Artificial Intelligence AI in Supply Chain and Logistics

​​With the majority of supply chain disruptions resulting from human error, the cost to businesses can be substantial. Supply chain management (SCM) is the process of managing and overseeing a company’s supply chain network and all activities involved. It includes the coordination, control, and integration of all processes in the journey a product takes from creation to consumption. By embracing these advancements, businesses can drive operational efficiencies, enhance customer experiences, and gain a competitive edge in the global marketplace.

More than 60% of supply chain managers who adopted AI in their processes saw a decrease in their costs, according to research by McKinsey & Co. According to that same study, most supply chain management respondents are likely to report savings specifically from spend analytics and logistics-network optimization. According to McKinsey, 61% of manufacturing executives report decreased costs, and 53% report increased revenues as a direct result of introducing AI in the supply chain. Some of the high impact areas in supply chain management include planning and scheduling, forecasting, spend analytics, logistics network optimization and more, further discussed below.

Machine Learning in Supply Chain Case Study

Technically, generative AI works by analyzing vast amounts of transportation, waste management, and resource usage data. It uses machine learning algorithms to identify patterns and predict outcomes that can lead to more sustainable practices. Additionally, it can dynamically adapt to unexpected circumstances in real-time, enhancing overall supply chain resilience.

• Only the right combination of AI and supply chain can help companies tide over this crisis.
• The main objective of using AI in the supply chain and logistics is to increase efficiency and productivity.
• Inventory management is extremely crucial for supply chain management as it allows enterprises to deal and adjust for any unexpected shortages.
• Teams have the power to apply approved technologies to the challenges that they face.

While AI can increase efficiency, reduce costs, and improve customer satisfaction, it also raises concerns about privacy, transparency, cybersecurity, and mass job losses. AI can be used to detect and prevent fraud in the logistics and supply chain industry. This is done by analyzing customer data and identifying any irregularities or suspicious activity, thus helping companies reduce the risk of fraud and protect their customers. AI is designed to automate order processing and optimize routing in order to improve efficiencies and reduce delivery time.

Annex: Taxonomy of AI that can be used in logistics

In this article, we explore a small but diverse set of use cases that can serve as a starting point for a supply chain organization’s foray into AI/ML. Supply chain leaders can expect to gain a high degree of cost and efficiency improvements from these applications. AI in supply chain and logistics helps streamline the ERP framework to make it future-ready and connect people, processes, and data in an intelligent way. AI-based automated tools can ensure smarter planning and efficient warehouse management, which can, in turn, enhance worker and material safety. AI can analyze workplace safety data and inform manufacturers about any possible risks.

Decision-making in this use context is not centralized but up to the discretion of the human operator. In unforeseen situations and circumstances, the system can react and is not paralysed due to a rigid structure, both in terms of traditional information technology solutions and hierarchical management approaches. The basic problem is optimal planning and scheduling of the supply chain, forecasting, and optimisation of production batches.

Platooning of semi-automated trucks is poised to revolutionize logistics in limited geographical areas like mines, military bases, and warehouses. Rolls Royce, legendary british automobile manufacturer, partnered with Intel to design an intelligent AI system that can make commercial shipping faster and safer. They claim that this technology will have the capabilities to independently manage navigation, obstacle detection and communications, developing a new system of autonomous ships.

It provides fleet managers with the intelligent armor to battle against the otherwise unrelenting fleet management issues that occur on a daily basis. One of the biggest challenges faced by supply chain companies is maintaining optimum stock levels to avoid ‘stock-out’ issues. At the same time overstocking can lead to high storage costs, which on the contrary, don’t lead to revenue generation either. With the complex network of supply chains that exist today, it is critical for manufacturers to get complete visibility of the entire supply value chain, with minimal effort.

Types of Application Software: A Detailed Guide for 2023

Generative AI in supply chain significantly enhances route optimization and logistics management in supply chain operations. It can devise optimal transportation strategies by considering traffic patterns, weather forecasts, vehicle capacities, and customer needs, thereby reducing fuel usage and delivery times and increasing customer satisfaction. Conventional AI methods typically utilize statistical models and historical data analysis. Techniques like time series analysis, regression models, and machine learning algorithms are employed to discern patterns and correlations in historical data. Predictions are made based on identified trends, seasonality, and other data-driven factors. The supply chain, a vital cog in the success of businesses across various sectors, comprises a complex network involving the production, distribution, and delivery of products and services.

For instance, IBM Watson leverages AI to monitor supply data, supplier cycle time performance, and manufacturing time, and helps to deal with unforeseen delays with inbound deliveries. The introduction of AI and machine learning into supply chain management is the most modern enterprise digital strategy out there. The relevant advanced computing tools allow you to learn more quickly from past problems, rectify current ones, and predict future threats and opportunities for your business. Open AI’s phenomenal ChatGPT program and similar generative AI models, such as AutoGPT and other AI Agents, can be used in logistics with the most impactful use cases around automating workflows and customer experience.

According to Gartner, 50% of supply chain business operations will be powered by AI-based software with progressive analytics features. Machine learning can help businesses improve supply chain management by making it more resilient to disruptions. Supply chains across the world are adopting Machine Learning to improve their processes, reduce costs and revenue. Machine learning in the supply chain can help retailers and distributors deliver transformational changes in their businesses. It can help them lower costs, improve efficiency, and enhance their customer service. Predictive models can forecast potential disruptions based on historical data patterns and other factors.

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