Supply Chain Digital Twin
Objective
A supply chain is better described as a network with nodes of activity, such as factories, warehouses, retail stores, with connections through which goods, money, and data flows. The operation of the nodes and configuration of the connections is determined by operational policies and parameters, for example, production scheduling techniques, procurement procedures, transport packaging, vehicle specifications. Any supply chain network has an extensive range of factors that interrelate and determine service, costs, capital requirements, and environmental impacts. To date, the full extent of the relationships between such factors has been difficult to perceive, understand, qualify, and manage. This is where supply chain digital twins come into play.
The project will apply digital twining to the mapping and assessment of the factors and levers that determine system dynamics operating across supply chain networks and that govern outcomes. Our objective for the Supply Chain Digital Twin is to use granular, operational level data to reveal actionable insights for businesses that are beyond intuition.
Key concepts
The AI based solution will use a unique implementation of graph-technology developed from over a decade of research and development. This will enable disconnected data to become connected, siloed analytics to be integrated and complex systemic relationships to be identified.
The project will focus on the use of digital twins to simulate, predict, and prescribe actions and changes in the configuration and operation of supply chain activities. It will harness the scalability of simulative digital twins; whether a business is seeking to optimize a single workstation or analyse a global supply chain, digital twins can be customized to fit any scope or complexity. This flexibility can address an endless list of critical questions regarding supply chain assets and nodes. That said, the core focus of this project will be process design and improvement to optimise supply chain flow in the specific context of a business.
Simulative Twining
Simulative digital twins are essential for testing and validating designs and processes before they are physically implemented. By enabling virtual testing, simulative digital twins reduce the risk for potentially disruptive and costly real-world changes and accelerate the design and development process
Predictive Twining
Predictive digital twins analyse historical and real-time data to forecast future states and outcomes. By anticipating future conditions, predictive digital twins enable businesses to take pre-emptive actions, reducing downtime, avoiding costly failures, and improving overall reliability.
Prescriptive Twining
Prescriptive digital twins go beyond prediction by recommending specific actions based on optimized agents. These digital twins not only predict outcomes but also provide actionable insights that optimize operations, reduce costs, and enhance decision-making processes.
These applications will deliver capabilities beyond that found in typical ‘monitoring’ digital twins that provide real-time insights into the status and performance of physical assets or processes and support the early detection of anomalies, prompting exceptions and potentially preventing supply chain failures.
Benefits
Digital twins allow businesses to test different scenarios in a risk-free, what-if environment. This is particularly valuable for supply chain operations in a VUCA world. Instead of relying on gut instincts or oversimplified linear models, supply chain managers can run detailed simulations to see how specific changes might affect overall performance.
Uniquely configured twins will provide much-needed clarity to help managers better understand system behaviour and how different elements interact. With this knowledge, businesses can align and synchronize their operations, mitigate the impact of events, and identify bottlenecks that hinder efficiency.
Moreover, with prescriptive capability, digital twins can take things one step further by offering actionable insights. These systems do not merely predict outcomes; they prescribe corrective actions that would lead to the desired outcomes. This feature addresses one of the main limitations of machine learning (ML) models, where decision-makers can’t always understand the reasons behind an ML model’s predictions. Our digital twins, in contrast, will aim to provide clarity by making the behaviour of a system visible over time and showing how corrective actions can guide the system toward improved performance.
Get Involved
To express your interest in participating in this innovation project, please send an email to: Innovations@b2wise.com quoting “Digital Twin”.