How can you ensure the accuracy of your logistics data?

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Logistics data is essential for plant engineering, as it helps you monitor and optimize your production, inventory, and distribution processes. However, if your data is inaccurate, incomplete, or outdated, you may face serious problems such as inefficiencies, errors, delays, or losses. How can you ensure the accuracy of your logistics data and improve your logistics performance measurement? Here are some tips to follow.

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Commander Jawad Ur Rashid PN (Retd)

Logistics Manager at Kenwood (R&I Electrical Appliances Pvt Ltd)
Guy Mills
Bala Panneerselvam

Cofounder @ZORP

1 Define clear metrics

The first step to ensure the accuracy of your logistics data is to define clear and relevant metrics that align with your goals and objectives. Metrics are the indicators that measure your logistics performance and help you evaluate your progress and identify areas of improvement. Some examples of logistics metrics are order fill rate, inventory turnover, cycle time, delivery accuracy, and cost per unit. You should choose metrics that are specific, measurable, achievable, realistic, and timely (SMART) and that reflect your customer needs and expectations.

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Commander Jawad Ur Rashid PN (Retd)

Logistics Manager at Kenwood (R&I Electrical Appliances Pvt Ltd)
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Ensuring the accuracy of logistics data is crucial for efficient supply chain management. Here are some steps to help you achieve this: 1. Data Validation 2. Automation 3. Regular Audits 4. Standardized Processes 5. Training and Education 6. Data Quality Tools 7. Real-Time Monitoring 8. Data Governance 9. Vendor and Partner Communication 10. Continuous Improvement 11. Error Reporting and Resolution 12. Data Backups 13. Data Security Following these steps, we can enhance the accuracy of logistics data and, in turn, improve the efficiency and reliability of supply chain operations.

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Oyelaran Paul mMBA

Data & Business Analyst ? YIL 2024 Fellow ? World Bank Group Delegate 2024 ? Community Builder ? Passionate about bridging the digital divide in my community through tech social project execution
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As a fleet Analyst here are what I do to ensure data accuracy: 1. Data validation so they won't enter A instead 9 for me 2. Reduce those who have access to the database 3. Organize a training session occasionally, so we will all be good. 4. When building template I involve them and we do it together. So they will understand how it work and what is expected of them to do when logging 5. I monitor my database for errors often 6. Available anytime to attend to my team questions when not cleared on any area before logging. 7. If anything goes wrong, I don't blame anyone or even mention names, I only inform them about better way to get it done need. It require team work to achieve it With the few said, I have been achieving my data accuracy ??

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Ifemidayo Odekunle

Demand Planner | Supply Chain Management
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Establish clear and standardized procedures for data entry and recording. Ensure that all personnel involved in logistics are trained on these procedures to minimize errors at the source.

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2 Use reliable data sources

The second step to ensure the accuracy of your logistics data is to use reliable and consistent data sources that provide you with the most accurate and up-to-date information. Data sources are the systems, tools, or methods that collect, store, and transmit your logistics data. Some examples of data sources are enterprise resource planning (ERP) systems, warehouse management systems (WMS), barcode scanners, radio-frequency identification (RFID) tags, and sensors. You should use data sources that are compatible, integrated, and secure and that minimize manual input and human error.

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Guy Mills
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A robust logistics data governance mechanism is essential. It involves the establishment of policies, procedures, standards and accountabilities to ensure that logistics data is accurate, reliable, secure, and used effectively to support business operations. Elements include: Ownership and Accountability Data Quality Standards Policies and Procedures Security and Compliance Data Catalog and Metadata Integration and Interoperability Quality Monitoring Training and Education Continuous Improvement

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3 Implement data quality checks

The third step to ensure the accuracy of your logistics data is to implement data quality checks that verify and validate your data before, during, and after you use it. Data quality checks are the processes, rules, or standards that ensure your data is accurate, complete, consistent, relevant, and timely. Some examples of data quality checks are data cleansing, data auditing, data validation, data reconciliation, and data governance. You should implement data quality checks that are automated, systematic, and continuous and that allow you to detect and correct any data issues or anomalies.

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Bala Panneerselvam

Cofounder @ZORP
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The golden words are "Garbage in, garbage out". Doesn't matter what system you use, if you do not have the right control mechanisms for what data goes into your system, it's not going to help you. There's a client of ours who implemented ZORP but did not put any process controls in place. After 6 months of usage, we see that 50% of the data they update are placeholders. Key tenants to think about: - Did your delivery person actually reach the location? Use location validations - Did the right product get delivered? Apply QR/barcode scan validation when the product is picked up and it is delivered. - Customer validation: Add a text based authentication when the parcel is handed over so the product is handed over to the right customer.

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4 Analyze and visualize your data

The fourth step to ensure the accuracy of your logistics data is to analyze and visualize your data in a way that helps you understand and communicate your logistics performance and insights. Data analysis and visualization are the techniques, tools, or methods that transform your data into meaningful and actionable information. Some examples of data analysis and visualization are descriptive statistics, trend analysis, benchmarking, dashboards, charts, and graphs. You should analyze and visualize your data in a way that is clear, concise, and comprehensive and that supports your decision making and problem solving.

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5 Review and update your data

The fifth step to ensure the accuracy of your logistics data is to review and update your data regularly and frequently to ensure it reflects the current reality and changes in your logistics environment. Data review and update are the activities, procedures, or practices that monitor and maintain your data quality and relevance over time. Some examples of data review and update are feedback loops, data audits, data refreshes, and data archiving. You should review and update your data in a way that is proactive, responsive, and adaptive and that aligns with your changing goals and objectives.

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6 Train and educate your staff

The sixth and final step to ensure the accuracy of your logistics data is to train and educate your staff on the importance, benefits, and best practices of data quality and management. Your staff are the key stakeholders and users of your logistics data and they play a vital role in ensuring its accuracy and value. Some examples of training and education are workshops, webinars, manuals, guidelines, and certifications. You should train and educate your staff in a way that is engaging, interactive, and continuous and that fosters a data-driven culture and mindset in your organization.

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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 can you ensure the accuracy of your logistics data?

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1 Define clear metrics

2 Use reliable data sources

3 Implement data quality checks

4 Analyze and visualize your data

5 Review and update your data

6 Train and educate your staff

7 Here’s what else to consider

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