Machine Learning

Using existing store data, retailers can utilize Machine Learning to create data-driven customer profiles to segment customers, understand their purchasing behaviors and predict their next action. In addition, retailers can use ML to identify which products, promotions, and strategies will resonate with each segment and effectively push marketing communications when and where the customer is most active. 

Companies can use Machine learning to determine the likelihood of workplace incidents during certain activities, weather events, environmental conditions, time of year, after significant overtime, surrounding sick days, and many other potentially predictable scenarios. Additionally, Computer Vision combined with Machine Learning can automate monitoring PPE compliance. This helps companies implement mitigation strategies that significantly improve the safety of employees and customers, especially in roles that involve physical labor, travel, vehicle operators, and all non-desk work. 

CFOs use Machine Learning and Predictive Analytics to reduce forecasting and budgeting time and effort while improving accuracy and consistency. ML uses historical data for autonomous predictive modeling while factoring in internal and external elements, including demographics and global economic factors.

Machine learning helps to understand where a package is during the entire logistics cycle. It allows supply chain professionals to:

• Track the location of goods during transportation
• Provides visibility into conditions in which the goods are transported
• Provides real-time route optimization

Machine learning can improve warehouse management by automating manual work predicting possible issues, and reducing paperwork with warehouse staff. Examples include:

• Controlling the work of the conveyor belt and predicting blockages
• Automatically detect the arrival of packages and update delivery statuses
• Scanning package barcodes and adding them to the system
• Programming autonomous vehicles and robots to help pack, unpack, transport, load, and unload

Machine learning can identify quality issues in line production early using computer vision. ML can also predict equipment maintenance based on real-time asset data, resulting in improved maintenance and decreased maintenance costs.

Machine learning algorithms can analyze vast amounts of data and draw patterns for every business to protect it from fraud.

Chatbots are used in supplier relationship management, sales, and procurement management. Chatbots allow staff to focus on value-added tasks instead of answering simple queries.

Netflix uses machine learning to curate what you’ve seen to predict what you’ll want next. The more you watch, the more the system learns what you’ll want to watch next.

Google Maps takes data from various sources and feeds it into machine learning models to predict traffic flows. This data includes live traffic information collected anonymously from Android devices, historical traffic data, information like speed limits and construction sites from local governments, and factors like the quality, size, and direction of any given road. 

Video Cameras can locally detect object classes, including people, vehicles, and packages, and send alerts for unusual behavior like hovering around the building, standing still too long, or snapping off curious photos.

Autonomous vehicles use ML to detect and identify ​​objects like vehicles, pedestrians, signals, traffic signs, driving lanes, license plates, etc. and provide the driver with parking assistance, collision warnings, traffic sign detection, and pedestrian detection.