As we speak, AI technologies are already been deployed in various Logistics & Supply Chain use cases, making them an invaluable tool for improving efficiency in the industry. Here is a comprehensive look at the 25 most popular AI use cases and applications transforming the Logistics & Supply Chain industry in 2023. In addition to all that we discussed, I’d like you to try out this supply chain simulation. If you’re new to the supply chain, this will give you an understanding of how supply chain management works.
For example, the system might identify that certain weather conditions often lead to delays in shipments or that certain suppliers are more prone to delivery issues. It can also simulate different scenarios by considering various factors and parameters, such as a supplier delay, a transportation breakdown, or increase in demand. Copilot also has the ability to learn from human input, as well, which is key in its predictive modeling. While it is almost obvious that artificial intelligence can revolutionise practices in the industrial supply chain, not all organisations are capable or ready to adopt this technology. Important areas where challenges exist include strategy and management, products and services, competencies and capabilities, collaboration, internal processes, data use and readiness for technology adoption .
Human oversight is crucial to ensure that AI-generated designs and solutions align with ethical, legal, and societal standards. In many scenarios, KPIs are reported at the month-end or quarter-end, and sometimes, it becomes a ritual because, by that time, SCM teams would have already initiated actions towards the next period. Leading SCM vendors do offer functionality for Regression modeling or causal analysis for forecasting demand.
AI and supply chain users, therefore, employ Natural Language Processing (NLP) techniques. AI can automate the return management process, from analyzing the reason for the return to suggesting the best corrective actions, thus enhancing customer satisfaction. Accenture’s Intelligent Revenue and Supply Chain (IRAS) platform, developed by Accenture, integrates insights and findings generated by ML and AI models into its business and technical ecosystem. We can build a deep reinforcement learning model using Ray and or-gym to optimize a multi-echelon inventory management model. Data visualization holds an important part in any ML project, also in the supply chain.
Based on your overarching strategy, we’ll help redefine your end-to-end supply chain and operations to support your enterprise objectives. Technical downtimes and breakdowns are caused by a lack of maintenance and poor equipment utilization, which can lead to an Operational Equipment Effectiveness problem (OEE). Add to this an unskilled, overworked, idle, or scarce workforce, and you have an Overall Labor Effectiveness (OLE) constraint as well.
This reduces transportation costs, as well as maximizes profits by cutting down on the time invesment. In general, advanced analytics in supply chain management is paving the way for new innovations where platforms are used for mining and analyzing cost-effective revenue-building standards. A Bloomberg report suggests that in the past two years, the overall cost in the supply chain has reduced to 12% leading to profits. Modern supply chain companies use a combination of software, hardware, and supply chain data analytics to get hands-on real-time visibility into the loading process. The gathered data can also be used to design less risky and quick process protocols to manage parcels. Handling sensitive supply chain data requires robust security measures to prevent data breaches and protect customer and business information.
In this scenario, the business chooses different models for different applications, balancing considerations like performance, cost, and privacy. EY is a global leader in assurance, consulting, strategy and transactions, and tax services. The insights and quality services we deliver help build trust and confidence in the capital markets and in economies the world over. We develop outstanding leaders who team to deliver on our promises to all of our stakeholders.
Generative AI can analyze equipment data and identify patterns to help predict when maintenance is required. The models can also be trained to predict the maintenance required based on historical data. Furthermore, Generative AI can help organizations manage their supplier relationships more effectively.
This paper published by ChainLink Research explores the use cases and machine learning from pricing to promotions, demand planning and forecasting, and inventory management. The use cases for artificial intelligence and machine learning abound in the supply chain and now is the time to take action. HAVI offers multiple AI-based solutions in the areas of supply chain management and logistics through the use of predictive analytics. The latter encompasses procurement, freight management, warehousing and distribution. By wielding this one-two punch, companies can digitize their operations to create more sustainable and resilient supply chains.
AI plays a crucial role in warehousing and manufacturing by monitoring energy consumption patterns. By doing so, it provides valuable insights for more efficient energy use or even the transition to renewable sources. By using sensors, AI delivers real-time monitoring of various supply chain processes.
Apart from long-term predictions, Demand Guru predicts the everyday demand for particular products. Moreover, this software can recognize the causes of increased demand and even create simulations of such situations. As a result, you receive more precise predictions generated by machine learning algorithms. The benefits of AI in supply chain provides data-driven insights that help supply chain and logistics organizations solve their hardest problems, drive success, and deliver real ROI. Stock level analysis can help identify when products are reaching the end of their life cycles in the retail marketplace. Generative AI can be used to autogenerate customs documents and other logistics documents through a process known as natural language generation (NLG).
Leveraging the power of generative models, which learn from patterns in data to create new and valuable content, this advanced approach offers a range of innovative solutions to longstanding challenges in the supply chain industry. As we speak, the future of the logistics and supply chain industry is already being revolutionized by AI in 2023 in ways that we’ve never seen before. With the integration of artificial intelligence, logistics, and supply chain operations can become faster, more efficient, and cost-effective. The 25 AI use cases and applications discussed in this article provide a glimpse of what is to be seen and how AI is poised to disrupt the industry in the coming years. Generative AI can analyze large volumes of data, including credit history, financial statements, and market information, to assess the creditworthiness of suppliers, partners, or customers. This helps supply chain stakeholders to manage financial risks, make informed decisions about extending credit, and identify potential defaults or disruptions in the chain.
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Supply market intelligence means gathering and analyzing data to support the management of specific categories. With market intelligence, procurement is in a better position to manage risks, negotiate with suppliers, ensure customers are satisfied, find cost savings, and gain a competitive advantage.