Supply chain organizations, however, must understand that AI also brings potential problems, such as loss of human expertise, algorithm bias, lack of quality data, system failures, and high initial capital investments.
Dynamic Scenario Modeling Optimizes Automated Supply Chain Planning
Ashish Kumar, Assistant Vice President of Operations | Chetu
Supply chains are intricate and vast networks that touch industries and countries.
The complexity of modern supply chains has led professionals to embrace Artificial Intelligence (AI) technologies that reduce human error and optimize operations. AI stands out as a desirable method because breakdowns in the supply chain can cause severe problems such as decreased productivity, revenue losses, and unhappy customers.
AI is a paradoxical technology, offering both benefits and challenges. Harvard Business Review highlights its ability to enhance “visibility into what’s happening in their supply chain.” Supply chain organizations, however, must understand that AI also brings potential problems, such as loss of human expertise, algorithm bias, lack of quality data, system failures, and high initial capital investments.
Despite these concerns, supply chain professionals know that AI’s advantages outweigh the disadvantages, and businesses are rapidly adopting this technology. A 2024 ZipDo study reports that “72% of organizations believe AI in supply chain management will be the leading competitive differentiator by 2025.” The study also stated that AI-powered “supply chain management” can result in a 10-20% reduction in supply chain costs.”
AI technology brings much-needed optimization and agility to meet the ever-changing demands of the modern supply chain. AI’s ability to analyze copious amounts of data and predict possible outcomes or disruptions allows supply chain professionals to maximize their operations.
Traditional Issues, Modern Solutions
Depending solely on historical data has created the chief issues within supply chain planning. These rigid, traditional analysis models may have worked for a while, but information is constantly updating in today’s technology landscape.
Keeping up with the constant flow of information makes real-time changes difficult. This leads to the problems many businesses encounter, such as:
- The inability to adequately respond to real-time changes
- Adapting to unforeseen events
- Inventory optimization
- Inefficient logistical planning
AI can address these four core issues through dynamic scenario modeling, which provides real-time data integration, predictive analytics, simulations, decision support, and optimization. More specifically, AI simultaneously studies historical trends, actualized market conditions, and potential risks. With dynamic scenario modeling, businesses can run “what-if” simulations to improve their decision-making and maximize their supply chains.
This technology allows companies to respond quickly to disruptions, enhance strategic and tactical decision-making, optimize operations, reduce expenses, and improve customer experience. As the engine that makes dynamic scenario modeling so effective, AI tech, particularly for supply chain automation, includes:
Machine/Deep/Reinforcement Learning
Machine Learning (ML) uses data and custom algorithms that allow AI to learn from new data. ML is integral for detecting anomalies within patterns, learning from trends, and delivering the correct information to make informed decisions about rectifying mistakes or enhancing the efficiency of the supply chain.
Supply chains are increasingly moving toward Deep Learning (DL) because it uses neural networks that analyze unstructured data, improve accuracy with complex patterns, and use a single model that learns from the raw data. In contrast, ML requires multiple algorithms to accomplish the new tasks.
Reinforcement Learning is a Machine Learning technique that teaches software by using a trial-and-error model to make decisions, simulating how people learn, i.e., positive or negative reinforcement. This results in highly effective AI that can identify complex supply chain process optimizations.
Natural Language Processing (NLP) and Computer Vision
But what if the data is unstructured? Ultimately, data is data, and through Natural Language Processing (NLP), companies can leverage sources of information like news articles or customer reviews. In turn, businesses can perform better market research. Additionally, computer vision technology can analyze visual data such as images and videos, allowing supply chain automation for tasks like inventory tracking, quality control, and warehouse management.
AI-Powered Optimization
With all those pieces working together, AI can enhance businesses in the following ways:
- Predictive Analytics
- Predictive analytics is the most fundamental technology behind AI. It is a tool for analyzing historical data and leveraging statistical algorithms to forecast an outcome accurately, a task that would take humans considerable time to perform. Meanwhile, AI can predict future outcomes, assist inventory management, and proactively assess risk.
- Demand Forecasting
- This application, a subset of predictive analytics, can study a wide range of factors, ranging from seasonality to market trends. It allows AI to forecast demand accurately and promptly. By providing forward-thinking insights, demand forecasting helps businesses better manage their inventory.
- Inventory Optimization
- Both overstocking and understocking lead to financial losses and waste. Artificial Intelligence can help avoid this by determining an ideal inventory level using historical data and real-time trend analysis. As a result, companies can reduce the issues of being understocked or overstocked.
- Transportation and Logistics Optimization
- Real-time information, specifically on traffic, weather, and overall carrier availability, is vital to providing customers with desired services and products. However, keeping track of all these things is incredibly challenging while providing better transportation routes, schedules, and driver selection. AI handles the heavy lifting, leaving managers to deliver that vital information—resulting in less stress, more cost savings, and improved delivery times.
- Production Planning
- Production planning involves various moving pieces that require the utmost attention. Therefore, Artificial Intelligence can simplify scheduling alongside demand and use resources effectively.
- Supplier Relationship Management
- Companies' relationships with their suppliers are fundamental to ensuring they have a business. AI can accurately evaluate a supplier's performance and allows supply chain managers to strengthen the vendor relationship. A robust tool like Artificial Intelligence can adequately bolster the customer-supplier relationship while determining potential risks of failure, minimizing disruptions, and laying the foundation for a better future.
Real-World Applications and Success Stories
With all the pain points, building blocks, and theoretical applications, the question becomes, “Are there any businesses that have taken the plunge?” The answer is a resounding “yes!”
Several companies have already reaped the benefits of AI because of their supply chain planning. An example of this is Echo Global Logistics, a transportation management company that, according to an article in builtin, uses AI for “rate negotiation; procurement of transportation; shipment execution and tracking; carrier management, selection, reporting, and compliance; executive dashboard presentations; and detailed shipment reports.”
Another example is UPS, which uses smart automation, ML, and AI at its UPS Velocity facility. By combining the latest technologies with “skilled team members,” UPS “can process over 350,000 items per day — significantly more than a non-automated warehouse.” This facility uses more than 700 bots.
By utilizing the abovementioned tools, companies can acquire much-needed agility to maintain their footing and excel in today's ever-changing markets. In the years to come, supply chain professionals will have multiple resources to help them adopt AI technologies, including off-the-shelf products and custom AI software solutions that can meet the unique needs of the operations by integrating with legacy platforms or replacing them.
Considerations and Conclusions
It’s well known that Artificial Intelligence has immense possibilities. The traditional supply chain cannot keep up with the new business dynamics. Adhering to outdated systems has become a barrier to efficiency rather than a speed bump.
Supply chain operations that adopt AI automation will have obstacles to address, such as data quality, AI integration, and workforce training. However, AI-automated supply chains have proven to reduce human error, save money, streamline operations, and improve customer experience.
Moreover, AI technology is not static. In the years to come, supply chain operators will be able to incorporate advanced tools and other technological advancements. Some of these AI upgrades will include digital twins, the Internet of Things (IoT), sensor networks, next-generation robotics, 3D printing, Augmented Reality, Virtual Reality, and quantum computing, which will deliver even greater optimization, transparency, and efficiency.
Embracing Artificial Intelligence and dynamic scenario modeling for supply chains isn’t just a way to get an edge on the competition; it’s an increasingly necessary tool for staying competitive and relevant within the sector.
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As Assistant Vice President of Operations at Chetu, a global software solutions and support services provider, Ashish Kumar oversees multiple portfolios, including Supply Chain, SAP, Cybersecurity, Manufacturing, Retail, Energy, and IT Service Management. He brings expertise in Artificial Intelligence/Machine Learning to these projects. With nearly 15 years of service at Chetu, Ashish is a Certified AWS Solution Architect, Project Management Professional, and Scrum Master.
The content & opinions in this article are the author’s and do not necessarily represent the views of ManufacturingTomorrow
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