Before moving forward, here are some quick definitions:
- AI refers to a branch of computer science that involves simulating intelligent human-like behavior in machines.
- Machine learning is a branch of AI concerned with using statistical techniques and algorithms to facilitate computer systems in improving their performance on specific tasks using data alone, without being explicitly programmed.
Benefits of Machine Learning and AI for Supply Chain Management
1. Predictive Analytics
Machine learning models are adept at predictive analytics for demand forecasting. These models can identify hidden patterns in historical demand data. For example, the models can correlate customer purchasing behavior with weather patterns.
We developed a manufacturing ERP systems list for organizations interested in this type of functionality:
2. Inventory Management
Thanks to machine learning and deep learning, image classification is now becoming more feasible, meaning computer systems can now recognize and classify objects in images with a high degree of accuracy – in some cases, even outperforming humans.
In terms of supply chain management, computer vision can enable more accurate inventory management. Target, for example, trialed a system in which a robot equipped with a camera tracked inventory on store shelves. (For information on other trends and key issues in contemporary supply chain management, read this article by Bringg.)
3. Optimized Procurement Management
A good example is Chyme, which opens up conversational interfaces between human operators and sales/marketing automation solutions, such as Salesforce. A large beverage company implemented Chyme as they were experiencing inefficiencies when employees sought information on procurement queries. Employees were required to call a helpdesk and wait for operators to access several systems to give them the required information. By implementing the AI-powered procurement bot and integrating it with various ERP systems for access to real-time information, inefficiencies were markedly reduced.
Chatbots provide instant information on shipment status, stock availability, stock price and other procurement queries. This is a clear case of AI benefiting supply chain management while augmenting the roles of staff and allowing them to focus on value-added tasks instead of getting frustrated answering simple queries.
SAP vs. Oracle Case Study
SAP and Oracle both invest heavily in cloud technology. However, our client was skeptical about cloud scalability and unsure if the products were mature and proven.
4. Automated Quality Inspections
IBM Watson is an artificial intelligence system that can be used for automated analysis of defects in industrial equipment. The system uses machine learning techniques to check for damage via image recognition.
The use of AI to power automated quality inspections reduces the chances of delivering faulty goods to customers.
5. Improved Compliance
Supplier quality management is costly and time-consuming because manufacturers in heavily-regulated industries need to track and monitor thousands, or even millions, of component parts from different suppliers to ensure they meet compliance standards. Machine learning models can streamline auditing and compliance monitoring of component parts.
6. Faster, Higher-output Shipping
If you’re pursuing digital transformation, AI may be just what you need for increasing operational efficiencies and competitive advantage.