Operations Management involves the various business processes that are concerned with the administration of an organization. It includes determining the size of the manufacturing plant, management of raw materials, quality control, maintenance policies etc. Operations managers deal with a lot of data that they analyze to make sure that everything in the organization is smoothly functioning and there are no problems of inventory shortage. Artificial Intelligence (AI) and Machine Learning (ML) can play a significant role in assisting the operations department with real-time monitoring of data to make better decisions in a timely manner.
Here are some use cases of AI in operations:
- Predictive Maintenance — Based on previous data, Machine Learning algorithms can analyze past incidents and predict the possible threats with the new system. This improves the reliability of the system and alerts the management before any harm is caused to the business.
- Threat Detection — The security of any organization is of utmost importance, especially when many of them are working in hybrid mode. AI can help in detecting the threat in real-time and reduce network downtime.
- Root Cause Analysis — AI not only detects threats but also help in investigating the origin of that issue. Doing a root cause analysis can help in correlating the origin and abnormality of the incident. This can help the operations team to respond more efficiently and effectively the next time a similar problem occurs.
- Automated Incident Management — AI algorithms can send automatic alerts to both the organization and customers to remediate the issue. Automating and resolving these issues in less time ensures a greater customer experience.
- Digital Transformation — Robotics and blockchain technologies are helping the operations managers to manage the data and even digitize it allowing the customers to use apps, and making the experience customer friendly.
- Event Correlation and Noise Reduction — A lot of data can be triggered by even the smallest incident; this results in data abundance which can be very difficult to manage. AI can help in categorizing this data into logical groups. This noise reduction helps the management system to look at critical data more efficiently.
- Inventory Management — AI uses advanced forecasting techniques based on the market trends to cope with the demand for all the products or services. This helps in managing the problem of overstocking and understocking.
- Automating Tasks — A lot of manpower and capital is incurred to do the repetitive tasks. AI can help to reduce the IT staff workloads and deploy those resources elsewhere to increase overall productivity of the organization.
USEFUL LINKS :-
- CellStrat Hub
- CellStrat Hub AI Project Packs
- CellStrat Hub AI Apps and API Marketplace
- CellStrat YouTube Channel
- CellStrat product demo : Vision Apps and NLP Apps
- CellStrat AI Lab community (Disrupt4.0)
- CellStrat research : https://bit.ly/CS-research and https://cellstrat.medium.com/
