Supply chain management (SCM) is the monitoring and optimization of the production and distribution of products and services of a company. A supply chain can be understood as a network that creates value through interconnected elements. It involves various business sectors working closely together, including production, services, funds and information. The primary purpose of SCM is to ensure that products are available to consumers. The main objective of efficient SCM is to enhance four key flows: goods, information, cash flows and overall processes. A major obstacle to managing the supply chain as an integrated system is thelack of visibility of goods and real time intelligent decision process keeping in eye overall operational efficiency and competitive advantages.
Supply Chain Management (SCM) Artificial Intelligence (AI) is the integration of advanced AI-enabled technologies and techniques within large organizations to enhance supply chain management (SCM), improving efficiency, resilience and strategic decision-making capabilities. SCM AI goes beyond simple automation. It involves using AI to solve complex business problems that require human-like intelligence like customer relationship management, inventory management, transportation networks, procurement, and demand forecasting and risk management. By leveraging large datasets and sophisticated algorithms, SCM AI can help unlock insights, optimize operations, and drive innovationacross various departments and functions and play critical role in shaping sustainable, flexible and resilient supply chains.
AI leads the way in revolutionizing every aspect of SCM and is employed to make educated predictions on demand and transportation routes, suggest innovative solutions and optimize operations costs. Yet, despite these advantages, enterprise adoption of AI-powered solutions remains fraught with challenges. The problem is rarely the technology itself - it’s the complexity of implementation with technological, operational and regulatory challenges. Key decision maker of most organization clearly understand the advantages of AI in SCM, the challenge is to integrate these AI solutions without disturbing their existing workflow. They are not interested in another standalone systems that provide isolated results without understanding contextual and real time information. The real challenge is how to integrate AI solutions that sit smoothly within existing SCM workflow, so it enhances productivity instead of creating more work. . A lack of enthusiasm for AI is never a question of uncertainty about AI’s potential but about how to do it right.
Challenges of VTO
Online shopping puts millions of products at consumers' fingertips and provides convenience for selection of various choices. However, there's a risk of receiving items that don't fit or look as stylish as they had imagined. According to Snap's Global Product Marketing Lead, businesses lose up to $550 billion annually to customers returning items, and 70% of customers find it difficult to get clothes that fit. Mckinsey discovered that poor fit or style accounts for 70% of returned fashion items. On the other hand, offline shoppers can try on items before buying, resulting in higher confidence in their purchases.
Application of AI in Supply Chain Management
AI-driven SCM optimization delivers a range of benefits that markedly enhance inventory management, improve demand projection, optimize logistics, increase efficiency and productivity and upgrade decision-making policies. We will describe application of AI in the following areas of SCM.
- Inventory Management
- Transportation Networks
- Demand Forecasting
- Decision Intelligence Systems
Inventory Management
One of the biggest challenges in SCM is inventory management and its relevant cost, as demand patterns change to facilitate diverse needs. AI-powered systems can optimize inventory, when taking into consideration factors such as demand, storage costs, and lead time and even supply chain constraints. Integrating AI in inventory management offers numerous advantages, namely, reduced stock-outs, minimized overstocking, strategic clearance sales and improved profit margins. AI techniques provide new, innovative ways to inventory control and planning challenges by capturing inventory patterns naked to the human eye. Machine Learning techniques, like reinforcement learning and anomaly detection, are capitalizing on data insights to fine-tune inventory levels. The analysis of historical stock quantities and abrupt changes in trends and the handling of large volumes of data produce informative reports on estimated inventory status.
Decision Intelligent Systems
Supply Chain Business success depends on decisions – decisions that range from target market selection to vendor engagement, production and sales targets, and logistics routes. However, decision making is plagued with challenges, and to make things worse, complex and critical decisions are often made by people with severely inadequate information, time, or experience. Making business decisions requires significant efforts in gathering and analyzing data. This often involves scrutinizing spreadsheets or similar legacy applications – leading to an inherently retrospective approach to decision-making.
Businesses, however, need real-time, relevant, and actionable intelligence to effectively manage increasingly complex supply chain networks. Without key, timely, and holistic supply chain decision intelligence, they run the risk of flawed decisions, process and operational gaps, and lost sales and revenue.
AI will help to develop decision intelligence systems that helps executive to understand cause and effect of various business process. The decision intelligence is different from business intelligence that most of the existing systems already have. The key difference between business intelligence and decision intelligence is in the type of intelligence that each provides. Business intelligence is the process of enabling the visualization of operational results in various formats such as dashboards and reports. Decision intelligence, on the other hand, empowers executives with insights and recommended actions that they need to solve business problems.
Demand Forecasting
AI applications observe and understand patterns through statistical, mathematical, and logical calculations of historical information, consumer perceptions and buying processes, and market volatility to forecast possible future demand precisely. These features help organizations avoid overstocking or understocking, optimize inventory costs, and enhance customer satisfaction through prompt service action.
Transportation Networks
AI in supply chain and ML in logistics can analyze historical data, traffic patterns, and external factors — such as traffic, weather, and shipper or receiver delivery constraints — to optimize route planning and scheduling. More efficient routing can reduce fuel consumption, improve delivery times, and cut costs. For example, UPS, a global leader in logistics, utilizes an AI-powered tool called ORION to determine the most efficient delivery routes, saving millions of miles and gallons of fuel annually. Similarly, any organization can also have a solution custom-designed for you to get the results it needs.
AI Platforms for Supply Chain Management
SO99
SO99, also known as ToolsGroup Service Optimizer 99+, is a supply chain planning and optimization platform. It is designed to help businesses improve demand forecasting, inventory optimization, and replenishment processes. SO99 utilizes advanced algorithms and AI to orchestrate planning variables and optimize service levels while minimizing inventory. It is particularly useful for managing demand uncertainty and improving overall supply chain efficiency.
Project44
Project44 is a supply chain visibility platform that is using its dataset gathered from tracking 1 billion shipments representing $1 trillion in customer inventory to enable Movement GPT, its new AI within its Movement platform, to respond to shipper queries. Shippers can ask Queries in plain natural language in its new AI platform known as Movement GPT: Shippers can ask questions like “Which of my shipments are impacted by weather in northern Europe?” and “Do I have more reliable routing options for my next shipment?”
LevelLoad
LevelLoad can help companies plan loads and create a more balanced transportation plan so they can work with preferred carriers and ensure adequate storage space and labor availability across their sites. LevelLoad solution from ProvisionAI analyzes shipment patterns and identifies spikes in demand over the next 30 days. The system can then adjust by shipping some products early or holding less-needed items a day or two.