Computer
Nowadays, the logistics and supply chain industry is taking the opportunity to adopt artificial intelligence at a very large scale. The core objective of leveragingAI in SCM is to boost productivity and increase efficiency. With diverse capabilities and huge potential, AI is rapidly revolutionizing traditional supply chain approaches to make the industry more sustainable, and future-ready. In this blog, we will discuss how artificial intelligence controls supply chain disruptions. Let’s get started.
AI-enabled use cases for supply chain management
AI and Analytics-based systems help supply chain businesses with automated and reliable data visual platforms. Let’s have a look at the top 5 AI use cases that efficiently manage supply chain disruptions.
1. Demand forecasting in warehouse management
Demand forecasting refers to data-rich modeling that helps optimize inventory levels, improve customer satisfaction, and manage warehouse operations efficiently. AI-based forecasts also allow warehouse experts to make informed decisions and build effective strategies for inventory stocking. Important use cases of AI-based demand forecasting are:
Data analysis
Inventory Optimization
Statistical forecasting
Collaborative Forecasting
Continuous Monitoring
Resource planning
2. AI-based vehicle maintenance
Artificial intelligence helps in vehicle maintenance recommendations by offering data-driven and proactive solutions. This leads to improved vehicle reliability, optimized vehicle maintenance schedules, and minimized downtime. Here is how artificial intelligence is used for vehicle maintenance:
Predictive maintenance
Anomaly Detection
Data Analytics
Cost optimization
Fleet management optimization
Prescriptive maintenance
3. AI in adding portability to the loading process
Supply chain operations involve detail-oriented analysis. This means how goods are loaded or unloaded from the containers. Logistics and supply chain agencies leverage accurate supply chain data analytics and the combination of hardware and software to get real-time visibility into the loading and unloading process. AI significantly improves the portability and visibility of the loading process in the supply chain by managing loading strategies, minimizing errors, and increasing efficiency. Some of the key benefits of AI in adding portability include:
Optimal loading strategies
Predictive analytics
Dynamic load balancing
Visualization & Simulation
Route optimization
Quality control
AI for revenue boost & cost-saving and in supply chain
AI & analytics-based solutions are the cost-effective options that help to minimize procurement and shipping rates and simplify courier contracts efficiently. Moreover, AI provides diverse opportunities for cost savings and revenue growth in supply chain management through automation, optimization & predictive analytics. Let’s have a look at how AI is applied for revenue boost & cost-saving:
Demand forecasting
Supply Chain Network (SCN) optimization
Inventory optimization
Predictive maintenance
Warehouse automation
Quality control
5. Strategic sourcing in the supply chain
AI-based data analytics helps in strategic sourcing and allows organizations to make well-informed decisions, improve cost savings, and manage supplier relationships.
Let’s have a look at how data analytics is applied in strategic sourcing:
Supplier Performance Analysis
Market Intelligence
Cost Analysis
Risk Management
Demand Forecasting
Performance Benchmarking
Supplier Relationship Management
Contract Management
