Guest Post

How Can Master Data Management Improve Supply Chain Operations?

April 23, 2020

Written by guest contributor Megan Ray Nichols, a freelance stem writer and the editor of Schooled By Science. For more from Megan, you can follow her on Twitter or subscribe to her blog here.

In an increasingly globalized and complex supply chain, accurate and comprehensive data is almost always necessary for informed decision-making.

However, supply chain data can often be siloed off between departments or individual systems — like fleet management tools, CRMs, warehouse platforms and other technology. As a result, many analysts will make decisions based on limited information, which can easily lead to mistakes like poor forecasting and route planning.

In these cases, centralizing data management can be a huge boon to supply chain operations.

What Is MDM?

Master data management (MDM) is the use of tools and procedures to integrate information from businesswide sources into a master data set. It offers a single point of reference. In the same way a master file provides a template for all copies, MDM seeks to create a central repository.

An MDM strategy can help synchronize information and reference points across departments to ensure analysts can access the same data. Typically, these systems are implemented to help businesses achieve company-wide data completeness and consistency, among other benefits.

How MDM Can Improve Supply Chain Operations:

MDM is primarily useful in boosting data transparency. This is the foundation of any successful supply chain operation. If the information is collected and relevant, it should be available — no matter the department, division or team a given analyst is working under.

A centralized data strategy will also help a supply chain business avoid reduplicated information. It will allow analysts to have faith that the data they have is as accurate as that stored anywhere else in the company.

A robust data management strategy, like MDM, will be especially important if your supply chain company wants to adopt a more aggressive data-collection approach, like those made possible by the rise of Industry 4.0.

Integrating an RFID or IoT data-collection solution will significantly increase the amount of data a business in the supply chain collects, as well as the risk of errors and redundancies. Having an MDM strategy and workflows in place can help ensure that info is stored in a way that is transparent, highly usable, coherent and compatible with existing systems.

Master data management can also allow for better forecasting and improve the usage of resources and sustainability. One study by analytics consultant Concentra found that supply chain companies carry up to 40% more stock than they need to. Better data management and improved forecasting can help supply chain companies only carry the inventory they need to meet expected customer demand.

Tips for Integrating MDM:

To successfully integrate MDM, your IT team or technical staff will need to select a master data hub and any tools necessary to make the system work. You and your team should know how to choose the right data management platforms and APIs for your specific business.

Your IT staff will need to prepare to tackle the challenge of extracting, collecting and standardizing data from all systems across your supply chain operation. These systems may be CRMs, IoT platforms and enterprise resource planning systems, as well as third-party sources like supplier systems, weather reports and social media. You may need additional tools to facilitate the transfer and storage of information from these platforms, so it will be useful when combined with the master data set.

To ensure the master data set can be smoothly updated, you will also need a central team that facilitates updates and handles conflicts.

Improving Supply Chain Operations With Master Data:

Master data management can help supply chain companies avoid siloing off valuable information and improve business transparency, ensuring that analysts can make highly informed decisions. As a result, companies can improve their demand forecasting and reduce inefficiencies.


A big thanks to Megan for this guest contribution!

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