How can data analysis revolutionize transportation management?

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.

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: otimiza??o de rotas para reduzir custos e tempo de entrega.2. Análise de desempenho de veículos: monitoramento do desempenho de veículos para manuten??o preventiva e eficiência.3. Análise de tempo de transito: avalia??o do tempo gasto em diferentes rotas para melhorar a precis?o das estimativas de entrega.4. Análise de custos: análise de custos operacionais, como combustível, manuten??o e m?o de obra.5. Análise de satisfa??o do cliente: avalia??o do feedback do cliente e métricas de satisfa??o para melhorar o servi?o.6. Análise de riscos: identifica??o e mitiga??o de riscos relacionados à seguran?a, conformidade e logística.7. Análise de dados de localiza??o: 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 a??o. Nas apresenta??es mensais junto aos clientes eu tenho abordado o tema com um viés voltado ao Customer Experience trazendo os big number da opera??o, melhoras, pontos críticos e plano de a??o de forma a fornecer ao meu cliente informa??es preciosas e que possa servir de base para a elabora??o 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.

Para o transporte de cargas em geral a melhor base para o seu negócio precisa ser o TMS, pois nele a informa??o necessita chegar primeiro, neste sistema necessitamos consolidar todos os dados conforme os eventos cronológicos para mensura??o e cria??o de dados e estatísticas para tomada de decis?o. Desta forma, avaliar o uso de planilhas paralelas e ou ferramentas sem integra??o com o TMS acaba gerando empobrecimento das informa??es e a analise com base em dados prejudicada pelo delay ou erros de input de dados por integra??o 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 Wertsch?pfungskette über die Zusammenfassung aus unterschiedlichen Datenquellen sehe ich als gr??te 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.

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 valoriza??o ao profissional em uma empresa de servi?o para extra??o e analise dos dados. Para esta tarefa se exige competências múltiplas, pois ser conhecedor de softwares e banco de dados em nível intermediário e avan?ado é uma premissa, assim como automa??o para a??es cíclicas para otimiza??o das atualiza??es, logo, conhecimento em programa??o, sejam em VBA, Phyton entre outros softwares já passa a ser desejável para ganho de escala e automa??o das tarefas, além disto, dados sem serem compilados, n?o trazem informa??es para tomada de decis?o, desta forma, conhecer do negócio e do mercado irá capacitar ao analista de dados fazer cruzamentos coerentes para tomada de decis?o.

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.

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 regi?o e mercado a ser atuado s?o determinantes para que a analise de dados seja bem sucedida. Sabemos que há margem bruta para diferentes mercados e regi?o, assim como o Prazo Médio de Entrega tem tolerancia maior para o Fracionado em detrimento ao Dedicado, temos regi?es com alta complexidade de entrega, assim como a rela??o de custo. Fazer a gest?o de custo de Gerenciamento de Risco no Rio de Janeiro comparando ao custo de Santa Catarina seria negligenciar a regi?o, por exemplo, assim como a média de consumo de combustível para as entregas fracionadas em S?o 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

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 m?os, que se usados de maneira certa muda totalmente a sua opera??o para ser mais eficiente.