Predictive Maintenance Using The Internet Of Things
Workshop
Introduction
Effective asset management aims to maximize asset performance and minimize operations & maintenance costs. Maintenance professionals must stay on top of the latest strategies, methodologies, and technologies to achieve these goals. One trend in the maintenance industry which cannot be ignored is predictive maintenance using the Internet of Things (IoT) or the Smart Factory concept (Industry 4.0).
Asset-intensive organizations are adopting predictive maintenance to reduce costs and the frequency of maintenance activities. The loT platforms provide good support for predictive maintenance as they can integrate information from different machines and manufacturing systems to predict failures.
As stated by McKinsey Global Institute – By moving to the loT platform, organizations will realize significant improvements, including a 25% reduction in maintenance costs and a 50% decrease in unplanned downtime.
This two-day workshop provides a practical methodology to implement the loT framework using intelligent connectivity of smart devices through the internet network. The workshop is relevant for mid-career professionals and senior managers from varied disciplines, including Maintenance, Operations, Engineering, Supply Chain, IT, Business and Finance. This covers organizations that manage large-scale physical assets such as auto, transportation, pharmaceuticals, shipping, mining, oil refineries, chemical processing plants, public utilities and infrastructures.
Learning Objective
The workshop’s objectives are to provide participants with a framework to successfully implement loT and smart machine concepts to analyze and visualize machine data to make timely decisions. Some specific objectives are to:
Familiarize the participants with the latest maintenance technologies, especially related to industrial challenges, digital transformation, computerized maintenance management systems (CMMS), and current analyses to optimize maintenance.
Boost operational performance and lower costs in factories, facilities, and the supply chain. Make companies focus more on outcomes, long-term strategy, and optimizing business processes.
Enhance decision-making capabilities by visualizing machine health data through loT connectivity and the ability to make timely decisions to avoid catastrophic failures. Create new opportunities for companies to enhance services, gain business insights, and make themselves more competitive and futuristic. Predictive Maintenance using the Internet of Things
Program Overview
By attending this workshop, participants will acquire knowledge from industry experts and academicians in the field of loT and maintenance management and learn from actual projects performed with measurable results. The participants will get practical hands-on information on essential topics including, but not limited to:
CONNECTIVITY
Learn about sensors and gain knowledge of hardware and software design.
ACQUISITION
Acquiring machine data and sending notifications to maintenance staff on machine health to take timely action.
DATA ANALYSIS
Use cloud services to monitor machine operation in real-time.
DATA VISUALISATION
Use of Graphical User Interface (GUI) to determine data trends to help technicians in decision making.
DRIVE TANGIBLE BENEFITS The participants will learn skills to achieve the following tangible benefits for their companies:
- Reduce Maintenance Costs by 15% Improve Machine Uptime by 10%
- Increase Asset Utilization by 15%
- Decrease Inventory Cost by 25%
- Reduce Safety, Health, Environment and Quality Risks by 15%
Return on Investment (ROI)
Institute Business Management (loBM) is committed to delivering high-quality, results-oriented, and benefit-driven training programs for our clients. Clients have increased profits by investing in our training programs and lowering expenses. Direct benefits have been realized in the form of reduced turnover, higher employee productivity, increased production, and decreased downtime.
Program Fees
PKR/- 30,000
Exclusive of 5% SST
Date
26th to 27th December 2023
Duration
8:30 am-5 pm (2 Days)
Registration
Workshop Trainers
Mehboob Karim
Mr. Karim is a Subject Matter Advisor for Digital Asset Performance Management (DAPM). He has over thirty years of experience in asset and operations management, of which the last eight years have been in Digital Asset Performance Management (DAPM). Part of his responsibilities under DAPM is to provide strategic leadership and support to organizations in digital asset performance initiatives. He has worked with private and public sector industries in Canada, the USA, and Australia.
As a Senior Advisor, Mr. Karim also provides direction on reliability improvement projects using asset management solutions with tools such as Asset Criticality and Risk Assessments, Spare Parts Criticality, Stocking Strategies, Maintenance Management Practice Assessments, Supply Chain and Operations Management, Business Case Studies, Reliability Centered Maintenance, Enterprise Asset Management (EAM) and Asset Performance Management (APM) Systems.
Mehboob Karim is a Registered Professional Engineer in the Province of Ontario and a senior member of the Pakistan Engineering Council. He holds certifications in Supply Chain and Operations Management (CISCOM), APM, and Meridium Solutions (GE Digital). Mr. Karim is a recipient of the Sergio Guy Award 2005, presented by PEMAC to outstanding engineering and maintenance practitioners of Canada.
*PEMAC – Plant Engineering and Maintenance Association of Canada (www.pemac.org)
Dr. Seema Ansari
Dr. Seema Ansari brings to academia 39 years of successful academic and top management career with local and foreign universities, including Staffordshire University in the United Kingdom, Asia Pacific Institute of Information Technology (APIIT)-MALAYSIA and top engineering universities of Pakistan, like NEDUET in the field of engineering, information technology and sciences. She was the Director of APIIT, Karachi Campus.
Her research includes 70+ publications in JCR Impact Factor Journals, International and National HEC Recognized Journals, Book Chapters and Conference Proceedings. Her recent research has the role of the Internet of Things in the fields of Healthcare, Industry 4.0, 5G And 6G Technologies, Smart Cities, and the Successful Design of a Smart Helmet for Bike Riders’ Safety with a team of EE Students at loBM. The Project has been approved for Commercialization by loBM.
Her recent research has been on Smart Agriculture and farm automation. She and her team members successfully completed IoT based Agricultural projects that can do remote monitoring of farms and send live data on mobile apps, PC and laptop.
