Deep Dive

Supply chain digital twins may integrate data from various sources, including manufacturing facilities, warehouses, transportation networks, and distribution centers, to provide a comprehensive view of an entire supply chain's status. These digital twins may be “point in time” models that reflect a supply chain as of a particular date, as commonly found in many agent-based modeling platforms, or real-time, providing an up-to-date detailed view of all a supply chain's operations.

Digital twins allow for the simulation of different scenarios, predictive analysis of potential disruptions, and optimization of operations, enabling companies to make informed, data-driven decisions to enhance efficiency, resilience, and responsiveness.

Without a robust Supply Chain Digital Twin, companies operate with relatively limited visibility into what may be complex and often fragmented supply networks. This lack of insight makes it difficult to anticipate and react to disruptions such as raw material shortages, production delays, or transportation bottlenecks, leading to increased risks of stockouts, expired products, and costly expedited shipments.

Absent digital twins, the inability to simulate “what-if” scenarios means decision-making is furthermore often reactive rather than proactive, resulting in suboptimal inventory levels, inefficient resource allocation, and a slower response to market changes or unforeseen events like pandemics, ultimately impacting patient access to critical medications and incurring significant financial losses. Read more about how HASH's agent-based modeling and digital twin capabilities were used to help optimize vaccine distribution during the COVID-19 pandemic.

Comprehensive data integration

The creation of a functional Supply Chain Digital Twin demands extensive and seamless data integration across numerous disparate systems.

This includes operational data from:

• Enterprise Resource Planning (ERP) systems,
• Manufacturing Execution Systems (MES),
• Warehouse Management Systems (WMS), and
• Transportation Management Systems (TMS).

In addition, real-time data feeds from IoT sensors embedded in manufacturing equipment, storage facilities (temperature, humidity), and transportation vehicles (GPS tracking, environmental conditions) may be incorporated into digital twins built within HASH, in contrast to many classic simulation software packages which do not support live or streaming data inputs to populate “initial state” or calibrate simulation models with historical actuals.

External data sources such as weather forecasts, geopolitical event data, public health advisories, and market demand signals can also be integrated to provide a holistic view and enable comprehensive scenario planning and predictive analytics. HASH specifically supports the integration of unstructured information sources as well as structured data within its platform.