit analysis estimates the total costs of hardware, software, assembly, programming and maintenance to be $14,440. The proposal claims embedded systems can increase productivity while maintaining quality standards and that the costs will be offset by improved efficiency over time.
Organizational Factors Affecting Propensity to Adopt Cloud ComputingNiki Kyriakou
Cloud computing (CC) is emerging as a new paradigm of ICT resources acquisition and management by firms. This paper empirically investigates and compares the effects of a set of organizational factors on the propensity to adopt CC, based on data from 676 European firms from the glass, ceramics and cement industries, collected through the e-Business Watch Survey of the European Commission. Our results do not confirm the initial expectations that CC would be adopted primarily by the SMEs, as they indicate that the size has a positive effect on the propensity to adopt CC. Furthermore, we have found that the latter is associated with ICT investment reduction strategy (quite usual today due to the existing economic crisis), and only to a lower extent with innovation oriented strategy. Our results also indicate that previous experience of ICT outsourcing and employment of ICT specialized personnel have positive effects on the propensity to adopt CC. Finally, we have found that firms with higher ICT infrastructure sophistication have higher CC adoption propensity.
Design and Analysis of Runout Measuring Machine using Feaijtsrd
Industrial engineering is a branch of engineering which deals with the optimization of complex processes or systems. It is concerned with the development, improvement, implementation and evaluation of integrated systems of people, money, knowledge, information, equipment, energy, materials, analysis and synthesis, as well as the mathematical, physical and social sciences together with the principles and methods of engineering design to specify, predict, and evaluate the results to be obtained from such systems or processes. While industrial engineering is a traditional and longstanding engineering discipline subject to and eligible for professional engineering licensure in most jurisdictions, its underlying concepts overlap considerably with certain business oriented disciplines such as operations management. Depending on the subspecialties involved, industrial engineering may also be known as, or overlap with, operations management, management science, operations research, systems engineering, management engineering, manufacturing engineering, ergonomics or human factors engineering, safety engineering, or others, depending on the viewpoint or motives of the user. For example, in health care, the engineers known as health management engineers or health systems engineers are, in essence, industrial engineers by another name. Mr. Sandip Subhash Narkhede | Mr. Vijay Liladhar Firke | Mr. Dhruvakumar B. Sharma "Design and Analysis of Runout Measuring Machine using Fea" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd28028.pdf Paper URL: https://www.ijtsrd.com/engineering/mechanical-engineering/28028/design-and-analysis-of-runout-measuring-machine-using-fea/mr-sandip-subhash-narkhede
Industrial IoT has many applications in manufacturing including:
1. Connecting machinery to remotely manage factory units and identify key performance areas.
2. Using sensors to monitor equipment condition and trigger maintenance alerts to reduce downtime.
3. Real-time monitoring of production lines to eliminate waste and optimize costs.
4. Tracking inventory globally to improve supply chain visibility and reduce costs.
A Review on Smart Manufacturing, Technologies and ChallengesIRJET Journal
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services of Administration of Servers, infrastructure, backup safety, data-bases, web services, security and network advocates for
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Green IT is one the very logical and important aspect to establish IT infrastructure as per eco-friendly concept norms to move one
step forward towards Next generation oriented Information technology. It will help to clear upcoming vision for future of advance
computing technology.
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This document summarizes research on losses due to equipment breakdown in construction projects. It discusses how proper maintenance of construction equipment is important for productivity and reducing costs. The document reviews several research papers on topics like maintenance management systems, criteria for sustainable equipment selection, and multi-criteria decision making for equipment selection. It also provides details on construction equipment planning, selection of appropriate equipment based on tasks, site constraints, and economic factors. The importance of maintenance planning and developing maintenance plans for individual equipment is highlighted.
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6. http://imu.ntua.gr
Industry 4.0
7/5/2018
6
Industry 4.0 indicates the flexibility that exists in value-
creating networks which enables machines and plants to
adapt their behaviour to changing orders and operating
conditions through self-optimization and
reconfiguration.
Platform Industrie 4.0. (2015). Platform Industrie 4.0: Umsetzunsstrategie Industrie 4.0. Berlin.
8. http://imu.ntua.gr
Industrial Maintenance
7/5/2018
ICCS – Alexandros Bousdekis 8
• Maintenance is a key operation function
within manufacturing enterprises.
• New practices put failure prediction at the
backbone of maintenance decision making.
Guillén, A. J. et al (2016): A framework for effective management of condition based maintenance programs in the context of
industrial development of E-Maintenance strategies. Computers in Industry, 82, 170-185.
• However, the stochastic degradation process
leads to high uncertainty in the decision
making process.
• E-maintenance refers to the convergence of
emerging information and communication
technologies with information systems to enable
decision making in a proactive way
– Technology
– Models, methods, algorithms
Muller, A., Marquez, A. C., & Iung, B. (2008). On the concept of e-maintenance: Review and current research. Reliability Engineering
& System Safety, 93(8), 1165-1187.
10. http://imu.ntua.gr
Proactive Computing (1/2)
7/5/2018
10
• Getting physical
– Proactive systems will be intimately
connected to the world around them
• using sensors and actuators to both
monitor and shape their physical
surroundings
• Getting real
– Proactive computers will routinely
respond to external stimuli
• at faster-than-human speeds.
• Getting out
– Interactive computing deliberately
places human beings in the loop
• Shrinking time constants and sheer
numbers demand research into proactive
modes of operation in which humans are
above the loop.
Tennenhouse, D. (2000). Proactive computing. Communications of the ACM, 43(5), 43-50.
11. http://imu.ntua.gr
Proactive Computing (2/2)
• Event-Driven Architecture (EDA) provides
an architectural computing paradigm that has
the ability to react to changes by processing
events.
7/5/2018
11
Source: Etzion O. (2016). Proactive Computing:
Changing the Future. RTInsights.
• Proactivity is referred to the ability to avoid or
eliminate the impact of undesired future events, or to
exploit future opportunities on the basis of real-time
predictions about a future event.
– “Detect-Predict-Decide-Act”
Engel, Y. & Etzion, O. (2011): Towards proactive event-driven computing. In Proceedings of the 5th ACM international conference on Distributed event-based system
(pp. 125-136). ACM.
12. http://imu.ntua.gr
Literature Review Outcomes
7/5/2018
12
Industry 4.0
• Documentation of “Platform Industrie 4.0”
• 19 scientific publications
• 7 consulting reports
Industrial Maintenance
• Standards
• 133 scientific publications
• 13 consulting and software reports
Proactive Computing
• 24 scientific publications
• 2 consulting and software reports
Manufacturing companies are slowly adopting
novel technologies.
Proactive computing needs successful applications in
order to further prove its effectiveness.
Maintenance and logistics
management are strongly
interconnected.
Industry 4.0 is at the very
first steps of its evolution.
The dynamic manufacturing environment poses
challenges to decision making.
Most applications currently supported by event
processing platforms are reactive by nature.
15. http://imu.iccs.gr
Next generation of maintenance in Industry 4.0
15
7/5/2018
RQ4
• How to incorporate context-awareness in
proactive decision making?
RQ1
• What is the next generation of industrial
maintenance in Industry 4.0?
16. http://imu.ntua.gr
Limitations of existing maintenance solutionsExistingLimitations
Physical models of equipment not easily extensible to other equipment.
Rarely exploit sensor-generated big data processing infrastructures.
Not incorporate data-driven decision making capabilities.
Focus on a specific aspect of maintenance
(e.g. condition monitoring, diagnostics, etc.).
At a conceptual level with limited practical applications in real industrial
environments.
7/5/2018
16
[J2] Bousdekis, A., Magoutas, B., Apostolou, D., & Mentzas, G. (2015). Review, analysis and synthesis of
prognostic-based decision support methods for condition based maintenance. Journal of Intelligent
Manufacturing, 1-14.
[C2] Bousdekis, A., Magoutas, B., Apostolou, D., & Mentzas, G. (2015). Supporting the Selection of
Prognostic-based Decision Support Methods in Manufacturing. In ICEIS (pp. 487-494).
17. http://imu.ntua.gr
Towards Proactive Maintenance
7/5/2018
17
Proactive Maintenance is a new
maintenance strategy that is based upon
4 technological pillars: Industry 4.0, IoT,
Big Data and Proactive Computing.
• It provides real-time monitoring,
detections, predictions and proactive
recommendations about maintenance
actions in a data-driven way.
• The aim is to support decision making
in order to eliminate or mitigate the
impact of future failures with the aim
to maximize reliability of operations
and improve business performance.
18. http://imu.ntua.gr
The Conceptual Architecture for Proactive Maintenance
7/5/2018
18
[C9] Bousdekis, A., & Mentzas, G. (2017). Condition-Based Predictive Maintenance in the Frame of
Industry 4.0. In IFIP International Conference on Advances in Production Management Systems (pp. 399-
406). Springer, Cham.
[C13] Bousdekis, A., Mentzas, G., Hribernik, K., Lewandowski, M., von Stietencron, M., & Thoben, K. D.
(2018). A Unified Architecture for Proactive Maintenance in Manufacturing Enterprises. In Enterprise
Interoperability: I-ESA ’18 Proceedings. Springer International Publishing. (In Press)
19. http://imu.iccs.gr
Proactive Decision Making in Maintenance Operations
19
7/5/2018
RQ4
• How to incorporate context-awareness in
proactive decision making?
RQ2
• How to support proactive decision making
in maintenance operations?
20. http://imu.ntua.gr
Limitations of existing maintenance decision making algorithmsExistingLimitations
They assume perfect maintenance. Imperfect maintenance actions with various
degrees is not usually considered.
Recommendations for immediate implementation of certain actions.
Not triggered by real-time predictions about future failures.
7/5/2018
20
Not embedded in an event-driven computational environment.
[J1] Bousdekis, A., Magoutas, B., Apostolou, D., & Mentzas, G. (2015). A proactive decision making
framework for condition-based maintenance. Industrial Management & Data Systems, 115(7), 1225-
1250.
21. http://imu.ntua.gr
Decision Making in the context of Proactivity
7/5/2018
21
Proactive decision making aims to
enable business analysts to create and
configure decision method instances
for mitigating a future undesired event,
which lays outside the desired states
space.
Based on the predictions for
undesirable situations derived on the
basis of streaming data, decision
methods instances are enacted online
to generate mitigating action
recommendations and optimal time of
action implementation.
22. http://imu.ntua.gr
Proactive Decision Making for Maintenance Actions
Proactive Expected Loss Rate Optimization
•Recommendation of the optimal time for a
pre-defined action
•PDF of the event occurrence can be of
arbitrary type.
Proactive Markov Decision Process
•Recommendation of the optimal (perfect or
imperfect) maintenance action and the
optimal time of applying it.
•PDF of the event occurrence belongs to
exponential family.
7/5/2018
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Proactive event-driven decision methods for maintenance actionsProactive Markov Decision Process
[J4] Bousdekis, A., Papageorgiou N., Magoutas, B., Apostolou, D., & Mentzas, G. (2018). Enabling
Condition-Based Maintenance Decisions with Proactive Event-driven Computing. Computers in Industry,
100, 173-183.
[C4] Bousdekis, A., & Mentzas, G. (2015). A Proactive Decision Support System for Maintenance Cost
Minimisation in Manufacturing Enterprises. In 4th Student Conference of Hellenic Operational Research
Society (HELORS) (pp. 61-66).
23. http://imu.ntua.gr
Proactive Decision Making for Logistics Actions
Proactive joint replacement and spare parts
inventory decision model
Proactive joint maintenance and spare parts
inventory decision model
7/5/2018
23
Proactive event-driven decision methods for joint maintenance and
spare parts ordering optimization
𝐶 𝑚 𝑡 = 𝑐𝑓 𝑡 ∗ 𝑃 𝜀
0, 𝑡 + 𝑐𝑓 𝑡 + 𝑐𝑝 𝑡 ∗ 𝑃𝑎
𝜀
𝑡, 𝑇 + 𝑐𝑝 𝑡 ∗ 𝑃 𝜀
0, 𝑇
𝐶𝑜 𝑡 = 𝑐𝑠 𝑡 ∗ 𝑃 𝜀
0, 𝑡 + 𝐿 + 𝑐𝑠 𝑡 ∗ 𝑃𝑎
𝜀
𝑡 + 𝐿, 𝑇 + 𝑐ℎ 𝑡 ∗ 𝑃 𝜀
0, 𝑇
Proactive Selection of Maintenance Spare Parts’ Suppliers
[C8] Bousdekis, A., Papageorgiou, N., Magoutas, B., Apostolou, D., & Mentzas, G. (2017). A Proactive
Event-driven Decision Model for Joint Equipment Predictive Maintenance and Spare Parts Inventory
Optimization. Procedia CIRP, 59, 184-189.
[C12] Bousdekis, A., & Mentzas, G. (2018). A proactive model for joint maintenance and logistics
optimization in the frame of Industrial Internet of Things. In Operational Research in Business and
Economics. Springer International Publishing. (In Press)
[C10] Bousdekis, A., Papageorgiou, N., Magoutas, B., Apostolou, D., & Mentzas, G. (2017). A Framework
for Integrated Proactive Maintenance Decision Making and Supplier Selection. In IFIP International
Conference on Advances in Production Management Systems (pp. 416-424). Springer, Cham.
• Markowitz Portfolio Theory (MPT)
optimization
• It recommends the optimal portfolio of
suppliers at the recommended future
ordering time.
24. http://imu.iccs.gr
Continuous Improvement of Proactive Decision Making
24
7/5/2018
RQ4
• How to incorporate context-awareness in
proactive decision making?
RQ3
• How to conduct continuous improvement
of proactive decision making?
25. http://imu.ntua.gr
Limitations of existing feedback mechanismsExistingLimitations
7/5/2018
25
Expert-driven and constant domain knowledge (e.g. cost functions) for the
configuration of decision methods.
Data acquisition not considered for improving maintenance decision making.
Limited research on eliminating user input inaccuracy and sensor noise.
Limited research on addressing sensitivity to input parameters in proactive
decision making, especially to those related to costs.
26. http://imu.ntua.gr
Continuous Improvement of Proactive Decision Making
7/5/2018
26
• Through Sensor-Enabled Feedback (SEF):
– the user is informed online about the estimated cost of action during its
implementation.
– the updated cost function of the specific action is used in the next recommendation in
which this action is involved.
27. http://imu.ntua.gr
Sensor-Enabled Feedback (SEF)
7/5/2018
27
Real-time Bayesian
changepoint detection
Kalman Filter
Curve Fitting with non-
negativity constraints
(Levenberg–Marquardt)
[C5] Bousdekis, A., Papageorgiou, N., Magoutas, B., Apostolou, D., & Mentzas, G. (2016). Continuous
Improvement of Proactive Event-driven Decision Making through Sensor-Enabled Feedback (SEF). In
ICEIS (pp. 166-173).
1
2
3
[J3] Bousdekis, A., Papageorgiou N., Magoutas, B., Apostolou, D., & Mentzas, G. (2018). Information
Processing for Generating Recommendations ahead of Time in an IoT-based Environment. International
Journal of Monitoring and Surveillance Technologies Research (IJMSTR), 5(4), 38-62.
28. http://imu.iccs.gr
Context-awareness in proactive decision making
28
7/5/2018
RQ1
• What is the next generation of industrial
maintenance in Industry 4.0?
RQ2
• How to support proactive decision making
in maintenance operations?
RQ3
• How to conduct continuous improvement
of proactive decision making?
RQ4
• How to incorporate context-awareness in
proactive decision making?RQ4
• How to incorporate context-awareness in
proactive decision making?
29. http://imu.iccs.gr
Context-awareness
29
7/5/2018
Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2014). Context aware computing for the internet of things: A survey. IEEE communications surveys &
tutorials, 16(1), 414-454.
Schmidt, B., Galar, D., & Wang, L. (2016). Context awareness in predictive maintenance. In Current trends in reliability, availability, maintainability and safety (pp.
197-211). Springer, Cham.
Thaduri, A., Kumar, U., & Verma, A. K. (2017). Computational intelligence framework for context-aware decision making. International Journal of System
Assurance Engineering and Management, 8(4), 2146-2157.
Context: “any information that can be used to characterize the situation of an entity.
An entity is a person, place, or object that is considered relevant to the interaction
between a user and an application, including the user and applications themselves.”
Although it is a well-known concept in pervasive and mobile computing, it has just
started to emerge in industrial maintenance.
Machine Learning is considered a context modelling approach in terms of its
objectives. It is the best approach for intelligent context-aware systems since it is able
to deal with uncertainty in a future context.
30. http://imu.ntua.gr
Limitations of existing context-aware mechanisms
ExistingLimitations
7/5/2018
30
Limited and conceptual research works for industrial maintenance in the frame
of Industry 4.0.
Focused on reactive applications rather than proactive ones.
Considered in detection and prediction algorithms, but not in decision making
algorithms.
Not considered the prediction of future context for proactive decision making
in industrial maintenance.
31. http://imu.ntua.gr
The probabilistic context-aware model
• Context-aware Model Initialization
– Bayesian Network
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31
– Bayesian Cost Risk Functions
𝐶 𝑛 𝑡 = 𝐶 𝑛,𝑖 𝑡 ∗ 𝑃 𝐶 𝑛 𝑡 = 𝐶 𝑛,𝑖 𝑡 |𝐶𝐸1 ∩ … ∩ 𝐶𝐸 𝑚
𝑖=𝑘
𝑖=1
• Context-aware Model Reasoning
– X-means clustering in BN effect nodes
𝐸𝐿𝑅 =
𝐶𝑢𝑒 𝑡 ∗ 𝐺 𝜀
𝑡
𝑡 𝑢𝑒
+
𝐶𝑝𝑎 𝑡 ∗ 𝐺 𝜀
𝑡
𝑡 𝑝𝑎
Proactive event-driven decision methods
E.g.
Implementation of recommended action
Sensor-Enabled Feedback (SEF)
[C7] Bousdekis, A., Papageorgiou, N., Magoutas, B., Apostolou, D., & Mentzas, G. (2016). A probabilistic
model for context-aware proactive decision making. In Information, Intelligence, Systems & Applications
(IISA), 2016 7th International Conference on (pp. 1-6). IEEE.
[C3] Bousdekis, A., Papageorgiou, N., Magoutas, B., Apostolou, D., & Mentzas, G. (2015). A real-time
architecture for proactive decision making in manufacturing enterprises. In OTM Confederated
International Conferences" On the Move to Meaningful Internet Systems" (pp. 137-146). Springer, Cham.
33. http://imu.ntua.gr 7/5/2018
33
PANDDA
• PANDDA (ProActive seNsing enterprise
Decision configurator DAshboard) is a
Python web-application developed
the web2py framework.
• The presentation layer occupies the top
level of the architecture and displays
information related to services available
on the web-based PANDDA
configuration.
• The logic layer controls application
functionality by performing detailed
processing.
• The data layer houses a relational
database engine where the information
needed by the main algorithms of
PANDDA is stored and retrieved.[C1] Magoutas, B., Stojanovic, N., Bousdekis, A., Apostolou, D., Mentzas, G., & Stojanovic, L. (2014).
Anticipation-driven Architecture for Proactive Enterprise Decision Making. In CAiSE (pp. 121-128).
PANDDA in Proactive Maintenance
ProActive seNsing enterprise Decision configurator DAshboardThe PANDDA technical architecture
34. http://imu.ntua.gr 7/5/2018
34
The user has created 3 instances for 3
parts of equipment. The failure prediction
triggers the part of equipment which is
referred to.
For the “Gearbox” instance, the user has
inserted 3 alternative (perfect and
imperfect) maintenance actions, their costs
and the cost of failure.
User Configuration
Proactive
Recommendation
Sensor-Enabled
Feedback
User Configuration
35. http://imu.ntua.gr 7/5/2018
35
Proactive Recommendations
• PANDDA is triggered by a prediction event that there is an exponential probability
distribution for the gearbox breakdown with a time-to-failure in 9 days.
• A proactive recommendation (action-time pair) that minimizes the expected loss is
generated.
• The recommendations of each instance are stored in the system.
User Configuration
Proactive
Recommendation
Sensor-Enabled
Feedback
Proactive
Recommendation
36. http://imu.ntua.gr 7/5/2018
36Alexandros Bousdekis
a. Online cost monitoring b. Cost function update
[C11] Bousdekis, A., Papageorgiou, N., Magoutas, B., Apostolou, D., & Mentzas, G. (2017). An Information
System for Deciding and Acting ahead of Time in Sensing Enterprises. In Information, Intelligence,
Systems & Applications (IISA), 2017 8th International Conference on (pp. 1-6). IEEE.
Proactive RecommendationsUser Configuration
Proactive
Recommendation
Sensor-Enabled
Feedback
Sensor-Enabled
Feedback
38. http://imu.ntua.gr
The Oil Drilling case (1/3)
• MHWirth is a leading global provider of first-
class drilling solutions and services.
• Global span covering 5 continents with offices
in more than 20 countries and employs 4,300
professionals.
• Its revenue is approx. 1 billion dollars.
7/5/2018
38
• Oil and gas projects are capital-intensive
investments, with severe consequences in financial
and environmental terms in case of breakdown.
• Since a typical production rate for an oil and gas
corresponds to USD 500,000, the reduction of
downtime is of great significance in the oil and gas.
39. http://imu.ntua.gr
The Oil Drilling case (2/3)
7/5/2018
39
Sensors measure parameters that are known to affect the oil rig’s gearbox:
temperature
vibration
friction losses
environmental conditions
Detect: a real-time detection service detects a
complex pattern that indicates an abnormal
behaviour of the equipment
Predict: a real-time predictive analytics service
provides a prediction about the gearbox
breakdown.
Decide: context-aware proactive recommendations
about joint maintenance and inventory actions.
Act: continuous monitoring and adaptation of the
whole cycle
40. http://imu.ntua.gr
Maintenance Spare parts ordering
Onshore maintenance in 98.26 hours Order the DDM in 49.12 hours
The Oil Drilling case (3/3)
7/5/2018
40
Context-aware Proactive Recommendations before SEF
Context-aware Proactive Recommendations after SEF
Maintenance Spare parts ordering
Offshore maintenance in 85.47 hours Order the gearbox in 42.36 hours
41. http://imu.ntua.gr
The Automotive Lighting Equipment case (1/3)
• A reduction of scrap rate in the automotive
lighting industry by just 1%, results in savings of
the order of 100,000 Euro per year.
• More than 60 different raw plastic materials may
be used for component production, each with its
own properties.
7/5/2018
41
• Hella Saturnus (Ljubljana, Slovenia) is a part
of the Hella Group.
• Core business is the production of lighting
equipment for motor vehicles.
• 95% of sales are exported worldwide.
• The last annual revenue was 257,000 EUR
• Employs approx. 2,800 people.
42. http://imu.iccs.gr
42
The Automotive Lighting Equipment case (2/3)
– Detect: a real-time detection service detects
a complex pattern that indicates an abnormal
behaviour of the equipment
– Predict: a real-time predictive analytics
service provides a prediction about the scrap
rate exceeding a threshold
– Decide: proactive recommendations about
joint maintenance and inventory actions.
– Act: continuous monitoring and adaptation of
the whole cycle
Sensors measure parameters that are known to affect the moulding machine
and therefore, the scrap rate of cover lens:
the dust levels in the shop floor
environmental factors, i.e. temperature and humidity
43. http://imu.ntua.gr
The Automotive Lighting Equipment case (3/3)
7/5/2018
43
Supplier A Supplier B Supplier C Supplier D
0.14 0.38 0.26 0.22
Maintenance Spare parts ordering
Clean the moulding machine in 3.54 hours Order the moulds in 1.32 hours
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Pilot Evaluation
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Primoz Puhar
Head of Test Engineering
Department
Tor Inge Waag
Specialist Engineer
“The system provides accurate
and reliable information, while
it improves and simplifies
decision making by drilling
operators and maintenance
planners.”
“Proactive Maintenance allows
us to mature and to avoid
defect-causing combinations in
order to move towards a ‘zero
defects’ approach.”
[C14] Bousdekis, A., Magoutas, B., Apostolou, D., Mentzas, G., & Puhar, P. (2018). The ProaSense
Platform for Predictive Maintenance in the Automotive Lighting Equipment Industry. In I-ESA ’18
Workshop Proceedings. ISTE-Wiley. (In Press)
[C6] Bousdekis, A., & Mentzas, G. (2016). A Multiple Criteria Approach Using ELECTRE for the Selection
of Maintenance Strategy in Manufacturing Companies. In Proceedings of 5th International Symposium
and 27th National Conference on Operational Research (pp. 117-121).
Questionnaire-based Evaluation Experts Evaluation
45. http://imu.ntua.gr
Simulation-based Evaluation
• Evaluation in real industrial environment is challenging due
to:
– The large timescales
– The operations lifecycle
– The criticality of equipment
• Therefore, a simulated computational environment was
created in order to deal with cases that did not arise in the
business cases during the evaluation period.
• Extensive simulation experiments for comparative and
sensitivity analyses show the added value of the thesis.
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Proactive Decision Making (2/2)
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• Proactive decision making leads to significantly lower losses
• by 29% to 77% with respect to reactive (breakdown maintenance) policy
• by 22% to 65% with respect to preventive (time-based maintenance)
policy
• by 7% to 61% with respect to myopic policy.
• Proactive decision making is highly sensitive with respect to its
input parameters and especially to those related to cost.
• The proactive recommendations significantly change
according to the prediction events.
• The earlier a failure is predicted
– the less the expected loss
– the decision maker has more time to be prepared
EvaluationResults
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Continuous Improvement of Proactive Decision Making
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EvaluationResults
• SEF leads to more reliable recommendations.
• The Standard Error of SEF is:
• 91% lower than the expert initial estimate
• 88% lower than processing noisy data
• The increased reliability of proactive recommendations leads to a more
accurate estimate of the maintenance expected losses by 9% to 88%.
• Noise filtering in SEF has a strong effect on cost function estimation.
• When cost function is high, uncertainty in sensor measurements leads to less
accurate results.
• Higher noise levels lead to less accurate estimations.
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Context-awareness in Proactive Decision Making
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Total Expected Loss for each approach (Euro)
Scenario Reactive Myopic Proactive Context-aware proactive
1 1,491,360 ± 185,150 827,635 ± 93,234 482,355 ± 71,566 376,810 ± 53,392
2 874,362 ± 41,275 596,122 ± 46,988 333,245 ± 37,461 281,245 ± 31,711
3 122,644 ± 12,476 93,532 ± 11,855 50,769 ± 11,450 42,712 ± 8,120
4 30,550 ± 3,122 22,550 ± 3,044 12,915 ± 2,988 9,675 ± 2,336
5 446,500 ± 23,110 315,000 ± 19,750 191,235 ± 16,814 122,651 ± 15,912
Approach Maintenance Action Logistics Action Total Expected Loss
Reactive Onshore maintenance after oil
rig moving
Immediate ordering of DDM 1,492,000 Euro
Myopic Gearbox replacement when
spare part arrives
Immediate ordering of gearbox 825,000 Euro
Proactive Operate at reduced equipment
load in 95.22 hours
Ordering of swivel hook in 84.23
hours
482,355 Euro
Context-aware
proactive
Offshore maintenance in 85.47
hours
Ordering of gearbox or gears in
42.36 hours
376,850 Euro
EvaluationResults
• Context-awareness in proactive decision making contributes to
higher accuracy in proactive decision methods’ input parameters.
• It increases the sensitivity of proactive decision making.
• To this end, the SEF mechanism acquires even higher importance.
• It leads to differences in expected losses with respect to
proactive decision making without context by 12% to 37%.
• It is sensitive to the time window between the time that a
prediction is received and the time of the predicted future failure.
50. http://imu.ntua.gr
Outline
• Introduction and Motivation
• Towards Proactive Maintenance Management
• The PhD Approach
• Deployment in Industrial Environments
• Evaluation
• Conclusions and Future Work
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Conclusions
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RQ1
• What is the next generation of industrial maintenance in Industry 4.0?
RQ4
• Framework for Proactive Maintenance
RQ2
• How to support proactive decision making in maintenance operations?
RQ4
• Proactive decision methods for: (i) maintenance actions; (ii) logistics actions;
RQ3
• How to conduct continuous improvement of proactive decision
making?
RQ4
• Sensor-Enabled Feedback (SEF)
RQ4
• How to incorporate context-awareness in proactive decision making?
RQ4
• Probabilistic context-aware model
52. http://imu.ntua.gr
Future Work
• To consider interdependencies among different parts of
equipment in proactive decision making.
– This would be applicable to complex manufacturing systems and sensor
networks.
– In this way, the failure predictions will be interrelated and proactive decision
making will provide recommendations about sets of maintenance actions.
• To utilize legacy data analytics and FMECA outcomes for the
configuration and enrichment of the proactive decision models.
• To develop a generic prescriptive analytics approach for supporting
proactive decision making.
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[C15] Lepenioti, K., Bousdekis, A., Apostolou, D., Mentzas, G. (2018). Prescriptive Analytics: A Survey of
Approaches and Methods. In International Conference on Business Information Systems (BIS). Springer,
Cham. (In Press)
53. http://imu.ntua.gr
Journal Publications
• [J1] Bousdekis, A., Magoutas, B., Apostolou, D., & Mentzas, G. (2015). A proactive
decision making framework for condition-based maintenance. Industrial Management & Data
Systems, 115(7), 1225-1250. Impact Factor: 2.948
• [J2] Bousdekis, A., Magoutas, B., Apostolou, D., & Mentzas, G. (2015). Review, analysis
and synthesis of prognostic-based decision support methods for condition based
maintenance. Journal of Intelligent Manufacturing, 1-14. Impact Factor: 3.667
(NTUA Thomaideio Award 2015)
• [J3] Bousdekis, A., Papageorgiou N., Magoutas, B., Apostolou, D., & Mentzas, G. (2018).
Information Processing for Generating Recommendations ahead of Time in an IoT-based
Environment. International Journal of Monitoring and Surveillance Technologies Research
(IJMSTR), 5(4), 38-62.
• [J4] Bousdekis, A., Papageorgiou N., Magoutas, B., Apostolou, D., & Mentzas, G. (2018).
Enabling Condition-Based Maintenance Decisions with Proactive Event-driven Computing.
Computers in Industry, 100, 173-183. Impact Factor: 2.850
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Conference Publications (1/2)
• [C1] Magoutas, B., Stojanovic, N., Bousdekis, A., Apostolou, D., Mentzas, G., & Stojanovic, L. (2014).
Anticipation-driven Architecture for Proactive Enterprise Decision Making. In CAiSE (pp. 121-128).
• [C2] Bousdekis, A., Magoutas, B., Apostolou, D., & Mentzas, G. (2015). Supporting the Selection of
Prognostic-based Decision Support Methods in Manufacturing. In ICEIS (pp. 487-494).
• [C3] Bousdekis, A., Papageorgiou, N., Magoutas, B., Apostolou, D., & Mentzas, G. (2015). A real-time
architecture for proactive decision making in manufacturing enterprises. In OTM Confederated International
Conferences" On the Move to Meaningful Internet Systems" (pp. 137-146). Springer, Cham.
• [C4] Bousdekis, A., & Mentzas, G. (2015). A Proactive Decision Support System for Maintenance Cost
Minimisation in Manufacturing Enterprises. In 4th Student Conference of Hellenic Operational Research
Society (HELORS) (pp. 61-66).
(Best Paper Award)
• [C5] Bousdekis, A., Papageorgiou, N., Magoutas, B., Apostolou, D., & Mentzas, G. (2016). Continuous
Improvement of Proactive Event-driven Decision Making through Sensor-Enabled Feedback (SEF). In
ICEIS (pp. 166-173).
• [C6] Bousdekis, A., & Mentzas, G. (2016). A Multiple Criteria Approach Using ELECTRE for the Selection
of Maintenance Strategy in Manufacturing Companies. In Proceedings of 5th International Symposium and
27th National Conference on Operational Research (pp. 117-121).
• [C7] Bousdekis, A., Papageorgiou, N., Magoutas, B., Apostolou, D., & Mentzas, G. (2016). A probabilistic
model for context-aware proactive decision making. In Information, Intelligence, Systems & Applications
(IISA), 2016 7th International Conference on (pp. 1-6). IEEE.
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Conference Publications (2/2)
• [C8] Bousdekis, A., Papageorgiou, N., Magoutas, B., Apostolou, D., & Mentzas, G. (2017). A Proactive Event-driven
Decision Model for Joint Equipment Predictive Maintenance and Spare Parts Inventory Optimization. Procedia CIRP,
59, 184-189.
(Best Paper Award)
(NTUA Thomaideio Award 2016)
• [C9] Bousdekis, A., & Mentzas, G. (2017). Condition-Based Predictive Maintenance in the Frame of Industry 4.0. In
IFIP International Conference on Advances in Production Management Systems (pp. 399-406). Springer, Cham.
• [C10] Bousdekis, A., Papageorgiou, N., Magoutas, B., Apostolou, D., & Mentzas, G. (2017). A Framework for
Integrated Proactive Maintenance Decision Making and Supplier Selection. In IFIP International Conference on
Advances in Production Management Systems (pp. 416-424). Springer, Cham.
• [C11] Bousdekis, A., Papageorgiou, N., Magoutas, B., Apostolou, D., & Mentzas, G. (2017). An Information System
for Deciding and Acting ahead of Time in Sensing Enterprises. In Information, Intelligence, Systems & Applications
(IISA), 2017 8th International Conference on (pp. 1-6). IEEE.
• [C12] Bousdekis, A., & Mentzas, G. (2018). A proactive model for joint maintenance and logistics optimization in the
frame of Industrial Internet of Things. In Operational Research in Business and Economics. Springer International
Publishing. (In Press)
• [C13] Bousdekis, A., Mentzas, G., Hribernik, K., Lewandowski, M., von Stietencron, M., & Thoben, K. D. (2018). A
Unified Architecture for Proactive Maintenance in Manufacturing Enterprises. In Enterprise Interoperability: I-ESA ’18
Proceedings. Springer International Publishing. (In Press)
• [C14] Bousdekis, A., Magoutas, B., Apostolou, D., Mentzas, G., & Puhar, P. (2018). The ProaSense Platform for
Predictive Maintenance in the Automotive Lighting Equipment Industry. In I-ESA ’18 Workshop Proceedings. ISTE-
Wiley. (In Press)
• [C15] Lepenioti, K., Bousdekis, A., Apostolou, D., Mentzas, G. (2018). Prescriptive Analytics: A Survey of Approaches
and Methods. In International Conference on Business Information Systems (BIS). Springer, Cham. (In Press)
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Awards
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CIRP Best Paper Award
Bousdekis, A., Papageorgiou, N., Magoutas, B., Apostolou, D., & Mentzas, G. (2017). A
Proactive Event-driven Decision Model for Joint Equipment Predictive Maintenance and
Spare Parts Inventory Optimization. Procedia CIRP, 59, 184-189.
HELORS Best Paper Award
Bousdekis, A., & Mentzas, G. (2015). A Proactive Decision Support System for Maintenance
Cost Minimisation in Manufacturing Enterprises. In 4th Student Conference of Hellenic
Operational Research Society (HELORS) (pp. 61-66).
NTUA Thomaideio Award 2015
Bousdekis, A., Magoutas, B., Apostolou, D., & Mentzas, G. (2015). Review, analysis and
synthesis of prognostic-based decision support methods for condition based maintenance.
Journal of Intelligent Manufacturing, 1-14.
NTUA Thomaideio Award 2016
Bousdekis, A., Papageorgiou, N., Magoutas, B., Apostolou, D., & Mentzas, G. (2017). A
Proactive Event-driven Decision Model for Joint Equipment Predictive Maintenance and
Spare Parts Inventory Optimization. Procedia CIRP, 59, 184-189.