Eliminating Data Silos for Supply Chain Analytics

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Meet the Experts

SAPinsider will be publishing a research report, Supply Chain Planning in The Cloud this month. As the survey for this research come to a close, many interesting insights emerge. One of the key ones is how SAPinsiders view supply chain planning as an important tool to build business resiliency and agility. Since the top business drivers focus on resiliency and agility, strategies that SAPinsiders are formulating to address business drivers also revolve around resiliency and agility. Building end-to-end visibility (67%), Developing a single source of truth for the end-to-end supply chain (39%), and leveraging real-time insights (39%), all pertain to the resiliency and agility and support associated business drivers, as shown in figure 1.

Data Source

Eliminating Data Silos in Supply Chains

Fragmentation of data remains a key obstacle on the path of integrated supply chain planning. Many organizations have invested in point systems for planning various elements of supply chains over the years, have legacy systems, and also have data pertaining to supply chain residing in enterprise systems like ERP, CRM etc. With the advent of Big Data, the number of data sources and opportunities for data fragmentation have also increased. Supply chain data is therefore fragmented in data silos across multiple systems and data sources across the enterprise. This is one of the factors behind SAPinsiders selecting creating a “single-source-of-truth” as one of their key strategies as it pertains to supply chain planning. And a critical step in supporting this strategy is to eliminate supply chain data silos.

This example screengrab from Pyramid Analytics Decision Intelligence solution is a good representation of how structured and unstructured data sources may be fragmented across various data sources. On the far left side you can see various types of data sources, ranging from JSON to SQL server, as well as open-source tools like R. Different pieces of information, may be distributed across these data sources, thereby hindering the “single-source-of-truth” view that is needed for optimal supply chain planning. While this example screengrab is for a contact center data silo, this illustration perfectly mimics data silos and data fragmentation for supply chains as well.

Supply Chain Data Silos

What organizations need, as a single source of truth, is the data source on the right-hand side of the visualization above. Many organizations try to alleviate this challenge by duplicating all the data sources into a data lake and creating a single source of truth from that, using the conventional ETL process. This approach, while may work, creates another big issue. In this SAPinsider article, we shared a quote on the perils of “information latency”. This is a critical aspect that we did not account for in supply chain planning for a while. This information delay is one of the reasons that many supply chain planning solutions were not able to address supply chain disruptions in the last few years. This is the reason real-time supply chain visibility and planning have become a critical focus.

In an era where timely insights are becoming increasingly critical, as highlighted by SAPinsiders in their responses to our survey on supply chain planning, this siloed approach will not work. And this is why the ability to build data models that can help eliminate these silos, without unnecessary and inefficient data duplication, is critical. A true single-source of truth can be created using a data model that leverages live data connections to data sources. The example screengrab shows a data model that can interact directly, with live data sources, without any need for duplication.

Data Silos

Advantages of having such live query data models go beyond the duplication as well as latency aspects. It is also about creating data threads leveraging data models that connect multiple data sources to create one “single-source-of-truth”, identified as a key strategy by SAPinsiders. This approach can also highlight discrepancies that may exist between data sources (ex: Total supplier spend across two different sources for a supplier), help eliminates redundant data sources which can then act as an input for systems portfolio optimization, and can also help build true supply chain control tower views. While the article uses a hands-on example from Pyramid Analytics, the good news is that data integration tools and solutions abound in terms of offerings both from SAP as well as SAP partners. SAP data services, bundled with SAP BTP is a comprehensive data management tool that helps connect data sources. Tools from partners like APOS can also help you leverage data connectivity to eliminate data silos by leveraging tools like SAP Analytics Cloud. MDO solution from Prospecta is another example of a tool that can be leveraged for eliminating data silos. These are just a few examples but the gist is that technology should not stop you from embarking on building this critical foundational capability for optimal supply chain planning.


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