Why CIOs are turning to digital twins to future-proof the supply chain
By Debajit Sen, Principal Architect, Calsoft
A disruption in Shenzhen has the potential to throw off a company’s entire launch plan in San Francisco. All the data available through dashboards, control towers and analytics may give you visibility into your supply chain across many levels; however, they do not correspond to the timeliness of a disruption itself.
Instead of just “what’s going on?” The focus now is on how technology will factor into every aspect of a company’s supply chain operations. Rather than waiting for problems to develop, businesses want to be proactive about them.
Digital twins are becoming an effective way to do this.
Unlike conventional analytics tools, which are structured to give a snapshot view of past performance, a supply chain digital twin utilizes artificial intelligence to maintain an evolving digital representation of physical business operations in recent history. Digital twins represent the present status of warehouse operations, logistics, inventory placements, and associated assets within the supply chain. In addition, Digital Twins enable businesses to run stress tests on predictions and evaluate their performance prior to real-world implementation in a low-risk way.
For CIOs, Digital Twins are beyond just an operational enhancement feature. They represent the interfaces between a company’s data architecture, artificial intelligence (AI) strategy, edge computing services, and the ongoing integration of their enterprise applications. The development of Digital Twins necessitates the implementation of an effective governance model, scalable technical infrastructure, and cross-functional integration of operational resources. Digital Twins can provide a competitive advantage when developed correctly and used appropriately; however, if not developed correctly, they can end up being costly digital simulations that have little impact on the business. An increasing number of organisations are deploying Digital Twins as pilots and they are fast becoming Board-level conversations, often instigated by the CIO.
Digital Twins Move Supply Chains from Reactive to Predictive
Digital twin models that represent digital copies of everything from sensors and devices to actions taken on those sensors or devices have become an integral component of supply chains. Digital twins are a model of a supply chain, where all of the information about the supply chain is available in one complete view, through all of the sources of signal data (e.g., ERP systems, WMS, and MES), throughout real-real-time, and the digital twin provides a means to analyze your entire supply chain simultaneously for all of the best practice methods of how those signals would affect your supply chain environment.
The ways in which digital twin models differ from traditional models are that they can be run as what-if scenarios and simulated by creating models based on cause-and-effect. Examples of this would include a demand increase in volume of supply chain product in a short time frame, or changes involving a facility shutting down because of severe weather conditions. The model will look at how this will affect a supply chain’s inventory levels, shipping schedule and delivery date, and even worker availability if any. All of this allows companies to move their decision-making process away from reactive firefighting to the more proactive planning process.
For a CIO, using a digital twin model eliminates the historical siloing of enterprise architecture of supply chain-related data. Data that once lived in a silo and was not acted upon becomes contextually available. AI models will not just be trained on historical data, but validated within future simulated environments. This closes the loop between signals, insights, and execution.
Importantly, digital twins serve as a means of safely automating processes. Before an AI agent can operate in the real world (e.g., part of a continuous cycle), both the risk and the ability to innovate quickly are Simplified through the use of a digital twin. Before creating an AI agent, the most common process is to train, test and enhance the AI agent in a digital twin, minimizing risk while speeding up time to market.
From Simulation to Intelligent Automation
Once established, a Digital Twin becomes more of a centralised platform for continuous intelligence than just a visualisation tool. As organisations frequently run scenario simulations (e.g., to run scenarios to simulate higher demand than anticipated, or how a supplier would perform poorly, etc.) through their digital twin without disrupting their operational environment, this helps them understand what disruptions may occur and what corrective actions may need to be taken to regain capacity.
Most organisations will use a Design of Experiments-type approach with their digital twins to evaluate how different variables (e.g., demand, supplier performance) may work together, and as organisations can test multiple combinations through the use of the Digital Twin, it allows leaders to determine what the levers are that are of the most significant value to their companies.
In the long run, organisations will start to see more AI Decision Loop opportunities develop from Digital Twins (path planning for Warehouse Robots, inventory threshold triggers, shipment rerouting for instances of poor service performance, predictive maintenance, etc.). Digital Twin metrics (added value KPIs, sensor fusion metrics, execution times) will become as important to organisations as traditional IT metrics.
In addition to demonstrating forecast accuracy, lower downtime, and shorter response cycles, there are many reasons for an organization’s CIO to provide evidence of the benefits being realized by investments made in digital twin technologies as a basis for scaling those investments.
Architecture, Governance, and the CIO’s Role
Although the value of the digital twin technology is evident, scaling digital twins remains a significant challenge. Integration of data from multiple sources including ERP, WMS, IoT, and partner systems is a primary challenge for all. High fidelity simulation requires high computational capacity, which in turn requires trade-offs between realism, performance, and cost.
There are also governance issues associated with digital twins. As digital twin models drift or are modified due to the physical state of the model changing, potential security vulnerabilities also increase as continuing data is streamed from cloud and edge environments. By nature, models will need to be maintained collectively through the organization and will typically require IT, Operations, and Analytics to function effectively and efficiently.
Consequently, CIO leadership is critical to the creation and implementation of digital twins. Digital twins can not be viewed simply as a project on the operational side of the organization but rather as a business platform that can enable and enhance all aspects of a business’ operations. As such, CIOs are in a position to create a reference architecture for digital twins, and ensure that the data quality of all sources of data used in the creation of digital twins are standardised and that the business objectives are aligned with the business strategies associated with AI, infrastructure, and security.
Digital twins represent a new paradigm for businesses thinking of their supply chain not as a fixed asset but rather a dynamic ecosystem that can adapt itself to changing environments. As a result, the next question for CIOs will be not whether to implement digital twin technology but how best to begin small and grow responsibly using this technology.
Therefore, choose a specific area to focus on proving your value through digital twins (DTs) as a subset of your overall supply chain network. Once you prove your value through DTs, measure both the technical and business value created as well as look to expand your use of DTs in other domains within your company.
As global supply chain disruption continues to occur on a daily basis, one of the most strategic advantages that any organization can gain over its competition is through the use of DTs to validate its future supply chains before they ever execute them.