Any successful manufacturer must go through the steps of producing, delivering, and selling their items while keeping track of them. When even a short delay in receiving data from the supply chain can lead to a major loss of revenues, relying on machine learning and real-time data in manufacturing is essential.
However, it's not easy for every manufacturer to implement this advanced technology in their system—but that doesn't mean these challenges are without solutions.
The Applications of Real-Time Data
Real-time streaming data in areas like the Internet of Things (IoT), provides access to sensor-collected data in real time and helps to recognize the latest trends. In addition, real-time data also:
- Enhances planning for both long and short-term projects
- Helps to boost customer experience
- Maximizes the profit in the supply chain
- Provides easier management and damage control
Challenges of using Real-Time Data Streaming with ML models
Consecutively Generated Data: Ordinarily, ML models use batch data. This type of data processes all the records and information, no matter how large, all at once. On the other hand, real-time data streaming processes real-time data received within seconds but provides no control over the order you will receive this data.
Temporary Data Storage: Any data received through real-time streaming is liable to get discarded after being analyzed. So, unlike batch data, you can't retrieve past data from specific timelines by using this data system with ML models.
Unsuitable for Common Software Systems: Because of this temporary data storage, data generation is continuous, putting strain on an average software system. Upgrading the system can lead to excessive additional costs.
Solutions for incorporating Streaming Data with ML models
Experienced Development Team: Building machine learning models for streaming data and integrating it are no easy tasks without having a proper development team. While a dedicated team is better suited to solving the constant issues that require attention any data science team, no matter how small, can meet the challenge as long as they are knowledgeable about the development, monitoring, and optimization of the models for real-time streaming data.
Computing Suitable Runtime Engines: To solve the issue of incompatible software systems, manufacturers should consider computing an event-by-event runtime engine that will help process supply chain data smoothly. These engines are specifically designed for handling copious amounts of data streaming in real time.
Switching to Newer Platforms: There are a few low-demanding platforms that can process a large amount of data stream with much lower computing costs. Some also help in reducing deployment time with a faster interface.
How High-Performance Deployment Platforms Can Help
To make the most out of your real-time data, you’ll need an ML deployment platform with an emphasis on scaling data while providing transparency on the performance of models live in production. Deployments of transformer models and other NLP (Natural Language Processing) models should be allowed to improve and/or maintain analysis times as the data changes without increasing infrastructure costs.
Some platforms allow you to not only automate model insights for more accuracy but also automate model deployment into production using just a single line of code. This helps run processes without interruption and reduces the need (and the cost) to hire more people. This is particularly beneficial for organizations like smaller CPG brands that are outside of major urban centers, where it may be harder to attract and employ ML engineers and data scientists.
You should also consider platforms where you can use A/B testing to compare performance and find the most suitable version to boost customer experience. This is extremely useful for understanding customer demands amid ever-changing market trends.
To make it easier to deploy models with dashboards and UI for personalized and simplified management, organizations should give greater consideration to platforms that provide an easy-to-navigate interface. In addition, they should consider whether a platform can handle high-volume computational errands for examining copious amounts of unstructured data and still run on a standard CPU instead of a GPU.
With constantly changing customer demands, trends, and tracking, optimizing your supply chain—whether it's for CPG, pharmaceuticals, textiles, electronics or industrial goods—with real-time data can provide meaningful business benefits. While it may seem daunting at first, there are solutions and platforms that make optimization possible for just about any organization.