The Digital Twin for an Organization (DTO for Supply Chains)
There is still a lot of confusion as to what a digital supply chain twin is and what capabilities it must have. The digital supply chain twin is becoming a reality to support higher quality decision making in an ideal world in real time. Supply leaders responsible for integrated supply chains can use this BVL blog entry to better understand what constitutes a digital supply chain twin.
Gartner first mentioned the term “digital supply chain twin” back in 2018. The digital twin a digital representation of the physical supply chain was originally created out of the world of supply chain planning and evolved over time into a more integrated end-to-end digital representations of an entire supply chain of plan, source, make deliver and return. Over the last couple of years, interest in the digital supply chain twin has grown as organizations look to digitize their supply chains and consequently their decision making for supply chain planning, but companies will need to look further how to integrate other supply chain functions such as sourcing, manufacturing logistics and transportation as well as how to integrate their economic ecosystem into their digital twin unlock the whole potential of value generation.
In the real world, the physical supply chain is described through the relationships between the entities and functions that make it up. In the digital world, we need to show these relationships in sufficient enough detail and accuracy so that any high quality and action orientated decisions can be made by using this digital representation of the real physical supply chains. Advanced predictive and prescriptive analytics can be applied to the digital twin of an organization to have real time data linked to core business management processes such as OTC or P2P to create different scenarios linked to different business outcomes incorporating opportunity and risk management, approve scenarios or different plans and order fulfillment. The essence is to work through high supply chain complexity with a digital twin as an enabler to act and to create better decision making as added value linked to tangible business outcomes. The digital supply chain twin is a key enabler where data from various sources and systems are linked into a meaningful representation of the supply chain.
What does this mean and what are the capabilities necessary for there to be a resulting digital supply chain twin?
A digital supply chain twin must be data-derived and must be created from granular data. Significant history of this data is used to initially create the digital supply chain twin which means master data management and clean data is the starting point before any digital twin can be developed. To keep the digital supply chain twin in sync which is a key characteristic of a digital twin with the real world, the near-real-time transactions and events data need to be fed in accurate manner into the digital supply chain twin visible for supply chain people to make decisions and to take actions.
A key capability for the digital supply chain twin is the ability to visualize the supply chain or a relevant part of it digitally. It is through this visualization that users have full visibility across and through the physical supply chain. As the digital supply chain twin is kept in sync and is integrated with the physical supply chain in near real time, this supply chain visualization provides near-real-time visibility into what is happening within the supply chain. The vast majority of used technologies for digital supply chain twins are using a graph model to represent the physical supply chain.
To summarize, the key benefit of any supply decision making improvement initiative is to enable higher quality of decision making to take the correct actions to deliver value. If decisions are of a higher quality, then resources are better used and deployed to achieve business outcomes, thereby creating more value for the business as a whole. As part of this goal to create a better decision-making model, a company’s key objective for improving its end-to-end supply chain decision making is to reduce the number of models used across their End-to-End supply chains. Having more than one model degrades the quality of any End-to-End decision making, increases the complexity and results in higher cost. Organizations can use a digital twin for all levels of an integrated end-to-end supply chain decision making, from strategic to tactical through to operational and executional. Appropriate predictive and prescriptive analytics, including machine learning (ML) and artificial intelligence (AI), can be applied to the digital supply chain twin, enabling aligned and to various degrees automatic decision making.
The impact of the digital supply chain twins is transformational as it enables end-to-end decision making connected and integrated with the real End -to- End supply chains and ecosystems. A digital supply chain twin should be in focus of any digital supply chain effort within organizations as a means to an end. It is through this twin that a company can drive the alignment of its decisions both horizontally and vertically throughout its supply chains and in the entire economic ecosystem. This alignment is key in the effort to significantly improve decision making and business value.
I will be attending the annual BVL International Supply Chain Conference and look forward to moderating a session related to the “Digital Twins of Supply Chains” on October 21st.
We will have excellent companies and people with Sven Markert from Siemens, Patric Spethman from Marco Polo and Volker Ruegheimer from Volkswagen presenting their best practice business cases of digital twins.
I look forward meeting you in the session.
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