Professional Certificate in
Manufacturing and Automotive Analytics Course
- Get Trained by Trainers from ISB, IIT & IIM
- 60 Hours of Intensive Classroom & Online Sessions
- 100+ Hours of Practical Assignments
- 2 Capstone Live Projects
- 100% Job Placement Assurance
Academic Partners & International Accreditations
"To improve product design and development, organizations are increasingly deploying manufacturing analytics solutions. The market size of Manufacturing Analytics is poised to grow to USD 9.01 Billion by 2021." - (Source). To improvise on product design and development, organizations are increasingly leveraging manufacturing analytics solutions to streamline their operations. Reducing the costs, improving productivity, foresee order volumes, improve product quality, track daily production, and minimizing risks are the most important criteria in any line of production. Marketing Analytics gives you the power to pioneer efficient methods to minimize costs in production operations, deliver innovative solutions and quality products with minimum risk involved. Manufacturing analytics facilitates digitalization and strengthens your analytical strategies that accelerate innovation in the age of IR4.0. Manufacturing Analytics Course embedded with AI helps to achieve a perfect balance of possibilities and control.
- Computer Skills
- Basic Mathematical Concepts
Manufacturing Analytics Course Overview
The time is uncertain, competition is fierce, customer demands have become complex and digital technology is being adopted like never before. During these quick-paced times, Machine learning, Data Science, and AI are the trending technologies that can give advantage to all the industries & sectors, and the manufacturing sector is no different. Manufacturing analytics concentrates on collecting and analyzing data to identify problem areas and work towards their improvement in terms of performance and quality metrics. The applications and the platforms developed using these technologies are helping manufacturers with new business models, fine-tune their product quality, optimize manufacturing operations, etc. The Manufacturing Analytics course is specially designed for working professionals & students from Mechanical, Electrical, Electronics & Communication, Automotive, Manufacturing educational background as well as experience. Manufacturing analytics course provides a new dimension and a plethora of opportunities for those who want to stay ahead of the race and competition.
What is Manufacturing Analytics?
Manufacturing analytics excels at collecting data and manipulating it to make sense out of it. Massive amounts of data can be combined and condensed into easy to understand metrics. There are a lot of scraps generated during the manufacturing process and tracking this scrap is supremely important to all manufacturing companies and manufacturing analytics provides a lot of tools in this area. In the past, if one wanted to know about the performance of a machine, it would potentially take weeks of manual effort to figure whether the problem is with the input, output, or with the supply of raw material. With Manufacturing Analytics in place, large quantities of data collected from machines with the help of industrial IoT can be now analyzed to monitor the performance of machines and to find out whether the problem was with performance or output quality.
Manufacturing Analytics Course Learning Outcomes
Analytics is like the crystal ball that has given the manufacturing industry the power to predict the future of modern manufacturing in order to cut down on the complexity and unpredictability and provide a path of solidity and reliability in a variant-rich value stream. Manufacturing Analytics contributes to every aspect of the product life cycle, it helps you spot problems before they become an issue and create systems that can gauge their own need for repairs. By combining analytics in manufacturing, one can generate actionable data that helps to create better forecasts in the demand area of various products thereby guiding the production chain of what purchasing trends will be on the rise leading to better risk management and less production waste. This course on Marketing Analytics focuses on all the aspects involved in the manufacturing industry which include planning, operations, production, and manufacturing methods. With this course students will understand why performing advanced analysis to manufacturing processes is crucial for determining and amending operational flaws in order to improve productivity and reduce costs. This course covers the basic principles and best practices behind Manufacturing Analytics course. Upon completion of this course, you will
Block Your Time
Who Should Sign Up?
- Manufacturing Analysts
- Mechanical Engineers
- Electrical & Electronic Engineering
- Plant Managers
- Design and Manufacturing Engineers
- Certified Financial Analysts
- Automotive Engineers
- Industry Heads
Manufacturing Analytics Course Modules
With advances in technology, Manufacturing Analytics has made collecting and analyzing data, interpreting results, and generating insights to measure the effectiveness of marketing strategies look seamless. This module will take you through the concepts and fundamental practices in marketing analytics and the critical milestones required for product development. It includes cost estimation, quality, and examples of best practices to equip you with the fundamentals of Manufacturing. The module also introduces students to the vital analytical techniques with an emphasis on the architecture of new-age systems as a single point of solution to manage complex manufacturing Data. On the other hand, generating strategic insights and interpreting results for marketing decision making along with the grip on manufacturing processes will be covered in-depth in this module. This course will provide you with a complete understanding of the advanced practices you will need in your business to stay current and competitive which is an integral part of the Manufacturing Analytics course.
Manufacturing 101 will enable you with a basic level of understanding about manufacturing processes. Gain understanding of the current Manufacturing Readiness Level (MRL). This module will help you more effectively achieve critical milestones for product development. It includes cost estimation, quality, and examples of best practices to equip you with the fundamentals of Manufacturing.
- About 360DigitMG & Innodatatics
- Data Explosion, Digital Transformation & Industrial Revolution 4.0
- Value Stream & Opportunities in Manufacturing
- Manufacturing Foundations and Understanding the Common Terminologies
- Manufacturing Variants in various Industries – Automotive, Mass Production Plants, etc.
- Bill of Materials and Bill of Process
- Cost Estimation of Supply Chain & Logistics
- Standards, Regulations, and Best Practices
AI and Data Science in manufacturing will drive the industry to higher productivity and improved efficiency. Exciting solutions are developed with the adoption of AI in the space of Predictive Maintenance, New Product Design, Production Quality Inspection, Inventory Management, etc. The availability and drop in the cost of sensors has encouraged industry to utilize sensors at every stage of the manufacturing to collect Data. This Data is being churned using AI and Data Science applications to give a competitive edge and these applications for AI will undoubtedly continue to increase with the availability of digital data.
- Understand the difference between AI, Data Science and Machine Learning
- Examples of AI Solutions in Manufacturing
- Solutions of Machine Learning in Manufacturing
- Automatic Video Analytics for Attendance Tracking & Behavioral Analytics
- Text Mining of Customer Reviews - Customize Product Design
- Cost Reduction through Predictive Maintenance
- AI-driven Automated Quality Assurance
- Supply Chain – Optimized Inventory Management
- Instant Access to Information via AI-Driven Chatbot
Thanks to Industry 4.0 and development of digital world, the Modern manufacturing is undergoing huge transformations. The application of Data science and AI in Manufacturing is high now in all aspects from raw materials procurement to finished products delivery.
The fourth industrial revolution deals with the principles of cyber-physical system (CPS). Machines capable to predict failures, understand the maintenance cycle, take appropriate actions autonomously in building smart systems.
- IR 4.0 & its Capability Maturity Roadmap
- IIoT Sensors for Manufacturing
- Wireless Sensor Networks
- IIoT Sensors Data & Cloud Computing
- Smart Manufacturing Setup
- Building Smart Factories & Role of Pillars of IR 4.0
Industry uses a variety of software solutions for Data storage and often have challenges to integrating all the Data together to draw meaningful insights. Big Data in manufacturing helps in identifying the patterns thereby the problems. This module discusses the distributed framework, which is the crux of Big Data systems in Manufacturing. We will learn about the architecture of new-age systems as a single point of solution to manage complex manufacturing Data.
- Introduction to Big Data in Manufacturing
- Hadoop and its Distributed Framework
- Data Collection - IoT sensors, Inventory, etc.
- Data Storage using Hadoop and its Eco-systems
- Data Processing using Spark on Big Data
Robotics is not new in Manufacturing, but machine learning is a new concept. According to one of the surveys by PWC, it was found that only around 50% in the industry are aware of Machine Learning. To impact the bottom-line, companies will have to use ML to the fullest. Machine Learning applications vary from Machine breakdown predictions, Quality Control, Optimize Supply Chain, etc.
- Data Mining Supervised Learning
- Regression for Predicting Entire Machine Breakdown
- Classification for Predicting Specific Equipment and Critical Component Failures
- Neural Network for Automatic Facial Recognition – Attendance Tracking as well as Behavior Analytics
- Unsupervised Learning
- Principle Component Analysis for Plant Operation Monitoring
- Automation of Overall Plant Quality Inspection
Hypothesis testing will allow manufacturers provide guidance to production control by using inferential statistics. Selection of appropriate tests; parametric or non-parametric methods to perform comparative analysis will be discussed. Descriptive analytics used to understand the conditions for High performance Machines to benchmark and compare these features to find the patterns of failures and reasons.
- Hypothesis Testing for Comparing Pre vs Post Improvement
- Inferential Statistics of High Performing Machines
- Hypothesis to compare the Machine Failure Pattern
- Weibull Probability Distribution for Machine Reliability Assessment
Route optimization is required to plan an efficient and cost-effective transportation model to use a limited number of vehicles with different capacities. Route optimization models can monitor the real-time challenges and dynamically plan the most economic route planning. Learn the trades of capturing the value of a network for managing the best route for streamlining the supply chain.
- Degree Centrality, Closeness Centrality Measures
- Betweenness Centrality, Eigenvector & Egocentric Centrality Measures
- Various Centrality Measures for Optimizing Network Route
- Route Optimization to Streamline Supply Chain
The most widely used application of Data Science in manufacturing is performing predictive analytics of machine break down. From the likes of Food and Beverages, Energy & Resources, Oil and Gas, Tobacco, Automobile, etc., all the industries are adopting the machine to break down models. Data gathered from SCADA systems, Cloud and combined with the Data obtained from sensors is used to train the algorithms to predict the incidents up front to manage the production cycle appropriately.
- SCADA Data, Server Data & Cloud Data of Machinery
- Critical Equipment Failures in Machinery
- Wind Turbine Breakdown Models
- Battery Breakdown Prediction Model
- Electrical Transformers Trip Breakdown Prediction Model
Survival Duration Analytics lets us plan the maintenance cycle to obtain zero downtime in production. Survival analysis is performed using the Data from censored and uncensored data to estimate the survival probability, of failure time, of machine components. Understand the life of the components so that this information is fed into logistics and inventory for better inventory management.
- Time to Component Failure Event Analysis
- Streamlining Inventory & Logistics based on Survival Analysis
- Censored Data of Components Surviving Failure
- Automotive Vehicles Breakdown Survival
- Building Survival Analytics directly on Cloud
Designing new products and enhancing the current product based on customer feedback will be critical for companies to survive in the industry. The customer feedback and reviews are unstructured in nature and extracting meaningful information from such data is performed using Text Mining and advanced Natural Language Processing techniques. Understanding the market sentiment and anticipating features in a product can guide to strategize the release of a new product to meet the customers’ requirements.
- “Customer Speak” Data Extraction from Web & Social Media
- Data Analysis from Customer Surveys
- Text Mining and Word Cloud
- Sentiment Analysis of Customers in New Product Manufacturing
- Forecasting New Trends based on Customers Digital Footprint
Bracing for the unexpected conditions and having an alternative plan is mandated in the current volatile market. Understanding upfront the factors like seasonal effects which influence the future will help decision makers to take appropriate steps to have business continuity. Marking yearly budget based on historical performance of manufacturers can streamline the supply chain value stream.
- Forecast the Market Demand to Adjust Manufacturing Levers
- Forecast using Trend & Seasonality Components
- Supply Chain Forecasting Strategies using Seasonal Index
- Supply-Demand Synergies based on Model Driven Approaches
- Improving Accuracy of Forecasting Models
IoT is the game-changer in the Manufacturing sector. Billions of devices interconnected to automate the machine to machine communication in the industry. IDC predicted that by 2025, above 95% of the IoT uses cases will be successfully implemented with real-time applications. Different types of IoT sensors like Smoke sensors, Temperature sensor, Image sensor, IR sensor, etc., are used to capture the Data from varied conditions. Effective collaboration and intercommunication are made possible via the sensors data which is stored and processed remotely on the cloud.
- IR4.0 Maturity Model for Manufacturing
- IIoT, Big Data, Cloud, Analytics, BI tools
- Architecture Preparation and Estimation
- Multi Year Project, Program & Portfolio Plan
- Measuring & Sustaining the Improvements
Adoption of Cyber security systems and new-age IoT sensor devices has boosted the production quality and efficiency of Manufacturing. The Industrial Internet of Things (IIoT), Machine Learning, and Big Data and Analytics are the pillars of smart Industries. The digital transformation in manufacturing has led the industry with new challenges from cyber threats. The need to look into the current systems to eliminate risks and compromise security is very much required.
- Data Security in Protecting the Infrastructure from Hackers
- Data Integration from varied Sources into a Data Lake
- Industrial Control Systems (ICS) & Varying Network Protocols
- Establishing Honey Webs for Hackers
- Making the Manufacturing Digital Process Hack Proof
The automotive industry is experiencing a paradigm shift from conventional, human-driven vehicles to self-driving, artificial intelligence-powered vehicles. Self-driving cars have emerged to be one of the most transformative technologies. Fuelled by deep learning algorithms, they are rapidly developing and creating new opportunities in the mobility sector. Deep learning jobs command some of the highest salaries in the development world.
- Perceptron, ANN, CNN and RNN algorithms
- Sharpen and blur images with convolution and detect edges in images with Sobel, Laplace, and Canny
- Extract image features with HOG and detects object corners with Harris
- Computer Vision techniques via OpenCV to identify lane lines for a self-driving car
- Train a perceptron-based neural network to classify between binary classes
- Convolutional neural networks to identify various traffic signs
The rising concern for climate change and the Governments across the globe promoting Electric vehicles and a lot of subsidies are on offer, a foreseeable future in EV for the automobile industry is evident. Usage of EV’s will have challenges as the availability of charging stations is a big concern. In this module, we will learn the application of Predictive Analytics to determine the Remaining Useful Life for the Electric Vehicle battery state of charge.
- Feature Analysis using Entropy
- Rules based Models
- Ensemble models for Complex dataset
- Random Forest & AdaBoost
Trends in Manufacturing Analytics Course
The Manufacturing Analytics Course Market is characterized by Deployment, Application, End-user, and Geography. With the emergence of IR 4.0, Predictive Maintenance and Asset Management have become the two important components of Manufacturing Analytics that ensure the quality of strategic decisions. Predicted to reach over US$4 Billion by the year 2025, It is bound to bring in significant profits and supplement a healthy drive to global growth. To remain pertinent in the ecosystem of IR4.0 organizations need to adopt a positive approach toward technology and innovation. The production establishment in this sector is using many Industrial Internet of Things (IIoT) solutions to get instant feedback from factories and their working environment. Utilizing the various Data mining methods production data is also being utilized to enable meaningful analysis.
The automotive industry is also moving towards predictive analytics along with a more data-driven approach to avoid the costs related to faulty assembly, overstocking, and maintenance of the assembly lines. One of the factors that are the strongest growth driver of the manufacturing analytics market is the increase in data sources such as in-factory databases, images, sensors. In addition, the rising demand to optimize business processes and make them more capable and accessible is further paving the growth of manufacturing analytics. North America is expected to be the largest market of manufacturing analytics due to increasing focus on innovations across the cloud segment which has led to an increase in its market presence. In Japan, Predictive Maintenance & Asset Management will reach a market size of US$349.1 Million by the end of 2025. Predictive Maintenance & Asset Management is expected to bring significant gains of over US$3.5 Billion by the year 2025 thereby adding thrust to global growth.
How we prepare you
Additional Assignments of over 100+ hours
Live Free Webinars
Resume and LinkedIn Review Sessions
Lifetime LMS Access
Job Placements in Manufacturing Analytics Fields
Unlimited Mock Interview and Quiz Session
Hands-on Experience in Live Projects
Offline Hiring Events
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Manufacturing Analytics Course Panel of Coaches
Bharani Kumar Depuru
- Areas of expertise: Data Analytics, Digital Transformation, Industrial Revolution 4.0.
- Over 14+ years of professional experience.
- Trained over 2,500 professionals from eight countries.
- Corporate clients include Hewlett Packard Enterprise, Computer Science Corporation, Akamai, IBS Software, Litmus7, Personiv, Ebreeze, Alshaya, Synchrony Financials, Deloitte.
- Professional certifications - PMP, PMI-ACP, PMI-RMP from Project Management Institute, Lean Six Sigma Master Black Belt, Tableau Certified Associate, Certified Scrum Practitioner, AgilePM (DSDM Atern).
- Alumnus of Indian Institute of Technology, Hyderabad and Indian School of Business.
Sharat Chandra Kumar
- Areas of expertise: Data Science, Machine Learning, Business Intelligence and Data Visualisation.
- Trained over 1,500 professional across 12 countries.
- Worked as a Data Scientist for 14+ years across several industry domains.
- Professional certifications: Lean Six Sigma Green and Black Belt, Information Technology, Infrastructure Library.
- Experienced in Big Data Hadoop, Spark, NoSQL, NewSQL, MongoDB, R, RStudio, Python, Tableau, Cognos.
- Corporate clients include DuPont, All-Scripts, Girnarsoft (College-dekho, Car-dekho) and many more.
- Areas of expertise: Data Science, Machine Learning, Business Intelligence and Data Visualisation.
- Over 20+ years of industry experience in Data Science and Business Intelligence.
- Trained professionals from Fortune 500 companies and students from prestigious colleges.
- Experienced in Cognos, Tableau, Big Data, NoSQL, NewSQL.
- Corporate clients include Time Inc., Hewlett Packard Enterprise, Dell, Metric Fox (Champions Group), TCS and many more.
Earn a Manufacturing Analytics & Automotive Analytics Course certificate and demonstrate your commitment to the profession. Use it to distinguish yourself in the job market, get recognized at the workplace and boost your confidence. The Data Science in Manufacturing Analytics Certificate is your passport to an accelerated career path.
FAQs for Manufacturing Analytics & Automotive Analytics Course
This course just assumes some basic computer familiarity and an analytical mindset. It definitely helps if the learner has some background in financial data analysis, SQL and Programming languages such as Python and R. Knowledge of manufacturing industry is expected for better understanding.
This course is specifically catered to learners intending to either begin or advance their careers in the finance industry.
Different organisations use different terms for data professionals. You will sometimes find these terms being used interchangeably. Though there are no hard rules that distinguish one from another, you should get the role descriptions clarified before you join an organisation.
With growing demand, there is a scarcity of data science professionals in the market. If you can demonstrate strong knowledge of data science concepts and algorithms, then there is a high chance for you to be able to make a career in this profession.
360DigiTMG provides internship opportunities through Innodatatics, our USA- based consulting partner, for deserving participants to help them gain real-life experience. This greatly helps students to bridge the gap between theory and practice.
There are plenty of jobs available for data professionals. Once you complete the training, assignments and the live projects, we will send your resume to the organisations with whom we have formal agreements on job placements.
We also conduct webinars to help you with your resume and job interviews. We cover all aspects of post-training activities that are required to get a successful placement.
After you have completed the classroom/online sessions, you will receive assignments through the online Learning Management System that you can access at your convenience. You will need to complete the assignments in order to obtain your data scientist certificate.
You will be attending 40 hours of classroom and/or virtual instructor-led sessions. After completion, you will have access to the Learning Management System for three months for recorded videos and assignments. Also you will have to spend another month after the classroom sessions to complete the live project.
If you miss a class, we will arrange for a recording of the session. You can then access it through the online Learning Management System.
We assign mentors to each student in this programme. Additionally, during the mentorship sessions, if the mentor feels that you require additional assistance, you may be referred to another mentor or trainer.
No.The cost of the certificate is included in the programme package.
Jobs in the field of Manufacturing Analytics
The job landscape in the field of manufacturing is promising with opportunities spanning across many industries. One can work in various domains of manufacturing like administration, quality inspector, electronic engineer, electronics assembler, mechanical technician, cad designer, etc.
Salaries for Manufacturing Analytics Professionals
The salary in the domain of analytics varies across experience levels. The average salary of Analytics Professionals in India is INR 14.4 Lakhs per annum while a fresher in this field will be able to earn anywhere between 3-6 Lakhs per annum.
Manufacturing Analytics Course Projects
Analytics plays a significant role in the manufacturing sector. Using the available data various projects on inventory management, supply chain management, the performance of targets, and risk mitigation plans can be implemented that offer meaningful insights.
Role of Open Source Tools in Manufacturing Analytics Course
The various tools in manufacturing analytics help in analyzing data to study and understand the business operations better, fix problem areas, and prepare the management for unforeseen events. In this program, you will explore tools like R, RStudio, Python, and jupyter.
Modes of Training for Manufacturing Analytics Course
The course in India is designed to suit the needs of students as well as working professionals. We at 360DigiTMG give our students the option of both classroom and online learning. We also support e-learning as part of our curriculum.
Industry Application of Manufacturing Analytics Course
Analytics in manufacturing helps refine the physical and financial standing of manufacturers. It is extensively used for continuous improvement in process, performance, quality metrics, and even in anticipating machine failure.