Top 5 AI Use Cases for Supply Chain Optimization

Being an ASCM member means joining a network of impassioned individuals dedicated to creating a better world through supply chain management. With discounts on APICS certification courses; professional development; events; and exclusive access to our insights, research and online community, you’ll have the tools you need to drive your career and your organization forward. The model was developed by translating the current supply chain into operational rules. These rules, combined with the data coming from Mendix, were used by the model to calculate the shortest throughput time. Greenfield Data Enhancing and DataMetric created a piece of software code that transforms the data from Mendix and performs the data sets necessary to continuously search for the shortest throughput time. Any misinterpreted supply chain condition in the model would result in a non-feasible and non-usable model output.

• However, the complexity and volatility across global supply chains present new problems every day.
• These intelligent systems can analyze and interpret huge datasets quickly, providing timely guidance on forecasting supply and demand.
• Cognitive and self-learning AI in supply chain use cases can prevent this by predicting what customers want, even before they realize they want it.
• With this, a staff member can point their phone at a shelf and the items will be automatically counted.
• Machine Learning serves as a robust analytical tool to help supply chain companies process large sets of data.
• Customers now expect a certain level of service, and companies need to manage a complex network of plants, providers, suppliers, and buyers that enable them to remain flexible, operate efficiently, and meet customer demand.

Weather forecasting and smart image processing enable growers to identify pests, weeds, and disease early on so they can protect healthy crops. Predictive analytics enable them to gauge how environmental factors will influence their crop yields, and real-time soil monitoring helps them adjust water levels to optimise growth. Supply chain companies can enjoy similar real-time and predictive benefits through AI solutions.

Data strategy and quality

The benefits of optimized warehouse space extend beyond employees’ productivity and efficient order fulfillment. Optimized use of warehouse space increases its storage capacity, enabling supply chain executives to purchase goods in bulk. Goods purchased in bulk cost less, resulting in lower expenditure and a higher profit margin. Supply chain operations are complex, and it’s difficult for a human to recognise patterns in inefficiencies, even with the aid of traditional business intelligence solutions. Operations teams can reduce the amount of time it takes to analyse data by leveraging AI tools. Analysts can use those insights to identify potential areas of improvement, forecast demand and inventory levels, schedule maintenance and downtime activities, and predict potential equipment failures.

Inventory management in supply chain is largely about striking a balance between timing the purchase orders to keep the operations going smoothly while not overstocking the items they won’t need or use. These powerful functionalities make it an ideal solution to address some of the main challenges of the supply chain industry. Currently, various strategies and optimizers are being used to generate a delivery schedule and truckload. In the future, AI/ML may be able to provide a more ‘perfect’ solution to the above problem, which balances the requirements mentioned above. A better approach will be segmenting SKUs using clustering (e. g. K-Means) and then applying different strategies to each segment.

Simplified inventory management

The detection and tacking procedures are facilitated using an EVLib component, a complete embedded vision software library developed by Irida Labs based on deep learning. For a lot of suppliers, distributors, manufacturers, and retailers, this is an open question. Also, in the current supply chain market dynamics, evolving workplace practices, and increasingly variable demand, businesses are contemplating how to make their supply chain AI Use Cases for Supply Chain Optimization businesses less susceptible to disruption. And this is where integrating Machine Learning andArtificial Intelligence in the supply chaincomes into the picture. A survey carried out by McKinsey indicated that 61% of supply chain executives had reported decreased costs following the adoption of AI. The business case has been made, and it is up to individual companies to continue adapting artificial intelligence to their advantage.

Supply chains can’t get the insight they need because data is siloed, and they lack end-to-end visibility — ultimately this impacts their ability to meet customer needs. A supply chain optimized through connected technology is the best solution for informational silos. One of the advantages of AI in this respect is that it can operate in the background without compromising current production.

Benefits Of Machine Learning In Supply Chain Management

All in all, they provide greater monitoring efficiency and improvement in warehousing operations. Lastly, if the above are taken care of by AI solutions, customer wait times will be shorter. Fujitsu is one company among many that have trained an AI model that uses object detection to spot damaged items on the assembly line. The inspection process is as a result more efficient, while the amount of defective products that are sent to consumers is greatly reduced.

One of the advantages of using AI in supply chain management is predicting demand and supply more accurately. This allows businesses to plan better and avoid under or overstocking products. It also means that companies will be able to better estimate costs and inventory levels, which helps them save money on storage space or labor costs while improving customer service. This is an area many businesses struggle with and aim to tackle by designing supply chains to match demand with supply .

Optimize Your Supply Chain with AI and ML

Back-office tasks such as document processing can be automated thanks to intelligent automation or digital workers that combine conversational AI with RPA. Back-office tasks such as document processing can be automated thanks to intelligent automation or digital workers that combineconversational AIwith RPA. Multiple cameras will need to be used for intrusion detection sensors to be successful, too. It uses computer vision to detect faces, with facial data stored away in a database, and it’s one of the most widely used applications of AI in logistics.