Highlights
- Supply chain businesses often face challenges in managing their demand, supplier efficiencies, inventory, customer data, compliance and regulatory demands, and pricing.
- They need to adopt a long-term master data management (MDM) strategy with the necessary agile implementations, data governance, and reporting systems.
- Mastering MDM will help these organizations optimize their processes and improve decision making, planning, and overall process efficiency.
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Building resilience
Resilient supply chain networks help enterprises ride the waves of disruption.
Most global supply chain networks have seen interruptions due to natural disasters, transportation disruptions, and political and trade disputes. The recent pandemic disrupted supply chains around the world, making it difficult for companies to keep up with the demand for necessities like food and medical supplies. It tested the resilience of top supply chain organizations, most of whom realized that over-reliance on a single supply source made it difficult for them to identify and address issues in real time.
In 2020, UNCTAD reported a negative trade growth of 5% compared to 2019. Although 2021 and 2022 witnessed trade growth overall, it was negative initially. While this was an industry-wide problem, certain sectors like automobile manufacturing saw a huge decrease in production. This impacted suppliers, supply chain planners, and retailers, and resulted in tightening of credit markets, which further impacted supply chains as businesses struggled to pay.
If there’s a lesson to be learned from the pandemic, it’s the need for supply chain resilience and innovation. A Gartner study revealed that enterprises can drive supply chain innovation by building purposeful value-aligned ecosystems, and leveraging hyperautomation, digital twins, and augmented digital intelligence.
This paper delves into how MDM can support the adoption of effective supply chain strategies and solutions to mitigate risks that may arise from disruptions.
Focus on data quality
Companies have a wealth of data at their disposal but there needs to be a razor-sharp focus on data quality rather than quantity.
Multiple data formats and sources though readily available may not be ideal for the analysis required to glean valuable insights. This is where MDM comes in. Today, companies are realizing the critical need for high quality master data, which defines the core underlying blocks of businesses. Master data helps answer questions like who the customers or suppliers are, what their sales channels, equipment, business assets look like, and so on.
While the need for master data is clear, faulty master data across systems and processes within organizations could be a challenge. If not managed on time, it can lead to disastrous consequences. That’s why enterprises need to be wary of faulty data that may fall under one of the scenarios listed below:
Inaccurate labeling: Poor understanding of the master data that arises from it being represented differently in different systems. For instance, a material labeled as a ‘design’ item in one system and as a ‘catalog’ or a ‘saleable’ item in another.
Data disparity: Frequently dispersed data across data silos that operate independently in varied landscapes, including many enterprise systems. The lack of a single point of reference across landscapes that provides a consolidated view of the enterprise data is an added challenge.
Data duplication: Duplication within and between systems that hinder accurate reporting and planning.
Data maintenance for efficient MDM can be an uphill task due to several challenges such as organizational silos and complex business models. While it comes with high associated costs, it is something that companies can’t ignore. Poor data maintenance can lead to invoice and delivery errors, loss of business and revenue to competitors, delayed time to market, and brand dilution. Integrating accurate master and transactional data from a data ecosystem provider will facilitate the required quality analytics to take timely decisions for mitigating risks.
Another aspect is that a wide variety of technology systems support supply chains that require a solid foundation of accurate and consistent master data to optimize and improve the processes. These include enterprise resource planning, transportation management systems, supply chain planning, warehouse management systems, and customer relationship management systems, as shown in Figure 1.
Figure 1: Typical supply chain conceptual master data model
Consolidate data from various sources to define the golden copy—an accurate, trusted version of the record and manage it consistently within the enterprise landscape. An MDM solution is the way to go, making the master data more responsive to change and the data management processes efficient.
Benefits of MDM
MDM enforces the discipline of ‘manage it right from now on’ with business rules, while also progressively improving the quality of pre-existing data.
Supply chains have their own nuances for MDM and require strong capabilities like normalization, match-merge, and cleansing for the corporate master data. MDM provides a platform for enabling these capabilities with business rules to enforce the quality of master data across managed domains over a period of time.
These supply chain environments have critical MDM domains such as organizations, business partners, items, locations, assets, and transportation. A simplified view of the typical conceptual master data model for supply chain environments with domains, entities, and their relationships are shown in Figure 2. This model can be adopted and enhanced as per organizational needs.
Figure 2: Typical supply chain MDM architecture
Figure 2: MDM model for supply chains
With MDM, enterprises can author and master important data attributes that include the following:
Addresses
Supplier risk ratings
Data universal number system (DUNS)
Customer banking information and supplier information
Manufacturer part numbers
Applicable unit of measurement (UOM), aliases,
Country of origin
Sustainability ratings
Regulatory compliance
Enterprises that optimize MDM for their supply chains can realize several benefits. They can:
Aid decision making with high quality data-based analytics to reduce data maintenance and costs.
Improve transportation planning, customer satisfaction, and crisis readiness and management.
Reduce invoice and delivery errors, interest expenses, and improve retention and corporate-level reporting.
Lower the overall purchasing costs from using accurate vendor master data and offer better opportunities to vendors and supply chain partners for price negotiations.
Categorize data better to improve sales teams’ abilities to cross or up-sell for increased sales.
Implementing MDM
For innovative supply chain management, a well-thought-out MDM strategy is key.
At the outset, get a clear picture of the enterprise data available. Multiple versions of data across suppliers, business partners, products, employees, locations, and assets may exist. When crafting the MDM strategy for supply chain management, enterprises need to:
Get business buy-in: Ensure business stakeholders and their needs drive MDM projects, and not IT. If not, it will become just another database that needs to be synchronized.
Think long-term and embrace agile implementation: Have a long-term MDM strategy but implement it in agile steps.
Implement strict data governance policies and procedures: Ensure that appropriate organizational change management processes are in place.
Carefully organize master data for consumption through planning and reporting systems: Poor quality data can pose multiple problems and incur high associated costs.
With an MDM strategy, master data will be more responsive to change and data management processes, making them more efficient. It would also help in maintaining a single version of truth for all the implemented master data objects in supply chains. Moreover, a business team, once trained, can easily define its own rules for maintaining the integrity of the data. MDM has the potential to improve business processes with a disciplined approach to handling master data.
