Artificial intelligence (AI) has the potential to become one of the most disruptive technologies of the 21st century. It’s driving innovation across sectors as disparate as healthcare and agriculture. Supply chain management is one area where AI has many applications and benefits.
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
Demand forecasting analyzes customer demand to optimize supply chain processes. Optimal inventory levels and reduced holding costs are key benefits of accurate demand forecasting.
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:
2019 Top 10 Manufacturing ERP Systems Report
Find out what ERP vendors made the Top 10 list this year!
2. Inventory Management
An important use case for AI is enhancing the computer vision capabilities of ERP systems and machines. Computer vision is a field of computer science that works on enabling computers to see, identify and process images.
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
Chatbots have dramatically improved in recent years, and while they are often used in the context of customer service, they also have benefits in 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.
4. Automated Quality Inspections
Manual quality inspections conducted at logistics hubs are often used to inspect packages or containers for any damage during transit. The possibility to automate quality inspections has emerged with the growth of AI.
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
Manufacturers in certain industries are required to comply with a range of industry-specific regulations governing product quality. In industries like aerospace and healthcare, supplier quality is paramount. A component part that fails to meet industry regulations in aerospace, for example, could lead to human fatalities.
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
The autonomous vehicles industry is still in its nascent stages. However, as it begins to mature, there is enormous potential for shortening shipping times. Human truck drivers can only be on the road for a limited amount of time within a certain time period. Autonomous vehicles, powered by AI and machine learning, do not have this limit on driving time.
The benefits of AI and machine learning in supply chain management are clear. These technologies augment the roles of skilled workers, allowing them to provide more value to their organizations.
If you’re pursuing digital transformation, Panorama’s ERP consultants can help you determine how AI can increase your organization’s operational efficiency and competitive advantage.
Written by Limor Wainstein. Limor is a technical writer and editor at Agile SEO, a boutique digital marketing agency focused on technology and SaaS markets. She has over 10 years’ experience writing technical articles and documentation for various audiences, including technical on-site content, software documentation, and dev guides. She specializes in big data analytics, computer/network security, middleware, software development and APIs.
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