This document describes a simulation model developed to calculate Overall Equipment Effectiveness (OEE) for a generic production line. The model takes input data from XML files generated by an optimization model that minimizes costs based on factors like work in progress inventory and machine idle time. Both crisp and fuzzy models are implemented to calculate availability, performance, quality, and overall OEE. The fuzzy model uses Mamdani inference with triangular membership functions. Simulation results in VB and Excel are presented and compared to world class standards. Goal seek and scenario manager tools are used to determine input parameter changes needed to meet standards. The model provides a way to evaluate a production line's efficiency and identify areas for improvement.
Review Paper On: Total Productive MaintenanceIJARIIT
Importance of TPM which stands for Total Productive maintenance for Liberalization of global economy has resulted tough competition in global market and for the sustainability in market for any product or service, the optimization of resources and costs in all sorts is required. The global competition is based on the innovation of advanced products, processes etc. and technology support is the essential requirement for any advancement in product or process where concept of Total productive maintenance has very much relevance today where it focus on improvement in equipment availability, performance and quality with assuring health and safety of employees and protection of environment.TPM provides a method for the achievement of world class levels of overall equipment effectiveness through people and not through technology or systems alone. It includes the organizational structures, human interactions, analytical tools and success criteria associated with the implementation of Total Productive Manufacturing programs.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Review Paper On: Total Productive MaintenanceIJARIIT
Importance of TPM which stands for Total Productive maintenance for Liberalization of global economy has resulted tough competition in global market and for the sustainability in market for any product or service, the optimization of resources and costs in all sorts is required. The global competition is based on the innovation of advanced products, processes etc. and technology support is the essential requirement for any advancement in product or process where concept of Total productive maintenance has very much relevance today where it focus on improvement in equipment availability, performance and quality with assuring health and safety of employees and protection of environment.TPM provides a method for the achievement of world class levels of overall equipment effectiveness through people and not through technology or systems alone. It includes the organizational structures, human interactions, analytical tools and success criteria associated with the implementation of Total Productive Manufacturing programs.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
A Review on Implementation of TPM in Manufacturing IndustryIJMER
Abstract: The intent of the study is to appraise the challenges faced by manufacturing industries to implement Total Productive Maintenance (TPM). The scheme of this research is to critically analyze the factors influencing TPM implementation in manufacturing organizations, and to formulate comprehensive strategy for overcoming impediments to successful TPM implementation . The introduction of several philosophies such as Corrective Maintenance (CM), Preventive Maintenance (PM) or Total Productive Maintenance (TPM) have allowed extra solutions to a process planning problem faced by company in comparison to the conventional fire-fighting syndrome.
This main purpose of this study was to focus on developing a framework of maintenance strategy
TPM initiatives to confront exponential global challenges.
Multi criteria Decision model (MCDM) for the evaluation of maintenance practi...IJERA Editor
The perceptible impact of Total Productive Maintenance (TPM) lies in raising productivity standards, gaining
profitability, and improving the quality besides cutting down the non value added costs greatly. This paper is
an attempt to provide a frame work and pragmatic approach in implementation of TPM. A number of novel
success factors or practices that are responsible for the decisive role to overture the process are identified.
These practices are interchangeably called as sub-attributes. These practices must have evolved from different
strategies. The sub-attributes are quantified using least square multi attribute decision model (LSMADM) for
three alternatives strategies viz. corrective maintenance, reliability centered maintenance(RCM), and TPM. Any
sub-attribute irrespective of its own high or low relative score among the number of sub attributes is evaluated
over three alternative strategies. To implement any sub-attribute, an investigation of its highest relative score
for given alternatives will guide the managers to opt the best alternative. The best practices must come from
different strategies to get most optimal results. The priorities established using LSMADM will act as base line
to implement the industrial activities in a more systematic and balanced way to gain far-reaching optimized
productivity and quality standards. The higher priority task will be given higher consideration in terms of
committing the resources vis a vis less priority task. This will aid in orienting the collective efforts for optimal
outcomes.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Impact of total productive maintenance methodology on the performanceeSAT Journals
Abstract These days, the organization and companies meet a lot of challenges(internally: to increase the performance, and externally: market share). This work focuses on the internal challenges: such as performance.The most important pillars of the production operations are employees, machines, equipment's, and etc. Maintenance represents the important activity that makes the machines and equipment's operate efficiently. Companies attempt to increase performance and minimize production costs by using a certain approach such as Total productive maintenance (TPM). This work introduces a methodology to improve the performance (operator and equipment) throughproposed model of TPM. Also, it indicates the importance of maintenance which minimizes or eliminates the production problems and increases the organizational productivity. Keywords:Maintenance, Maintenance Management System (MMS), Maintenance Office (MO), Preventive Maintenance (PM), Total productive maintenance (TPM), andOverall Equipment Effectiveness (OEE).
Link Stability and Energy Aware routing Protocol for Mobile Adhoc NetworkIOSR Journals
Abstract: MOBILE ad hoc networks (MANETs) have more popularity among mobile network devices and
wireless communication technologies. A MANET is multihop mobile wireless network that have neither a fixed
infrastructure nor a central server. Every node in a MANET will act as a router, and also communicates with
each other. The mobility constraints in mobile nodes will lead to problems in link stability. Energy saving, path
duration and stability will be two major efforts and to satisfy them can be difficult task. A self node which is
present in the network may also consume little energy during the transmission. This proposed approach tries to
account for link stability and for minimum drain rate energy consumption. In order to verify the correctness of
the proposed solution a objective optimization formulation has been designed and a novel routing protocol
called Link-Stability and Energy aware Routing protocols is proposed. This novel routing scheme has been
compared with other two protocols: PERRA and GPSR. The protocol performance has been evaluated in terms
of Data Packet Delivery Ratio, Normalized Control Overhead, Link duration, Nodes lifetime, and Average
energy consumption.
Keywords-component; Energy Consumption, Link Stability, Routing, Self node
Designing of a AMBA-AHB Multilayer Bus matrix Self-Motivated Arbitration schemeIOSR Journals
Abstract: The AMBA-AHB Multilayer Bus matrix Self-Motivated Arbitration scheme proposed three methods for data transmiting from master to slave for on chip communication. Multilayer advanced high-performance bus (ML-AHB) busmatrix employs slave-side arbitration. Slave-side arbitration is different from master-side arbitration in terms of request and grant signals since, in the former, the master merely starts a burst transaction and waits for the slave response to proceed to the next transfer. Therefore, in the former, the unit of arbitration can be a transaction or a transfer. However, the ML-AHB busmatrix of ARM offers only transfer-based fixed-pri-ority and round-robin arbitration schemes. In this paper, we propose the design and implementation of a flexible arbiter for the ML-AHB busmatrix to support three priority policies fixed priority, round robin, and dynamic priority and three data multiplexing modes transfer, transaction, and desired transfer length. In total, there are nine possible arbitration schemes. The proposed arbiter, which is self-motivated (SM), selects one of the nine possible arbitration schemes based upon the priority-level notifications and the desired transfer length from the masters so that arbitration leads to the maximum performance. Experimental results show that, although the area overhead of the proposed SM arbitration scheme is 9%–25% larger than those of the other arbitration schemes, our arbiter improves the throughput by 14%–62% compared to other schemes.
A Review on Implementation of TPM in Manufacturing IndustryIJMER
Abstract: The intent of the study is to appraise the challenges faced by manufacturing industries to implement Total Productive Maintenance (TPM). The scheme of this research is to critically analyze the factors influencing TPM implementation in manufacturing organizations, and to formulate comprehensive strategy for overcoming impediments to successful TPM implementation . The introduction of several philosophies such as Corrective Maintenance (CM), Preventive Maintenance (PM) or Total Productive Maintenance (TPM) have allowed extra solutions to a process planning problem faced by company in comparison to the conventional fire-fighting syndrome.
This main purpose of this study was to focus on developing a framework of maintenance strategy
TPM initiatives to confront exponential global challenges.
Multi criteria Decision model (MCDM) for the evaluation of maintenance practi...IJERA Editor
The perceptible impact of Total Productive Maintenance (TPM) lies in raising productivity standards, gaining
profitability, and improving the quality besides cutting down the non value added costs greatly. This paper is
an attempt to provide a frame work and pragmatic approach in implementation of TPM. A number of novel
success factors or practices that are responsible for the decisive role to overture the process are identified.
These practices are interchangeably called as sub-attributes. These practices must have evolved from different
strategies. The sub-attributes are quantified using least square multi attribute decision model (LSMADM) for
three alternatives strategies viz. corrective maintenance, reliability centered maintenance(RCM), and TPM. Any
sub-attribute irrespective of its own high or low relative score among the number of sub attributes is evaluated
over three alternative strategies. To implement any sub-attribute, an investigation of its highest relative score
for given alternatives will guide the managers to opt the best alternative. The best practices must come from
different strategies to get most optimal results. The priorities established using LSMADM will act as base line
to implement the industrial activities in a more systematic and balanced way to gain far-reaching optimized
productivity and quality standards. The higher priority task will be given higher consideration in terms of
committing the resources vis a vis less priority task. This will aid in orienting the collective efforts for optimal
outcomes.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Impact of total productive maintenance methodology on the performanceeSAT Journals
Abstract These days, the organization and companies meet a lot of challenges(internally: to increase the performance, and externally: market share). This work focuses on the internal challenges: such as performance.The most important pillars of the production operations are employees, machines, equipment's, and etc. Maintenance represents the important activity that makes the machines and equipment's operate efficiently. Companies attempt to increase performance and minimize production costs by using a certain approach such as Total productive maintenance (TPM). This work introduces a methodology to improve the performance (operator and equipment) throughproposed model of TPM. Also, it indicates the importance of maintenance which minimizes or eliminates the production problems and increases the organizational productivity. Keywords:Maintenance, Maintenance Management System (MMS), Maintenance Office (MO), Preventive Maintenance (PM), Total productive maintenance (TPM), andOverall Equipment Effectiveness (OEE).
Link Stability and Energy Aware routing Protocol for Mobile Adhoc NetworkIOSR Journals
Abstract: MOBILE ad hoc networks (MANETs) have more popularity among mobile network devices and
wireless communication technologies. A MANET is multihop mobile wireless network that have neither a fixed
infrastructure nor a central server. Every node in a MANET will act as a router, and also communicates with
each other. The mobility constraints in mobile nodes will lead to problems in link stability. Energy saving, path
duration and stability will be two major efforts and to satisfy them can be difficult task. A self node which is
present in the network may also consume little energy during the transmission. This proposed approach tries to
account for link stability and for minimum drain rate energy consumption. In order to verify the correctness of
the proposed solution a objective optimization formulation has been designed and a novel routing protocol
called Link-Stability and Energy aware Routing protocols is proposed. This novel routing scheme has been
compared with other two protocols: PERRA and GPSR. The protocol performance has been evaluated in terms
of Data Packet Delivery Ratio, Normalized Control Overhead, Link duration, Nodes lifetime, and Average
energy consumption.
Keywords-component; Energy Consumption, Link Stability, Routing, Self node
Designing of a AMBA-AHB Multilayer Bus matrix Self-Motivated Arbitration schemeIOSR Journals
Abstract: The AMBA-AHB Multilayer Bus matrix Self-Motivated Arbitration scheme proposed three methods for data transmiting from master to slave for on chip communication. Multilayer advanced high-performance bus (ML-AHB) busmatrix employs slave-side arbitration. Slave-side arbitration is different from master-side arbitration in terms of request and grant signals since, in the former, the master merely starts a burst transaction and waits for the slave response to proceed to the next transfer. Therefore, in the former, the unit of arbitration can be a transaction or a transfer. However, the ML-AHB busmatrix of ARM offers only transfer-based fixed-pri-ority and round-robin arbitration schemes. In this paper, we propose the design and implementation of a flexible arbiter for the ML-AHB busmatrix to support three priority policies fixed priority, round robin, and dynamic priority and three data multiplexing modes transfer, transaction, and desired transfer length. In total, there are nine possible arbitration schemes. The proposed arbiter, which is self-motivated (SM), selects one of the nine possible arbitration schemes based upon the priority-level notifications and the desired transfer length from the masters so that arbitration leads to the maximum performance. Experimental results show that, although the area overhead of the proposed SM arbitration scheme is 9%–25% larger than those of the other arbitration schemes, our arbiter improves the throughput by 14%–62% compared to other schemes.
A Review on Implementation of TPM in Manufacturing IndustryIJMER
The intent of the study is to appraise the challenges faced by manufacturing industries to
implement Total Productive Maintenance (TPM). The scheme of this research is to critically
analyze the factors influencing TPM implementation in manufacturing organizations, and to
formulate comprehensive strategy for overcoming impediments to successful TPM implementation .
The introduction of several philosophies such as Corrective Maintenance (CM), Preventive
Maintenance (PM) or Total Productive Maintenance (TPM) have allowed extra solutions to a
process planning problem faced by company in comparison to the conventional fire-fighting syndrome.
This main purpose of this study was to focus on developing a framework of maintenance strategy
TPM initiatives to confront exponential global challenges.
OPTIMIZING TIME WITH INCREASING PRODUCTIVITY USING LEAN MANUFACTURING AND OVE...IAEME Publication
Lean Manufacturing is the production control technique for eliminating the waste from manufacturing. Lean is concentrated on preserving value with less work. Lean manufacturing is a theme of efficiency based on optimizing flow; it is a present-day instance of the recurring theme in
human history toward increasing efficiency, decreasing waste, and using empirical methods to decide what matters, rather than uncritically accepting pre-existing ideas. Manufacturers are under tremendous pressure to improve productivity and quality while reducing costs.
Failure Mode Effect Analysis and Total Productive Maintenance: A ReviewAM Publications
The goal of quality and reliability systems is the same-to achieve customer satisfaction. Quality and reliability are
synonymous. A system cannot be reliable if it does not have high quality. Likewise, a system cannot be of high quality if it is not
reliable. The quality performance of a firm is often assessed by the reliability of the firm's equipment or machinery. If a system is
unreliable, it is unpredictable and if it is unpredictable, it is not of high quality. FMEA is a one of the most important quality
management Techniques. Total Productive Maintenance is useful technique to increase the productivity of plant and equipment
with a modest investment in maintenance. The paper reviews various approaches of Failure Mode Effect Analysis and Total
Productive Maintenance has been developed so far and discussion about use of FMEA-TPM in integrated approach.
The Critical KPI to drive Manufacturing ProductivityJason Corder
A net reduction in cost of operations directly and positively affects the bottom line. Companies can boost revenue without sacrificing profitability by factoring in long-term debt-to capital ratio. Since finance puts a premium on a company’s ability to maximize productivity and use existing assets, you have to continually measure, analyze, and adjust your processes. This is accomplished by a rigorous practice of productivity gains, cost cutting with increased efficiencies, and maximizing returns on fixed assets.
Total Productive Maintenance - A Systematic Reviewijsrd.com
TPM is an effective tool and a practical technique, which is aimed at maximizing the effectiveness of the facility by minimizing the downtime of machine, production losses and the material, production losses that occurs during the continuous production process. This also increase the working efficiency and productivity of the employee and a positive inclination is registered in the overall environment of a company. This paper presents the literature review of total productive maintenance which is a positive approach for solving the manufacturing problem, also gives the objective of TPM, a detail about a six big losses, 8 pillar of TPM also give the tool used for improvement, implementation stages, methodology for calculating the overall equipment efficiency and also give the direct and indirect benefits of TPM. The aim of this paper is to study the TPM concept and its implementation program which gives a successful improvement in overall equipment efficiency.
DEMATEL (siglas en inglés de Decision Making Trial and Evaluation Laboratory) es una técnica desarrollada en 1972 por Fontela y Gabus en el Centro de Investigación de Ginebra del Battelle Memorial Institute. Se utiliza para analizar la interdependencia (relación o influencia) entre componentes, variables o atributos de un sistema complejo, identificar aquellos que son críticos y estudiar sus relaciones causa-efecto, utilizando un diagrama de relaciones de
DEMATEL (siglas en inglés de Decision Making Trial and Evaluation Laboratory) es una técnica desarrollada en 1972 por Fontela y Gabus en el Centro de Investigación de Ginebra del Battelle Memorial Institute. Se utiliza para analizar la interdependencia (relación o influencia) entre componentes, variables o atributos de un sistema complejo, identificar aquellos que son críticos y estudiar sus relaciones causa-efecto, utilizando un diagrama de relaciones de DEMATEL (siglas en inglés de Decision Making Trial and Evaluation Laboratory) es una técnica desarrollada en 1972 por Fontela y Gabus en el Centro de Investigación de Ginebra del Battelle Memorial Institute. Se utiliza para analizar la interdependencia (relación o influencia) entre componentes, variables o atributos de un sistema complejo, identificar aquellos que son críticos y estudiar sus relaciones causa-efecto, utilizando un diagrama de relaciones de DEMATEL (siglas en inglés de Decision Making Trial and Evaluation Laboratory) es una técnica desarrollada en 1972 por Fontela y Gabus en el Centro de Investigación de Ginebra del Battelle Memorial Institute. Se utiliza para analizar la interdependencia (relación o influencia) entre componentes, variables o atributos de un sistema complejo, identificar aquellos que son críticos y estudiar sus relaciones causa-efecto, utilizando un diagrama de relaciones de
EMATEL (siglas en inglés de Decision Making Trial and Evaluation Laboratory) es una técnica desarrollada en 1972 por Fontela y Gabus en el Centro de Investigación de Ginebra del Battelle Memorial Institute. Se utiliza para analizar la interdependencia (relación o influencia) entre componentes, variables o atributos de un sistema complejo, identificar aquellos que son críticos y estudiar sus relaciones causa-efecto, utilizando un diagrama de relaciones de
DEMATEL (siglas en inglés de Decision Making Trial and Evaluation Laboratory) es una técnica desarrollada en 1972 por Fontela y Gabus en el Centro de Investigación de Ginebra del Battelle Memorial Institute. Se utiliza para analizar la interdependencia (relación o influenci
OVERALL EQUIPMENT EFFECTIVENESS OF CRITICAL MACHINES IN MANUFACTURING INDUSTR...IAEME Publication
Overall Equipment Effectiveness (OEE) is a measure used in Total Productive Maintenance (TPM), a maintenance program which involves a newly defined concept for maintaining plants and equipment, to calculate the percentage of actual effectiveness of the equipment, taking into consideration the availability of the equipment, the performance rate when running and the quality rate of the manufactured product measured over a period of time (days, weeks or months). The equipment criticality is decided by considering how and how much the equipment affects the production volume and quality.
Good maintenance is fundamental to productive manufacturing system. Total Productive Maintenance (TPM) is an alternative approach to equipment maintenance that seeks to achieve zero breakdowns and zero defects. TPM is an approach to keep the current plant and equipment at its higher productive level through cooperation of all areas of organization. The eight pillars of TPM are very important and serve as guidance to effectively implement TPM programme to improve overall manufacturing performance. In this paper the basic issues like planning, training, overall equipment effectiveness and implementation pertaining to the TPM are discussed.
Methodology used for improving overall equipment effectiveness by Implementi...IJMER
The global marketplace is highly competitive and organizations who want to survive long-term, have to continuously improve, change and adapt in response to market demands. Improvements in
a company's performance should focus on cost cutting, increasing productivity levels, quality and
guaranteeing deliveries in order to satisfy customers. Total Productive Maintenance (TPM) is one
method, which can be used to achieve these goals. TPM is an approach to equipment management that
involves employees from both production and maintenance departments. Its purpose is to eliminate major
production losses by introducing a program of continuous and systematic improvements to production
equipment.
The purpose of this study is to determine the effectiveness of the gas turbine engine work through the
value of OEE (availability, performance, quality) and identify losses affecting the OEE value of the gas turbine
engine. The method used is a descriptive analysis, which exposes availability, performance and quality of gas
turbine engines based on actual data and information by collecting, compiling, classifying and analyzing data
and information about the effectiveness of gas turbine engines
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
1. IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE)
e-ISSN: 2278-1684,p-ISSN: 2320-334X, Volume 12, Issue 5 Ver. III (Sep. - Oct. 2015), PP 52-63
www.iosrjournals.org
DOI: 10.9790/1684-12535263 www.iosrjournals.org 52 | Page
A Simulation Model for Overall Equipment Effectiveness of a
Generic Production Line
Mr. Girish R. Naik1
, Dr. V.A.Raikar2
, Dr. Poornima.G. Naik3
,
1
(Associate Professor, Production Department, KIT’s College of Engineering, Kolhapur, India.)
2
(Sanjay Ghodawat Group of Institutions, Atigre, Kolhapur, India.)
3
(Professor, Department of Computer Studies/CSIBER, Kolhapur, India.)
Abstract: Overall equipment effectiveness (OEE) is one of the performance evaluation methods most common
in manufacturing industries. It plays a vital role in improving the efficiency of a manufacturing process which in
turn ensures quality, consistency and productivity. In this paper, the authors have designed and implemented a
simulation model for OEE computation. The input data needed by the model is derived from XML files
generated by the cost optimized production line based on multiple criteria such as (Work In Progress) WIP
inventory minimization, idle time minimization and application of Theory of Constraints. Both the crisp model
and the fuzzy model based on Mamdani inference system with triangular membership functions are implemented
and compared. In the current model fuzzy input variables corresponding to machine down time and machine
setup time and the fuzzy output variable corresponding to the availability parameter of OEE are considered.
The rule set consists of nine different rules. The front end of the application model is implemented in VB and
the simulation model is presented in MS-Excel. It is observed that the fuzzy model deviates from the crisp model
as the overlap of the member functions is increased.
Keywords: Availability, Fuzzy model, Performance, Quality, Rule Set, Triangular Membership Functions,
XML.
I. Introduction
While managing change, organizations can deploy change management tools like total productive
maintenance and six sigma to remove redundancies and elimination of rework. The objective of Total
Productive Maintenance (TPM) is to manage equipment/machine to deliver the most it can by completely
eliminating machine down time in all forms. The benefits flow both directly and tangentially, for instance the
quality pay offs in terms of fewer defects and rejections mean lower cost and implementation of TPM can play a
pivotal role in cost rationalization, resulting in direct cost advantage from reduction in man power, stocks,
inventories and repairs. The basic approach is loss analysis, continuous improvement and maintenance of
equipment to prevent downtime. This is a participatory management technique which significantly contributes
in enhancing productivity and quality, reducing cost, improving adherence to delivery schedules, bettering
safety conditions and increasing employee morale. Like all transformation imperatives TPM begins by
understanding what is wrong and why it is so by applying rules like kaizen and employee involvement to
maintenance. Overall Equipment Effectiveness can be attained with a focus on zero loss, zero break downs, zero
defects and zero accidents. TPM is the ideal integrator and the extent of the change and impact on the cost can
be huge one. The best approach to combat shop floor cost is through higher machine uptimes and better process
capabilities. The measures are overall equipment efficiency, production cost efficiency and production lead time
efficiency. Equipment availability is calculated on several fronts including break down, changeover, fixture
change and startup time.
OEE is one of the performance evaluation methods that is most common in manufacturing industries.
OEE is a mechanism to continuously monitor and improve the efficiency of a manufacturing process. The three
prime measuring metrics for OEE are Availability, Performance and Quality which help gauge manufacturing
process‟s efficiency and effectiveness. Further they enable categorization of key productivity losses that occur
within the manufacturing process. As such OEE aims towards improving manufacturing processes and in turn
ensures quality, consistency, and productivity. By definition, OEE is the multiplication of Availability,
Performance, and Quality.
The formula to calculate Overall Equipment Effectiveness is as follows [1]:
OEE = Availability x Performance x Quality
The formula to calculate the three parameters are given below:
Availability =
Operating Time
Planned Production Time
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DOI: 10.9790/1684-12535263 www.iosrjournals.org 53 | Page
Performance =
Ideal Cycle Time
TotalPieces
=
Total Pieces /Operating Time
Ideal Runtime
Quality =
Good Pieces
TotalPieces
Figure1. depicts six major losses to equipment effectiveness.
Figure 1. Six Major Losses to Equipment Effectiveness
The six major losses, which fall under three OEE loss categories along are depicted in Table 1. along
with possible causes of losses.
Six Major Loss Category OEE Loss Category Reason
Breakdowns Availability 1. Equipment failure
2. Major component failure
3. Unplanned maintenance
Set up and adjustments Availability 1. Equipment setup
2. Raw material shortage
3. Operator shortage
Minor stops Performance or, Availability 1. Equipment failure <5mins
2. Fallen product
3. Obstruction blockages
Speed loss Performance 1. Running lower than rated speed
2. Untrained operator not able to run at
nominal speed
3. Machine idling
Production rejects Quality 1. Scrap
2. Rework
3. In process damage
Rejects on start up Quality 1. Scrap
2. Rework
3. In process damage
Table 1 . OEE Loss Categories for Six Major Losses
Figure 2. shows improvement goals for major losses affecting OEE.
Figure 2. Improvement Goals for Major Losses Affecting OEE.
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World class OEE
World class standard for OEE parameters is shown in Table 2 .
OEE Factor World Class
Availability 90.0%
Performance 95.0%
Quality 99.9%
OEE 85%
Table 2. World Class Standard for OEE
Availability Matrices
The availability data for production line of a manufacturing organization is shown in Table 3.
Serial No. Production Data Value
01 Shift Length (8 Hours) 60x8 = 480 min
02 Short Breaks ( 2@15) 2x15=30 min
03 Meal Break (1@30) 1x30=30 min
04 Down Time 47 min
Table 3. Availability data
Performance Matrices
The performance data for production line of a manufacturing organization is shown in Table 4.
Serial No. Production Data Value
01 Ideal Runtime 60 pieces per min
02 Total Pieces 19,271
Table 4. Performance data
Quality Matrices
The quality data for production line of a manufacturing organization is shown in Table 5.
Serial No. Production Data Value
01 Total Pieces 19,271
02 Rejection and Rework 423
Table 5. Quality data
II. Literature Review
The outcome of TPM implementation is measured using OEE, serves as an indicator of process
improvement activities within the manufacturing environment [2]. The three elements of OEE measure the
equipment and production losses experienced. Nakajima grouped and defined these losses into what is termed
as production‟s six big losses. The six major losses are equipment breakdown losses, setup and adjustment
losses, minor stoppage losses, speed reduction losses, defective losses and start up losses. Equipment and setup
losses are considered as time lost which are used for equipment availability calculation. The minor stoppages
and speed reduction losses are used to determine equipment process performance efficiency. On the other hand,
defective and start up losses are categorized as quality yield losses [3]. In order to be classified as „world class
manufacturing‟, the company must achieve equipment availability that is greater than 90%, process performance
efficiency that is more than 95%, achieve a quality yield that is greater than 99% and obtained overall
equipment effectiveness (OEE) that is greater than 85%”[4]. Total Productive Maintenance (TPM) is a world
class manufacturing strategy which aims at manufacturing near to ideal condition with zero downtime, zero
defect, lean production, just in time (JIT) production leading to competitive advantage. The most common
metric utilized by management to gauge the effectiveness and the successful implementation of TPM is OEE. In
their paper, authors have employed DMAIC approach to systematically define, measure, analyze, improve and
control the equipment performance. In their paper the authors have highlighted the use of six sigma
methodology to mitigate the bottleneck processes which focuses on OEE performance in a semiconductor firm
[5]. In literature there exist number of papers focusing on state of TMP implementation in SMIs and
interrelationship of TMP with TQM and JIT [6-9]. The authors of paper provide a brief study on the literature
related to the application of TPM in the manufacturing industry. The study focuses on the main role of TPM in
supporting the established quality improvement initiative such as lean production. Effort was made to discuss
the published research related to TPM and lean production. This literature review-based research revealed an
important research gap, i.e. the need of a comprehensive integration between these two methodologies. The
significance role of TPM as an important complementary to lean production is observed has not been well
addressed in the available literature. Most of the researches available investigate these initiatives separately,
rather than addressing on the significant role of TPM as one of the main thrust. The beneficial outcome from
4. A Simulation Model for Overall Equipment Effectiveness of a Generic Production
DOI: 10.9790/1684-12535263 www.iosrjournals.org 55 | Page
TPM methodology is quite hindered and unexposed in some literatures related to lean production. The outcomes
from this review is hope justify the needs of further research in the area of TPM integration with lean
production, aimed at strengthening its philosophy towards more realistic applications [10].The literature review
demonstrates that the implementation of TPM is one of the business philosophies which is basically used to
improve the technological base by enhancing equipment efficiency and improving the morale of employees
[12]. TPM implementation brings both production and maintenance functions together after initiating good
working practices, team working and continuous improvement [13]. The goal of TPM is to continually maintain,
improve and maximize the condition and effectiveness of equipment through complete involvement of every
employee from top management to shop floor workers [14].
III. Application Architecture
The authors have designed and developed various tools for the selection of manufacturing method
based on single organizational objective/multi objective and/or single/multi organizational function. The tools
are based on crisp and fuzzy expert systems which can be queried in a human language parsed used Natural
Language Processing (NLP) and Deterministic Finite Automata (DFA) parsers. The authors have designed and
implemented their own query language name as manufacturing query Language (MQL) for this purpose. A
production line is redesigned for total cost optimization based on multiple criteria such as WIP minimization,
machine idle time minimization and application of theory of constraints using Genetic Algorithm. There is a
trade off between machine idle time and WIP and the objective is total cost minimization. Finally, the line
efficiency is analyzed using OEE Analysis Tool designed and developed by us and the results are compared
with world class standard. Figure 3. depicts the overall application architecture for the setting up of production
line, redesigning and analyzing line efficiency using OEE tool.
Figure 3. Application Architecture for Production Line Setup, Redesign and Analysis
The output of single channel multiphase optimization model of a production line based on multiple
criteria is routed to several XML files which are input to OEE analysis tool for analyzing line efficiency. A
simulation model is developed and the input parameters are fine tuned for achieving the world class standard for
OEE parameters. The corresponding layered architecture based on service-oriented model is shown in Figure 4.
As seen from the figure, each layer except the first derives its input from the previous layer and generates output
to the next layer.
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Figure 4. Layered Architecture based on Service Oriented Model.
IV. Results and Discussions
The OEE part of the model discussed above is implemented in VB and Excel for the data presented
above. The input data is derived from the XML files generated by the cost optimized production line. The
structure of the XML files are shown in Table 6.
availability.xml
<config>
<shiftLength>8</shiftLength>
<shortBreak>
<count>2</count>
<duration>15</duration>
<min>10</min>
</shortBreak>
<mealBreak>
<count>1</count>
<duration>30</duration>
<min>25</min>
</mealBreak>
<machine>
<breakdownTime>32</breakdownTime>
<setupTime>15</setupTime>
<minSetupTime>10</minSetupTime>
<minBreakDownTime>25</minBreakDownTime>
</machine>
</config>
efficiency.xml
<config>
<idealRuntime>60</idealRuntime>
<totalPieces>19271</totalPieces>
</config>
quality.xml
<config>
<rejectedPieces>423</rejectedPieces>
</config>
worldclass.xml
<config>
<availability>90</availability>
<efficiency>95</efficiency>
<quality>99.9</quality>
<oee>85</oee>
</config>
Table 6. Structure pf XML files generated by the cost optimized production line
The code for parsing of XML files using Microsoft's XML parser is given in Appendix A.
Figure 5- 7 depict the simulation results for machine availability, performance and quality. All the three
parameters can be fine tuned based on the priority levels set. As a visual aid, the corresponding world class
standard is highlighted.
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Figure 5. Simulation result for Machine Availability
Figure 6. Simulation result for Machine Performance
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Figure 7. Simulation result for Machine Quality
Figures 8(a) - 8 (b) show the corresponding simulation model results implemented in MS-Excel.
Figure 8 (a)- 8 (b). Simulation Model Results in MS-Excel
Goal seek is employed for determining the changes in the input parameters desirable to attain the world
class standard by changing one parameter at a time. Scenario manager is used for storing different scenarios
corresponding to the goal for attaining world class standard as shown in Figures 9 (a)- 9 (b) and Figures 10 (a)-
10 (c), respectively.
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Figure 9 (a)- 9 (b) - Goal Seek Tool for reaching World Class Standard for Availability Parameter
Waste must be reduced by 2959 in order to reach world class standard.
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Figure 10 (a)- 10 (c) - Scenario Manager for Storing World Class Standard for Availability Parameter
The relative comparison of OEE parameters between the actual value and the world class values is shown in
two Figure 11.
Figure 11. Relative comparison of OEE parameters between the actual value and the world class values
Fuzzy Simulation Model for OEE.
Figure 12. depicts the fuzzy model for availability parameter of OEE using the Mamdani inference
system for two fuzzy input parameters, machine down time and machine setup time and fuzzy output parameter
OEE availability.
Figure 12. Fuzzy Model for Availability Parameter of OEE using the Mamdani Inference System
10. A Simulation Model for Overall Equipment Effectiveness of a Generic Production
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Figure 13 shows the rules generated for different combinations of linguistic variables and their impact
on the output variable.
Figure 13. Fuzzy Rules Generated for Model
The triangular membership functions for input and output variables are shown in Figures 14 (a) - 14 (b) and
Figure 15, respectively.
Triangular Membership Function for input variable, Machine
Down Time
Triangular Membership Function for input variable, Machine Setup
Time
Figure 14 (a) - 14 (b). Triangular membership functions for Input Variables.
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Figure 15. Triangular membership function for Output Variable.
Figure 16 shows the simulated model for availability parameter of OEE based on machine down time and
machine setup time.
Figure 16. Fuzzy Simulation Model for Availability Parameter Conclusion and Future Work
V. Conclusions and Future Work
In the current work authors have designed and implemented a simulation model for OEE computation.
OEE computation module is the smaller part of the application framework designed by the authors for a
production line which analyzes the line efficiency before and after re-design of production line and compares
the same with world class standards. The GUI is developed as a visual aid which clearly signifies the gap
between the current line efficiency parameters and those needed by the world class standards. The input data
needed by the model is derived from XML files generated by the cost optimized production line based on
multiple criteria such as (Work In Progress) WIP inventory minimization, idle time minimization and
application of Theory of Constraints. Both the crisp model and the fuzzy model based on Mamdani inference
system with triangular membership functions are implemented and compared. In the current model fuzzy input
variables corresponding to machine down time and machine setup time and the fuzzy output variable
corresponding to the availability parameter of OEE are considered. The rule set consists of nine different rules.
The front end of the application model is implemented in VB and the simulation model is presented in MS-
Excel. It is observed that the fuzzy model deviates from the crisp model as the overlap of the member functions
is increased. Our future work focuses on development of fuzzy simulation model incorporating hybrid soft
computing technologies such as artificial neural networks and fuzzy logic.
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