- How AIOps can Transform Manufacturing and Logistics
- AIOps for the manufacturing industry
- 1. Conducting quality checks
- 2. Predicting exhaustive capacity
- 3. Maintaining equipment
- 4. Testing equipment virtually
- 5. Improving process visibility
- 6. Bridging the gap between cost and quality
- 7. Detecting threats proactively
- How AIOps is used in the logistics industry?
- n a nutshell
In the past few years, AI has proved transformational for numerous industries, including the manufacturing and logistics industries. AIOps (Artificial Intelligence for IT Operations) is being used by manufacturing and logistics firms to improve their productivity.
Since user requirements are becoming more complex, data-driven platforms are used by firms to cater to the needs of customers. Due to increased competition and customer demands, there is a need for autonomous supply chains. AIOps based analytics platforms can help firms to cope up with the increased customer demand.
AIOps for the manufacturing industry
Do you know that more than 60% of manufacturing firms have already adopted an AI strategy? AIOps based analytics platforms are widely used for information systems by manufacturing firms for the following:
1. Conducting quality checks
Many software systems are used in the manufacturing industry for making finished goods and services. It also uses diverse equipment and pieces of machinery. However, a manufacturing firm finds it hard to identify the internal defects of machinery or equipment. If a piece of equipment is not performing on its optimal level, the finished products or services may not be up to the mark. An AIOps based analytics platform can help in conducting quality checks and making sure the machinery is free from any kind of issues.
2. Predicting exhaustive capacity
Every equipment used in the manufacturing industry has an expiry date. However, it is a challenge for manufacturing firms to predict the exhaustive capacity of their equipment and machinery. If an important software system shuts down abruptly, it may have serious impacts on service availability. AI for application monitoring can predict the exhaustive capacity of machines based on historical and real-time data.
3. Maintaining equipment
Manufacturing firms spend funds on the maintenance of their equipment and machinery at regular intervals. With AI for application monitoring, you can adopt a predictive maintenance strategy for your manufacturing firm. Predictive maintenance is a process that determines the best time when a machine required maintenance services. Why waste funds on maintaining a piece of machinery that is working just fine? Predictive maintenance will help in decreasing your downtime thus increasing the service availability.
4. Testing equipment virtually
AI for application monitoring can create a digital twin of your essential equipment. It will then check for any shortcomings by analyzing the virtual and physical specifications of the equipment. With AIOps-powered real-time user monitoring tools, you can detect and solve issues with your equipment quickly.
5. Improving process visibility
If your manufacturing firm is using any software systems, AIOps will offer improved observability. Improved observability means you can easily track the internal states of a software system. An AIOps platform will also improve process visibility where you can test if a process is going as it was planned.
6. Bridging the gap between cost and quality
Manufacturing firms want to close the gap between cost and quality. Customers want to get the best out of their money while choosing products/services. With smart insights and round-the-clock monitoring, an AIOps based analytics platform will help you in bridging the gap between cost and quality.
7. Detecting threats proactively
AI for application monitoring doesn’t wait for threats to affect service availability. Instead, it analyzes the behavior of machines to find any abnormality. If there is any abnormality in the behavior of machines, you can solve it in real-time to avoid any future threat or downtime. Managing threats proactively with AIOps is the best way to ensure high service availability.
How AIOps is used in the logistics industry?
Once a manufacturing firm finishes making a product or developing a service, it must be transported to end-users. The logistics industry has also started using AIOps based analytics platforms for boosting productivity. Top companies like Google, Amazon, and Intel have an AI strategy in place for the supply chain management. The use-cases of AIOps in the logistics industry are as follows:
- Tracing various components of your supply-chain system is easy with AIOps. Supply chain traceability means viewing various components of the supply-chain system separately and identifying the relationship between them. From raw materials to distributors, an AIOps based analytics platform will provide granular traceability into various components.
- Do you know that logistics firms are expected to automate 30% of their warehousing process in the coming years? AI for application monitoring can predict the demand for any product. By analyzing user behavior, an AIOps platform can predict the customer preferences for coming business quarters. Once a logistics firm knows about future demands, it can fill its regional warehouses with that product for higher ROI (Return on Investment).
- Every logistics firm has back-office operations that are necessary. For example, there may be some organizational data that needs to be analyzed daily. Instead of hiring a data analyst, a firm can automate it via an AIOps based analytics platform. A robotic process automation service desk can be introduced to make sure all the back-office operations are automated.
- An AIOps based analytics platform can collect data about customer experiences regarding your services. Collecting feedback is essential for achieving operational excellence.
Now, the rise of AIOps based analytics platforms is forcing logistics firms to adopt an AI strategy. AI in operations management service can help in gaining visibility into the logistics processes and slashing costs wherever possible.
n a nutshell
By using AI for supply-chain management, companies can gain more than a trillion US dollars in a year. Using AIOps platforms can significantly boost service availability for manufacturing and logistics firms. You can also forecast operational risks with an AIOps based analytics platform and can resolve them proactively. Start using AIOps for logistics & manufacturing!