You have a lot of data to manage in logistics. What’s the best way to stay on top of it?

O mais importante sobre os dados é transformar em informa??es de forma simples e direta, através de Indicadores Chavende Desempenho. Tenha em mente que eles precisam se conectar com benefícios tangíveis ao negócio, como aumento de receitas ou redu??o de despesas. Encontre essa correla??o e causalidade no plano de metas e de dados. Assim, haverá seguran?a da sua parte e de todos os stakeholders quanto ao bom direcionamento das metas definidas.

In order to efficiently manage a lot of data available for logistics management, it is important to clearly define the goals associated. --data may be required to study the historical trend, vehicle types, load sizes, destination wise distribution, destination wise per MT per km cost, total spend on various logistics cost factors and as a whole etc. --data would be required to study and monitor the KPIs, timely placement of vehicles, timely delivery, vendor performance etc. -- data would be required to prepare RFQs for contract finalization. --data would be required for audit purposes.

First off you've to decide what comes first. The human capital data or the headcount for each day, the volume expected every hour, scheduling and outbounds and of course quality. These are very important in alignment of your data goals with business objectives. Defining what comes first for you is how you draw data that matters most to avoid redundant information.

The best way to stay on top of logistics data is to implement a robust data management system that integrates real-time tracking, analytics, and predictive modeling. Utilizing advanced technologies like IoT sensors, AI-driven algorithms, and cloud-based platforms can streamline data collection, analysis, and decision-making processes. Regularly updating and optimizing this system ensures accuracy, efficiency, and adaptability to changing logistics demands and market conditions. Additionally, fostering a culture of data-driven decision-making among team members enhances collaboration and ensures everyone is aligned with the overarching logistics objectives.

Além disso, é importante assegurar que os dados estejam disponíveis para consumo para todas as pessoas interessadas já de forma tratada, evitando erros de interpreta??o, métricas diferentes para o mesmo fen?meno ou até mesmo escassez de informa??es necessárias. O bom fluir dos dados corretos facilita a gera??o de insights e.boas decis?es, além dessas serem mais rápidas. N?o desperdice tempo da sua equipe fazendo ela preparar os dados antes das análises

There are more methods and programs to house and gather data than ever before, but not all of them have the same utility or compatibility with a given party. For example, if you don't provide your packaging department with the method and specifications to calculate LTL freight class, then you may be missing on tremendous cost-savings by avoiding arbitrary classifications, therefore potentially spiking your base freight rate. In this example, your packaging department shouldn't stop at weight and dimensions when picking their data sources to make a commodity's packaging, but should have also incorporated estimated freight class (which uses a pounds per cubic foot, or "pcf", calculation). Different data sources have different utilities!

Prefira as ferramentas que permitam que todas as pessoas possam acessar as informa??es de forma segura, idêntica e reproduzir os mesmos resultados. é muito comum usarem planilhas a partir de arquivos de texto tratados. Isso é relativamente barato, passa uma certa sensa??o de autonomia ao criador do insight, mas na verdade pode se tornar uma armadilha de disponibilidade, valida??o e prontid?o de informa??es. Invista.em um sistema de curadoria dos dados e centralize a informa??o.

A tenta??o é ter um painel.com muitas metas de indicadores, isso gera perda de foco e acaba por impactar o resultado global das equipes. Nada contra monitorar muitos indicadores, mas priorize doos ou três indicadores no máximo que represente 80% dos problemas que est?o sendo focados no momento. Muitas vezes uma escolha acertada mostrará que ao melhorar os três indicadores priorizados, os demais ser?o no mínimo mantidos ou alavancados com eles.

Some points to consider while managing too much data in logistics: 1- Filter data by looking at the required end result 2- Make it a daily task to update systems such as CRM or WMS/TMS 3- Make data analysis a part of staff performance review, at all applicable levels 4- Encourage knowledge sharing and cross-analysis between different departments so that data does not accumulate without proper usage

Implement a robust logistics management system that integrates real-time tracking, inventory management, and analytics. Utilize advanced technologies such as RFID, GPS, and IoT devices for accurate data collection. Employ automation for routine tasks and alerts, reducing human error. Embrace cloud-based platforms for centralized data access and collaboration. Regularly analyze performance metrics to identify areas for improvement. Foster collaboration with suppliers and partners for seamless information exchange. Stay abreast of technological advancements in logistics for continual optimization. Regularly update and train staff on system usage, ensuring efficiency and adaptability to evolving industry standards.

Effectively manage a large volume of logistics data by implementing robust data management systems and tools. Utilize automation for routine tasks, ensuring accuracy and efficiency. Prioritize data security to safeguard sensitive information. Implement regular data quality checks and validations to maintain accuracy. Foster collaboration among team members to share insights and collectively address challenges. Stay updated on technological advancements to leverage new solutions that enhance data management capabilities.

Logística quase sempre envolve equilibrar muitas situa??es antag?nicas, como nível de servi?o e custo, qualidade e velocidade, etc. Isso significa uma rotina de trade-offs. O sistema de dados e metas precisa estar preparado para que sua equipe consiga lidar com essa quest?o, ou seja, consiga priorizar, repriorizar e ajustar a rota sistematicamente. N?o basta um sistema de dados robusto, é necessário ter sensibilidade aos sinais de mudan?a e também na gest?o do risco no negócio.

Keep in mind that while working with data can provide great insights, any model will only reflect an extremely narrow portion of reality. Even with perfect data quality, this introduces biases and distortions, especially when dealing with complex environments.