Supply Chain Digital Twin Simulation and Predictive Modeling Implementation - 20 Week Implementation

A digital twin is a digital representation of a hypothetical or real-world product, system, or process. The digital twin serves as a digital copy of the counterpart for practical purposes such as simulation, integration, testing, monitoring, and maintenance.

For the manufacturing industry, digital twins can be used to optimize production processes and reduce downtime. The use of digital twin technology is growing rapidly, and new applications are coming into development.

Digital Twin provides solutions for various key challenges including...

• Limited and time-consuming access to accurate, real-time data.
• Costs associated with planned/unplanned downtime.
• Lack of automated planning and reporting tools.
• Labor-intensive process operations.

Implementing a digital twin starts with identifying the problem to solve and determining the appropriate target KPIs that measure it. Then, the system can be modeled and the KPIs can be improved iteratively.

Activities to be Completed:

Phase 1

• Document current system and processes
• Collect point in time data
• Deploy Azure Digital Twin in client environment
• Build baseline model and data schema

Phase 2

• Connect twin to current systems
• Model actions in digital twin
• Create queries for analysis of twin
• Develop reports

Phase 3

• Run simulations / “what-if” scenarios
• Deployment of chatbot
• Creation of predictive models
• Training and handoff

Deliverables:

Phase 1

• Documented current state
• Baseline Azure Digital Twin
• Reports/dashboards of current systems and processes using point in time data

Phase 2

• Deployed Azure Digital Twin using real time data
• Queries and reports using real time data

Phase 3

• Simulator for “what-if” scenarios
• Deployed chatbot and predictive models
• Completed training and hand-off materials

Protiviti Services; subject to change based on scope

Note: licensing additional