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Global Smart Labels Market to reach USD 33.69 billion by the end of 2030.

Global Smart Labels Market Size study & Forecast, by Product Type (RFID, Electronic Article Surveillance Label, Sensing Label, Dynamic Display Label), by Application (Retail Inventory, Perishable Goods), by End User (Retail, Healthcare, Logistics, Others) and Regional Analysis, 2023-2030

Product Code: CMP-14793699
Publish Date: 30-10-2023
Page: 200

Global Smart Labels Market is valued at approximately USD 10.42 billion in 2022 and is anticipated to grow with a healthy growth rate of more than 15.80% over the forecast period 2023-2030. Smart labels are tags with information about the product that can be printed or affixed to the packing material. Smart labels also employ printed paper, plastic, or fabric labels that have a unique intelligent inlay technology added to them so they get digitally record and transmit more information than is printed on the physical label. The Smart Labels market is expanding because of factors such as the rising e-commerce industry, the increase in adoption in the packaging industry.

The increasing popularity of online shopping and the rapid growth of e-commerce businesses have led to higher consumer expectations for efficient and transparent product delivery. Smart labels play a crucial role in meeting these demands by providing real-time tracking and monitoring of products throughout the supply chain. They enable businesses to manage inventory more effectively, optimize shipping processes, and provide customers with accurate delivery information, ultimately improving the overall customer experience. According to the International Trade Administration, in the year 2019, the global sales from the e-commerce industry stood at USD 3354 billion which increased to USD 5424 billion in the year 2022 and it is projected to reach USD 6388 billion by year 2024. Thus, the rising e-commerce industry is driving market growth. In addition, the rising adoption of Near Field Communication (NFC) Technology, the increasing adoption of RFID technology in the retail industry and rising initiatives for the development of smart tags is creating new opportunities for market growth. However, data security and privacy concerns hamper the growth throughout the forecast period of 2023-2030.

The key regions considered for the Global Smart Labels Market study includes Asia Pacific, North America, Europe, Latin America, and Middle East & Africa. North America dominated the market in 2022 owing to factors such as the increasing number of retail stores, the rising e-commerce industry, and increasing technological advancement in label technology. Whereas, Asia Pacific is projected to witness the fastest growth owing to factors such as rising manufacturing industries, increasing retail stores, and rising e-commerce industry in the region.

Major market player included in this report are:
Muehlbauer Holding AG Zebra Technologies Corp.
CGL Industries Inc.
SATO Holdings Corporation
Graphic Label, Inc.
Alien Technology Pvt Ltd.
Advantech Co. Ltd.
Thin Film Technology Corp.
Invengo Information Technology Co., Ltd.

Recent Developments in the Market:
Ø In April 2023, CCL Industries Inc. announced the acquisition eAgile Inc. A vital part of CCL Label’s Healthcare & Specialty division, the new company would bring RFID expertise to the entire company. In addition, privately held Alert in Denmark offers exclusive anti-theft goods to European retailers in addition to Checkpoint’s merchandise availability solutions (MAS) product lines.
Ø In February 2023, Danavation Technologies Corp., based in North America, stated that it has been awarded fresh contracts for four further installations of innovative digital smart labels. Since the beginning of 2023, the company has installed digital smart labels in ten recent retail sites in Canada and the US.

Global Smart Labels Market Report Scope:
ü Historical Data – 2020 – 2021
ü Base Year for Estimation – 2022
ü Forecast period – 2023-2030
ü Report Coverage – Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
ü Segments Covered – Product Type, Application, End User, Region
ü Regional Scope – North America; Europe; Asia Pacific; Latin America; Middle East & Africa
ü Customization Scope – Free report customization (equivalent up to 8 analyst’s working hours) with purchase. Addition or alteration to country, regional & segment scope*

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within countries involved in the study.

The report also caters detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, it also incorporates potential opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Product Type:
Electronic Article Surveillance Label
Sensing Label
Dynamic Display Label
By Application:
Retail Inventory
Perishable Goods
By End User:

By Region:

North America


Asia Pacific
South Korea

Latin America

Middle East & Africa
Saudi Arabia
South Africa
Rest of Middle East & Africa

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2020-2030 (USD Billion)
1.2.1. Smart Labels Market, by Region, 2020-2030 (USD Billion)
1.2.2. Smart Labels Market, by Product Type, 2020-2030 (USD Billion)
1.2.3. Smart Labels Market, by Application, 2020-2030 (USD Billion)
1.2.4. Smart Labels Market, by End User, 2020-2030 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Smart Labels Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Industry Evolution
2.2.2. Scope of the Study
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global Smart Labels Market Dynamics
3.1. Smart Labels Market Impact Analysis (2020-2030)
3.1.1. Market Drivers Rising e-commerce industry Increase in adoption in the packaging industry
3.1.2. Market Challenges Data security and privacy concerns
3.1.3. Market Opportunities Rising Adoption of near field communication (NFC) Technology Increasing adoption of RFID technology in retail industry Rising initiatives for development of smart tags
Chapter 4. Global Smart Labels Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.2. Porter’s 5 Force Impact Analysis
4.3. PEST Analysis
4.3.1. Political
4.3.2. Economical
4.3.3. Social
4.3.4. Technological
4.3.5. Environmental
4.3.6. Legal
4.4. Top investment opportunity
4.5. Top winning strategies
4.6. COVID-19 Impact Analysis
4.7. Disruptive Trends
4.8. Industry Expert Perspective
4.9. Analyst Recommendation & Conclusion
Chapter 5. Global Smart Labels Market, by Product Type
5.1. Market Snapshot
5.2. Global Smart Labels Market by Product Type, Performance – Potential Analysis
5.3. Global Smart Labels Market Estimates & Forecasts by Product Type 2020-2030 (USD Billion)
5.4. Smart Labels Market, Sub Segment Analysis
5.4.1. RFID
5.4.2. Electronic Article Surveillance Label
5.4.3. Sensing Label
5.4.4. Dynamic Display Label
Chapter 6. Global Smart Labels Market, by Application
6.1. Market Snapshot
6.2. Global Smart Labels Market by Application, Performance – Potential Analysis
6.3. Global Smart Labels Market Estimates & Forecasts by Application 2020-2030 (USD Billion)
6.4. Smart Labels Market, Sub Segment Analysis
6.4.1. Retail Inventory
6.4.2. Perishable Goods
Chapter 7. Global Smart Labels Market, by End User
7.1. Market Snapshot
7.2. Global Smart Labels Market by End User, Performance – Potential Analysis
7.3. Global Smart Labels Market Estimates & Forecasts by End User 2020-2030 (USD Billion)
7.4. Smart Labels Market, Sub Segment Analysis
7.4.1. Retail
7.4.2. Healthcare
7.4.3. Logistics 7.4.4. Others
Chapter 8. Global Smart Labels Market, Regional Analysis
8.1. Top Leading Countries
8.2. Top Emerging Countries
8.3. Smart Labels Market, Regional Market Snapshot
8.4. North America Smart Labels Market
8.4.1. U.S. Smart Labels Market Product Type breakdown estimates & forecasts, 2020-2030 Application breakdown estimates & forecasts, 2020-2030 End User breakdown estimates & forecasts, 2020-2030
8.4.2. Canada Smart Labels Market
8.5. Europe Smart Labels Market Snapshot
8.5.1. U.K. Smart Labels Market
8.5.2. Germany Smart Labels Market
8.5.3. France Smart Labels Market
8.5.4. Spain Smart Labels Market
8.5.5. Italy Smart Labels Market
8.5.6. Rest of Europe Smart Labels Market
8.6. Asia-Pacific Smart Labels Market Snapshot
8.6.1. China Smart Labels Market
8.6.2. India Smart Labels Market
8.6.3. Japan Smart Labels Market
8.6.4. Australia Smart Labels Market
8.6.5. South Korea Smart Labels Market
8.6.6. Rest of Asia Pacific Smart Labels Market
8.7. Latin America Smart Labels Market Snapshot
8.7.1. Brazil Smart Labels Market
8.7.2. Mexico Smart Labels Market
8.8. Middle East & Africa Smart Labels Market
8.8.1. Saudi Arabia Smart Labels Market
8.8.2. South Africa Smart Labels Market
8.8.3. Rest of Middle East & Africa Smart Labels Market

Chapter 9. Competitive Intelligence
9.1. Key Company SWOT Analysis
9.1.1. Company 1
9.1.2. Company 2
9.1.3. Company 3
9.2. Top Market Strategies
9.3. Company Profiles
9.3.1. Muehlbauer Holding AG Key Information Overview Financial (Subject to Data Availability) Product Summary Recent Developments
9.3.2. Zebra Technologies Corp.
9.3.3. CGL Manufacturing Inc.
9.3.4. SATO Holdings Corporation
9.3.5. Graphic Label, Inc.
9.3.6. Alien Technology Pvt Ltd.
9.3.7. Advantech Co. Ltd
9.3.9. Thin Film Technology Corp
9.3.10. Invengo Information Technology Co., Ltd.

Chapter 10. Research Process
10.1. Research Process
10.1.1. Data Mining
10.1.2. Analysis
10.1.3. Market Estimation
10.1.4. Validation
10.1.5. Publishing
10.2. Research Attributes
10.3. Research Assumption

At Bizwit Research and Consultancy, we employ a thorough and iterative research methodology with the goal of minimizing discrepancies, ensuring the provision of highly accurate estimates and predictions over the forecast period. Our approach involves a combination of bottom-up and top-down strategies to effectively segment and estimate quantitative aspects of the market, utilizing our proprietary data & AI tools. Our Proprietary Tools allow us for the creation of customized models specific to the research objectives. This enables us to develop tailored statistical models and forecasting algorithms to estimate market trends, future growth, or consumer behavior. The customization enhances the accuracy and relevance of the research findings.
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Data Collection:
To determine the appropriate methods of data collection based on the research objectives, we consider both primary and secondary sources. Primary data collection involves gathering information directly from various industry experts in core and related fields, original equipment manufacturers (OEMs), vendors, suppliers, technology developers, alliances, and organizations. These sources encompass all segments of the value chain within the specific industry. Through in-depth interviews, we engage with key industry participants, subject-matter experts, C-level executives of major market players, industry consultants, and other relevant experts. This allows us to obtain and validate critical qualitative and quantitative information while evaluating market prospects. AI and Big Data are instrumental in our primary research, providing us with powerful tools to collect, analyze, and derive insights from data efficiently. These technologies contribute to the advancement of research methodologies, enabling us to make data-driven decisions and uncover valuable findings.
In addition to primary sources, we extensively utilize secondary sources to enhance our research. These include directories, databases, journals focusing on related industries, company newsletters, and information portals such as Bloomberg, D&B Hoovers, and Factiva. These secondary sources enable us to identify and gather valuable information for our comprehensive, technical, market-oriented, and commercial study of the market. Additionally, we utilize AI algorithms to automate the collection of vast amounts of data from various sources such as surveys, social media platforms, online transactions, and web scraping. And employ Big Data technologies for storage and processing of large datasets, ensuring that no valuable information is missed during the data collection process.
Data Analysis:
Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
Our proprietary data tools play a crucial role in deriving our market estimates and forecasts. Each study involves the creation of a unique and customized model. The model incorporates the gathered information on market dynamics, technology landscape, application development, and pricing trends. AI techniques, such as machine learning and deep learning, aid us to analyze patterns within the data to identify correlations, trends, and relationships. By recognizing patterns in consumer behavior, purchasing habits, or market dynamics, our AI algorithms aid us in more precise estimations of market size. These factors are simultaneously analyzed within the model, allowing for a comprehensive assessment. To quantify their impact over the forecast period, correlation, regression, and time series analysis are employed.
To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
Insight Generation & Report Presentation:
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Next, we create a well-structured research report that effectively communicates the research findings, insights, and recommendations to the client. To enhance clarity and comprehension, we utilize visual aids such as charts, graphs, and tables. These visual elements are employed to present the data in an engaging and easily understandable format, ensuring that the information is accessible and visually appealing to the client. Our aim is to deliver a clear and concise report that conveys the research findings effectively and provides actionable recommendations to meet the client’s specific needs.

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