# Digital Twins: Enabling Interoperability in Smart Manufacturing Networks
## 2 Interoperability in Manufacturing  
Interoperability can be defined as the capacity of two or more products, programs, or systems to communicate and understand one another's data. Currently, there is no one agreed definition of interoperability that fits all projects and uses cases. However, the IEEE defines interoperability as \"the ability of two or more systems or components [from different manufacturers/vendors] to exchange information and to use the information that has been exchanged\". Harvesting inputs from numerous sensors, a substantial amount of data can be gathered in mere moments. Manufacturing architectures have evolved into interconnected networks of automation devices, services, and businesses as a result of recent developments in manufacturing technology, including cyber-physical systems, industrial internet, AI (artificial intelligence), and machine learning. The rising requirement for interoperability at all levels of the manufacturing ecosystem is one of the difficulties that have come about as a result of this growth. To help operators maximise performance, industrial IoT (IIoT) links the factory floor to the company, boosting visualisation, data analysis, and holistic insights. Data synergy, or the integration of data from various sources to produce value, is a crucial part of the industrial IoT [4]. There are several challenges associated with achieving interoperability in manufacturing, including the following:  
* Technical Complexity: Manufacturing systems often consist of a variety of different equipment, machines, and software applications from different vendors, each with its own data format and communication protocols. Achieving interoperability between these systems can be technically complex and challenging.  
Figure 1: Automation pyramid (adapted from [1]).  
Data Incompatibility: Incompatible data formats and standards can make it difficult to integrate different systems and share data between them. Data formats, protocols, and standards can vary widely across different manufacturing systems, leading to incompatibility issues.
* Security Risks: Interoperability between different systems and devices can increase the risk of security breaches, as it creates more opportunities for hackers to exploit vulnerabilities.
* Lack of Standardisation: There is currently no single standard for achieving interoperability in manufacturing, which can lead to confusion and incompatibility issues between different systems and devices.
* Cost: Achieving interoperability can be expensive, as it often requires significant investment in hardware, software, and personnel resources.
* Legacy Systems: Many manufacturing systems and equipment are older and may not have been designed with interoperability in mind. Retrofitting these systems to achieve interoperability can be costly and time-consuming.
* Organizational Resistance: Achieving interoperability often requires changes in business processes and workflows, which can be met with resistance from employees and management.  
Nowadays, interoperability is more of a need than a desirable quality, especially when smart things start to take the form of widespread technology. Interoperability is crucial both between smart devices from various manufacturers and between smart devices and current infrastructures [5]. According to [PERSON], the technological architecture of smart objects must be specifically tasked to facilitate interoperability to accomplish the delivery of interoperability [6]. Figure 2 below displays a summary of the six levels of conceptual interoperability and their alignment with the expected interfacing architecture of the data sharing between various interoperable platforms.