Leveraging ML and Analytics to streamline business operations
CoreFlex uses automation and artificial intelligence to improve sales productivity
The client is one of the largest beverage bottlers in the United States, the United Kingdom, and Canada. The company delivers over 2 billion cans of products to more than 6,000 customers each day using a network of distribution facilities that maintain tight control over product quality and freshness.
The client’s order processing management infrastructure was manual, resulting in delayed order processing. Several factors influenced the delay, from resource unavailability to the lack of management skills.
The client needed an automation system driven by Machine Learning and Power BI, capable of making informed decisions to dictate smooth operations.
CoreFlex began by identifying all anomalies, whether they were caused by a specific person or by a common pattern of behavior. This was a crucial step to identify the systems affecting the overall performance. CoreFlex also collected, cleaned, and organized all the data required for training and testing.
The next step was pre-processing. This involved converting raw data into a format that Machine Learning algorithms could use. Some of these steps required the CoreFlex teams to remove irrelevant information, such as noise and false positives, normalize the values of variables according to their range and standard deviations, feature extraction from raw data, etc.
Once the data was gathered, CoreFlex ensured it was accurate and valid before proceeding with model selection (training). The team also performed thorough research on model selection to choose an appropriate model for the application. For model selection, the CoreFlex teams spent time choosing between different models based on their performance metrics, such as accuracy or precision/recall, etc., to best depict the process.
Ability to analyze data using AI from various sources quickly without writing a single piece of code
Ability to predict future sales with AI and ML
Ease of submitting reports hassle free
Better data-driven decision making
No more manual sales anomaly detection
Detection of unusual behaviors across data sourcesthat were initially not flagged when using traditional methods