XenonStack Data Insights on DevOps, Cloud and Data Science

Master Data Management in Supply Chain

Written by Dr. Jagreet Kaur Gill | 05 September 2024

Master Data Management in Supply Chain provides businesses a single authoritative view of information and eliminates expensive inefficiencies caused by data silos. This is why it is called that Master data management feeds your business with better data. Now, the question is why should we use it in Supply Chain or what are the best practices to do so. Well, to understand this fact, first of all, you need to understand both of the terms, which are elaborated in the subsequent section.

Master Data is the core that refer to the business information shared across the organization. Click to explore about our, Master Data Management

What is supply chain?

It consists of the entire network of entities linked directly or indirectly in serving the consumers or customers. It comprises various things, such as vendors that supply the material, producers who convert the content into products, and warehouses to store the products, distribution, centers, retailers, etc.
  • It consists of individual contributors involved in creating the product. It underlie this chain of product creation without the supply chain producers wouldn’t know the requirements of consumers and what they need and when they need it.
  • Any deficiencies in it can affect the capability of a producer to withstand the competition, as there are no improvements that a producer can make. And so, using Supply Chain Security best practices becomes essential.
  • Many organizations want their model to have capabilities of its six models tha compromise efficiency, fast, agile, continuous,custom-configured, and flexible.
These factors ensure high asset utilization and end to end efficiency. The productive that are in now have taken the basic models and added certain features to meet their specific needs. Unlike the efficient models, these models need human interaction, which makes the system prone to error as from outside, it is difficult to define which model to use.

Drive data for smarter choices with a fully managed database for modern app development. Download to explore the potential of Azure Cosmos DB

What is Master Data?

Master data is a core data that refers to the business information shared across the organization. It consists of the structural and hierarchical reference, which is essential for a particular business. Eventually, it remains constant; however, we need it to update regularly. Nowadays data is valuable. When information is managed correctly, it supports a company to achieve a particular set of goals as it displays all data, which in return provides quick and accurate access to the data.
  • The information present in the master data differs as per the industry type from industry to industry. This is the reason why Supply Chain Master Data Management is essential.
Master data comprises four major components:

Reference Data

Reference data are classified schemas (data organized in the form of classes or groups) refer to applications, data stores, and processes. It includes code list, status, codes, flags, and product hierarchy.

Enterprise Master Data

It represents the single source of market data used across multiple system applications and processes regardless of locations. Enterprises often store and retrieve the crucial. Doing this helps to identify and maintain a set of master data across the enterprise efficiently.

Market Master Data

On the flip side, the enterprise master data, the single source of market data is used across marketplaces regardless of the locations. Usually, master data is non-transactional, but in some cases, information is contained in the form of orders and receipts. It is considered as master data.

Master Data Management

It is the core process used to manage, centralize, and organize data according to business marketing and operations.
Big Data Security is the collective term for all the measures and tools used to guard both the data and analytics methods from attacks. Click to explore about our, Big Data Security and Management

Why Master Data Management in Supply Chain is important?

  • Many organizations face data-related issues as they grow and prepare for data insights. And it results in inefficiency if the prepared data is error-prone.
  • It consists of tools and management that coordinate and organize data across the enterprise, which helps to access accurate information in the organization.
  • It helps in managing the critical portion of the data and provides data integration as a single source.
  • It may be stored in a single repo, but data is stored at various places in the organization when accessed by multiple functions.
  • As multiple applications access master data, errors in one application can also cause errors in all other applications that access master data.

 

Challenges of Master Data Management in Supply Chain

Implementing Master Data Management (MDM) in supply chains presents several challenges that organizations must navigate to achieve effective data governance and operational efficiency. Some of the most common challenges include:

  1. Data Silos: Many organizations struggle with fragmented data stored across various systems and departments. This leads to inconsistencies and a lack of a unified view of critical information, making it difficult to make informed decisions. For instance, if supplier data is stored in separate databases, discrepancies can arise, impacting procurement and inventory management processes 
  2. Integration Issues: Integrating diverse data sources into a cohesive MDM system can be complex. Organizations often face difficulties in standardizing data formats and ensuring seamless communication between legacy systems and new applications. This integration challenge can lead to delays in data availability and hinder real-time analytics 
  3. Resistance to Change: Implementing MDM often requires changes to existing workflows and processes, which can meet resistance from employees accustomed to traditional methods. Change management strategies are essential to ensure buy-in from stakeholders across the organization 
     

  4. Data Quality Concerns: Poor data quality can significantly impact supply chain operations, leading to inaccuracies in forecasting, inventory management, and order fulfillment. Organizations must invest in data cleansing and validation processes to maintain high-quality master data 
  5. Compliance and Regulatory Challenges: As supply chains become more complex, ensuring compliance with regulations regarding data privacy and security becomes increasingly challenging. Organizations must establish robust governance policies to manage sensitive information effectively 

Master Data Management for Supply Chain

It stays at the core of the supply chain. As the consistency of data and performance are the primary factors, Industries with its are looking or appropriate ways to conduct it. It can help to achieve data consistency, these are highly dependent on MDM to track and manage the data. Image it helps in cleansing and validating the data and provides an exact image of customer products and suppliers.

  • It provides homogeneity and transparency, and it acts as a bridge between the supplier systems.
  • The better the transparency is, the more convenient the cut cost and, in result, better revenue generation.
  • Helps with cross-organizational data alignment and achieves reliable data for the enterprises.

These days as an increase in the data domains consisting of data of products, locations, finance, and employees, most organizations maintain multiple MDM technology. And this leads to various challenges especially while using Master Data Management in Banking Sector.

Whether you want to constantly move your data, deploy applications, accuracy, or ensure enterprise security, XenonStack is here to help. Check out our Managed Analytics Services and Solutions

Benefits of Master Data Management for Supply Chain

The most prominent feature of it consists of the elimination of duplicate data and feeding accurate information, which helps monitor integrity. It also offers data consistency (changes that should be made on data) and eliminates redundancy for better and consistent workflows, for better Supply Chain Master Data Management.
  • Practical data analysis is essential to discover useful information to prepare a better business model regarding what type of data goes into the management for beneficial business needs.
  • Providing multiple platforms, as businesses run on various platforms, should be accessible to the data; it can be done, and data is made available on physical, online, and cloud.
  • It comes with efficient backup options to retrieve the lost or corrupted data during the times of disaster.
  • Eliminates the sparse quality data by making the data more securable and can be accessed from a single point.
  • The core features of data management include cloud, BPM(business process management, and data integrity.
  • Different applications, information, and the process can be integrated at a faster rate.

Best Practices for Implementing MDM in Supply Chain

To successfully implement MDM in supply chains, organizations should consider the following best practices:

  1. Stakeholder Engagement: Involve key stakeholders from various departments early in the MDM implementation process. Their insights can help identify specific data needs and ensure that the MDM solution aligns with organizational goals 
     

  2. Technology Selection: Choose an MDM solution that fits the organization's specific requirements, including scalability, integration capabilities, and ease of use. A multi-domain MDM platform can provide comprehensive visibility across different data domains 
     

  3. Establish Governance Policies: Develop clear governance frameworks that outline roles, responsibilities, and processes for managing master data. This includes defining data ownership and establishing standards for data entry and maintenance 
  4. Ongoing Maintenance and Training: Ensure continuous monitoring and maintenance of the MDM system to adapt to changing business needs. Providing regular training for employees on best practices for data management will help maintain high-quality master data over time
  5. Leverage Automation: Utilize automation tools within the MDM system to streamline data entry, validation, and cleansing processes. This reduces manual errors and enhances overall efficiency

Future Trends in Master Data Management

  1. AI and ML Integration: AI and ML are enhancing MDM by automating data cleansing and predictive analytics for demand forecasting, improving data quality and anomaly detection.

  2. Focus on Data Governance: With stricter data privacy regulations like GDPR, organizations are prioritizing strong data governance frameworks to ensure compliance and operational efficiency.

  3. Cloud-Based MDM: Cloud solutions offer scalability, flexibility, and lower costs, allowing organizations to access and update master data remotely.

  4. Real-Time Data Processing: Real-time MDM capabilities enable faster decision-making with up-to-date data insights, improving responsiveness.
View full post