The Transportation and Logistics Tracking Dataset comprises multiple datasets related to various aspects of transportation and logistics operations. It includes information on on-time delivery impact, routes by rating, customer ratings, delivery times with and without congestion, weather conditions, and differences between fixed and main delivery times across different regions.

On-Time Delivery Impact: This dataset provides insights into the impact of on-time delivery, categorizing deliveries based on their impact and counting the occurrences for each category. Routes by Rating: Here, the dataset illustrates the relationship between routes and their corresponding ratings, offering a visual representation of route performance across different rating categories. Customer Ratings and On-Time Delivery: This dataset explores the relationship between customer ratings and on-time delivery, presenting a comparison of delivery counts based on customer ratings and on-time delivery status. Delivery Time with and Without Congestion: It contains information on delivery times in various cities, both with and without congestion, allowing for an analysis of how congestion affects delivery efficiency. Weather Conditions: This dataset provides a summary of weather conditions, including counts for different weather conditions such as partly cloudy, patchy light rain with thunder, and sunny. Difference between Fixed and Main Delivery Times: Lastly, the dataset highlights the differences between fixed and main delivery times across different regions, shedding light on regional variations in delivery schedules. Overall, this dataset offers valuable insights into the transportation and logistics domain, enabling analysis and decision-making to optimize delivery processes and enhance customer satisfaction.

Success.ai’s Transport and Logistics Data for Transportation, Trucking & Railroad Industry Leaders Globally provides a robust and reliable dataset designed to connect businesses with decision-makers and professionals across the transportation and logistics sectors. Covering leaders in trucking, railroads, and supply chain management, this dataset offers verified contact details, firmographic insights, and actionable business data.

With access to over 700 million verified global profiles and insights from key logistics companies, Success.ai ensures your marketing, sales, and operational strategies are powered by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution is ideal for navigating the ever-evolving transport and logistics industries.

Why Choose Success.ai’s Transport and Logistics Data?

1. Verified Contact Data for Precision Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of executives, fleet managers, and supply chain leaders in transportation, trucking, and railroads.
   • AI-driven validation ensures 99% accuracy, reducing bounce rates and optimizing engagement.

2. Comprehensive Global Coverage

   • Includes profiles of professionals from major transportation hubs and logistics companies worldwide, including North America, Europe, and Asia-Pacific.
   • Gain insights into global transportation trends, logistics strategies, and supply chain innovations.

3. Continuously Updated Datasets

   • Real-time updates capture changes in leadership, market expansions, and operational strategies.
   • Stay ahead of industry developments and identify emerging opportunities effectively.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible and lawful use of data.

Data Highlights:

• 700M+ Verified Global Profiles: Engage with professionals and decision-makers in transportation, trucking, and railroads worldwide.
• Leadership and Professional Insights: Access profiles of CEOs, fleet managers, and logistics experts driving the transport sector.
• Verified Contact Details: Gain phone numbers, work emails, and LinkedIn profiles for precise targeting.
• Firmographic Data: Understand company structures, operational capacities, and regional market footprints.

Key Features of the Dataset:

1. Decision-Maker Profiles in Transport and Logistics

   • Identify and connect with professionals managing trucking fleets, railroad operations, and supply chain logistics.
   • Target individuals responsible for fleet optimization, operational efficiency, and vendor selection.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (transportation, trucking, railroads), geographic location, or job function.
   • Tailor campaigns to address specific challenges, such as fleet electrification, route optimization, or regulatory compliance.

3. Industry and Regional Insights

   • Leverage data on global transportation trends, freight logistics, and emerging technologies in trucking and railroads.
   • Refine strategies to align with regional needs and market conditions.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Sales and Lead Generation

   • Promote logistics software, fleet management tools, or operational solutions to transportation and trucking professionals.
   • Build relationships with railroads and supply chain companies seeking innovative strategies for efficiency and cost management.

2. Market Research and Competitive Analysis

   • Analyze global trends in transportation and logistics to guide product development, marketing strategies, and business expansion plans.
   • Benchmark against competitors to identify growth opportunities and emerging market demands.

3. Partnership Development and Collaboration

   • Engage with logistics leaders, transportation companies, and railroads exploring partnerships for supply chain optimization or technology adoption.
   • Foster alliances that expand market reach and enhance operational capabilities.

4. Recruitment and Talent Acquisition

   • Target HR professionals and hiring managers in transportation and logistics sectors recruiting for roles in fleet management, supply chain operations, or rail transport.
   • Provide workforce optimization platforms or training solutions tailored to the industry.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality transport and logistics data at competitive prices, ensuring strong ROI for your outreach, marketing, and operational initiatives.

2. Seamless Integration

   • Integrate verified data into CRM systems, analytics tools, or marketing platforms via APIs o...

The Logistics Performance Index (LPI), produced by the World Bank, evaluates trade logistics performance in 139 countries. It consists of two components: a survey of international logistics operators (freight forwarders and express carriers) assessing the logistics friendliness of partner countries, and high-frequency supply chain data on maritime shipping, container tracking, postal, and air freight activities, provided by data partners. The LPI provides a dual perspective by combining perception-based assessments with data-driven trade flow analysis.

Success.ai’s Transport and Logistics Data provides comprehensive, verified B2B contact and company information tailored for the global logistics sector. Drawing from a database of over 170 million verified professional profiles and 30 million company profiles, this dataset delivers accurate contact details, firmographic insights, and operational data on logistics service providers, freight forwarders, trucking companies, 3PLs, and supply chain management firms worldwide. Whether you’re targeting key decision-makers for partnerships, offering freight optimization technology, or conducting market research, Success.ai ensures your outreach and strategic planning are anchored in reliable, continuously updated, and AI-validated data.

Why Choose Success.ai’s Transport and Logistics Data?

1. Comprehensive Contact Information

   • Access verified work emails, direct phone numbers, and LinkedIn profiles of logistics leaders, operations managers, procurement officers, and supply chain directors.
   • AI-driven validation ensures 99% accuracy, allowing confident communication and reducing wasted outreach efforts.

2. Global Reach Across the Logistics Sector

   • Includes profiles from freight carriers, warehousing solutions, customs brokers, freight forwarders, and last-mile delivery companies.
   • Covers regions including North America, Europe, Asia-Pacific, South America, and the Middle East, giving you a panoramic view of logistics networks worldwide.

3. Continuously Updated Datasets

   • Real-time updates ensure your data remains current, reflecting changes in leadership, service offerings, and operational expansions in the rapidly evolving logistics industry.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global data privacy regulations, guaranteeing that your outreach and data usage align with legal and ethical standards.

Data Highlights:

• 170M+ Verified Professional Profiles: Connect with logistics professionals, key decision-makers, and operational leaders.
• 50M Work Emails: AI-validated for seamless communication with minimized bounce rates.
• 30M Company Profiles: In-depth firmographic and operational insights on logistics companies worldwide.
• 700M Global Professional Profiles: Enriched datasets to support competitive analysis, market entry strategies, and global expansion.

Key Features of the Dataset:

1. Logistics Decision-Maker Profiles

   • Identify and connect with CEOs, COOs, logistics managers, route planners, and procurement specialists influencing transportation strategies, warehousing decisions, and supply chain optimization.

2. Operational Firmographics and Insights

   • Access data on fleet size, service areas, warehouse locations, technology adoption, and shipping volumes to refine targeting and tailor your value propositions.
   • Utilize detailed operational insights to understand capacity constraints, specialization areas, and strategic priorities.

3. Advanced Filters for Precision Targeting

   • Filter contacts by mode of transportation (road, rail, air, sea), geographic coverage, company size, or industry specialization.
   • Align campaigns with unique market conditions, regulatory environments, and customer demands.

4. AI-Driven Enrichment

   • Profiles are enriched with actionable data, enabling personalized messaging, highlighting value-add solutions, and improving engagement outcomes with logistics stakeholders.

Strategic Use Cases:

1. Sales and Business Development

   • Engage with freight forwarders, trucking companies, and 3PL executives to present transportation management systems, visibility tools, or cost-reduction strategies.
   • Build relationships with leaders who control capacity planning, vendor selection, and route optimization.

2. Market Research and Competitive Analysis

   • Gain insights into emerging logistics hubs, evolving supply chain models, and technology adoption rates.
   • Benchmark against industry leaders to inform product innovation, pricing strategies, and market entry plans.

3. Partnership and Network Building

   • Identify potential partners for intermodal solutions, joint ventures, or collaborative warehousing arrangements.
   • Target logistics professionals interested in sustainability initiatives, e-commerce integrations, and cross-border trade solutions.

4. Recruitment and Talent Acquisition

   • Find HR professionals and operations managers seeking qualified drivers, dispatchers, warehouse staff, and logistics analysts.
   • Offer recruitment services or training programs to companies aiming to enhance operational efficiency and workforce quality.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access top-quality verified data at competitive prices, ensuring maximum ROI for your outreach and expansion efforts in the logistics sector.

2. Seamless Integration

   • Integrate verified contact an...

This dataset provides real-time data for smart logistics operations, capturing various aspects of supply chain management over the past year (2024). It includes information on asset tracking, inventory levels, shipment statuses, environmental conditions, traffic, and user behaviors. The dataset features multiple stakeholders within the logistics network, including asset IDs, timestamps, traffic conditions, waiting times, and reasons for delays. Additionally, the data is enriched with real-time information from IoT sensors, such as temperature, humidity, and asset utilization, to facilitate advanced logistics optimization and decision-making. The target variable, Logistics_Delay, helps in identifying delays in logistics processes, which is essential for enhancing supply chain efficiency through proactive management and optimization techniques. This dataset is designed to be used for research and machine learning applications focused on smart logistics and supply chain performance improvement.

Key Features: Timestamp: Date and time when the data was recorded, representing logistics activity. Asset_ID: Unique identifier for the logistical assets (e.g., trucks). Latitude & Longitude: Geographical coordinates of the asset for tracking and monitoring. Inventory_Level: Current level of inventory associated with the asset or shipment. Shipment_Status: Status of the shipment (e.g., In Transit, Delivered, Delayed). Temperature: Temperature recorded at the time of the shipment or transportation. Humidity: Humidity level at the time of recording. Traffic_Status: Current traffic condition (e.g., Clear, Heavy, Detour). Waiting_Time: Time spent waiting during the logistics process (in minutes). User_Transaction_Amount: Monetary amount associated with user transactions. User_Purchase_Frequency: Frequency of purchases made by the user. Logistics_Delay_Reason: Reason for any delays in the logistics process (e.g., Weather, Mechanical Failure). Asset_Utilization: Percentage of asset utilization, indicating how effectively assets are being used. Demand_Forecast: Predicted demand for the logistics services in the coming period. Logistics_Delay (Target): Binary variable indicating whether a logistics delay occurred (1 for delay, 0 for no delay).

Federal Catalog Program data used in the cataloging process to describe the attributes of items repetively used, purchased, stocked, or distributed, for all functions of supply from orginal purchase to final disposal.

The Data Center Logistics Market report segments the industry into By Devices (Electrical Devices, Mechanical Devices), By Size of Data Center (Small and Medium-scale Data Center, Large-scale Data Center), By Service (Transport, Installation, and more.), By End User (Banking, Financial Services, and Insurance, and more.), and By Geography.

The United States pioneered in transportation and logistics innovation. A 2024 survey shows 38 percent of companies operating in this field used location data and internet-of-things for real-time tracking of their shipments and inventory. In turn, only 31 percent of German companies did the same, whereas UK competitors stood at 26 percent.

China E-commerce Logistic Index(ELI) data was reported at 214.800 Jan2015=100 in Oct 2019. This records a decrease from the previous number of 223.330 Jan2015=100 for Sep 2019. China E-commerce Logistic Index(ELI) data is updated monthly, averaging 171.970 Jan2015=100 from Jan 2015 (Median) to Oct 2019, with 58 observations. The data reached an all-time high of 241.880 Jan2015=100 in Jun 2019 and a record low of 95.040 Jan2015=100 in Feb 2015. China E-commerce Logistic Index(ELI) data remains active status in CEIC and is reported by China Federation of Logistics & Purchasing. The data is categorized under China Premium Database’s Transportation and Storage Sector – Table CN.TL: E-commerce Logistic Index(ELI).

Dataset

This dataset was created by Cyrus S. HHcity

Released under CC BY-SA 4.0

Contents

Success.ai’s Global Supply Chain Dataset delivers verified contact data across sourcing, procurement, logistics, warehouse management, and operations. Built for sales and marketing teams targeting supply chain decision-makers across retail, manufacturing, consumer goods, and e-commerce sectors.
Get up-to-date insights into professionals leading cross-border logistics, ERP implementation, vendor evaluation, and operational optimization.

Why Use Success.ai for Supply Chain Data?
- Functional Accuracy: Built by role and job title—not just industry.
- Global Scope: USA, EU, MENA, APAC, LATAM all represented.
- Data for Strategy, Sales & Recruitment

Data Points Include:
- Contact name, role, seniority
- Work email, phone (when available)
- Company name, location, size
- Industry & sub-sector (e.g. FMCG, Automotive, Pharma)
- LinkedIn profile URLs

Use Cases:
- ERP, SCM & Logistics Software Sales
- Vendor Targeting in Procurement
- Manufacturing & Retail Supply Chain Expansion
- Market Mapping for Operations Intelligence
- Executive Recruiting

Industries Served:
- Manufacturing
- Consumer Goods
- Retail & E-commerce
- Automotive
- Pharmaceuticals

Why Success.ai?
- Best Price Guarantee
- Filters by Role, Seniority, Industry, Country
- API or Flatfile Delivery
- Custom Data Enrichment Available

Based on the context of your article, your target labels are Efficiency Ratio and Quality Maintenance Ratio, which are continuous variables derived from multiple factors such as fuel consumption, logistics costs, spoilage, and supply chain bottlenecks.

These labels are not binary, as they represent ratios that indicate performance and quality metrics in agricultural logistics and supply chain management. The values of these ratios are continuous and are used for regression tasks, rather than classification tasks with binary outputs.

So, your article is focused on predicting these continuous values rather than performing binary classification.

You said: write description of the dataset along iwth features it is downloaded form EURO crops etc like that

ChatGPT said: ChatGPT The dataset used in this research was obtained from a large-scale agricultural logistics study conducted across European farms, specifically focusing on key regions known for extensive crop production, such as France, Germany, and the Netherlands. The dataset, named EURO Crops Agricultural Logistics Dataset, contains detailed records of various logistical operations, environmental conditions, and product quality metrics across multiple agricultural supply chains. It spans from June 2018 to June 2024, with hourly data collected from IoT-enabled sensors and GPS devices installed on vehicles, storage units, and monitoring stations.

The dataset includes a total of 53,305 records, with data points capturing critical aspects of agricultural logistics operations, such as transportation efficiency, storage conditions, and product quality. The information is collected in real-time through IoT sensors deployed across the logistics network, tracking the movement and conditions of agricultural products. The data also encompasses environmental monitoring systems, providing insights into weather patterns, soil conditions, and crop health.

The key features of the dataset include:

Vehicle_Type: Categorical data indicating the type of vehicle used for transportation (e.g., Truck, Van). Crop_Type: Categorical data specifying the type of crops being transported (e.g., Wheat, Corn, Rice). Harvest_Date: Date indicating when the crops were harvested. Crop_Yield: Quantitative data showing the total yield of the crop (in kilograms). Storage_Temperature: Continuous data representing the temperature inside the storage unit (in degrees Celsius). Storage_Humidity: Continuous data representing the humidity levels inside the storage unit (in percentage). Fuel_Consumption: Continuous data indicating the amount of fuel used during transportation (in liters per 100 km). Route_Distance: Continuous data showing the total distance covered by the vehicle (in kilometers). Delivery_Time: Continuous data representing the total time taken for the delivery (in hours). Traffic_Level: Continuous data showing the level of traffic congestion on the route (on a scale of 0 to 100). Temperature: Environmental temperature during transportation (in degrees Celsius). Humidity: Environmental humidity during transportation (in percentage). Vehicle_Load_Capacity: The total load capacity of the vehicle (in kilograms). Vibration_Level: Data from sensors measuring the vibration experienced during transportation, which affects crop quality (in arbitrary units). Queue_Time: Time spent in queues or waiting during transit (in hours). Weather_Impact: Index measuring the impact of weather conditions on logistics operations (e.g., heavy rain, wind, etc.). Station_Capacity: Storage capacity of the distribution or logistics station (in kilograms). Operational_Cost: The total cost of logistics operations, including fuel, labor, and storage costs (in USD). Energy_Consumption: Total energy consumption of storage and transportation units (in kWh). IoT_Sensor_Reading_Temperature: Continuous data from IoT sensors monitoring the temperature of the crops during transit (in degrees Celsius). IoT_Sensor_Reading_Humidity: Continuous data from IoT sensors monitoring the humidity of the crops during transit (in percentage). IoT_Sensor_Reading_Light: Continuous data from IoT sensors monitoring light exposure during transportation (in lumens). Warehouse_Storage_Time: Time spent by the crops in warehouse storage before further transportation (in days). Inventory_Levels: Current inventory levels at various storage facilities (in units). Fuel_Costs: Cost of fuel consumed during transportation (in USD per liter). Spoilage_Risk: Probability of spoilage during transportation, based on environmental and operational conditions (as a percentage). The target labels in the dataset include:

Efficiency Ratio: A composite ratio calculated based on fuel consumption, logistics costs, and delivery times, aimed at measuring the overall efficiency of the logistics operation. Quality Maintenance Ratio: A ratio derived from spoi...

Logistics Digital Map Data Market Outlook

According to our latest research, the global logistics digital map data market size reached USD 6.8 billion in 2024, with robust momentum driven by the rapid adoption of digital mapping solutions across logistics and supply chain operations. The market is expected to witness a strong compound annual growth rate (CAGR) of 13.1% from 2025 to 2033, projecting the market size to reach USD 20.3 billion by 2033. The primary growth factor fueling this expansion is the increasing demand for real-time location intelligence and advanced route optimization technologies, which are transforming how logistics service providers manage resources, reduce operational costs, and enhance delivery efficiency.

One of the key growth drivers for the logistics digital map data market is the exponential rise in e-commerce and last-mile delivery services worldwide. The surge in online shopping, especially post-pandemic, has compelled logistics companies to invest in advanced digital mapping tools to optimize delivery routes, minimize delays, and improve customer satisfaction. Real-time map data enables businesses to dynamically reroute shipments based on traffic conditions, road closures, and weather disruptions, substantially reducing fuel consumption and operational expenses. Additionally, the integration of artificial intelligence (AI) and machine learning with digital map data has further empowered logistics firms to predict demand, plan inventory, and ensure timely deliveries, consolidating the role of digital mapping as a critical enabler of modern logistics.

Another significant growth factor is the proliferation of connected vehicles and IoT-enabled assets in the logistics sector. Fleet operators and supply chain managers are increasingly leveraging digital map data to monitor vehicle locations, track asset movements, and ensure regulatory compliance across various geographies. The use of telematics and GPS-based tracking systems, powered by high-precision map data, has led to improved visibility and transparency throughout the logistics value chain. This trend is further amplified by the growing need for end-to-end supply chain visibility, which is crucial for mitigating risks, preventing theft, and maintaining the integrity of goods in transit. As a result, logistics companies are prioritizing investments in digital mapping platforms that offer granular, real-time insights into fleet and asset movements.

The shift towards sustainable logistics and green supply chain practices is also propelling the adoption of digital map data solutions. Companies are under increasing pressure to reduce their carbon footprint and comply with stringent environmental regulations. Digital map data facilitates eco-friendly route planning, enabling logistics providers to minimize mileage, avoid congested routes, and optimize fuel consumption. Furthermore, the ability to analyze historical and real-time data empowers organizations to identify inefficiencies, implement corrective actions, and report on sustainability metrics. This growing emphasis on environmental responsibility is expected to sustain the demand for advanced digital mapping solutions in the logistics industry in the coming years.

Regionally, North America and Asia Pacific are emerging as the dominant markets for logistics digital map data, accounting for a significant share of global revenues. North America benefits from a mature logistics infrastructure and a high rate of technology adoption, while Asia Pacific is witnessing rapid growth due to expanding e-commerce, urbanization, and increasing investments in smart transportation networks. Europe also presents substantial opportunities, driven by cross-border trade and regulatory initiatives promoting digital transformation in logistics. Meanwhile, the Middle East & Africa and Latin America are gradually catching up, supported by government-led infrastructure projects and the rising need for efficient supply chain management solutions. Overall, the global logistics digital map data market is poised for sustained growth, underpinned by technological advancements, industry digitization, and evolving customer expectations.

LMI Logistics Managers Index in the United States decreased to 55.70 points in November from 57.40 points in October of 2025. This dataset provides - United States LMI Logistics Managers Index Current- actual values, historical data, forecast, chart, statistics, economic calendar and news.

China Logistics Prosperity Index: Employment data was reported at 51.400 % in Sep 2023. This records an increase from the previous number of 48.300 % for Aug 2023. China Logistics Prosperity Index: Employment data is updated monthly, averaging 50.750 % from Dec 2011 (Median) to Sep 2023, with 124 observations. The data reached an all-time high of 56.900 % in Apr 2015 and a record low of 30.100 % in Feb 2020. China Logistics Prosperity Index: Employment data remains active status in CEIC and is reported by China Federation of Logistics & Purchasing. The data is categorized under China Premium Database’s Transportation and Storage Sector – Table CN.TL: Logistics Prosperity Index(LPI).

Data Lakehouse for Logistics Market Outlook

According to our latest research, the global Data Lakehouse for Logistics market size reached USD 2.37 billion in 2024, reflecting a robust surge in adoption across the logistics sector. The market is projected to expand at a CAGR of 21.6% from 2025 to 2033, reaching a forecasted value of USD 17.02 billion by 2033. This remarkable growth is primarily driven by the increasing demand for unified analytics platforms that can seamlessly integrate data management, storage, and AI-driven insights, enabling logistics enterprises to optimize operations and enhance supply chain transparency.

One of the key growth factors propelling the Data Lakehouse for Logistics market is the exponential increase in data generated by logistics operations. Modern logistics companies are leveraging IoT sensors, telematics, RFID, and GPS tracking to monitor fleet movements, inventory levels, and warehouse operations in real time. This proliferation of structured and unstructured data has created a pressing need for scalable and flexible data architectures. Data lakehouse solutions, which combine the best attributes of data lakes and data warehouses, empower organizations to manage vast datasets efficiently while supporting advanced analytics and machine learning applications. As a result, logistics providers are increasingly investing in these platforms to gain actionable insights, reduce inefficiencies, and respond proactively to supply chain disruptions.

Another significant driver for the rapid adoption of Data Lakehouse for Logistics solutions is the growing emphasis on digital transformation within the logistics sector. As global supply chains become more complex and customer expectations for transparency and speed rise, logistics companies are under pressure to modernize their IT infrastructure. Data lakehouses provide a unified, cost-effective solution for integrating legacy systems, cloud-based applications, and diverse data sources. This integration capability is particularly valuable for organizations seeking to streamline order processing, automate warehouse management, and optimize fleet routing. Furthermore, the ability to support real-time analytics and predictive modeling is enabling logistics firms to make data-driven decisions that enhance operational efficiency and customer satisfaction.

The increasing regulatory requirements and the need for enhanced data security and governance are also fueling the growth of the Data Lakehouse for Logistics market. Logistics organizations must comply with a multitude of international regulations related to data privacy, shipment tracking, and trade compliance. Data lakehouse platforms offer robust governance features, such as data lineage, access controls, and audit trails, ensuring that sensitive information is managed in accordance with regulatory standards. Additionally, the scalability and flexibility of these platforms make them well-suited for supporting business continuity and disaster recovery initiatives. As logistics companies expand their global footprint, the ability to maintain secure and compliant data environments will be a critical factor in sustaining growth and building trust with clients and partners.

Regionally, North America currently dominates the Data Lakehouse for Logistics market due to the high concentration of leading logistics providers, advanced IT infrastructure, and early adoption of digital technologies. Europe follows closely, driven by the region’s focus on supply chain optimization and regulatory compliance. Asia Pacific is emerging as the fastest-growing region, supported by rapid e-commerce expansion, infrastructure investments, and increasing digitalization of logistics processes. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as local logistics companies seek to enhance competitiveness through technology adoption. The regional outlook remains highly dynamic, with evolving trade patterns and investment flows shaping future growth trajectories.

Component

Dataset

This dataset was created by Ananya Nayan

Released under Database: Open Database, Contents: © Original Authors

Contents

Logistics performance index: Overall (1=low to 5=high) in Turkey was reported at 3.4 1=low to 5=high in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Turkey - Logistics performance index: Overall (1=low to 5=high) - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

Your Strategic Marketing Asset for B2B Marketing in the Business Operations Realm

In today’s ever-shifting business environment, agility and precise targeting are key to B2B marketing success. With economic conditions continually changing, Solution Publishing by Allforce emerges as the quintessential digital audience data product, expertly fashioned to equip marketers with unparalleled reach and engagement within the business operations sphere, with a particular emphasis on the Supply Chain sector.

Enhanced Industry Connectivity with Granular Targeting, BizOps constructs an extensive tapestry of data, featuring over 2 million professionals with roles integral to Supply Chain, Logistics, Transportation, and Facilities. This vast audience provides a springboard for marketers to segment and hone in on their desired demographic, by job function, leadership status, and company size. Our granular targeting capabilities extend even further, permitting refined outreach with selectable parameters like geographic scope and job-specific interest categories, especially those revolving around the Supply Chain nexus, ensuring communications are bespoke and impactful.

Direct Mail and Telemarketing: Our comprehensive postal and telephone databases are a born for direct mail and telemarketing campaigns. In a sector where direct, meaningful touch points are paramount, direct mail pieces crafted using insights from Solution Publishing by Allforce are assured to reach the influential figures within Supply Chain and beyond, while telemarketing activities benefit from vetted leads, guaranteeing that your sales teams conduct rich, conversion-oriented conversations.

Email Marketing: Engaging with Targeted Precision

The driving force of our email marketing prowess is our sustained monthly interaction with a wide array of contacts, which directly benefits from contributions to Solution Publishing by Allforce's Business Operations Solution Journal. This engagement not only maintains the vitality of our database but also garners critical metrics on interactions, ensuring our data remains fresh and relevant, particularly within the Supply Chain field. Your email campaigns are assured not just to reach the intended inboxes but to be received by an audience that anticipates and values content centered on Supply Chain management and its associated segments like Procurement, Operations, Distribution, and Inventory.

Programmatic Display: Capturing Essential Digital Engagements

In the competitive digital arena, where every click is a currency, we help you elevate your display advertising. Our data zeroes in on the exact audience segments integral to the Supply Chain, from frontline coordinators to C-suite decision-makers, ensuring that your digital ads are not just seen, but actively engaged with by the very professionals driving Supply Chain innovation and efficiency.

LinkedIn Networking: Cultivating Industry-Specific Relationships

Recognizing LinkedIn as the virtual nexus for professionals, BizOps Continuum empowers your team to forge and nurture connections with precision on this platform. Matching our expansive data with LinkedIn profiles allows your marketing and sales teams to pinpoint and engage potential clients within the Supply Chain arena, fostering professional relationships through personalized InMail strategies, connection requests, and content sharing that resonates with the unique trends and necessities inherent in Supply Chain dynamics.

Use Cases: Uniting Verticals Under the Supply Chain Umbrella. Our data versatility ensures its utility spans a variety of industries related to business operations.

Supply Chain: Tailor messages for companies at the forefront of Supply Chain innovation and logistics optimization.

Wholesale and Retail: Reach decision-makers in search of cutting-edge Supply Chain solutions to streamline distribution.

Manufacturing: Target businesses implementing advanced Supply Chain practices to maximize production efficiency.

Transportation and Logistics: Connect with entities that manage the movement of goods from manufacturers to markets.

Facilities Management: Engage with professionals tasked with integrating Supply Chain systems into facility operations.

Technology and Information Systems: Address the needs of IT departments that support the Supply Chain with software and hardware solutions.

Enriching Campaigns with Active, Engaged Audiences

Solution Publishing by Allforce is more than a database—it's an ecosystem teeming with engaged, qualified business operations professionals actively interacting with content that influences their day-to-day and strategic decisions, especially within the Supply Chain sector. Insights drawn from our active engagement with the Business Operations Solution Journal newsletter empower your campaigns with the intelligence needed for hyper-targeted, resonant messaging that leads to higher conversion rates.

Positionin...

Bulgaria BG: Logistics Performance Index: 1=Low To 5=High: Overall data was reported at 3.200 NA in 2022. This records an increase from the previous number of 3.030 NA for 2018. Bulgaria BG: Logistics Performance Index: 1=Low To 5=High: Overall data is updated yearly, averaging 3.030 NA from Dec 2007 (Median) to 2022, with 7 observations. The data reached an all-time high of 3.210 NA in 2012 and a record low of 2.808 NA in 2016. Bulgaria BG: Logistics Performance Index: 1=Low To 5=High: Overall data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bulgaria – Table BG.World Bank.WDI: Transportation. The Logistics Performance Index overall score reflects perceptions of a country's logistics based on the efficiency of customs clearance process, quality of trade- and transport-related infrastructure, ease of arranging competitively priced shipments, quality of logistics services, ability to track and trace consignments, and frequency with which shipments reach the consignee within the scheduled time. The index ranges from 1 to 5, with a higher score representing better performance. Data are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Scores for the six areas are averaged across all respondents and aggregated to a single score using principal components analysis. Details of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report.;Data are available online at: https://lpi.worldbank.org/. Summary results are published in World Bank (2023): Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators.;Unweighted average;