Simio Process Digital Twin Applications

Understanding begins with identifying all physical constraints in the process of sourcing materials, as well as producing and distributing products to customers. Additionally, numerous documents describe the business rules management wants to apply to govern the process, which often contradict the current reality. In most organizations, much of the execution know-how and detailed decision logic remains tribal knowledge, making it difficult to replicate in other systems, as it resides in the minds of those making day-to-day decisions on the shop floor.

Understanding the current quality and correlation of data between various enterprise systems is a significant challenge, as values for identical fields frequently vary across different systems, making it difficult to ascertain accuracy. The level of detail and recording frequency vary between systems based on their application, complicating data correlation and aggregation further. Identifying the sources and flow of data to establish a relevant data pipeline to support process modeling, control, dashboarding, and analysis is key to the transformation process.

It is difficult to accurately identify and determine the value that certain process changes and optimizations can deliver to increase performance in the factory or supply chain. Certain performance or value gains are often perceived, leading to large capital investments in capacity and extensions to physical infrastructure for future growth and new products, without a detailed understanding of the requirements and potential impact on the business. Transformation projects are often developed in isolation, resulting in a shortfall in delivering the overall expected value and the anticipated process transformation required to advance the business towards achieving its digital transformation goals.

Transformation typically involves many concurrent aspects of a business, such as personnel, processes, equipment, new products, sales, global reach, distribution, and more. Without understanding the end-to-end impact of proposed changes in business operations, including policies and processes, there is a risk of falling short of expectations and potentially wasting money on investments that do not deliver the expected value. This includes understanding the impact of automation (robotics, AMRs, material handling, etc.), evaluating alternatives to understand the ROI of various options, and visualizing and presenting future results to all stakeholders for buy-in and decision-making.