Artificial Intelligence in Supply Chain Management Market Trends Technology Innovation, Analysis Application, Demand Forecast 2026
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Solution providers integrated artificial intelligence (AI) technology in supply chain management to improve productivity and workflow. The term ‘artificial intelligence’ refers to the design of computer systems that can imitate human behavioral patterns by understanding the phenomenon of human intelligence. AI can be divided into sub-fields: artificial neural networks (ANNs), machine learning, expert systems, fuzzy logic, and agent-based systems.
Supply chain management includes purchasing & supply management, demand planning & forecasting, transportation & network design, order picking issues, and customer relationship management. Maintenance is made easy due to the automated processing, as regular repairs and upkeep are required for maintaining equipment. Artificial intelligence gathers information through sensors, which is combined with maintenance data. The best time to repair equipment in an organization is analyzed by the system, which is called predictive maintenance. Inventory can be improved by reducing redundancy with the help of smart storage processing and deploying advanced technology. Further, artificial intelligence in supply management offers to track and maintain database of suppliers and shipping.
Growing adoption of artificial intelligence in supply chain management is also attributable to factors such as demand for better transparency and visibility in supply chain data and processes. Rising adoption of Big Data is another factor driving for artificial intelligence in supply chain management market for improving consumer services & satisfaction. Further, increasing demand for accuracy and safety in warehouses would drive growth of AI in supply chain management market in coming years.
Lack of awareness about developments in the artificial intelligence technology can be a restraining factor for the artificial intelligence in supply chain management market growth. Demand for using the artificial intelligence technology in data collection and automated systems is likely to increase in the near future and would also provide opportunity for the artificial intelligence in supply chain management market.
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The artificial intelligence in supply chain management market can be segmented, based on technology which is divided into natural language processing (NLP), machine learning (ML), computer vision, and context-aware computing. The computer vision segment is expected to expand at a high pace during the forecast period, due to increasing adoption of computer vision for semi-autonomous or autonomous applications in several industries across the world such as automotive and manufacturing. In terms of end-user industry, the artificial intelligence in supply chain management market can be segmented into retail, consumer-packaged goods, health care, automotive, aerospace, manufacturing, and food & beverages.
The consumer-packaged goods segment is expected to expand at a rapid pace during the forecast period. Geographically, the global artificial intelligence in supply chain management market can be divided into North America, Europe, Middle East & Africa and Asia Pacific. The artificial intelligence in supply chain management market in Asia Pacific is expected to expand at a significant rate during the forecast period. Increasing adoption of deep learning and NLP technologies for use in automotive, retail, and manufacturing applications in Asia Pacific is driving the artificial intelligence in supply chain management market in the region.
Key players operating in the artificial intelligence in supply chain management market are United Parcel Inc. (which uses artificial intelligence to develop the most efficient route for its fleet); Rolls-Royce Motor Cars Limited (which uses AI to safely transport its cargo); Blue Marble Logistics, LLC (which uses robots to deliver drugs, groceries, and packages with the help of AI); Lineage Logistics, LLC (which uses an artificial intelligence algorithm that can forecast the time when orders arrive and leave a warehouse); and Infinera Corporation (which uses machine learning to analyze production times and logistics and predict delivery dates in a better manner).
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