Machine Learning in Logistic: Use Cases in Supply Chain Management

There’s software to identify the frequency and severity of common customer pain points, and their causes can be rooted out more quickly. Customer queries can be anticipated by AI tools providing them automatic information updates. This process is vital for stock to be sorted in the local language of a warehouse and stored properly to be easily accessible.

Digital transformation doesn’t occur in a vacuum —existing personnel and processes across the organization will be impacted, even if the implementation is on a relatively small scale. AI in supply chain and logistics provides real-time tracking mechanisms to gain timely insights including the optimal times by where, when, and how deliveries must and should be made. Such powerful multi-dimensional data analytics further aids in reducing unplanned fleet downtime, optimizing fuel efficiencies, detecting and avoiding bottlenecks.

Top 5 AI and Analytics-Enabled Use Cases to Control Supply Chain Disruption

The platform’s functionality is tested in manufacturing, where it will improve production monitoring and planning and enable human intervention and learning. The AI application is expected to increase performance on various business and production indicators, which will also have an impact beyond the factory floor. With this approach, managers can respond quickly to changing customer requirements, while deviations in planned processes can be addressed more effectively. Additionally, the research conducted by the project will provide insights into future management and learning in SC. Accurate demand forecasting is critical to supply chain management, as it enables organizations to optimize inventory levels, minimize stockouts, and enhance customer satisfaction.

They are now working on these technologies for future frozen AI in supply chain use cases such as gene therapy. By analyzing past data and current market trends, AI can more accurately forecast demand, allowing companies to better plan their resources and operations. Analyzing financial data and predicting future costs and revenue streams, generative AI helps companies in budgeting, forecasting, and optimizing their financial resources. That being said, AI is a promising technology that offers many advantages as well as some disadvantages for the supply chain and logistics industry. This raises concerns for businesses about being able to reach their contractual commitments on time.

Generative AI in Supply Chain Use Cases- One Perspective

Additionally, selecting appropriate model architectures, fine-tuning parameters, and handling trade-offs between model complexity and performance can pose challenges. With this technology, Copilot can refine order fulfillment strategies by automating, identifying and implementing the most efficient fulfillment decisions. In scenarios where the AI’s recommendations fall short of the ideal, the system uses a unique training, feedback, and improvement method to adapt and optimize its decision-making processes continually.

This AI technology aids in identifying the best routes for returned products, deciding on repair or disposal actions, and optimizing inventory distribution for refurbished items. Generative AI in supply chain significantly elevates fraud detection in supply chain management by analyzing financial data for irregular patterns indicative of fraud. It uses machine learning algorithms, particularly deep learning neural networks, to examine past transactional data, invoice information, shipping details, and more, spotting anomalies that could signal fraud. In recent IDC surveys, global organizations have expressed the need for improved supply chain visibility to mitigate challenges like cost increases and demand volatility. Generative AI holds the capability to fulfill these needs and more, aiding businesses in enhancing their transparency, efficiency, and overall resilience.

The Future of Logistics and Supply Chain industry: 25 AI Use Cases and Applications Disrupting the Industry in 2023

This is followed by the Response Time needed to create a new production plan based on real-time data, which should also be reduced by half. This KPI reflects both the time it takes to respond to a disruption or unexpected event in the supply chain and a robust supply chain design. The simulation capabilities of the solution aim to increase the Overall Equipment Effectiveness (OEE) by two to five percent when compared with data based on historical performance. OEE refers to the measurement of the actual availability of production equipment, its utilisation and quality. This KPI is therefore significant because it aims to show the assurance of reliability, availability and end-to-end process stability. Conversely, underutilisation and low plant efficiency can have an impact on the stability of value chains.

By leveraging Generative AI, organizations can generate optimal transportation plans, minimize fuel consumption, reduce delivery lead times, and enhance customer satisfaction. Moreover, Generative AI can dynamically adapt plans in real-time, considering unforeseen circumstances or disruptions, thus improving overall supply chain resilience. Generative AI in supply chain transforms demand forecasting in supply chain management, enhancing inventory streamlining, reducing product shortages, and elevating customer satisfaction.

Predictive maintenance is a process in which AI-driven systems help to identify potential problems before they arise. This allows companies to address issues before they cause major disruption or lead to costly repairs. Harnessing AI not only optimizes production processes but also strengthens supply chain resilience. This application can streamline common inquiries from vendors and customers, enabling employees to focus on more complex tasks. As our world becomes increasingly interconnected, entire supply chain networks are becoming increasingly complex. AI has the potential to help organizations address these complexities and optimize their supply chains more effectively.

This process involves identifying the most efficient and fastest driver’s stop sequence while minimizing driving time and distance. Such features also help to monitor and predict traffic patterns impacting delivery times, such as peak hours at logistics hubs. By utilizing ML in the supply chain, businesses can anticipate and proactively mitigate challenges. For example, UPS — a leading shipping company — has employed machine learning and artificial intelligence to optimize its package delivery operations. This allows UPS to allocate resources efficiently, reducing delays and improving customer satisfaction.

There are three main applications of AI in the supply chain that can benefit businesses of all sizes. These applications help merchants make smarter decisions around procurement, transportation, and final mile delivery. Artificial Intelligence (or AI) enables a machine to respond in real-time to a challenge, request, or question the way a human would. AI in the supply chain can be used to observe patters, play out scenarios, or make digital twins. This helps retailers and merchants make more informed decisions and build supply chain resilience.

The earlier companies begin planning, the sooner they can start reaping the rewards of ML. At this stage, it can be useful to establish new KPIs to measure the impact of integrating AI in supply chain management. At a more granular level, professionals should understand how AI and automation will contribute to specific company operations.

Use cases of AI in supply chain management that increase resilience

Before integrating Artificial Intelligence because it’s hype tech, take a look around. Lenovo Brazil turned to DataRobot to build machine learning models at a faster rate, while improving prediction accuracy. Supply chain and logistics industries worldwide lose over $1 trillion a year due to out-of-stock or overstocked items1. We have a team of experts who understand the customer’s needs and build customised solutions accordingly.

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