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Machine Learning Applications in Industry

Machine learning is nothing more than algorithms to analyze data and make rules based on the finding. It sounds simple now, right? However, it will create more than powerful processing in every single industry and business.
© Sungkyunkwan University

Machine learning is nothing more than algorithms to analyze data and make rules based on the finding. It sounds simple now, right? However, it will create more than powerful processing in every single industry and business.

Machine Learning Development

Many industries are now developing more robust machine learning algorithms with more complex data while the algorithms deliver faster and accurate results. The practical application of machine learning drives businesses’ success and many companies have become the pioneers of new machine algorithms and techniques. Machine learning offers almost limitless opportunities.

I want you to open your eyes wide and pay attention to the recent developments in different industries. I am sure that you will have a pretty good sense of what these new techniques are after understanding the fundamentals of machine learning and its application to business problems.

Industries That Use Machine Learning

1. Governments.

Some examples of compelling applications include those that identify tax-evasion patterns, sort through infrastructure data to target bridge inspections, or sift through health and social-service data to prioritize cases for child welfare and support, or predicting the spread of infectious diseases. Machine learning enables governments to perform more efficiently, both improving outcomes and keeping costs down.

2. Marketing and Sales.

According to the Forbes magazine article, Knowing what’s driving more Marketing Qualified Leads (MQLs), Sales Qualified Leads (SQL), how best to optimize marketing campaigns, and improving the precision and profitability of pricing are just a few of the many areas machine learning is revolutionizing marketing. Machine learning takes contextual content, marketing automation, including cross-channel marketing campaigns and lead scoring, personalization, and sales forecasting, to a new level of accuracy and speed.

3. Transportation.

This transportation-related data and algorithms are directly connected to supply chain problems. Discovering new patterns in supply chain data has the potential to revolutionize any business. Machine learning algorithms find these new patterns in supply chain data daily without needing manual intervention or the definition of taxonomy to guide the analysis. The algorithms iteratively query data using constraint-based modeling to find the core set of factors with the most excellent predictive accuracy.

Key factors influencing inventory levels, supplier quality, demand forecasting, procure-to-pay, order-to-cash, production planning, transportation management, and more are becoming known for the first time. New knowledge and insights from machine learning are revolutionizing supply chain management as a result.

4. Finance

Again, machine learning and algorithms enable individual investors to access the professional level service with very little or almost no fee.

5. Manufacturing (especially using automation).

Machine learning applications in manufacturing are about improving operations from conceptualization to final delivery, significantly reducing error rates, improving predictive maintenance, and increasing inventory turn. These days, we are seeing revolutionary changes in every single manufacturing industry using machine learning.

© Sungkyunkwan University
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