AUTOMOTIVE & TRANSPORTATION
Global E-Commerce Logistics Market - Industry Trends and Forecast to 2032
REPORT OVERVIEW
Global E-Commerce Logistics Market, By Service Type (Warehousing, Transportation, and Others), Operational Area (Domestic and International), Model (3PL, 4PL, and Others), Type (Forward Logistics and Reverse Logistics), End-User (B2B and B2C), Vertical (Apparels, Consumer Electronics, Automotive, Healthcare, Food and Beverage, and Others), Region (North America, Europe, Asia-Pacific, South America, Middle East and Africa) – Industry Trends and Forecast to 2032.
Market Insights
The global e-commerce logistics market size is valued to be USD xx million in 2023 and is expected to reach USD xx million by 2032, and it is expected to register a CAGR of xx% over the forecast period 2024-2032.
The e-commerce logistics market refers to the segment of the logistics industry that specifically caters to the transportation, storage, and distribution of goods bought and sold through online platforms. It encompasses the processes involved in fulfilling orders placed by consumers or businesses on various E-Commerce platforms, including inventory management, order processing, packaging, shipping, and last-mile delivery.
The respective global report analyses market trends, consumer behaviour and industry dynamics to guide towards entry into new markets with ease. Also, it assists in tailoring market specific and related products and services to meet the needs, preferences, and expectations of target audience by delving into their psychology. The report also specializes with comprehensive and extensive competitive analysis which offers useful insights into competitor strengths, weaknesses, opportunities, and threats. The respective report offers exclusive insights into the potential impact of disruptive developments and technologies that are expected to completely transform corporate operations. The context includes tailor-made research solutions to create a stronger footprint in their particular industries thereby offering dedicated customized solutions according to the client needs which helps in addressing unique business challenges with more simplified and efficient decision-making solutions.
Market Dynamics
DRIVERS
- Expansion of omni-channel retailing
- Globalization of e-commerce
RESTRAINTS
- Infrastructure limitations
- Last-mile delivery challenges
OPPORTUNITIES
- Same-day and instant delivery services
- Integration of AI and automation
CHALLENGES
- Reverse logistics
- Supply chain visibility and transparency
SEGMENTATION
MARKET SEGMENTATION
- Service Type
- Warehousing
- Mega Centers
- Hubs / Delivery Centers
- Returns Processing Centers
- Transportation
- Air / Express Delivery
- Freight / Rail
- Trucking / Over Road
- Maritime
- Others
- Operational Area
- Domestic
- International
- Model
- 3PL
- 4PL
- Others
- Type
- Forward Logistics
- Reverse Logistics
- End-User
- B2B
- B2C
- Vertical
- Apparels
- Consumer Electronics
- Automotive
- Healthcare
- Food and Beverage
- Others
The respective global report is completely customizable specific to regions (North America, Europe, Asia-Pacific, South America, Middle East and Africa), countries, segments, and key players as per the client requirements.
REGIONAL SEGMENTATION
- North America
- U.S.
- Canada
- Mexico
- Rest of North America
- Europe
- Germany
- U.K.
- France
- Italy
- Spain
- Russia
- The Netherlands
- Belgium
- Turkey
- Rest of Europe
- Asia-Pacific
- China
- India
- Japan
- South Korea
- Singapore
- Malaysia
- Australia
- Thailand
- Philippines
- Rest of Asia-Pacific
- South America
- Brazil
- Argentina
- Chile
- Colombia
- Rest of South America
- Middle East and Africa
- Kingdom of Saudi Arabia
- South Africa
- U.A.E.
- Egypt
- Rest of Middle East and Africa
KEY MARKET PLAYERS
- Gati Limited
- DHL International GmbH
- Aramex International
- FedEx Corporation
- S.F. Express
- Amazon
- Kenco Group, Inc.
- Ceva Holdings LLC
- United Parcel Service
- XPO Logistics
Table OF CONTENTS
- SECTION 1 - INTRODUCTION
- 1.1 Taxonomy
- 1.2 Market Overview
- 1.3 Currency and Limitations
- 1.3.1 Currency
- 1.3.2 Limitations
- 1.4 Key Competitors
- SECTION 2 - RESEARCH METHODOLOGY
- 2.1 Research Approach
- 2.2 Data Collection and Validation
- 2.2.1 Secondary Research
- 2.2.2 Primary Research
- 2.3 Market Assessment
- 2.3.1 Market Size Estimation
- 2.3.2 Bottom-up Approach
- 2.3.3 Top-down Approach
- 2.3.4 Growth Forecast
- 2.4 Market Study Assumptions
- 2.5 Data Sources
- SECTION 3 - EXECUTIVE SUMMARY
- 3.1 Global E-Commerce Logistics Market, by Service Type
- 3.2 Global E-Commerce Logistics Market, by Operational Area
- 3.3 Global E-Commerce Logistics Market, by Model
- 3.4 Global E-Commerce Logistics Market, by Type
- 3.5 Global E-Commerce Logistics Market, by End-User
- 3.6 Global E-Commerce Logistics Market, by Vertical
- 3.7 Global E-Commerce Logistics Market, by Geography
- 3.8 Market Position Grid
- SECTION 4 - PREMIUM INSIGHTS
- 4.1 Regulatory Framework
- 4.1.1 Standards
- 4.1.2 Regulatory Landscape
- 4.2 Value Chain Analysis
- 4.3 Supply Chain Analysis
- 4.4 COVID-19 Impact
- 4.5 Russia-Ukraine War Impact
- 4.6 PORTER's Five Force Analysis
- 4.7 PESTLE Analysis
- 4.8 SWOT Analysis
- 4.9 Go to Market Strategy
- 4.10 Opportunity Orbit
- 4.11 Multivariate Modelling
- 4.12 Pricing Analysis
- SECTION 5 - MARKET DYNAMICS
- 5.1 Trends
- 5.1.1 Trend 1
- 5.1.2 Trend 2
- 5.1.3 Trend 3
- 5.2 Drivers
- 5.2.1 Expansion of omni-channel retailing
- 5.2.2 Globalization of e-commerce
- 5.2.3 Driver 3
- 5.2.4 Driver 4
- 5.3 Restraints
- 5.3.1 Infrastructure limitations
- 5.3.2 Last-mile delivery challenges
- 5.3.3 Restraint 3
- 5.4 Opportunities
- 5.4.1 Same-day and instant delivery services
- 5.4.2 Integration of AI and automation
- 5.4.3 Opportunity 3
- 5.4.4 Opportunity 4
- 5.5 Challenges
- 5.5.1 Reverse logistics
- 5.5.2 Supply chain visibility and transparency
- 5.5.3 Challenge 3
- SECTION 6 - GLOBAL E-COMMERCE LOGISTICS MARKET, BY SERVICE TYPE
- 6.1 Service Type Summary
- 6.2 Market Attractive Index
- 6.3 Global E-Commerce Logistics Market, by Service Type (2019-2032)
- SECTION 7 - GLOBAL E-COMMERCE LOGISTICS MARKET, BY OPERATIONAL AREA
- 7.1 Operational Area Summary
- 7.2 Market Attractive Index
- 7.3 Global E-Commerce Logistics Market, by Operational Area (2019-2032)
- SECTION 8 - GLOBAL E-COMMERCE LOGISTICS MARKET, BY MODEL
- 8.1 Model Summary
- 8.2 Market Attractive Index
- 8.3 Global E-Commerce Logistics Market, by Model (2019-2032)
- SECTION 9 - GLOBAL E-COMMERCE LOGISTICS MARKET, BY TYPE
- 9.1 Type Summary
- 9.2 Market Attractive Index
- 9.3 Global E-Commerce Logistics Market, by Type (2019-2032)
- SECTION 10 - GLOBAL E-COMMERCE LOGISTICS MARKET, BY END-USER
- 10.1 End-User Summary
- 10.2 Market Attractive Index
- 10.3 Global E-Commerce Logistics Market, by End-User (2019-2032)
- SECTION 11 - GLOBAL E-COMMERCE LOGISTICS MARKET, BY VERTICAL
- 11.1 Vertical Summary
- 11.2 Market Attractive Index
- 11.3 Global E-Commerce Logistics Market, by Vertical (2019-2032)
- SECTION 12 - GLOBAL E-COMMERCE LOGISTICS MARKET, BY GEOGRAPHY
- 12.1 Regional Summary
- 12.2 Market Attractive Index
- 12.3 Global E-Commerce Logistics Market, by Geography (2019-2032)
- SECTION 13 - NORTH AMERICA E-COMMERCE LOGISTICS MARKET
- 13.1 North America Summary
- 13.2 Market Attractive Index
- 13.3 North America E-Commerce Logistics Market, by Service Type (2019-2032)
- 13.4 North America E-Commerce Logistics Market, by Operational Area (2019-2032)
- 13.5 North America E-Commerce Logistics Market, by Model (2019-2032)
- 13.6 North America E-Commerce Logistics Market, by Type (2019-2032)
- 13.7 North America E-Commerce Logistics Market, by End-User (2019-2032)
- 13.8 North America E-Commerce Logistics Market, by Vertical (2019-2032)
- 13.9 North America E-Commerce Logistics Market, by Country (2019-2032)
- 13.9.1 U.S.
- 13.9.2 Canada
- 13.9.3 Mexico
- 13.9.4 Rest of North America
- SECTION 14 - EUROPE E-COMMERCE LOGISTICS MARKET
- 14.1 Europe Summary
- 14.2 Market Attractive Index
- 14.3 Europe E-Commerce Logistics Market, by Service Type (2019-2032)
- 14.4 Europe E-Commerce Logistics Market, by Operational Area (2019-2032)
- 14.5 Europe E-Commerce Logistics Market, by Model (2019-2032)
- 14.6 Europe E-Commerce Logistics Market, by Type (2019-2032)
- 14.7 Europe E-Commerce Logistics Market, by End-User (2019-2032)
- 14.8 Europe E-Commerce Logistics Market, by Vertical (2019-2032)
- 14.9 Europe E-Commerce Logistics Market, by Country (2019-2032)
- 14.9.1 Germany
- 14.9.2 U.K.
- 14.9.3 France
- 14.9.4 Italy
- 14.9.5 Spain
- 14.9.6 Russia
- 14.9.7 The Netherlands
- 14.9.8 Belgium
- 14.9.9 Turkey
- 14.9.10 Rest of Europe
- SECTION 15 - ASIA-PACIFIC E-COMMERCE LOGISTICS MARKET
- 15.1 Asia-Pacific Summary
- 15.2 Market Attractive Index
- 15.3 Asia-Pacific E-Commerce Logistics Market, by Service Type (2019-2032)
- 15.4 Asia-Pacific E-Commerce Logistics Market, by Operational Area (2019-2032)
- 15.5 Asia-Pacific E-Commerce Logistics Market, by Model (2019-2032)
- 15.6 Asia-Pacific E-Commerce Logistics Market, by Type (2019-2032)
- 15.7 Asia-Pacific E-Commerce Logistics Market, by End-User (2019-2032)
- 15.8 Asia-Pacific E-Commerce Logistics Market, by Vertical (2019-2032)
- 15.9 Asia-Pacific E-Commerce Logistics Market, by Country (2019-2032)
- 15.9.1 China
- 15.9.2 India
- 15.9.3 Japan
- 15.9.4 South Korea
- 15.9.5 Singapore
- 15.9.6 Malaysia
- 15.9.7 Australia
- 15.9.8 Thailand
- 15.9.9 Philippines
- 15.9.10 Rest of Asia-Pacific
- SECTION 16 - SOUTH AMERICA E-COMMERCE LOGISTICS MARKET
- 16.1 South America Summary
- 16.2 Market Attractive Index
- 16.3 South America E-Commerce Logistics Market, by Service Type (2019-2032)
- 16.4 South America E-Commerce Logistics Market, by Operational Area (2019-2032)
- 16.5 South America E-Commerce Logistics Market, by Model (2019-2032)
- 16.6 South America E-Commerce Logistics Market, by Type (2019-2032)
- 16.7 South America E-Commerce Logistics Market, by End-User (2019-2032)
- 16.8 South America E-Commerce Logistics Market, by Vertical (2019-2032)
- 16.9 South America E-Commerce Logistics Market, by Country (2019-2032)
- 16.9.1 Brazil
- 16.9.2 Argentina
- 16.9.3 Chile
- 16.9.4 Colombia
- 16.9.5 Rest of South America
- SECTION 17 - MIDDLE EAST AND AFRICA E-COMMERCE LOGISTICS MARKET
- 17.1 Middle East and Africa Summary
- 17.2 Market Attractive Index
- 17.3 Middle East and Africa E-Commerce Logistics Market, by Service Type (2019-2032)
- 17.4 Middle East and Africa E-Commerce Logistics Market, by Operational Area (2019-2032)
- 17.5 Middle East and Africa E-Commerce Logistics Market, by Model (2019-2032)
- 17.6 Middle East and Africa E-Commerce Logistics Market, by Type (2019-2032)
- 17.7 Middle East and Africa E-Commerce Logistics Market, by End-User (2019-2032)
- 17.8 Middle East and Africa E-Commerce Logistics Market, by Vertical (2019-2032)
- 17.9 Middle East and Africa E-Commerce Logistics Market, by Country (2019-2032)
- 17.9.1 Kingdom of Saudi Arabia
- 17.9.2 South Africa
- 17.9.3 U.A.E.
- 17.9.4 Egypt
- 17.9.5 Rest of Middle East and Africa
- SECTION 18 - COMPANY SHARE ANALYSIS
- 18.1 Global E-Commerce Logistics Market, Company Share Analysis
- 18.2 North America E-Commerce Logistics Market, Company Share Analysis
- 18.3 Europe E-Commerce Logistics Market, Company Share Analysis
- 18.4 Asia-Pacific E-Commerce Logistics Market, Company Share Analysis
- SECTION 19 - COMPANY PROFILES
- 19.1 Gati Limited
- 19.1.1 Company Snapshot
- 19.1.2 Financial Overview
- 19.1.3 Product Portfolio
- 19.1.4 Recent Developments
- 19.2 DHL International GmbH
- 19.2.1 Company Snapshot
- 19.2.2 Financial Overview
- 19.2.3 Product Portfolio
- 19.2.4 Recent Developments
- 19.3 Aramex International
- 19.3.1 Company Snapshot
- 19.3.2 Financial Overview
- 19.3.3 Product Portfolio
- 19.3.4 Recent Developments
- 19.4 FedEx Corporation
- 19.4.1 Company Snapshot
- 19.4.2 Financial Overview
- 19.4.3 Product Portfolio
- 19.4.4 Recent Developments
- 19.5 S.F. Express
- 19.5.1 Company Snapshot
- 19.5.2 Financial Overview
- 19.5.3 Product Portfolio
- 19.5.4 Recent Developments
- 19.6 Amazon
- 19.6.1 Company Snapshot
- 19.6.2 Financial Overview
- 19.6.3 Product Portfolio
- 19.6.4 Recent Developments
- 19.7 Kenco Group, Inc.
- 19.7.1 Company Snapshot
- 19.7.2 Financial Overview
- 19.7.3 Product Portfolio
- 19.7.4 Recent Developments
- 19.8 Ceva Holdings LLC
- 19.8.1 Company Snapshot
- 19.8.2 Financial Overview
- 19.8.3 Product Portfolio
- 19.8.4 Recent Developments
- 19.9 United Parcel Service
- 19.9.1 Company Snapshot
- 19.9.2 Financial Overview
- 19.9.3 Product Portfolio
- 19.9.4 Recent Developments
- 19.10 XPO Logistics
- 19.10.1 Company Snapshot
- 19.10.2 Financial Overview
- 19.10.3 Product Portfolio
- 19.10.4 Recent Developments
- SECTION 20 - RELATED REPORTS
- SECTION 21 - DISCLAIMER
RESEARCH METHODOLOGY
The research methodology employed in Uniprism Market Research involves four basic steps namely research and data collection, data pre-processing, modeling and forecasting, quality assurance and output.
RESEARCH AND DATA COLLECTION
A tripod model research technique is followed for research and data collection in which various approaches such as primary research, secondary research, and product mapping are considered.
Primary research basically involves the process of conducting personalized interviews with market related professionals of major market players, investors, distributors, vendors and many more.
The secondary research include data published by government, annual reports, press releases, investor presentations of companies, white papers, certified publications, annual manufacturing limit of the respective industries related to the market, production consumption analysis of certain products respective to the market and many more.
Below mention are few of the sources which we have considered while estimating the market size:
For instance,
- Research articles published on Technium
- Science and MDPI
- Research publications by government approved associations and societies
Product mapping means the process of mapping the list of products that a key player contributes to the market as well as estimating the revenue of those products in order to define the Global Company share analysis of the respective Global Company in global, regional, and country level markets.
DATA PRE-PROCESSING
The term "data pre-processing" refers to the collection of procedures and methods used to clean, modify, and make ready for analysis the raw data gathered during research and data collection. The completion of this phase is necessary to guarantee that the data are reliable, consistent, and appropriate for statistical analysis and other data-driven tasks. The data pre-processing ensures that the information gathered from research and data collection is comparable and expressed in standard units, by the integration of missing data pointers and algorithmic approaches.
MODELING AND FORECASTING
The process of developing mathematical, statistical, or computational representations of real-world occurrences or relationships is known as modelling. These models are intended to replicate and explain market interactions, interdependence, and dynamics. These models are used by Uniprism Market Research to acquire a better knowledge of numerous market characteristics such as customer preferences, pricing elasticity, competition dynamics, and more. Depending on the individual study aims, many types of models are utilized, such as regression models, econometric models, decision tree models, and machine learning models.
Forecasting is the process of predicting future market conditions, trends, and occurrences using past data and models. Forecasting is used by Uniprism Market Research to estimate future sales, demand for products or services, market growth, and other important performance metrics. Forecasting accurately can assist organizations in making educated decisions about resource allocation, pricing, inventory management, and marketing tactics.
We create standardized bottom-up or top-down models that scale by leveraging data science and machine learning technology. All our market models consider the unique market characteristics of each country. Forecasting is based on major market indicators and a combination of traditional methodologies, such as exponential smoothing, time series analysis, regression analysis, and more modern techniques such as machine learning algorithms are all forecasting methodologies. The method chosen is determined on the nature of the data and the specific forecasting aims.
QUALITY ASSURANCE AND OUTPUT
Quality assurance and output involves the process of validation, adjustments, further publications of key market indicators. Extensive plausibility and consistency tests are performed on derived time series to ensure the high degree of quality of our market analysis. This quality assurance procedure also includes rigorous inspection, validation, and editing by an experienced management team to assure the dependability of the published data.