1. Introduction
Industry 3.0 was responsible for the computerization and automation of the manufacturing industry, resulting in extensive transactional and execution level data being created, stored, and analyzed to fine tune and improve the system performance, creating a Digital Shadow of the factory (digital snapshot). As part of this Industry 3.0 transformation, data analytics was applied to discover and communicate meaningful patterns and trends. While applying data analytics of historical information is useful, it is tedious and attempts to help companies make decisions about the future by looking in the rearview mirror.
In today’s world, companies need to be highly agile to endure a constantly changing and increasingly uncertain business environment – while also dealing with a fast-growing combination of products, services, materials, technologies, machines, and people skills. A successful manufacturing supply chain requires the orchestration, coordination, and synchronization of each of these elements operating independently and cohesively together. As Industry 4.0 unfolds, computers are connected and communicate with the aim to ultimately make decisions and run operations with minimal human involvement, but companies are struggling to manage these multi-faceted and complex digital transformation projects. Below are some of the key challenges stakeholders and transformation projects face in their journey to a highly agile and “smart” (low touch/no touch) manufacturing supply chain:
- Understanding the current processes and constraints Although people have been working in factories and supply chains for over a century, it is still difficult to fully understand and articulate all the processes in detail since much of the information is compartmentalized between departments or different organizational structures within the company. Understanding starts by identifying all the physical constraints in the process of sourcing material, as well as producing and distributing products to customers. There are also many different documents describing the business rules that govern the process, often contradicting current reality. In most organizations, a large amount of the execution know-how and detailed decision logic is still tribal knowledge and hard to replicate in any system, as it is contained in the minds of people making these day-to-day decisions on the shop floor.
- Identifying the best data sources and aggregating accurate and relevant data Understanding the current quality and correlation of data between the various enterprise systems is a significant challenge, given that values for identical fields frequently vary across different systems, making it difficult to ascertain accuracy. The level of detail and recording frequency between systems is different based on the system application, making correlation and aggregation of data even more complex. Synchronizing different data sources to ensure they are all time relevant (same timestamp) is a challenge as some systems are running close to real-time while others are batch-oriented running only once per day. 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.
- Identifying and exploring areas for transformation and modernization It is difficult to accurately identify and determine the value that certain process changes and optimization can deliver to increase performance in the factory or supply chain. Certain performance or value gains are often overstated, resulting in 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. Automation and digitalization initiatives to improve efficiency and performance are also challenging. These projects are often developed in isolation, thereby missing the mark: underdelivering the overall expected value and anticipated process transformation required to advance the business towards reaching its digital transformation goals.
- Accurately predicting future behavior and performance Transformation usually involves many concurrent aspects of a business, including but not limited to personnel, processes, equipment, new products, sales, global reach and distribution. Without understanding the end-to-end impact of proposed changes on business operations, there is a risk of falling short of expectations, 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.
Based on years of simulation and analysis experience, it is clear that the most effective way to enable and facilitate digital transformation and address the challenges discussed above is by creating and using a detailed simulation-based virtual model or offline Process Digital Twin of the process (i.e., factory and/or supply chain). This model can be used for design and analysis of the current and future processes as a predictive solution. The virtual model can also then be connected to real-time data of the enterprise systems to become the online Process Digital Twin for operational deployment and near-real-time decision making as a prescriptive solution. The underlying technology is described in more detail in the Simio Simulation Solution Whitepaper, also available on the Simio website.
This whitepaper describes the Simio Intelligent Adaptive Process Digital Twin solution and the various digital transformation workstreams that can be supported by using this technology. During the lifecycle of a digital and business transformation project, different requirements emerge during the various phases of the project. A single integrated Process Digital Twin of the business can facilitate the continuous evaluation of both current and future performance. Additionally, the Process Digital Twin model can also be deployed on the cloud, providing operational decision support as well as scheduling and orchestration of the ongoing operations.
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