How do you use data to improve your logistics decisions?

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Data is a powerful tool for improving your logistics decisions, whether you are planning, executing, or evaluating your supply chain and logistics operations. In this article, you will learn how to use data to optimize your logistics performance, reduce costs, increase customer satisfaction, and gain a competitive edge.

本文章的要点总结

Integrate real-time tracking:

Implement tracking systems that provide real-time data to monitor shipments and logistics operations. This allows for quick analysis and performance adjustments, enhancing end-to-end management.
Empower with data literacy:

Develop a culture where everyone understands the value of data. Train your team to analyze and apply information effectively, making smarter decisions that streamline operations and improve service.

本摘要由 AI 和以下专家提供支持

Francisco Gomes

Green Belt Lean Six Sigma | Excelência…
Irina Lavrova

MBA/Agile Certified/Dedicated…

1 Data sources and types

The first step to using data to improve your logistics decisions is to identify the sources and types of data that are relevant for your objectives. Some common sources of data include internal systems, such as enterprise resource planning (ERP), warehouse management system (WMS), transportation management system (TMS), and inventory management system (IMS), as well as external sources, such as suppliers, customers, carriers, and third-party logistics providers (3PLs). Some common types of data include historical data, real-time data, and predictive data. Historical data can help you analyze past performance, identify trends, and benchmark against best practices. Real-time data can help you monitor current operations, track shipments, and respond to disruptions. Predictive data can help you forecast demand, optimize routes, and allocate resources.

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Francisco Gomes

Green Belt Lean Six Sigma | Excelência Operacional | Lean Manufacturing | Gest?o de Projetos | Gest?o ágil | Logística | Gerenciamento de Transporte | Inventário | Auditoria | ESG
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Todas as decis?es devem ser baseadas e amparadas em dados. Temos além do Sistema ERP com os dados macros de movimenta??o, faturamento, pedidos, etc, Sistema de Rastreamento, Checklists Digitais e principalmente Sistemas TMS para acompanhamento intralogístico trazendo informa??es etapa por etapa, com dados em tempo real, permitindo análises rápidas e de performance. Se faz necessário o acompanhamento End to End da Cadeia Logística permitindo uma real e melhor Gest?o das opera??es!

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Irina Lavrova

MBA/Agile Certified/Dedicated professional with strong background in business development
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I fully agree , also would like to admit that tracking platforms are not always implemented in TMS systems and work requires using it separately. However much or internal sensitive data can be collected just from the individuals to who it may concern.

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Muhammad Ali Saeed

| FMCG | Food & Beverage | Textiles | Building Material | Global Operations Mgmt | Project Mgmt | Greenfield Proj. | Sys Dev & Implmnt.| Sourcing & Proc. | Strategic Supply Chain | Consultancy | Coaching & Teaching |
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Data-driven logistics decisions leverage insights gleaned from comprehensive analysis of various factors including demand forecasting, route optimization, inventory mngmnt, warehouse organization, supplier collaboration, performance monitoring, technology integration, risk assessment, cost analysis, and CI. By harnessing real-time and historical data, logistics managers can optimize transportation routes, adjust inventory levels, enhance warehouse efficiency, mitigate risks, reduce costs, and continuously refine operations for maximum effectiveness. Through the integration of data-driven strategies, logistics professionals can make informed decisions that streamline the SC, improve customer satisfaction, and drive overall business success

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Muhammad Ali Saeed

| FMCG | Food & Beverage | Textiles | Building Material | Global Operations Mgmt | Project Mgmt | Greenfield Proj. | Sys Dev & Implmnt.| Sourcing & Proc. | Strategic Supply Chain | Consultancy | Coaching & Teaching |
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Data is crucial for optimizing logistics decisions. Firstly, historical shipping data aids in route planning, identifying the most efficient and cost-effective paths. Real-time tracking data enhances visibility, allowing for proactive issue resolution & improved delivery accuracy. Inventory data helps in demand forecasting, preventing stockouts or excess inventory. Customer feedback and market trends, when analyzed, provide insights for service enhancements. Utilizing data analytics, machine learning, AI algorithms optimizes resource allocation, reducing operational costs & enhancing overall efficiency. In summary, leveraging data across various aspects of logistics enables informed decision-making, ensuring a streamlined & responsive SC.

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2 Data analysis and visualization

The second step to using data to improve your logistics decisions is to analyze and visualize the data in a meaningful way. Data analysis involves applying various methods, such as descriptive, diagnostic, prescriptive, and predictive analytics, to extract insights and recommendations from the data. Data visualization involves presenting the data in a clear and engaging way, such as using charts, graphs, dashboards, and maps, to communicate the results and support decision making. Data analysis and visualization can help you identify patterns, problems, opportunities, and solutions for your logistics operations.

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Irina Lavrova

MBA/Agile Certified/Dedicated professional with strong background in business development
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In my area as Air freight expedition Data analysis is mostly used for optimisation- we are using data analysis to identify trends in flight schedules, delays, and routes. Optimize our freight deliveries by selecting the most efficient and well priced flight options. Also this helps to adjust our transportation chains. Helps us to determine which cargo items are time-sensitive or have specific handling requirements. We should ensure that critical shipments receive top priority.

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3 Data integration and collaboration

The third step to using data to improve your logistics decisions is to integrate and collaborate with the data across your supply chain and logistics network. Data integration involves connecting and sharing the data among different systems, platforms, and devices, such as using cloud computing, internet of things (IoT), and application programming interfaces (APIs), to enable seamless data flow and access. Data collaboration involves working with the data with different stakeholders, such as suppliers, customers, carriers, and 3PLs, to enhance coordination, transparency, and trust. Data integration and collaboration can help you streamline your processes, improve your efficiency, and increase your responsiveness.

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4 Data quality and security

The fourth step to using data to improve your logistics decisions is to ensure the quality and security of the data. Data quality involves verifying and validating the data for accuracy, completeness, consistency, timeliness, and relevance, such as using data cleansing, auditing, and governance techniques, to avoid errors and biases. Data security involves protecting the data from unauthorized access, use, modification, or disclosure, such as using encryption, authentication, and authorization methods, to prevent breaches and losses. Data quality and security can help you maintain your reliability, credibility, and compliance.

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5 Data-driven culture and learning

The fifth step to using data to improve your logistics decisions is to foster a data-driven culture and learning environment. Data-driven culture involves embracing and promoting the use of data for decision making, problem solving, and innovation, such as using data literacy, leadership, and incentives, to encourage data adoption and usage. Data-driven learning involves continuously and systematically collecting, analyzing, and applying the data for improvement and growth, such as using feedback, experimentation, and evaluation, to measure and enhance data impact and value. Data-driven culture and learning can help you improve your agility, adaptability, and competitiveness.

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Bellamy Blake

Freight Spend Manager | Lead Project Manager | Freight Payables Analyst at Interlog Group for Stellantis N.V.
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The field of logistics has for a long time been an arena of hunches—but, without even knowing it, these hunches are driven by data! It may not be digitized data, but rather cerebralized. There is a give and take with logisticians and supply chain managers in finding the balance between making decisions driven by your gut or by data. It is not Intuition vs. Data, as many think. It should be Intuition + Data. Because there are things that our minds are good at that are hard to replicate or extract from data. Our human outputs of feeling or intuition towards SCM decisions come from the very same inputs that drive KPIs, metrics, and forecasts. Data should be our navigation tool in the future's uncharted waters, or the past's paved history.

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6 Data challenges and opportunities

The final step to using data to improve your logistics decisions is to recognize the challenges and opportunities that data presents for your supply chain and logistics operations. Some common challenges include data availability, accessibility, compatibility, scalability, and ethics, which require careful planning, management, and regulation. Some common opportunities include data automation, optimization, personalization, and innovation, which offer potential benefits, such as cost savings, quality improvement, customer satisfaction, and market differentiation. Data challenges and opportunities can help you identify your strengths, weaknesses, threats, and opportunities for your logistics performance.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you use data to improve your logistics decisions?

1

2

3

4

5

6

7

1 Data sources and types

2 Data analysis and visualization

3 Data integration and collaboration

4 Data quality and security

5 Data-driven culture and learning

6 Data challenges and opportunities

7 Here’s what else to consider

Industrial Engineering

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How do you use data to improve your logistics decisions?

1

2

3

4

5

6

7

1 Data sources and types

2 Data analysis and visualization

3 Data integration and collaboration

4 Data quality and security

5 Data-driven culture and learning

6 Data challenges and opportunities

7 Here’s what else to consider

Industrial Engineering

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