Wie kann Datenanalyse das Transportmanagement revolutionieren?

Data analysis has helped in revolutionising transportation industry. Firstly Data analysis has helped in reducing cost of logistics by optimising truck routes & particularly in case of milk run, loading pattern, use of multi modal transport. It has helped in improving customer experience owing to track n trace of all shipments and precise calculations of shipment delivery dates & times. It has helped internal customers and logistics teams in terms by creating SCORE CARD of all transporter on various parameters. Since these SCORE CARD are based on data, they are neutral and have no personal biases and are acceptable to one & all. It has improved the safety owing to continuous monitoring of truck and generation of alerts and reports.

Data analysis can transform transportation management by offering information, insights, and solutions for optimising operations, increasing customer happiness, improving safety and security, promoting sustainability and social responsibility, and driving innovation and growth.

Data analysis transforms transport management by optimizing routes, predicting demand, improving safety, reducing congestion, minimizing emissions, and enhancing the travel experience.

Data analysis decodes information, revealing valuable insights for businesses and individuals. It guides evidence-based decision-making, minimizing risks and optimizing processes. This process acts as a guiding force in daily choices, empowering individuals to make informed decisions, akin to having a personal advisor. Overall, data analysis unlocks the potential within information, shaping a world where informed decisions drive success efficiently and intelligently

Data analysis in the trucking industry offers significant benefits: operational efficiency, safety enhancement, cost reduction, improved customer service, and proactive asset management. It optimizes operations, improves safety protocols, minimizes costs, enhances customer satisfaction, and maximizes asset utilization. Analyzing data uncovers inefficiencies, aiding data-driven decision-making. Tracking driver behavior and accidents enables targeted safety training. Route optimization optimizes fuel consumption and timely deliveries. Monitoring fuel usage, maintenance schedules, and driver productivity aids cost control. Analyzing customer feedback and service quality enhances satisfaction. Proactive maintenance prolongs asset lifespan.

Data analysis will increasingly become the backbone of transportation management, offering insights that outpaces traditional methods. By sifting through extensive datasets, it will pinpoint optimal routes, forecast maintenance needs, and bolster safety more accurately. This forward-looking approach promises to streamline operations, unveil cost-saving strategies, and elevate reliability, showcasing the transformative potential of data in steering logistics toward unparalleled efficiency and sustainability.

Big Data Analytics in Logistics What is big data? Big Data encompasses datasets that surpass the capabilities of traditional databases, typically characterized by large volumes, high velocity, or diverse data types. Stock and Storage Control Route Optimization Capacity Planning Risk Management Customer Satisfaction Predictive Analytics in Logistics Predictive analytics, a subset of big data analytics, plays a crucial role in enhancing the predictive capabilities of logistics operations. By analyzing historical data, identifying patterns, and applying statistical algorithms, predictive analytics enables organizations to anticipate future events and make data-driven decisions.

Baseado na minha Experiência A análise de dados pode revolucionar a gestão de transportes de várias maneiras. Aqui estão algumas delas: 1. Planejamento de rotas mais eficientes; 2. Manutenção preditiva; 3. Melhoria da segurança: A análise de dados pode ajudar a identificar padrões de acidentes e compreender as causas subjacentes. 4. Otimização da gestão de estoque: A análise de dados pode ajudar a melhorar o gerenciamento de estoque, identificando as demandas de produtos em diferentes regiões e horários. 5. Melhoria da gestão de frota: A análise de dados pode ser usada para acompanhar o desempenho dos motoristas, avaliar sua eficiência e segurança e fornecer feedback para melhorar seu desempenho.

In my experience Data analysis is key 🔑 to making transport more efficient, safe, sustainable, and user-focused. It transforms management by optimizing routes for time and cost savings, enabling predictive maintenance to prevent failures, and aligning services with real demand for better efficiency and satisfaction. Integrating diverse data enhances strategic decisions, while personalizing services increases user loyalty. Data-driven innovation fosters new technologies like MaaS and autonomous vehicles, promoting sustainability.

Data analysis in transportation management helps in reducing costs and risks. It also helps in improved services to consumers. Innovative design strategies can be made if accurate data is managed and available.

Datos a considerar, Número de entregas o recolecciones a realizar. Volumen de la carga vs capacidad de la misma. Kilómetros recorridos por Litro de combustible. Radio de atención y tiempos de recorrido. Entregas en tiempo. Entregas no realizadas. Horas trabajadas. Son algunas de las métricas o KPI´s a tomar en consideración.

Análise de rotas: otimização de rotas para reduzir custos e tempo de entrega.2. Análise de desempenho de veículos: monitoramento do desempenho de veículos para manutenção preventiva e eficiência.3. Análise de tempo de trânsito: avaliação do tempo gasto em diferentes rotas para melhorar a precisão das estimativas de entrega.4. Análise de custos: análise de custos operacionais, como combustível, manutenção e mão de obra.5. Análise de satisfação do cliente: avaliação do feedback do cliente e métricas de satisfação para melhorar o serviço.6. Análise de riscos: identificação e mitigação de riscos relacionados à segurança, conformidade e logística.7. Análise de dados de localização: uso de dados de GPS para rastreamento de veículos em tempo real

By analyzing historical traffic data, weather patterns, and other factors, transportation companies can optimize routes for efficiency, reducing fuel consumption, delivery times, and operational costs.Data analysis is essential for the development and operation of self-driving cars, which have the potential to revolutionize transportation by improving safety, efficiency, and accessibility.

A analise de dados, hoje, bastante facilitada pelo uso do Microsoft BI, permite o cruzamento de dados e uma abordagem para tomada de ação. Nas apresentações mensais junto aos clientes eu tenho abordado o tema com um viés voltado ao Customer Experience trazendo os big number da operação, melhoras, pontos críticos e plano de ação de forma a fornecer ao meu cliente informações preciosas e que possa servir de base para a elaboração de seu material para junto aos seus gestores de forma transparente, informativa e que contribua para o fortalecimento da parceria entre as partes.

Various types of data analysis techniques, such as descriptive, diagnostic, predictive, and prescriptive analysis, enable transportation managers to gain a comprehensive understanding of past trends, current challenges, and future opportunities. By applying the appropriate data analysis methods, organizations can extract actionable insights, optimize resource allocation, and mitigate risks across the transportation network.

Actually, I disagree with the notion that there's a one-size-fits-all approach to data analysis in transportation management. Instead, understanding the various types of data analysis available and when to use each one is crucial for effectively leveraging data in this context. Different types of data analysis, such as descriptive, diagnostic, predictive, and prescriptive analysis, serve unique purposes and can provide valuable insights when applied appropriately to transportation data. By utilizing a combination of these types of data analysis, transportation managers can gain a comprehensive understanding of their operations and make informed decisions.

In transportation management, data analysis types involve descriptive analysis for understanding traffic patterns and fleet performance, diagnostic analysis to identify causes of delays or accidents, predictive analysis for forecasting demand and optimizing routes, and prescriptive analysis to recommend actions for improving efficiency and reducing costs.

Otimização de Rotas: Utilizando dados de tráfego em tempo real e históricos, é possível otimizar rotas para minimizar o tempo de viagem e reduzir custos operacionais.

In my opinion, the most important thing before deciding on the type of analysis to be carried out is to clearly define the company's problem : - Why do we want to analyze the transportation business? - What are the pain points or dysfunctions in current operations? - What analyses are already being carried out, and are they relevant? It is therefore important to understand the context before choosing the type of analysis. This will avoid wasting time and money collecting data for analyses that are inappropriate for the purpose in hand...

Para o transporte de cargas em geral a melhor base para o seu negócio precisa ser o TMS, pois nele a informação necessita chegar primeiro, neste sistema necessitamos consolidar todos os dados conforme os eventos cronológicos para mensuração e criação de dados e estatísticas para tomada de decisão. Desta forma, avaliar o uso de planilhas paralelas e ou ferramentas sem integração com o TMS acaba gerando empobrecimento das informações e a analise com base em dados prejudicada pelo delay ou erros de input de dados por integração e ou outro meio.

Un sistema de gestión del transporte (TMS) es una plataforma logística que utiliza tecnología para ayudar a las empresas a planificar, ejecutar y optimizar el movimiento físico de mercancías, tanto de entrada como de salida, y asegurarse de que el envío cumpla con las normas y la documentación adecuada esté disponible. Es indispensable contar con las herramientas y tecnología que nos permita elaborar rutas más eficientes para lograr ahorros en tiempo y combustible. Así como cubrir un mayor número de entregas o recolecciones con menor número de unidades de transporte.

Die Darstellung der kompletten Warenbewegungen entlang der Wertschöpfungskette über die Zusammenfassung aus unterschiedlichen Datenquellen sehe ich als größte Herausforderung für eine transparente Datenanalyse. Schon alleine der Begriff Sendung in der Distributionslogistik wird von unterschiedlichen Systemen unterschiedlich oder gar nicht dargestellt. So kennt das ERP System meist eher Bestellung, Auftrag, Auftragsposition und Rechnung. Ob die Auftragsposition nun Wagenladung, Palette oder Multipaketversand ist obliegt meist schon einer Transportsoftware oder -modul, das nicht unbedingt komplett ins ERP integriert ist.

Data Sources: Telematics Devices: Collect real-time data on vehicle location, speed, and status. GPS and Navigation Systems: Provide route data and help optimize routes for efficiency. RFID and Barcodes: Track shipments and inventory in real-time. Customer Feedback: Gather information on service quality and areas for improvement. Data Analysis Tools: Geographic Information System: Analyze spatial data for route optimization and location-based insights. Machine Learning Algorithms: Predictive analytics for demand forecasting, maintenance predictions, and route optimization. Big Data Platforms: Handle large volumes of data efficiently for real-time analysis. Business Intelligence (BI) Tools: Create dashboards and reports for decision-makers.

Data from diverse sources, including GPS tracking systems, telematics devices, electronic logging devices (ELDs), IoT sensors, and transportation management systems (TMS), provide a rich reservoir of information for analysis. Leveraging advanced data analysis tools and technologies, such as data visualization software, machine learning algorithms, and predictive analytics models, empowers transportation managers to extract meaningful insights, optimize routing decisions, and enhance operational efficiency.

An example I've seen is the use of diverse data sources and tools in transportation management to gather, analyze, and derive insights from data. Internal data sources such as orders, invoices, shipments, and vehicle records provide valuable information about operational processes, while external data sources from customers, suppliers, competitors, and industry associations offer additional insights into market trends and industry dynamics. Transportation managers leverage a variety of tools and technologies, including transportation management systems (TMS), data visualization software, and predictive analytics tools, to process and analyze this data effectively.

Data sources for analysis include internal company records, customer databases, social media interactions, sensor data from IoT devices, transactional data, public datasets, and research studies. Tools for data analysis range from spreadsheets like Microsoft Excel to advanced analytics platforms like SAS, SPSS, and Tableau for visualization, as well as programming languages such as Python and R with libraries for statistical analysis and machine learning. Cloud-based services like Google Analytics provide web and marketing analytics, while databases and data management systems like SQL Server and MongoDB support data storage and manipulation.

There are several data sources and tools available: FHWA Freight Management and Operations: The FHWA provides various data sources for freight transportation analysis, including the Freight Analysis Framework, freight demand modeling, and national statistics and maps. Transportation Management Systems (TMS): A TMS is a software category that helps in planning and executing the physical movement of goods. Order Management System (OMS): An OMS helps in managing the portfolio of orders. It includes features like order entry, order overview, tendering, and shipment rate management. Warehouse Management System (WMS): A WMS is responsible for planning, executing, and controlling warehouse operations.

Data sources include GPS tracking data from vehicles, traffic cameras and sensors for real-time traffic conditions, customer feedback and usage patterns from ticketing systems, weather data for route optimization, and logistics databases for freight and supply chain management. Tools used in transportation encompass transportation management systems (TMS) for logistics and route planning, geographic information systems (GIS) for mapping and spatial analysis, real-time traffic management systems like Intelligent Transportation Systems (ITS), data analytics platforms like Python with geospatial libraries (e.g., GeoPandas), and specialized software for fleet management that integrates telematics data for vehicle tracking and maintenance.

1. Statistical Analysis: Understand statistical methods to analyze trends, patterns, and correlations in transportation data. 2. Programming Skills: Proficiency in programming languages like Python or R for data manipulation and analysis. 3. Machine Learning Knowledge: Apply machine learning algorithms for predictive modeling and optimization. 4. Data Visualization: Communicate insights effectively using charts, graphs, and interactive dashboards. 5. Domain Knowledge: Understand transportation logistics and industry-specific challenges to contextualize data findings.

Developing data analysis skills and competencies among transportation personnel is essential for maximizing the value of available data assets. Training programs, workshops, and certifications focused on data analysis techniques, statistical methodologies, and data visualization tools enable employees to interpret data effectively, identify patterns, and derive actionable recommendations to drive continuous improvement in transportation management practices.

In order to develop and improve these skills, professionals can pursue relevant education and training programs, gain practical experience through internships or entry-level positions, and stay updated on industry trends and best practices. Continuous learning and professional development opportunities are also important in keeping up with emerging trends and technologies in the transportation industry

Atualmente percebemos que existe uma melhor valorização ao profissional em uma empresa de serviço para extração e analise dos dados. Para esta tarefa se exige competências múltiplas, pois ser conhecedor de softwares e banco de dados em nível intermediário e avançado é uma premissa, assim como automação para ações cíclicas para otimização das atualizações, logo, conhecimento em programação, sejam em VBA, Phyton entre outros softwares já passa a ser desejável para ganho de escala e automação das tarefas, além disto, dados sem serem compilados, não trazem informações para tomada de decisão, desta forma, conhecer do negócio e do mercado irá capacitar ao analista de dados fazer cruzamentos coerentes para tomada de decisão.

In transportation, data analysis skills and competencies specifically include understanding of geographic information systems (GIS) for route optimization, expertise in time series analysis for demand forecasting, knowledge of transportation regulations and safety standards, ability to work with large datasets from sensors and telematics for predictive maintenance, proficiency in traffic flow and congestion management techniques, familiarity with logistics and supply chain management concepts, and skills in environmental impact analysis to support sustainable transportation initiatives.

Afin de bien exploiter le potentiel des Données de transport, il est souvent très utile de combiner au sein de l'équipe projet des compétences en analyse mais aussi des représentants des métiers du transport. La donnée seule peut ne pas révéler la totalité de la réalité opérationnelle et faciliter à perturber la qualité des analyses. La connaissance et l'expérience opérationnelle aide à cadrer les analyses.

Es vital contar con las habilidades y competencias en análisis de datos para revolucionar la gestión del transporte. En un entorno logístico cada vez más complejo y competitivo, contar con profesionales capacitados en análisis de datos nos permite desbloquear el verdadero potencial de nuestras operaciones. Desde la capacidad para recopilar, limpiar y procesar grandes volúmenes de datos, hasta la habilidad para interpretar tendencias y patrones significativos, estas competencias nos ayudan a tomar decisiones estratégicas basadas en evidencia. Al invertir en el desarrollo de estas habilidades entre nuestro equipo, estamos preparando el camino para una gestión del transporte más eficiente, ágil y orientada hacia el futuro.

Die beste Umsetzung von Datenanalyse im Transportmanagement sind die vielfach angebotenen digitalen TMS (Transport Management Systems). Mithilfe der Datenanalyse der vorhandenen und erfassten Daten optimieren diese Systeme die Transportrouten sowie den Treibstoff- und Ressourcenverbrauch. Zudem wird das Fahrverhalten der Fahrer analysiert. Daraus kann eine künftig verbesserte Fahrweise der Fahrer erreicht werden. Vielfach werten diese Systeme auch das Verhalten der Kühlketten aus, z. B. bei Lebensmittel- oder Pharmatransporten.

1. Data Quality: Ensure data accuracy, completeness, and consistency for reliable analysis. 2. Real-time Analysis: Implement real-time analytics to respond promptly to changing conditions and optimize operations dynamically. 3. Predictive Analytics: Use predictive modeling to forecast demand, identify maintenance needs, and optimize routes. 4. Collaboration: Foster collaboration between data analysts and transportation experts to develop actionable insights. 5. Continuous Improvement: Regularly assess and refine analysis methods to adapt to evolving transportation needs and technological advancements. 6. Security and Privacy: Implement robust security measures to protect sensitive transportation data.

Embracing data analysis best practices, such as data quality assurance, data governance frameworks, and data-driven decision-making processes, ensures the accuracy, reliability, and relevance of insights derived from transportation data. By establishing robust data management protocols and fostering a culture of data-driven decision-making, organizations can harness the full potential of data analysis to drive innovation, optimize operations, and deliver superior customer experiences.

Obter um planejamento estratégico bem definido, definindo nicho e segmento de uma empresa de transporte, assim como região e mercado a ser atuado são determinantes para que a analise de dados seja bem sucedida. Sabemos que há margem bruta para diferentes mercados e região, assim como o Prazo Médio de Entrega tem tolerância maior para o Fracionado em detrimento ao Dedicado, temos regiões com alta complexidade de entrega, assim como a relação de custo. Fazer a gestão de custo de Gerenciamento de Risco no Rio de Janeiro comparando ao custo de Santa Catarina seria negligenciar a região, por exemplo, assim como a média de consumo de combustível para as entregas fracionadas em São Paulo Capital, comparado ao custo do interior do mesmo Estado.

El análisis de estos datos no debería estar contaminado por intereses personales ,por lo que la recomendación es que sea realizada por factores externos ,pero también por los involucrados ,generando así una imagen más real de la situación Debe ser un análisis objetivo y duro para poder sacar un dato real y no solo palabrerio

Afin d'améliorer la qualité des analyses de Données Transport, je pense qu'un des nouveaux leviers est la capacité nouvelle à collecter, structurer et analyser plus facilement les activités avec l'aide d'outils tels l'I.A, le NoCode et l'automatisation. Cela me semble une opportunité réelle pour sortir de la faiblesse récurrente actuelle : le manque de Données opérationnelles fiables et pertinentes. Une fois ces bases disponible, les méthodes d'analyse et outils sont nombreux. L'enjeu reste encore trop souvent cette carence ou fragilité des Données Transport disponibles.

Para asegurar un impacto significativo, es crucial adoptar prácticas recomendadas de análisis de datos. Esto incluye la recopilación sistemática de datos relevantes, desde la ubicación de la carga hasta los tiempos de entrega, asegurando su calidad y coherencia. Además, es esencial emplear técnicas avanzadas de análisis, como el modelado predictivo y la visualización de datos, para identificar tendencias y patrones significativos. Al integrar estas prácticas en nuestra estrategia logística, podemos tomar decisiones más informadas y proactivas, optimizando nuestras operaciones de transporte para garantizar una entrega eficiente y oportuna de nuestros productos a los clientes, mientras mantenemos la competitividad.

Work on data Decisions made by the historical, plan and possibilities. Data is one of the key points that is readable and possible to organize without doing any experiment or action in actual situations. Based on the data we could know what things happened and what has happened and the next thing will happen. We could do analysis, do some simulation and output any possibilities of impact from the decisions. Not only the data, but the analyst is super important that could drive the data, where to go and what next.

Considerar la rentabilidad de cada envío es de suma importancia, ya que, este costo logístico impacta de manera directa a la ganancia esperada. Enviar paquetes, pallets, camiones, etc. sin analizar la afectación de este costo puede terminar mermando o incluso dejando perdidas por una mala gestión. Muchas startups no cuentan con personal experimentado, en mi experiencia he visto que con el afán de cumplir con la entrega caen en gastos que superan incluso el costo del producto, todo por su promesa de entregas en tiempos muy cortos. Llevándolos a la quiebra.

Today, many organizations recognize the importance of logistics-related data management as a crucial factor in standing out in the marketplace. The logistics activity is directly related to the improvement in customer service and is seen as a strategic differential. In short, the analysis of systematic data that encompasses the entire logistics chain, generates value, as it seeks to optimize costs, ensure deadlines and quality. In this perspective, data management offers numerous opportunities to improve the performance and profitability of the organization.

By effectively harnessing data sources, utilizing appropriate tools, developing relevant skills, and following best practices, transportation management can benefit from data analysis to optimize routes, reduce costs, improve efficiency, and enhance overall decision-making processes.

Dados é um dos bens mais precisos que temos hoje em nossas mãos, que se usados de maneira certa muda totalmente a sua operação para ser mais eficiente.

Integrating data analysis into transportation management processes enables organizations to unlock new levels of operational excellence, agility, and resilience. By embracing data-driven insights, organizations can proactively identify emerging trends, anticipate market shifts, and optimize resource allocation to adapt to changing customer demands and competitive pressures. Through continuous innovation and optimization, data analysis revolutionizes transportation management, enabling organizations to achieve sustainable growth and competitive advantage in the dynamic global marketplace.

In addition to the technical aspects of data analysis, it's important to consider the human and organizational factors that impact its success in transportation management. This includes fostering a data-driven culture within the organization, investing in employee training and development to enhance data analysis skills and competencies, and promoting collaboration and knowledge sharing among different departments and stakeholders. Furthermore, staying informed about emerging trends and technologies in data analysis and transportation management and continuously evaluating and adapting data analysis strategies are essential for driving innovation and improvement in transportation operations.

Data analysis can revolutionize transportation management by providing insights into traffic patterns, optimizing routes, improving vehicle maintenance schedules, enhancing fuel efficiency, and enabling predictive maintenance, ultimately leading to cost savings, increased safety, and better overall efficiency.

Gestão de Frota: Dados de telemetria podem ser usados para monitorar o desempenho de veículos, combustível e padrões de condução, permitindo uma gestão mais eficiente da frota. Manutenção Preventiva: A análise de dados pode prever falhas mecânicas em veículos, permitindo a manutenção antes que ocorram problemas graves, evitando atrasos e reduzindo custos de reparo.

as mentioned we do not all have the position to integrate all data, but even the smallest transpararancy can and will help to get more steering and optimization into your network. In many cases also you service providers (transport or 3pl) may be able to support on that as well. Knowing what you spend, on what and where the room for improvement is doesn't need a full TMS system. You can start right away! Last but not least.. Shit in is Shit out.. make sure your data is correctly managed in your systems!