The Importance of Input Data in the Logistics Industry

In today's economy, whether large corporations or non-profit organizations, data is the lifeblood that helps companies operate and manage. A recent study by Hackett Group showed that more than 70% of companies are planning to implement technology and data analytics applications into their business operations. This further proves that input data greatly influences the company's strategic business decisions and its success potential, especially when the company operates in the logistics sector.

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What is Input Data?

Input data is defined as the primary, essential data for businesses and business units and is used for specific software, e.g., customers, suppliers, products, prices, costs, and services. The types of information used as input data are diverse and depend on various industries, organizations, and software. However, not all quality input data is sufficient to create a good data source for the business. This depends on the quality and how the business organizes and manages its input data.

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How to Control the Quality of Input Data?

Data quality can be measured in many ways. Generally, a set of input data is considered good when it is suitable for its purpose. The quality of input data is evaluated based on the following criteria:

• Completeness: Are the values complete?
  

• Validity: Does the data truly comply with the rules?
  

• Uniqueness: Is the data duplicated anywhere?
  

• Consistency: Is the data consistent with other data sources?
  

• Timeliness: How realistic is the data representation?
  

Why is Input Data Extremely Important in the Logistics Industry?

Input data is considered the foundation that helps businesses build an operational system based on information collected over time. Most companies allow only a small group of people to access input data to keep it accurate and avoid misuse, leading to unintended consequences.

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To operate a logistics software solution effectively, data is the key to every transaction, from production, customer search to price control. A simple example is deciding to sell a completely new product to an existing customer. In this case, the customer and product are already in the data storage, but the relationship between the customer and the business is not stored. To complete the transaction correctly, the software system needs to store accurate order quantities and shipping modes. Customer historical data is crucial to help sales staff know information such as which supply sources or distribution centers help reduce costs and optimize the transportation process. All this data needs to be stored.

On the contrary, erroneous or faulty data can lead to business operation errors. Imagine if the Transport Management System (TMS) or Route Optimization System (ROS) uses incorrect or outdated data (e.g., vehicle numbers, warehouses, carriers, total weight, traffic conditions...), many adverse scenarios could occur. For example, if the transport coordinator adds many wrong customer addresses or incorrect cargo weights, the software will not be able to provide accurate routes or, worse, a completely wrong route. Additionally, entering incorrect weight information could cause the system to deliver orders on inappropriate vehicles, significantly increasing costs and time, negatively affecting customer relationships. Therefore, data accuracy is an indispensable part of logistics and the supply chain. To avoid errors from using poor-quality data, an input data management strategy is always important.

How to Avoid Using Incorrect or Faulty Input Data?

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For a logistics company, there are many types of input data on a large scale - data from ERP software, CRM, route data from the fleet, personnel data, suppliers... A good input data management strategy should be built on the following principles:

• Governance: Guidelines in company system management, policies, regulations, and quality required for a reasonable input data set.
  

• Measurement: Quality check of data and subsequent improvement opportunities.
  

• Process: Selecting the right people to manage input data.
  

• Technology: Technology in input data management.
  

There are several important steps to storing input data, e.g., organizing data governance, building a data storage system... Refer to the article  “10 Steps To Store And Prepare Master Data” to learn how to manage data effectively.

Conclusion

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Input data is an important part of every company today, regardless of the industry they operate in. However, not all organizations can overcome the challenge of storing and maintaining an effective data system. Managing input data is an effective way to control workflows and business analysis, especially for companies in the logistics industry.

With good input data quality, you can fully utilize Abivin vRoute to reduce logistics costs and manage fleets efficiently.

Visit https://abivin.odoo.com/vroute to learn more!

References:

1.https://mccoy-partners.com/updates/the-importance-of-master-data

2.https://www.inddist.com/article/2011/08/how-master-data-affects-supply-chain-planning

3.https://profisee.com/master-data-management-what-why-how-who/

4.http://spendmatters.com/2013/05/09/the-importance-of-master-data/

5.https://www.bisnode.com/globalassets/global2/pdf/the-truth-about-data_v3.pdf

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