This white paper covers various aspects from the evolution of Machine Learning to challenges involved in implementation and how to overcome them. It also covers various applications of Machine Learning and game changing use cases across industries. Towards the end it provides an example of application of Machine Learning in predictive maintenance in Automotive Industry.
The Evolution of Machine Learning: In 1990s, a concept called Artificial Intelligence (AI) came into existence. Today, this concept is fast gaining popularity with applications such as Google Chauffeur. Machine Learning is a part of AI that continuously observes a series of actions performed over a period of time, and puts this knowledge to use by devising ways to perform similar processes better, in a new environment. Big Data technology, with the capacity to process large volumes of data, is accelerating the growth of Machine Learning applications. These algorithms are remarkable in their ability to bring out hidden relationships among data sets and make predictions.
Approach to Machine Learning: While Machine Learning techniques have found an increasing level of applicability and relevance to real world scenarios, they pose a few implementation challenges. Some of them are:
- Lack of expertise in applying Machine Learning techniques to business problems,
- Lack of a culture that can apply the Machine Learning process to day-to-day operations,
- Availability of the right data from various operations and processes
- Lack of technological competence in using Big Data for Machine Learning algorithms
The implementation approach must be tailored for specific requirements to make it relevant to a particular organization. Following checklist can help in the implementation process:
- Define the business case
- Set in place a core team with expertise
- Get guidance from renowned universities
- Deploy dedicated operational resources
- Institute change management programs
Applications of Machine Learning in Different Industries: Machine Learning can be applied to high volumes of data in order to gain deeper insights and to improve decision making in manufacturing, retail, healthcare and life sciences, travel and hospitality, financial services and energy, feedstock and utilities.
Machine Learning systems can estimate the predicted outcome accurately based on training set data or past experiences. By gathering valuable insights for better and more accurate decision-making, Machine Learning systems can help manufacturers improve their operations and competitiveness.
Use cases: The paper also covers some potential game changing use cases from across industries including Condition Monitoring, Quality Diagnostics, Energy Optimization, Demand Prediction and Propensity to Buy.
Case in Point covered in the paper is the Application of Machine Learning for Predictive Maintenance in the Automotive Industry. The scenario consists of a stamping plant at an automotive OEM that manufactures vehicle panels. The operation involves an intensive workload, requiring high availability of hydraulic press lines. The Overall Equipment Effectiveness (OEE) of the press line is as low as 65 percent, with the breakdown time ranging from 17-20 percent. In addition, the maintenance process is largely based on preventive scheduling, leading to high unplanned downtime and maintenance cost as well as lost capacity during the maintenance tasks. This paper demonstrates how use of Machine Learning Analytics can help improve the equipment availability through accurate prediction of potential events such as part failure and functional degradation.
Across industries, Big Data technology has tremendous potential to leverage Machine Learning capabilities in enabling accurate decision-making for superior performance. There are many applications of Machine Learning techniques in the manufacturing industry, but successful implementation requires commitment from top management to enable changes in processes, active involvement of operational resources, availability of data, and collaboration with academia and technology partners with expertise in Machine Learning models and Big Data technology. The solution for predictive maintenance analytics using Stochastic Gradient Descent, as presented in this paper, demonstrates how Machine Learning can enable accurate prediction of failure events in the press line. Recent developments in advanced computing, analytics, and low cost sensing have the potential to bring about a transformation in the manufacturing industry. The implementation of Machine Learning and Big Data may drive the next wave of innovation and may soon prove to be an unavoidable tactical move in achieving higher levels of optimization.