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ASCM Insights

The Internet of Supply Chains (IoSC), a Model in the Cloud


Imagine that you’ve captured all your supply chain data in real time, including your customers’ demand and preferences, suppliers’ capacities and delivery schedules, and all the transaction data as your plans are executed. Would this visibility of your end-to-end supply chain status and performance help you make better decisions? If you have a supply chain, the answer should be yes.

The greater challenge is not just collecting all this data, but finding a way to provide shared meaning to the data in the context of where it is being used by you and your suppliers, customers, and other business partners. It can be extremely difficult to integrate data from multiple sources and turn it into meaningful indicators to take collective action. Collaboration in an open ecosystem is essential in today’s digital world.

Why the Semantic Web Should be on Your Radar

Digital supply chains are the future and their data is at the center of value creation.  However, if you can’t turn that data into something meaningful for a wide range of users then it is just a vast, difficult to navigate sea of ones and zeros. 

Three emerging concepts that should be on your radar going forward are the semantic web, linked data technologies, and blockchain. Collectively, these make up the Internet of Supply Chains (IoSC). The IoSC is not even a web, but it is the webbing of data that connects a value network based on a conceptual data model. It is at the center of the web and at the edges simultaneously. It puts data at the center of value creation in an ecosystem of partners from suppliers to customers to providers delivering cloud-based services such as advanced analytics as-a-service (AAaaS).

What’s needed? A shared, secure data space where the conceptual data model resides and provides the language (linking) of data logic. The data science of semantic data management is used to provide meaning, not just a collection of stored data in a big data repository. What’s NOT needed? A data warehouse in the cloud and point-to-point data exchange based on proprietary models.

Legacy connectivity technologies of electronic data interchange (EDI) have challenges in speed, cost, and performance in this global and real time world of today. Such legacy interactions are characterized by point-to-point or protocol-based connectivity where a dominant player in a business ecosystem forces suppliers into supplier portals or to use EDI for data exchange. Supply chain decision makers need to understand the transactional value stream, the cost attached to it, and the value that data in an isolated process can add to e2e processes, in real time.

The value of an actively optimized supply network is only as good as the capability to visualize, analyze, and make predictions based on data from across the network. That value is then amplified to create real time fact-based decision support. This data feeds algorithms and artificial intelligence to augment staff in decision support, automate tasks, and increase productivity in a variety of ways. This is also known as the cognitive supply chain.

What exactly are we getting from the IoSC web of data we weave? Greater connectivity of meaning rather than just data between partners provides for better integrated business planning, more real-time response between planning and execution, and significant power added to the analytics that help predict and prevent disruptive supply chain events.

The digital giants (Amazon, Google, IBM, etc.) are already successfully applying IoSC with these new technologies. They are standardizing the language of the Internet of Things (IoT). You touch these technologies every day when using search engines, social networks, online shopping and banking, etc. You see how they track and analyze historic data and present context to provide you with better information.

Developing Internet of Supply Chains Proof of Concept 

The APICS Special Focus Forum “Internet of Supply Chains” was founded last year to create a learning platform and to run proof of concepts (PoC). The efforts were led by Nokia, Ericsson, Infineon, Rhode & Schwarz, SAP, Fraunhofer, and eccenca.

Has progress been made? Yes! The first proof of concept projects were successful.  The group worked in a co-innovation mode, first focusing on the order process between buyer and supplier. In each case, the supplier was Infineon and buyers were Nokia, Ericsson, and Rhode & Schwarz.

The PoC used a shared logic language (ontology) or semantic data management scoped for the order process between buyer and supplier. Current EDI protocols have limited data scope and expanding that scope with EDI is slow and expensive. The PoC went beyond the EDI data scope and including product master data. The previously manual process of synchronizing updates in product master data has been eliminated through full automation and happens at near-zero cost. Implementation of an Industrial Data Space in a sandbox environment enabled the proof of concepts and an IT reference architecture necessary for implementation.

Next steps for IoSC

So what is next in developing the IoSC? Pilots will continue in the sandbox environment created by the IoSC Special Focus Forum team. Each company in the team is working with their respective IT organizations to implement the IT reference architecture. Of course, there are many legacy solutions such as ERP that will co-exist with the new technology. The team will analyze the impact on next generation ERP, supplier connectivity and collaboration, and master data management (just to name a few). Also, the thinking of this Special Focus Forum is benefiting the revision of SCOR (Supply Chain Operations Reference Model) now being developed and soon to be released as version 12. SCOR can make an impact enabling the digital supply chain to support digital business.

With IoSC, intelligent collaboration –  rather than simple data exchange – becomes possible and brings greater value to all supply chain partners.

To learn more, check out the following:

Linked Data 

IDS and Semantic Science

The Predictive Enterprise & Data Science

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