Use-Cases of AI in Supply Chain Management
1. Supply chain Robotization
Nowadays supply chain robotization isn’t possible without AI. AI gives supply chain robotization technologies similar as digital workers, storehouse robots, autonomous vehicles, RPA, etc., the capability to perform repetitive, error-prone and indeed semi-technical tasks automatically.
Back- office tasks similar as document processing can be automated thanks to intelligent automation or digital hands that combine conversational AI with RPA.
Transportation robotization in a supply chain can also be achieved through AI. Companies like Amazon, Tusimple, and Nuro are considerably investing in transport robotization technologies similar as autonomous trucks.
Warehouse Robotization is another use case of AI in supply chain operation. AI- enabled technologies similar as cobots are helping drive effectiveness, productivity, and safety in warehouse operation.
2. Accurate predictive analytics forecast
Supply chain executives would want to know what the future will look like in terms of demand, market trends, etc. And although no forecast is bullet- proof, using AI can help executives make further accurate decisons.
AI- enabled demand forecasting operations can significantly increase prediction accuracy. The benefits of high accuracy include, but are limited to enhanced optimal inventory level determination.
Detailed inventory necessities for specific regions. Reduced demand and supply inconstancy across the supply chain( Reducing stock- outs, backlogs) and reduced storehouse costs.
3. Enhanced supplier relationship operation
Many of the usual issues we face in global force chains are related to weak supplier relationship operation. Due to a lack of collaboration and integration with suppliers, multiple supply chains, similar as food and automotive, faced serious dislocations during the global pandemic.
AI can help enhance supplier relationship management (SRM) by making it more harmonious and effective. AI- enabled SRM software can assist in supplier selection predicated on factors such as pricing, historical purchase history, sustainability, etc. AI can also help assess supplier performance and rank them
Artificial Intelligence in supply chain management can help automate routine supplier communications like statement sharing and payment reminders.
4. Computer vision in supply chains
AI- enabled computer vision systems are also changing how supply chains work. From enhancing quality control to inventory operation, computer vision has various operations in supply chain optimization.
5. Amending sustainability
Sustainability is a growing concern of supply chain executives since utmost of an association’s indirect emissions are produced through its supply chain. Artificial Intelligence in supply chain can help ameliorate supply chain operations to make them greener and further sustainable.
AI can support optimize logistics routes to reduce gas consumption. AI can also be used for accurate demand forecasting, which can respond in optimizing inventory levels, waste, and carbon emissions across the supply chain.
AI powered with big data can help the supply chain become not only sustainable but flexible at the same time.
The AI ecosystem allows the supreme position of agility to the supply chain business. The equal goes for the use case of data science for supply chain prediction, where you can determine your customer’s requirements before they even know it. This is like accessing your supply chain enterprise into the future to achieve the maximum client satisfaction level.
The above benefits prove nothing but the ever- expanding extent of AI and analytics in the supply chain and logistics. Your decision to invest in AI- based predictive analytics solutions will be more simplified once you check out the use cases of these technologies in your business.
We hope this article was insightful and helped you to understand how AI can help supply chain management and deliver more engaging experiences for your customers. FutureAnalytica improves operational efficiency by automating tasks. Thank you for reading our blog and if you have any questions related to Predictive analytics, Machine Learning, or AI-based platforms, please send us an email at [email protected]