Why do you need a digital supply chain twin? Complex global supply chains are a standard feature of major international companies operating in today’s connected world. Being able to effectively track, collate and manage data from all the steps in the end-to-end product flow is critical for visualizing the bigger picture and mitigating supply chain threats as they emerge.
With each internal and external stakeholder potentially using multiple informatics platforms, all complex supply chains suffer from the same problem: the data is scattered across a range of different systems, including ERP, MES, WMS, TMS, QA/QC, CRM, SRM and many others.
→ Having a highly distributed network of specialized systems is simply an unavoidable fact of life for large businesses striving to optimize the delivery of their various commercial operations.
Not only do different business functions require dedicated technologies, but as organizations expand, additional systems can be brought into the mix via mergers and acquisitions, often even duplicating existing capabilities. For instance, many life science companies have multiple instances of ERP systems across different departments and sourced from different vendors, all contributing to the wider supply chain without being harmonized.
As a result, it can be extremely difficult for companies to capture an accurate and up-to-date overview of all the digital assets in their end-to-end product flows. It’s not unusual for value stream teams to resort to manually creating representational value streams based on experience, gut feeling and hands-on trawling through the available data, spread across the different contributing systems. Needless to say, this is both a highly time-consuming and demotivating task—and one that requires constant monitoring as new information becomes available.
→ Due to the changing and dynamic nature of supply chains, those value streams are often already outdated as soon as they are published!
In recent years, many of the big movers and shakers have implemented initiatives to establish centralized systems for consolidating and storing the structured, semi-structured and unstructured data that flows from their diverse list of internal and external sources. Whether companies set up a data lake, data warehouse or data factory, we believe this trend represents a positive evolution since such infrastructure undeniably makes it easier to identify data availability and enhance data accessibility. Nevertheless, centralized consolidation is only one part of the data jigsaw puzzle.
Implementing a data lake, for instance, does not mean that your data will automatically be connected, nor that data originating from heterogeneous source systems will in any way be harmonized. Nomenclature and standards will rarely map in a 1:1 fashion between platforms, and if the same product has a different identifier in two different systems, channeling the divergent data streams into a single lake isn’t going change that.
→ Being built to serve as a giant repository for your data means that a lake simply isn’t designed (or intended) to trace supply chain flows in a multi-node or end-to-end way.
On the other hand, this is precisely what a digital supply chain twin is designed to achieve. It is the perfect way to complement the functionality of a data lake (or warehouse or factory).
The Axon digital supply chain twin seamlessly integrates in both directions, connects data automatically via its proprietary machine learning models and provides a single digital representation of your entire supply chain that is continuously in sync with reality. Implemented alongside a comprehensive data repository, this combination enhances the utility of your existing systems while representing a significant value enabler for your wider organization.
