The Digital Supply Chain Twin – An Emerging Trend to Reveal Interconnected Insights
There is no single formula for supply chain digitization, and you may be like many companies who are either exploring or actively implementing any combination of the trends we outlined in our previous blog. In its recently published report, The 2019 Top Supply Chain Trends You Can’t Ignore, Gartner maps out eight supply chain trends. One thing is very apparent – change is coming at a rapid pace.
Per the Gartner report, “By 2023, at least 50% of large global companies will be using AI, advanced analytics and IoT in supply chain operations.”
It’s an impressive number that may prompt you to redouble your efforts to bring these technologies into your own enterprises – or risk falling behind the competition. But just because emerging technologies are generating massive amounts of buzz right now, it doesn’t mean success is guaranteed. If you’re considering supply chain applications and technology, beware of new solutions looking for a problem to solve. It would be wise to evaluate each technology’s impact on business objectives to determine its ability to move the needle regarding the business objectives.
When considering these trends, leaders are prudent to place organizational energy and efforts towards technologies that have broad applicability across industries and use cases. The digital supply chain twin, one of Gartner’s eight emerging supply chain tech trends, is a great example.
Supply Chain Digital Twin
According to Gartner, “The digital supply chain twin is a digital representation of the physical (often multi-enterprise) supply chain. It is a dynamic, real-time and time-phased representation of the various associations between the data objects that ultimately make up how the physical supply chain operates.”
It’s an end-to-end time phased model of your supply chain. On the horizontal level, your supply chain is recreated in a digital space in a farm-to-fork fashion encapsulating your suppliers, manufacturing locations, distribution facilities, transportation lanes and customer locations. Vertically, it contains a view of each facility. Models can be built from an extremely fine level of granularity to years in the future to allow for the evaluation of different tradeoffs. Reviewing and evaluating all the costs flows allow for a complete tradeoff analysis. Decisions driven in this manner by nature are interconnected.
Evolution of Supply Chain Modeling
In 1996, a global chemical company used supply chain modeling to determine the time phased production plan that provided the lowest cost to serve option for production and distribution to their customers. They considered their customers’ phased demands in relation to the various assets that could produce the chemicals, the time phased capacities of those facilities and all the costs of sourcing, conversion, duties, and transportation. Solvers would then derive the optimal answer as to how much to produce in each plant and when. Those sourcing rules were loaded into their ERP system for execution.
That is a small example of a part of a supply chain – far from a comprehensive digital twin being discussed today. The catalyst for being able to develop a comprehensive digital supply chain twin has been the explosion of cloud-based computing infrastructure. The scale of what practitioners can model has greatly expanded, causing greater insight into interconnected decisions.
Today, one major automotive manufacturer has deployed a digital twin of their in-bound supply chain model and were able to cut their in-bound transportation spending by over 11%. That level of savings is far from unique and companies report an average savings of around 10%.
Enterprise Level Systems for Enterprise Decisions
Organizations for decades have deployed ERP systems to reduce operating costs, drive efficiencies and standardize processes. While those systems are generally optimized upon setup, the master data is often not revisited as the business’s operating environment changes. Sales orders may be fulfilled and shipped from locations that are not the most cost effective and upstream supplier sourcing decisions may not optimized, or even taken into account.
True enterprise-level decision making is still missing for many organizations; hence the key reason why the end-to-end digital supply chain twin is an emerging trend. The ability of the digital twin to sit above these systems provides insights into interconnected decisions that are inherent in supply chains. It allows the flexibility for performing scenarios that can be evaluated without having to necessarily conform to the design constraints of your current supply chain.
While no future predictions will ever be 100% accurate, within the supply chain space, we believe, Gartner keeps its finger on the pulse of what’s ahead. Supply chain digitization has become an imperative and the establishment of a digital twin should be considered within the context of your organization’s supply chain design and enterprise decisioning process.
1Gartner The 2019 Top Supply Chain Technology Trends You Can’t Ignore, Christian Titze, Andrew Stevens, 11 March 2019
The Figure 1. graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document.