Smart implementation of Machine learning to solve Real-world problems: 5 Uses, Applications & Benefits of ML in Industries

Published on 06 Aug 2018 | Abhijeet Bawadhankar

Enterprise Azure machine learning implementation

Experts say that, AI and Machine Learning will power the 4th industry revolution, where 50 billion devices will be connected by 2020 that will produce 600 Zetta bytes of structured and unstructured data every year. Here, Machine Learning implementation will help extract insights from this Big Data, to evaluate and build the modelling system. This would help in improving business offerings and gain competitive advantage. Now’s the high time for enterprises to take a much closer look at ML and check how you can utilize the ML potential.

With the launch of new deep learning interface called Gluon, building and executing Machine learning models has become a simpler task for developers of all abilities. This is a ground-breaking step launched by arch rivals, Microsoft and Amazon Web Services. Gluon eliminates all the difficult work in building AI systems and provides neural network models & training algorithms for quick Machine Learning implementation.

Let's discuss 5 ways businesses can utilize ML to innovate and lead in this digital transformation world.

  1. IoT and IIoT Predictive Maintenance:

    One of the costly challenges that every enterprise face is the equipment maintenance. Today, IoT and IIoT implementations are everywhere, in every industry, from wind turbine blades to temperature gauges, to collect data and analyze. Combining this data with Machine Learning will help to know how the system/machine is working, when it needs maintenance, or when a failure can occur. With ML, businesses can predict the need for maintenance or prevent failures and save time & cost.

    World’s leading Renewable Energy Solutions provider uses wind turbine generated (WTG) data to uncover patterns on wind turbines' mechanical part failure, gear box failure, generator malfunction, etc. The company is using the scalable data management and Machine Learning platform powered by Microsoft Azure; to enable quick & intelligent decisions to prevent operational errors and minimize asset failures.

  2. Inbound Logistics Planning:

    Logistics planning involves providing right supplies to the right person at the right place & right time; which is a significant focus for any logistics enterprise. It involves complex process of inventory control, managing orders, warehousing, shipping, and utilization. However, gathering data & preparing information is very time-consuming. This Machine Learning use case can help such enterprises utilize the data generated in this process. Furthermore, this technology is also helpful in the repetition of recurring planning for strategic inbound logistic planning. It can potentially use the previously generated plan and save time.

    A Premier Logistics service provider gears-up to launch Fleet Management solution; to optimize fleet of vehicles’ operations using Azure Machine Learning platform. The company utilizes data of the vehicles, drivers and history of unplanned events obtained from the installed GPS enabled devices (IoT assets) in the vehicles. It then uses ML for predictive maintenance of vehicles and determine fuel usage trends to help improve Fleet operational efficiency.

  3. Retail Commerce:

    E-commerce companies have been gathering demographic data, from quite some time, on store or online consumers, their preferences and spending habits. They can utilize this data, using Machine Learning applications, to unlock insights, which could positively influence inventory, profitability, pricing, and customer experience. Also, Machine Learning implementation helps businesses to change the pricing depending on the various factors like demand, time, competitor’s prices, and much more. ML considers all such parameters and offers the dynamic pricing to win your customers.

  4. Marketing Personalization & Recommendations:

    Statista predicts that by 2021, e-Commerce will share 17.5% of the total retail worldwide. At the same time, it’s important for e-Commerce websites to segment and offer the personalized experience to the customers. However, it’s difficult to deal with the vast amount of data for a tailored experience. Here, Machine Learning platform can help to analyze the big data and offer a personalized experience that will boost the sales and revenue.

    According to the report, product recommendation increases 24% orders and almost 26% revenue. We all know Amazon has already proven product recommendation works. Manually such recommendation is possible, but it will be time-consuming, error-prone, and outdated quickly. Machine Learning applications can analyze the customers’ behavior, identify the trend, and provide insights swiftly. Retailers can leverage these insights along with the market trends to offer customers with personalized product recommendations, which can ultimately increase their sales.

  5. Data Security:

    In this digital transformation world, one of the growing and huge problems is the Malware. In the year 2014, Kaspersky Lab said that over 325,000 new malware files have been detected every day. However, an intelligence company says that each malware file tends to have similar code as its previous versions and only 2 – 10% of files gets modify from one iteration to another. Their Machine Learning models can predict which are the malware files with great accuracy and has no problem with 2-10% variations. Apart from this, ML algorithms can help to unlock patterns about cloud data access, and report anomalies that help predict security breaches.

Hit the Road

The Machine Learning use cases are not limited to the above scenarios and can be implemented in many other processes, like quality control, safety & security, and inventory management. It is quite apparent that Machine Learning implementation offers the wide range of benefits, and it’s no doubt that it will help businesses optimize their processes and increase revenue. Understand your structured & unstructured data and determine which ML models can be consumed to transform your business processes into intelligent systems for competitive advantage and achieve digital transformation.

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