Posted by Indusa Admin on June 21, 2016 4:53 am
Manufacturing companies need to have processes running around-the-clock with the utmost precision. Often, manufacturing plants encounter linear failure; where a defect repeats until the end of the operating cycle, messing up the entire batch. Then, several micro level process failures remain unidentified until the last phase of production. How can you mitigate such a failure and ensure smooth process flow? This is where machine learning comes to the rescue – using artificial intelligence, you can continuously observe a series of actions performed over a period of time and devise new ways to carry out similar processes in a new and improved environment.
With the explosion of manufacturing data, and the pressure of continuous process improvement and waste reduction, engineers are beginning to use machine learning techniques along with traditional statistical methods to ensure process stability. However, implementing machine learning in real world scenarios is met with certain challenges:
- Insufficient expertise in applying machine learning techniques to business problems
- Absence of the right data from various processes and operations
- Inadequate technical competence in using big data for machine learning algorithms
- Lack of culture that can apply machine learning techniques to day-to-day operations
Machine learning aims to impact an array of industries and the diverse roles that reside within them. By taking on unsolvable problems using all the information around the plant, machine learning brings the right insight to your fingertips right when it’s most needed. While machine learning techniques are increasingly being used, the implementation framework must be tailored for specific requirements:
Machine learning enables you to predict demand fluctuations and arrive at the optimum demand response in real time, ultimately optimizing the demand and supply from various sources at minimum cost and minimizing scrap and redundancies. Machine learning can also be applied to various modules for long term production planning and output optimization, for instance:
- Production and operation management
- Plant optimization
- Demand forecast
- Error detection
- Performance management
- Maintenance and interoperability
- Prediction of equipment failures
- Pattern reorganization
As the manufacturing industry is moving away from the traditional long term service contract to an ‘Analytics-as-a-Service’ model, big data applications are increasingly being used to collect data from manufacturing operations. Using big data, you can accurately predict failure in operations well ahead of time, increasing the service revenue and reducing the cost of service. In addition, you can predict the health of your equipment in real time, and release equipment for maintenance only when necessary. Through the use of neural networks, support vector machines, and decision trees, you can identify complex interdependencies within operational parameters and detect anomalies that lead to equipment failures.
Improving Business Performance
Over the next couple of years, machine learning applications will lead to innovative breakthroughs in the manufacturing space. As manufacturing organizations generate a lot of data, machine learning systems can help accurately estimate the predicted outcome based on certain parameters and past experiences. With strong commitment from top management, high involvement of operational resources, availability of the right data and sufficient expertise in machine learning techniques, you can increase the accuracy in decision-making and enjoy a radical improvement in performance.
Contact us to learn more about Artificial Intelligence.
About the Author – Manan Thakkar
Manan Thakkar is a Solution Architect who spearheads&Indusa’s strategic initiatives to maximize customer benefits. His expertise is in enterprise consulting and solution design globally and he plays a key role in institutionalizing mobility to position Indusa’s services in leading enterprises.