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Consumer Products | Cross-Industry | Discrete Manufacturing | Process Manufacturing | Retail

How Master Data Makes or Breaks a Transformation

The quality of a digital transformation depends on the quality of data.


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Published: July 5, 2023

Over the past few years, global supply chains have been affected by uncertainty. Climate change, global health concerns, an inflationary environment and geopolitical conflicts have exposed significant vulnerabilities in how organizations operate their supply chains.

In response, CXOs at Fortune 500 companies are expressing a pressing need to accelerate supply chain-related digital transformation initiatives. 

One of their biggest challenges is a lack of supply chain visibility. They struggle to gain a comprehensive view of their capacities, constraints, inventory and alternative options across their networks. As a result, most organizations have been unable to promptly respond to sudden disruptions.

Most large organizations remain predominantly focused on what they sell to customers rather than understanding the key drivers behind end-consumer demand. For instance, many large consumer product companies forecast their future demand based on historical shipments into the retail channel (sell-in). However, this approach often falls short when the underlying market undergoes dramatic changes. Instead, companies would greatly benefit from incorporating sell-out/consumption drivers and adopting advanced analytics and machine-learning techniques for a more responsive forecasting process.

A critical question that comes to mind is: why are companies struggling to prepare for surprises? The most common answer is a lack of comprehensive data. In Gartner’s “Future of Supply Chain, Reshaping the Profession” report, access to reliable and high-quality data is identified as a significant obstacle to initiating a digital transformation. Large organizations tend to echo similar sentiments: “We do not have the data to take the next step in our digitization journey.” “We would love to implement driver-based forecasting, but we do not have enough data.” “We have been working on improving supply chain visibility for years, but our master data is scattered across different systems and often inaccurate.”

While master data is often inaccurate or might not exist, there is immense value in approaching the problem with a fresh perspective. First and foremost, nearly all organizations are data-rich but struggle to extract meaningful insights from it.

Many organizations undertake labor-intensive “data cleansing” projects. But where should they start? Is it worth exploring alternative approaches to traditional methods? How do you ensure that data cleansing is not a one-time exercise but an ongoing process? 

This blog provides seven practical approaches to solving common data challenges and how the o9 platform, with the underlying Enterprise Knowledge Graph (EKG), enables it.

1. Streamlining multiple ERPs or different systems with inconsistent naming conventions

For example, when a product is named differently in different systems (e.g., Sku12, SKU12, SKU-12), the o9 platform can create a planning model that maps transactional data from different approaches to a unified model. At a large electronics client, the o9 platform’s intelligent algorithms were able to detect similar entities across systems and reconcile them within the EKG model.

2. Establishing a system of record for forward-looking master data

Placeholder products must be maintained in master data for planning purposes until they become fully realized products. Within the o9 EKG, mappings between placeholders and fully realized products can be established, allowing forecasts and actuals to be reconciled over planning cycles.

3. Creating affinity graphs/relations through analytics

Demand planning often involves understanding the impact of one product on the demand for others, such as cannibalization or halo effects. When demand for one product increases, demand for another could go down or up. Instead of relying on planners to manually maintain these relationships, the o9 platform can leverage analytics to infer such links automatically.

4. Facilitating collaboration over master data

In some scenarios, such as outsourced manufacturing, master data (e.g., capacities and routings) may originate from other actors in the supply chain. Collaboratively reviewing and accepting this data is crucial to develop meaningful joint plans. The o9 platform, with its high-fidelity representation of the value chain, facilitates the collaboration needed to align on master data before it influences planning.

5. Connecting transactional data with smart analytics to update master data

For instance, when supplier lead times in the master data differ from observed lead times in transactional data, applying smart analytics helps to identify deviations. In the o9 platform, such a lead-time difference triggers alerts to update planning policies, communicate with suppliers, and update master data and inventory policies for future periods.

6. Integrating real-time data with intelligent analytics to update master data

Organizations are increasingly investing in IoT capabilities to obtain real-time information from their supply chain networks. Imagine a sensor at a factory line or from an inventory location. By leveraging the o9 platform’s cloud-first architecture, robust analytics and dataflow pipelines can be built using the cloud provider’s data warehousing and management services. These pipelines ensure master data remains up-to-date by incorporating real-time data.

7. Leveraging tribal knowledge to establish a planning system of record

In some instances where there isn’t an existing system to maintain planning-related data, organizations may rely on tribal knowledge stored in spreadsheets or stand-alone systems. o9’s EKG enables the accurate representation of the supply chain network, allowing attributes and associated parameters to be built gradually over time. One of the world’s largest retailers has successfully utilized this capability, creating a planning system of record close to where they made planning decisions.

Effective master data management and maintenance are crucial requirements for digital transformation. Organizations need a platform that offers a comprehensive representation of the enterprise and possesses the technical capabilities to support these efforts. The o9 platform, with its underlying Enterprise Knowledge Graph, enables a digital model of the entire enterprise, including interconnected customer and supplier relationships, functioning as a digital twin of the supply chain, making it an ideal solution for organizations seeking to embark on a digital transformation journey.

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