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市场调查报告书

商品编码

1544558

食品追溯市场、机会、成长动力、产业趋势分析与预测，2024-2032

Food Traceability Market, Opportunity, Growth Drivers, Industry Trend Analysis and Forecast, 2024-2032

出版日期: 2024年07月02日 | 出版商:

Global Market Insights Inc. | 英文 210 Pages | 商品交期: 2-3个工作天内

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简介目录

在人工智慧用于预测分析的推动下，食品追溯市场规模在 2024 年至 2032 年期间将以超过 8% 的复合年增长率成长。据 Exploding Topics 称，大约 48% 的企业正在利用人工智慧来有效利用巨量资料的力量。人工智慧技术正在彻底改变食品追溯系统管理和解释资料的方式，提供先进的预测和决策功能。透过利用机器学习演算法和巨量资料分析，人工智慧使组织能够预测供应链中的潜在中断、预测需求波动并识别可能影响食品安全和品质的模式。这种预测能力可以提高营运效率，降低风险，并支持采取主动措施，在问题影响供应链之前解决问题。

随着消费者越来越关注环境影响和道德采购，对食品可追溯性解决方案的需求不断增加，这些解决方案可以清楚地了解食品的起源和旅程。公司正在投资技术和实践，不仅增强可追溯性，而且支持永续发展目标，例如减少碳足迹和最大限度地减少浪费。这种向永续实践的转变正在推动市场创新，增加收入。

食品追溯产业依照组件、部署模型、技术、软体、应用程式和区域进行分类。

到 2032 年，服务领域将快速成长，因为它们确保了追溯系统的准确性和可靠性。透过实施先进的资料分析和区块链技术，食品追溯服务可以即时追踪食品从农场到餐桌的整个过程，从而增强消费者的安全和信心。提供这些服务的公司不断创新，以满足食品生产商、零售商和监管机构不断变化的需求，创造一个充满活力和竞争的格局。

混合部署模型将在 2032 年受到关注，因为它结合了本地解决方案和基于云端的解决方案，为食品追溯系统提供了一个灵活且可扩展的框架。它具有改进的资料整合、增强的安全性和成本效益等优势。透过利用本地基础设施和云端技术的优势，组织可以在管理其可追溯性操作方面实现更高的敏捷性和回应能力。这种方法可以实现无缝资料同步，跨多个平台即时监控追溯资料，满足食品产业利害关係人的多样化需求。

由于严格的食品安全法规的实施，到 2032 年，欧洲食品追溯市场规模将以良好的速度成长。欧洲食品安全局 (EFSA) 和其他监管机构为食品可追溯性制定了高标准，要求食品生产商和分销商采用强大的系统。因此，人们越来越重视将物联网、区块链和人工智慧等先进技术整合到可追溯性解决方案中，以确保合规性并提高透明度。

产业生态系统分析
供应商矩阵
利润率分析
技术与创新格局
专利分析
重要新闻和倡议
监管环境
衝击力
- 成长动力
  - 监理合规要求
  - 消费者对透明度的需求
  - 物联网技术进步
  - 食源性疾病增加
  - 食品供应链全球化
- 产业陷阱与挑战
  - 实施成本高
  - 资料隐私问题
成长潜力分析
波特的分析
PESTEL分析

第 4 章：竞争格局

介绍
公司市占率分析
竞争定位矩阵
战略展望矩阵

第 5 章：市场估计与预测：按组成部分，2021 - 2032 年

硬体
软体
服务

第 6 章：市场估计与预测：按部署模式，2021 - 2032 年

基于云端
现场
杂交种

第 7 章：市场估计与预测：按技术划分，2021 - 2032 年

射频识别
条码
红外线的
生物辨识技术
全球定位系统
其他的

第 8 章：市场估计与预测：按软体划分，2021 - 2032 年

企业资源规划（ERP）
摩擦焊
实验室资讯系统
仓库软体
供应链管理（SCM）
其他的

第 9 章：市场估计与预测：按应用分类，2021 - 2032

水果和蔬菜
果浆和浓缩物
乳製品
鱼、肉和海鲜
加工食品
麵包店和糖果店
其他的

第 10 章：市场估计与预测：按地区，2021 - 2032

主要趋势
北美洲
- 我们
- 加拿大
欧洲
- 英国
- 德国
- 法国
- 义大利
- 西班牙
- 欧洲其他地区
亚太地区
- 中国
- 印度
- 日本
- 韩国
- 澳新银行
- 亚太地区其他地区
拉丁美洲
- 巴西
- 墨西哥
- 拉丁美洲其他地区
MEA
- 阿联酋
- 南非
- 沙乌地阿拉伯
- 我的其余部分

第 11 章：公司简介

Bar Code Integrators
Bext360
C.H. Robinson
Carlisle
Cognex
Covectra
Food Forensics
FoodLogiQ
Honeywell International Inc
Merit-Trax
rfxcel
Safe Traces
SGS SA
Zebra Technologies

简介目录

Product Code: 10088

The Food Traceability Market size will grow at over 8% CAGR during 2024-2032, driven by the increasing use of AI for predictive analytics. According to Exploding Topics, approximately 48% of businesses are leveraging AI to harness the power of big data effectively. AI technologies are revolutionizing how food traceability systems manage and interpret data, offering advanced capabilities for forecasting and decision-making. By leveraging machine learning algorithms and big data analytics, AI enables organizations to predict potential disruptions in the supply chain, anticipate demand fluctuations, and identify patterns that may affect food safety and quality. This predictive power enhances operational efficiency, reduces risk, and supports proactive measures to address issues before they impact the supply chain.

As consumers become increasingly concerned about environmental impact and ethical sourcing, there is a heightened demand for food traceability solutions that provide clear visibility into the origins and journey of food products. Companies are investing in technologies and practices that not only enhance traceability but also support sustainability goals, such as reducing carbon footprints and minimizing waste. This shift towards sustainable practices is driving innovation in the market, adding to the revenues.

The food traceability industry is classified based on component, deployment model, technology, software, application, and region.

The services segment will grow rapidly through 2032, as they ensure the accuracy and reliability of traceability systems. With the implementation of sophisticated data analytics and blockchain technology, food traceability services enable real-time tracking of food products from farm to table, thereby enhancing consumer safety and confidence. Companies offering these services are continuously innovating to meet the evolving needs of food producers, retailers, and regulators, creating a dynamic and competitive landscape.

The hybrid deployment model will gain traction through 2032, as it combines both on-premises and cloud-based solutions, providing a flexible and scalable framework for food traceability systems. It provides advantages like improved data integration, enhanced security, and cost-effectiveness. By leveraging the strengths of both on-premises infrastructure and cloud technology, organizations can achieve greater agility and responsiveness in managing their traceability operations. This approach allows for seamless data synchronization, real-time monitoring of traceability data across multiple platforms, catering to the diverse needs of food industry stakeholders.

Europe food traceability market size will grow at a decent pace through 2032, owing to the implementation of stringent food safety regulations. The European Food Safety Authority (EFSA) and other regulatory bodies have set high standards for food traceability, necessitating the adoption of robust systems by food producers and distributors. As a result, there is a growing emphasis on integrating advanced technologies such as IoT, blockchain, and AI into traceability solutions to ensure compliance and enhance transparency.

Chapter 1 Methodology and Scope

1.1 Market scope and definition
1.2 Base estimates and calculations
1.3 Forecast calculation
1.4 Data sources
- 1.4.1 Primary
- 1.4.2 Secondary
  - 1.4.2.1 Paid sources
  - 1.4.2.2 Public sources

Chapter 2 Executive Summary

2.1 Industry 360º synopsis, 2021 - 2032

Chapter 3 Industry Insights

3.1 Industry ecosystem analysis
3.2 Vendor matrix
3.3 Profit margin analysis
3.4 Technology and innovation landscape
3.5 Patent analysis
3.6 Key news and initiatives
3.7 Regulatory landscape
3.8 Impact forces
- 3.8.1 Growth drivers
  - 3.8.1.1 Regulatory compliance requirements
  - 3.8.1.2 Consumer demand for transparency
  - 3.8.1.3 Technological advancements in IOT
  - 3.8.1.4 Increase in foodborne illnesses
  - 3.8.1.5 Globalization of food supply chain
- 3.8.2 Industry pitfalls and challenges
  - 3.8.2.1 High implementation costs
  - 3.8.2.2 Data privacy concerns
3.9 Growth potential analysis
3.10 Porter's analysis
- 3.10.1 Supplier power
- 3.10.2 Buyer power
- 3.10.3 Threat of new entrants
- 3.10.4 Threat of substitutes
- 3.10.5 Industry rivalry
3.11 PESTEL analysis

Chapter 4 Competitive Landscape, 2023

4.1 Introduction
4.2 Company market share analysis
4.3 Competitive positioning matrix
4.4 Strategic outlook matrix

Chapter 5 Market Estimates and Forecast, By Component, 2021 - 2032 (USD Billion)

5.1 Hardware
5.2 Software
5.3 Services

Chapter 6 Market Estimates and Forecast, By Deployment mode, 2021 - 2032 (USD Billion)

6.1 Cloude based
6.2 On- premises
6.3 Hybrid

Chapter 7 Market Estimates and Forecast, By Technology, 2021 - 2032 (USD Billion)

7.1 Radio frequency identification
7.2 Barcodes
7.3 Infrared
7.4 Biometrics
7.5 Global positioning systems
7.6 Others

Chapter 8 Market Estimates and Forecast, By Software, 2021 - 2032 (USD Billion)

8.1 Enterprise resources planning (ERP)
8.2 Friction welding
8.3 Laboratory information systems
8.4 Warehouse software
8.5 Supply chain management (SCM)
8.6 Others

Chapter 9 Market Estimates and Forecast, By Application, 2021 - 2032 (USD Billion)

9.1 Fruits and vegetables
9.2 Fruit pulp and concentrates
9.3 Dairy products
9.4 Fish, meat, and seafood
9.5 Processed food
9.6 Bakery and confectionary
9.7 Others

Chapter 10 Market Estimates and Forecast, By Region, 2021 - 2032 (USD Billion)

10.1 Key trends
10.2 North America
- 10.2.1 U.S.
- 10.2.2 Canada
10.3 Europe
- 10.3.1 UK
- 10.3.2 Germany
- 10.3.3 France
- 10.3.4 Italy
- 10.3.5 Spain
- 10.3.6 Rest of Europe
10.4 Asia Pacific
- 10.4.1 China
- 10.4.2 India
- 10.4.3 Japan
- 10.4.4 South Korea
- 10.4.5 ANZ
- 10.4.6 Rest of Asia Pacific
10.5 Latin America
- 10.5.1 Brazil
- 10.5.2 Mexico
- 10.5.3 Rest of Latin America
10.6 MEA
- 10.6.1 UAE
- 10.6.2 South Africa
- 10.6.3 Saudi Arabia
- 10.6.4 Rest of ME