Enabling Flexible and Robust Business Process Automation for the Agile Enterprise

During the last decade process-aware information systems (PAISs) have become increasingly popular to digitize business processes and to effectively support them at the operational level. In many application domains, however, PAISs will not be accepted by users if rigidity comes with them. Ensuring PAIS robustness, in turn, becomes extremely complicated if high flexibility demands need to be fulfilled. To cope with the dynamic nature of business processes, we developed AristaFlow, a next generation process management technology that enables comprehensive process lifecycle support. In addition to standard process management services, AristaFlow can handle exceptions, change the execution of running business cases on the fly, efficiently deal with uncertainty, and support the evolution of business processes over time. This paper discusses how AristaFlow assists the various stakeholders of a PAIS to cope with errors and exceptional situations, while still meeting robustness needs. In particular, we focus on new error handling procedures and capabilities utilizing the flexibility provided by ad-hoc changes

Realising the open virtual commissioning of modular automation systems

To address the challenges in the automotive industry posed by the need to rapidly manufacture more product variants, and the resultant need for more adaptable production systems, radical changes are now required in the way in which such systems are developed and implemented. In this context, two enabling approaches for achieving more agile manufacturing, namely modular automation systems and virtual commissioning, are briefly reviewed in this contribution. Ongoing research conducted at Loughborough University which aims to provide a modular approach to automation systems design coupled with a virtual engineering toolset for the (re)configuration of such manufacturing automation systems is reported. The problems faced in the virtual commissioning of modular automation systems are outlined. AutomationML - an emerging neutral data format which has potential to address integration problems is discussed. The paper proposes and illustrates a collaborative framework in which AutomationML is adopted for the data exchange and data representation of related models to enable efficient open virtual prototype construction and virtual commissioning of modular automation systems. A case study is provided to show how to create the data model based on AutomationML for describing a modular automation system

SOA and BPM, a Partnership for Successful Organizations

In order to stay effective and competitive, companies have to be able to adapt themselves to permanent market requirements, to improve constantly their business process, to act as flexible and proactive economic agents. To achieve these goals, the IT systems within the organization have to be standardized and integrated, in order to provide fast and reliable data access to users both inside and outside the company. A proper system architecture for integrating company’s IT assets is a service oriented one. A service-oriented architecture (SOA) is an IT architectural style that allows integration of the company’s business as linked, repeatable tasks called services. A subject closely related to SOA is Business Process Management (BPM), an approach that aims to improve business processes. The paper also presents some aspects of this topic, as well as the relationship between SOA and BPM. They complement each other and help companies improve their business performance.Information Systems, SOA, Web Services, BPM

Modeling 4.0: Conceptual Modeling in a Digital Era

Digitization provides entirely new affordances for our economies and societies. This leads to previously unseen design opportunities and complexities as systems and their boundaries are re-defined, creating a demand for appropriate methods to support design that caters to these new demands. Conceptual modeling is an established means for this, but it needs to be advanced to adequately depict the requirements of digitization. However, unlike the actual deployment of digital technologies in various industries, the domain of conceptual modeling itself has not yet undergone a comprehensive renewal in light of digitization. Therefore, inspired by the notion of Industry 4.0, an overarching concept for digital manufacturing, in this commentary paper, we propose Modeling 4.0 as the notion for conceptual modeling mechanisms in a digital environment. In total, 12 mechanisms of conceptual modeling are distinguished, providing ample guidance for academics and professionals interested in ensuring that modeling techniques and methods continue to fit contemporary and emerging requirements

Innovative configurable and collaborative approach to automation systems engineering for automotive powertrain assembly

Presently the automotive industry is facing enormous pressure due to global competition and ever changing legislative, economic and customer demands. Both, agility and reconfiguration are widely recognised as important attributes for manufacturing systems to satisfy the needs of competitive global markets. To facilitate and accommodate unforeseen business changes within the automotive industry, a new proactive methodology is urgently required for the design, build, assembly and reconfiguration of automation systems. There is also need for the promotion of new technologies and engineering methods to enable true engineering concurrency between product and process development. Virtual construction and testing of new automation systems prior to build is now identified as a crucial requirement to enable system verification and to allow the investigation of design alternatives prior to building and testing physical systems. The main focus of this research was to design and develop reconfigurable assembly systems within the powertrain sector of the automotive industry by capturing and modelling relevant business and engineering processes. This research has proposed and developed a more process-efficient and robust automation system design, build and implementation approach via new engineering services and a standard library of reusable mechanisms. Existing research at Loughborough had created the basic technology for a component based approach to automation. However, no research had been previously undertaken on the application of this approach in a user engineering and business context. The objective of this research was therefore to utilise this prototype method and associated engineering tools and to devise novel business and engineering processes to enable the component-based approach to be applied in industry. This new approach has been named Configurable and Collaborative Automation Systems (CO AS). In particular this new research has studied the implications of migration to a COAS approach in terms of I) necessary changes to the end-users business processes, 2) potential to improve the robustness of the resultant system and 3) potential for improved efficiency and greater collaboration across the supply chain... cont'

Design of an innovation platform for manufacturing SMES

This paper reports on the conception of a collaborative, internet-based innovation platform with semantic capabilities, which implements a new methodology for the adoption of a systematic innovation process in globally-acting networked SMEs. The main objective of the innovation platform is to stimulate the generation of ideas, the selection of good ideas and their ultimate implementation. The platform will support SMEs to manage and implement the complex innovation processes arisen in a networked environment, taking into account their internal and external links, by enabling an open multi-agent focused innovation system, facilitating customer, provider, supplier and employee- focused innovation. The solution is specifically focused on the needs of manufacturing SMEs and will observe product, process and management innovation. The paper presents the key elements of the innovation model and makes references to a novel approach concerning the development of a robust and flexible Central Knowledge Repository for the innovation platform

Is Ambient Intelligence a truly Human-Centric Paradigm in Industry? Current Research and Application Scenario

The use of pervasive networked devices is nowadays a reality in the service sector. It impacts almost all aspects of our daily lives, although most times we are not aware of its influence. This is a fundamental characteristic of the concept of Ambient Intelligence (AmI). Ambient Intelligence aims to change the form of human-computer interaction, focusing on the user needs so they can interact in a more seamless way, with emphasis on greater user-friendliness. The idea of recognizing people and their context situation is not new and has been successfully applied with limitations, for instance, in the health and military sectors. However its appearance in the manufacturing industry has been elusive. Could the concept of AmI turn the current shop floor into a truly human centric environment enabling comprehensive reaction to human presence and action? In this article an AmI scenario is presented and detailed with applications in human’s integrity and safety.Ambient Intelligence, networks, human-computer interaction

ACCOUNTING AND FINANCIAL STATEMENTS AUTO ANALYSIS SYSTEM

This project was motivated by the need to revolutionize the generation of financial statements and financial analysis process thus speeding up business decision making. The research questions were: 1) How can machine learning increase the speed of financial statement preparation and automate financial statements analysis? 2) How can businesses balance the benefits of automating financial analysis with potential concerns around privacy, data security, and bias? 3) Can the Java J2EE framework provide a reliable running environment for machine learning? The findings were: 1) Machine learning can significantly increase the accuracy and speed of financial analysis. Using machine learning algorithms, financial data can be processed and analyzed in real-time, allowing for quicker and more precise financial analysis. Machine learning models can identify patterns and trends in financial data that may not be easily detectable by humans, leading to more accurate financial statements and analysis. Additionally, machine learning can automate repetitive tasks in the financial analysis process, saving time and resources for businesses. 2) Businesses need to carefully balance the benefits of automating financial analysis with potential concerns around privacy, data security, and bias. While machine learning can offer significant advantages in terms of accuracy and speed, it also requires handling sensitive financial data. Therefore, it is crucial for businesses to implement robust data security measures to protect against potential data breaches and ensure compliance with privacy regulations. Additionally, businesses need to be mindful of potential biases in machine learning algorithms, as biased algorithms can result in biased financial analysis. Regular audits and monitoring of machine learning models should be conducted to address and mitigate any potential biases. 3) The Java J2EE framework can provide a reliable running environment for machine learning. Java J2EE (Java 2 Platform, Enterprise Edition) is a widely used and mature framework for developing enterprise applications, including machine learning applications. It offers scalability, reliability, and security features that are essential for running machine learning algorithms in a production environment. Java J2EE provides robust support for distributed computing, allowing for efficient processing of large financial datasets. Furthermore, it offers a wide range of libraries and tools for implementing machine learning algorithms, making it a viable choice for running machine learning applications in the financial industry. The conclusions were: 1) Machine learning has the potential to significantly increase the accuracy and speed of financial analysis, thereby revolutionizing the generation of financial statements and the financial analysis process. Various machine learning algorithms, such as decision trees, random forests, and deep learning algorithms, can be utilized to identify patterns, trends, and hidden risks in financial data, leading to more informed and efficient business decision making. 2) Businesses need to carefully balance the benefits of automating financial analysis with potential concerns around privacy, data security, and bias. While machine learning can offer significant advantages in terms of accuracy and speed, there are ethical considerations that need to be addressed, such as ensuring data privacy, implementing effective data security measures, and mitigating biases in machine learning algorithms used in financial analysis. Businesses should adopt a responsible approach to machine learning implementation, considering the potential risks and benefits. 3) The Java J2EE framework can provide a reliable running environment for machine learning applications, but further research is needed to evaluate the performance and scalability of machine learning models in this framework. Identifying potential optimizations for running machine learning applications at scale in the Java J2EE framework can lead to more efficient and effective implementation of machine learning in financial analysis and decision-making processes. Further research in this area can contribute to the development of robust and scalable machine learning applications for financial analysis in the business domain. Areas for further study include: 1) Exploring different machine learning algorithms and techniques to further improve the accuracy and speed of financial analysis. 2) Conducting research on the impact of machine learning on financial decision making and business performance. 3) Investigating methods for addressing and mitigating biases in machine learning algorithms used in financial analysis. 4) Evaluating the effectiveness of different data security measures in protecting sensitive financial data in machine learning applications. 5) Studying the performance and scalability of machine learning models in the Java J2EE framework and identifying potential optimizations for running machine learning applications at scale

Product to process lifecycle management in assembly automation systems

Presently, the automotive industry is facing enormous pressure due to global competition and ever changing legislative, economic and customer demands. Product and process development in the automotive manufacturing industry is a challenging task for many reasons. Current product life cycle management (PLM) systems tend to be product-focussed. Though, information about processes and resources are there but mostly linked to the product. Process is an important aspect, especially in assembly automation systems that link products to their manufacturing resources. This paper presents a process-centric approach to improve PLM systems in large-scale manufacturing companies, especially in the powertrain sector of the automotive industry. The idea is to integrate the information related to key engineering chains i.e. products, processes and resources based upon PLM philosophy and shift the trend of product-focussed lifecycle management to process-focussed lifecycle management, the outcome of which is the Product, Process and Resource Lifecycle Management not PLM only