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市場調查報告書

商品編碼

1544558

食品追溯市場、機會、成長動力、產業趨勢分析與預測，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個工作天內

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簡介目錄

在人工智慧用於預測分析的推動下，食品追溯市場規模在 2024 年至 2032 年期間將以超過 8% 的複合年成長率成長。據 Exploding Topics 稱，大約 48% 的企業正在利用人工智慧來有效利用巨量資料的力量。人工智慧技術正在徹底改變食品追溯系統管理和解釋資料的方式，提供先進的預測和決策功能。透過利用機器學習演算法和巨量資料分析，人工智慧使組織能夠預測供應鏈中的潛在中斷、預測需求波動並識別可能影響食品安全和品質的模式。這種預測能力可以提高營運效率，降低風險，並支持採取主動措施，在問題影響供應鏈之前解決問題。

隨著消費者越來越關注環境影響和道德採購，對食品可追溯性解決方案的需求不斷增加，這些解決方案可以清楚地了解食品的起源和旅程。公司正在投資技術和實踐，不僅增強可追溯性，而且支持永續發展目標，例如減少碳足跡和最大限度地減少浪費。這種向永續實踐的轉變正在推動市場創新，增加收入。

食品追溯產業依照組件、部署模型、技術、軟體、應用程式和區域進行分類。

到 2032 年，服務領域將快速成長，因為它們確保了追溯系統的準確性和可靠性。透過實施先進的資料分析和區塊鏈技術，食品追溯服務可以即時追蹤食品從農場到餐桌的整個過程，從而增強消費者的安全和信心。提供這些服務的公司不斷創新，以滿足食品生產商、零售商和監管機構不斷變化的需求，創造一個充滿活力和競爭的格局。

混合部署模型將在 2032 年受到關注，因為它結合了本地解決方案和基於雲端的解決方案，為食品追溯系統提供了一個靈活且可擴展的框架。它具有改進的資料整合、增強的安全性和成本效益等優勢。透過利用本地基礎設施和雲端技術的優勢，組織可以在管理其可追溯性操作方面實現更高的敏捷性和回應能力。這種方法可以實現無縫資料同步，跨多個平台即時監控追溯資料，滿足食品產業利害關係人的多樣化需求。

由於嚴格的食品安全法規的實施，到 2032 年，歐洲食品追溯市場規模將以良好的速度成長。歐洲食品安全局 (EFSA) 和其他監管機構為食品可追溯性制定了高標準，要求食品生產商和分銷商採用強大的系統。因此，人們越來越重視將物聯網、區塊鏈和人工智慧等先進技術整合到可追溯性解決方案中，以確保合規性並提高透明度。

產業生態系統分析
供應商矩陣
利潤率分析
技術與創新格局
專利分析
重要新聞和舉措
監管環境
衝擊力
- 成長動力
  - 監理合規要求
  - 消費者對透明度的需求
  - 物聯網技術進步
  - 食源性疾病增加
  - 食品供應鏈全球化
- 產業陷阱與挑戰
  - 實施成本高
  - 資料隱私問題
成長潛力分析
波特的分析
PESTEL分析

第 4 章：競爭格局

介紹
公司市佔率分析
競爭定位矩陣
戰略展望矩陣

第 5 章：市場估計與預測：按組成部分，2021 - 2032 年

硬體
軟體
服務

第 6 章：市場估計與預測：按部署模式，2021 - 2032 年

基於雲端
現場
混合

第 7 章：市場估計與預測：按技術分類，2021 - 2032 年

射頻識別
條碼
紅外線的
生物辨識技術
全球定位系統
其他

第 8 章：市場估計與預測：按軟體分類，2021 - 2032 年

企業資源規劃（ERP）
摩擦焊
實驗室資訊系統
倉庫軟體
供應鏈管理（SCM）
其他

第 9 章：市場估計與預測：按應用分類，2021 - 2032

水果和蔬菜
果漿和濃縮物
乳製品
魚、肉和海鮮
加工食品
麵包店和糖果店
其他

第 10 章：市場估計與預測：按地區，2021 - 2032

主要趨勢
北美洲
- 美國
- 加拿大
歐洲
- 英國
- 德國
- 法國
- 義大利
- 西班牙
- 歐洲其他地區
亞太地區
- 中國
- 印度
- 日本
- 韓國
- 澳新銀行
- 亞太地區其他地區
拉丁美洲
- 巴西
- 墨西哥
- 拉丁美洲其他地區
MEA
- 阿拉伯聯合大公國
- 南非
- 沙烏地阿拉伯
- 我的其餘部分

第 11 章：公司簡介

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

簡介目錄

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