To effectively manage your supply chain ecosystem, you need accurate and timely data. A lack of real-time visibility into your supply chain can create significant challenges. It can mask problems until it’s too late to take action to prevent missed deliveries or shortfalls.
Based on several years of supply chain disruption and uncertainty, it’s not surprising that more than two-thirds of CEOs surveyed say they are increasing their investment in disruption detection and innovative processes to manage supply chains more effectively.
Supply chain leaders are deploying digital tools to enhance supply chain visibility and artificial intelligence (AI) and machine learning (ML) to uncover valuable insights to improve performance. Advanced analysis powers the predictive ability to manage inventory levels and demand forecasting, and signal potential supply chain disruptions.
When you have better supply chain visibility and incorporate AI in supply chain operations, you can significantly improve performance.
What is Artificial Intelligence (AI)?
Artificial intelligence in supply chain management uses algorithms to analyze data, look for anomalies and patterns, and produce insights that can help supply chain managers make better decisions.
How is AI used in supply chain management? One example is that AI can automatically adjust inventory levels or orders to meet anticipated demand, reducing the risk of stockouts or overstock, suggesting changes to reorder points, or adjusting the amount of safety stock. However, there are many more benefits of AI in supply chain management.
How AI Is Helping Overcome Supply Chain Challenges
When you have the right technology to track real-time data, you can leverage AI to overcome supply chain challenges.
Sensors, IoT devices, and passive radio frequency identification (RFID) tags enable tracking throughout the supply chain, gathering real-time data for analysis. Application programming interfaces (APIs) can help overcome the complexity of modern supply chain solutions to power AI. APIs allow for the sharing of data to communicate between different systems to ensure compatibility and scalability.
This data and technology give AI what it needs to work. Some of the bigger benefits of AI in supply chain management include:
- Increased productivity – automating tasks so employees can focus attention elsewhere.
- Minimizing operating costs – overcoming supply-demand mismatches and identifying supply chain process inefficiencies.
- Improving inventory management – calibrating optimal stock levels to meet demand.
- Predictive trends – analyzing the entire supply chain ecosystem for potential problems
Gives Recommendations of Where to Work
Data in the supply chain and AI can help companies focus their efforts on areas that need work. This can help businesses isolate bottlenecks and recommend potential solutions to improve throughput. Using simulation modeling, AI can run through various scenarios to identify root causes and help identify solutions.
Using baseline data, AI can project the impact of process changes and surface solutions to improve efficiency and cost-effectiveness. With the right data, AI can process a vast number of potential changes to recommend optimal approaches.
Monitors Trends and Recommends Strategies
AI is adept at spotting trends that might otherwise get overlooked. By analyzing data points, AI can uncover causality that humans may not be able to see. By looking for correlations between cause and effect, AI can demonstrate how even small anomalies can have ripple effects throughout the entire supply chain.
For example, by analyzing patterns of historical trends, AI might uncover seasonal trends that impact the on-time supply of raw materials from specific vendors. Algorithms can then extrapolate the impact of these delays on production schedules, staffing, machine capacity and utilization, warehousing, and finished product delivery to customers. AI might recommend switching to a different supplier during peak times or adjusting production schedules to mitigate the impact.
AI has become especially useful in helping companies manage logistics, such as finding alternate or optimal routing for intermodal shipments as a proactive measure to overcome escalating shipping and freight costs. Analyzing transportation data, tracking inbound and outbound shipments, and providing visibility into trailer contents can produce a treasure trove of data for AI to analyze and help optimize.
Determines When Inventory and Container Management May Be at Risk
Supply chain leaders are deploying highly sophisticated AI tools that look at vast data points that are adaptive to changes in the environment. For example, the right AI tools can analyze the impact of global tensions and governmental policies on raw material mining and the downstream impact on supply chains or monitor weather patterns and potential labor unrest in foreign countries and assess risk.
In modern manufacturing and warehousing, AI plays an important role in better inventory management. By analyzing trends, historical data, and external sources, AI can better forecast demand and maintain optimal inventory levels, automatically making adjustments as necessary. This helps avoid stockouts or tying up capital in excess inventory that may never be needed.
AI can also help determine when container management may be at risk. Inefficient container management can cause all sorts of logistics problems. Imagine having finished products ready to go and not enough capacity to ship them or the inability to reorder from certain suppliers because returnable containers were not shipped back promptly. AI can anticipate such problems and help uncover solutions.
What This Means for Your Supply Chain Management
Artificial intelligence in modern supply chain management can significantly improve operations, cost-efficiency, and cash flow. Businesses can use AI to analyze logistics, processes, equipment, and just about any aspect of the entire supply chain ecosystem to improve performance and avoid potential problems.
For AI to work, however, you need accurate, consistent data.
A lack of supply chain transparency is often the result of inconsistent or incomplete data. Surgere provides the real-time data you need to generate reports and recommendations for where to work and how to optimize operations. This enables AI to monitor the data points to predict trends and accurately forecast things like when container and inventory management may be at risk.
Real-time data is essential for companies to build a more resilient supply chain by anticipating events, isolating problems and bottlenecks, and where to go to avoid ripple effects throughout the entire supply chain.
Contact Surgere today and improve your supply chain visibility.
