MIT-2025人工智能在仓储领域中的应用状况研究报告

Global insights from supply chain leaders on AI’s economic and workforce impact – 2025The state of AI in warehousing2 I MIT Intelligent Logistics Systems Lab × Mecalux StudyIntroductionKey findingsTheme 1: Current state of AI/ML adoptionAI adoption in warehousing reaches a new level of maturity • The expanding footprint of AI in warehouse operations • AI becomes part of the daily workflow• A tool for operational complexity• An enabler of high-impact valueTheme 2: AI/ML investment and ROIAI investments move from pilot budgets to proven returns• Budget allocation and ROI from AI• AI investment drivers• Payback periods reflect the scope of transformationTheme 3: Current AI/ML implementation challenges and enablersOvercoming challenges to accelerate AI adoption in warehouses• Main barriers• Internal capabilities for AI implementation• Resources to speed up AI adoptionTheme 4: AI/ML impact on the workforcePeople at the centre of warehouse transformation• AI raises skills across the board• AI creates new rolesTheme 5: Future AI/ML implementation outlook and prioritiesThe next wave: From prediction to decision• Expanding capabilities and investmentContentsTheme 6: Dominant methods and technologiesFrom prediction to generation: How AI methods create value• The technologies powering today’s smart warehouses• Integration: The foundation of effective AISynthesis across themes• Key insights and implications for AI in warehousing• RecommendationsCross-sectional analysisRegional nuances and differences• The state of warehouse automation across countries• AI/ML budget allocation by country• Barriers to AI and automation adoption vary by regionIndustry-specific differencesClosing remarksSurvey methodology MIT Intelligent Logistics Systems Lab × Mecalux Study I 3Year: 2025Participants: 2,000+ experienced supply chain and warehousing professionals.Reach: 21 countries across Europe, North America, Asia-Pacific and Latin America.Industry sectors: Agriculture, automotive, chemicals, construction, consumer goods, e-commerce & retail, energy, food & beverage, government, logistics, manufacturing, pharma, technology, textiles and transport.Company size: Respondents represented companies ranging from 100 to over 5,000 employees, with the largest share coming from organisations with 1,000–4,999 employees.Revenue: Most respondents (32%) represent companies with annual revenues between $251–999 million, followed by 26% in the $51–250 million range, 19% above $1 billion, 18% between $10–50 million, and 5% under $10 million.About the survey4 I MIT Intelligent Logistics Systems Lab × Mecalux StudyIntroductionA data-driven view of AI adoption in warehouse operationsMatthias WinkenbachDirector, MIT Intelligent Logistics Systems LabThe MIT Intelligent Logistics Systems Lab and Mecalux jointly fielded a global survey of more than 2,000 experienced supply chain and warehousing professionals to assess the current state, investment patterns, challenges, wo