Manufacturing ML algorithms. This can be applied to

Manufacturing         

Predictive
Maintenance
Use of ML can predict when maintenance should be performed and thereby, prevents
unexpected equipment failures. The underlying principle is to perform
maintenance at a scheduled point in time when the maintenance activity is most
cost-effective and before the equipment loses performance within a threshold.

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Condition Monitoring
Manufacturing systems and processes are becoming increasingly complex, making decision-making
in process control a necessity. With the use of Condition monitoring, maintenance
and other actions can be scheduled beforehand to prevent failure and avoid consequences.
Using ML, predictions can be made in advance for those conditions which shorten
a normal lifespan of an activity.

Process Optimization
When optimizing a
process, it is important to maximize one or more of the process specifications while
keeping other constraints same as before. The goal to be achieved is to minimize
the cost and maximize the efficiency.

Demand Forecasting
It is easier to predict
the future demand for a product or a service on the basis of past events and
current trends using ML algorithms. This can be applied to inventory management,
production planning or in assessing the future capacity requirements.                                                                                     

Banking and
Financial Services

Fraud Detection
Machine learning algorithms are able to detect and recognize thousands of
patterns on a user’s purchasing journey and thus, is more useful in preventing
fraud detection. Automated fraud screening systems powered by machine learning
can help businesses in reducing fraud.

Risk Analytics
Predictions about
risk scores for individual customers can be made with certainty using ML
algorithms. It can also help to accelerate and streamline risk processes to
reduce costs from credit losses and manage operational risk.

Credit Worthiness Evaluation
When a business applies for a loan, the lender must evaluate whether the
business can reliably repay the loan principal and interest. Lenders commonly use
measures of profitability and leverage to assess credit risk. Machine learning
contributes significantly to modeling these applications.

Customer
Segmentation
There are different
algorithms available which can help in segmenting the customers based on their
purchasing habits. It helps to identify the likelihood of future purchases.

Retail

Market Segmentation
and Targeting
With the help of
large datasets available, it is now possible for marketers to improve
targeting, response rates, and overall marketing ROI by studying and analyzing
the consumer spending habits and purchasing behavior.

Product Recommendations
E-Commerce websites are an excellent example in this category. Based on
customer purchases, it is possible to predict and recommend similar products or
product accessories. In this way, it provides a personalized experience to each
customer.

Inventory Planning
Rapid changes in business requirements and the complexity of factors
influencing demand are making it difficult to accurately model the causes of
demand variation. Machine learning can help companies overcome this challenge.
They help in forecasting the demands and thus, in planning the inventory
effectively.

Energy and Utilities

Smart Grid
Management
It is now possible to connect sensors, smart meters, and various soft wares to forecast
the consumption and monitor assets to improve efficiency. Machine Learning
helps in building an energy grid system with smart solutions which helps in
reducing outrages.

Power Usage
Analytics
Applying machine learning algorithms to large datasets available for utilities data
can help in uncovering consumer consumption patterns, managing energy
constraints and, detecting and preventing fraud.

Energy Utilization and Optimization
Significant energy savings can be achieved in many manufacturing industries
through process integration. Energy utilization can be monitored and optimized
based on consumption patterns and seasonal variations.  

Travel and
Hospitality

Dynamic Pricing
Without the actual need to manually define complex pricing rules, it is now
possible to fix the prices dynamically. Thanks to machine learning by which the
prices get optimized every time whenever a user is performing any action on it.
It keeps on learning itself and predicts whether to display the original price
or the discounted price.

Aircraft Scheduling
Many airlines use different techniques to create robust and reusable predictive
models to provide a holistic view of operations and enhance business value. Using
ML algorithms, it is possible to predict the flight delays, monitor, and
improve flight operations.

Traffic patterns and congestion
management
Advanced machine learning algorithms can be augmented with real-time data
visualization to predict the traffic patterns so as to manage congestion. It is
now possible to identify the relationships and gain meaningful insights to take
appropriate actions to improve the traffic flow and minimize delays.