# Technical Interoperability for Machine Connectivity on the Shop Floor
## Background  
### Industrial Digitization and Industry 4.0 Maturity  
A key issue for manufacturing enterprises is to ensure that the application of new technologies adds value to their core business. The goal of Industry 4.0 is to create cyber-physical production systems (CPPS) from which data-driven autonomous systems can emerge. The Industry 4.0 Maturity Index model [10] is a way to measure how close an enterprise is to reaching this goal. The model sets several prerequisites needed for an industrial system to take advantage of new capabilities. To reach these different steps, many things need to be in place. Figure 1 shows a high-level list of what technological solutions, or paradigms, that can be loosely connected with each step. The first step, computerization, is to have a digital system as opposed to a purely mechanical one, a development that triggered the third industrial revolution during the 1950s. The second step is to connect the various digital systems. Academic discussion around the complexity of such interconnections intensified during the 1980s, during the Computer Integrated Manufacturing (CIM) era, which leads to multiple frameworks of enterprise integration and interoperability [2; 11].  
The third step is termed visibility, i.e., having a digital model of the factory. Data from machines and sensors need to be collected and stored continuously so that the current state of the production system is always known and based on facts. Collecting all the relevant data in a complex manufacturing environment requires an efficient and scalable information system. A solution is to utilize shared resource pooling and Cloud Computing technologies [12]. According to the National Institute of Standards and Technology (NIST), cloud computing \"is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction\" [3].  
Industry 4.0 maturity index four is called transparency, which means to make sense of the digital models to understand the current situation. A central aspect of this is to use algorithms for data analysis in Big Data applications. A popular definition of Big Data includes the four important V's of data, drawn from an IDC (International Data Corporation) report 2011 [13]. \"Big data technologies describe a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling high-velocity capture, discovery, and/or analysis.\"  
In the fifth step, predictive capacity, real-time data and analyzed information needs are linked, aggregated, simulated, etc., into useful information which can be used for decision-making. This alignswith a certain aspect of IoT that focuses on a layered architecture and the use of middleware [5]. Encapsulating functionality in well-defined services enables and simplifies data sharing between new and legacy systems with IoS [6; 14].  
The sixth and last step in the Industry 4.0 Maturity Index model is called adaptability. This is the end goal of both Industry 4.0 and IoT, which is also referred to as smart manufacturing or smart industry [15; 16]. A Cyber-Physical Production System (CPPS) is an adaptable system with the capability to sense its environment and dynamically react to it. Since this reaction needs to function at different time scales, depending on which part of the system needs to adapt, there is a need for programming and modeling languages with such support [7].