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Editor's Pick (1 - 4 of 8)
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Democratizing Machine Learning Algorithms for Integrated Data-Sharing

Democratizing Machine Learning Algorithms for Integrated Data-Sharing
Andre Mutovic, CIO, ION Media Networks

The Tango of AI and Big Data

The Tango of AI and Big Data
Monica Khurana, CTO/CIO in Financial Services, Guardian Life Insurance

Transforming the Art Museum in the 21st Century.

Transforming the Art Museum in the 21st Century.
Jane Alexander, CIO, Cleveland Museum of Art

Machine Learning: The Future from the Perspective of Model Building

Machine Learning: The Future from the Perspective of Model Building
Markus Levy, Director of Enabling Technology, NXP Semiconductors

The State-Of Machine Learning Adoption in the Enterprise

The State-Of Machine Learning Adoption in the Enterprise
Sangeeta Edwin, Vice President, Data, Analytics & Insights, Rockwell Automation

Inside the Latest in Machine Learning

Inside the Latest in Machine Learning
Troy Lau, Group Leader in Machine Intelligence, Draper

Recent Trends in Machine Learning

Recent Trends in Machine Learning
Sears Merritt, Head of Data, Strategy & Architecture, MassMutual

Balancing Accuracy With Interpretability in Machine Learning Models

Balancing Accuracy With Interpretability in Machine Learning Models
Joshua Mabry Expert in Advanced Analytics practice

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How can Machine Learning Streamline the Supply Chain Management

By CIO Applications| Friday, February 15, 2019
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The digital revolution has made way for many innovative and efficient technologies that have allowed organizations to revamp their business process. These technologies have helped to bridge the gap between the service providers and the customers. The supply chain industry is also leveraging many intelligent tools like machine learning (ML) to rev-up their services. Let’s delve deeper to understand the significance of ML techniques in streamlining and optimizing the operation of supply chain industry:

Scalability: The fierce competition among companies requires them to scale up or scale down their services according to the requirement. Companies need to be prepared for any upcoming challenges to stay relevant in the market. Machine learning tools help enterprises to address any sudden changes in the scale of operation and keep the delivery system clear of backorders.

Smart Machines: A combination of machine learning tools and big data solutions provide a high level operating values, helping companies to perform operations efficiently. Ml tools can also automate repeated tasks, which help an enterprise to use their workforce in more productive ways.

Decision Making: The supply chain industry is gradually acknowledging the significance of predictive analysis. A right prediction allows companies to be prepared for any contingencies. Machine learning tools offer an innovative analytical approach by processing large data sets, providing valuable insights into the business processes to make informed decisions.

 Quality Management: Machine learning tools have a powerful pattern recognition technique, which offers effective suppliers quality level and log creation. These tools ensure that the suppliers adhere to the minimum criteria of material quality, which helps to improve supplier quality management.

Demand and production planning: The pattern recognition of ML tools also helps to identify a customer’s demand pattern and offer appropriate solutions for multiple scenarios. This makes ML tools, one of the best available techniques for demand and production planning.

An appropriate implementation of intelligent technologies like artificial intelligence, machine learning, and augmented reality can work wonders for the growth of an organization. Many companies in the supply chain and logistics industry have already ventured into using intelligent technologies in their services to outclass their counterparts.

Check Out: Top Machine Learning Consulting/Services Companies

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