Current challenges and recent advances on the path towards continuous biomanufacturing.

Continuous biopharmaceutical manufacturing is currently a field of intense research due to its potential to make the entire production process more optimal for the modern, ever-evolving biopharmaceutical market. Compared to traditional batch manufacturing, continuous bioprocessing is more efficient, adjustable, and sustainable and has reduced capital costs. However, despite its clear advantages, continuous bioprocessing is yet to be widely adopted in commercial manufacturing. This article provides an overview of the technological roadblocks for extensive adoptions and points out the recent advances that could help overcome them. In total, three key areas for improvement are identified: Quality by Design (QbD) implementation, integration of upstream and downstream technologies, and data and knowledge management. First, the challenges to QbD implementation are explored. Specifically, process control, process analytical technology (PAT), critical process parameter (CPP) identification, and mathematical models for bioprocess control and design are recognized as crucial for successful QbD realizations. Next, the difficulties of end-to-end process integration are examined, with a particular emphasis on downstream processing. Finally, the problem of data and knowledge management and its potential solutions are outlined where ontologies and data standards are pointed out as key drivers of progress.

Contextualized Filtering for Shared Cyber Threat Information.

Cyber threat information sharing is an imperative process towards achieving collaborative security, but it poses several challenges. One crucial challenge is the plethora of shared threat information. Therefore, there is a need to advance filtering of such information. While the state-of-the-art in filtering relies primarily on keyword- and domain-based searching, these approaches require sizable human involvement and rarely available domain expertise. Recent research revealed the need for harvesting of business information to fill the gap in filtering, albeit it resulted in providing coarse-grained filtering based on the utilization of such information. This paper presents a novel contextualized filtering approach that exploits standardized and multi-level contextual information of business processes. The contextual information describes the conditions under which a given threat information is actionable from an organization perspective. Therefore, it can automate filtering by measuring the equivalence between the context of the shared threat information and the context of the consuming organization. The paper directly contributes to filtering challenge and indirectly to automated customized threat information sharing. Moreover, the paper proposes the architecture of a cyber threat information sharing ecosystem that operates according to the proposed filtering approach and defines the characteristics that are advantageous to filtering approaches. Implementation of the proposed approach can support compliance with the Special Publication 800-150 of the National Institute of Standards and Technology.

ARES: Automated Risk Estimation in Smart Sensor Environments.

Industry 4.0 adoption demands integrability, interoperability, composability, and security. Currently, integrability, interoperability and composability are addressed by next-generation approaches for enterprise systems integration such as model-based standards, ontology, business process model life cycle management and the context of business processes. Security is addressed by conducting risk management as a first step. Nevertheless, security risks are very much influenced by the assets that the business processes are supported. To this end, this paper proposes an approach for automated risk estimation in smart sensor environments, called ARES, which integrates with the business process model life cycle management. To do so, ARES utilizes standards for platform, vulnerability, weakness, and attack pattern enumeration in conjunction with a well-known vulnerability scoring system. The applicability of ARES is demonstrated with an application example that concerns a typical case of a microSCADA controller and a prototype tool called Business Process Cataloging and Classification System. Moreover, a computer-aided procedure for mapping attack patterns-to-platforms is proposed, and evaluation results are discussed revealing few limitations.

D4I - Digital forensics framework for reviewing and investigating cyber attacks.

Many companies have cited lack of cyber-security as the main barrier to Industrie 4.0 or digitalization. Security functions include protection, detection, response and investigation. Cyber-attack investigation is important as it can support the mitigation of damages and maturing future prevention approaches. Nowadays, the investigation of cyber-attacks has evolved more than ever leveraging combinations of intelligent tools and digital forensics processes. Intelligent tools (e.g., YARA rules and Indicators of Compromise) are effective only when there is prior knowledge about software and mechanisms used in the cyber-attack, i.e., they are not attack-agnostic. Therefore, the effectiveness of these intelligent tools is inversely proportional to the number of the never-seen-before software and mechanisms utilized. Digital forensic processes, while not suffering from such issue, lack the ability to provide in-depth support to a cyber-attack investigation mainly due to insufficient detailed instructions in the examination and analysis phases. This paper proposes a digital forensics framework for reviewing and investigating cyber-attacks, called D4I, which focuses on enhancing the examination and analysis phases. First, the framework proposes a digital artifacts categorization and mapping to the Cyber-Kill-Chain steps of attacks. Second, it provides detailed instructing steps for the examination and analysis phases. The applicability of D4I is demonstrated with an application example that concerns a typical case of a spear phishing attack.

Standards-based Semantic Integration of Manufacturing Information: Past, Present, and Future.

Service-oriented architecture (SOA) has been identified as a key to enabling the emerging manufacturing paradigms such as smart manufacturing, Industrie 4.0, and cloud manufacturing where things (i.e., various kinds of devices and software systems) from heterogeneous sources have to be dynamically connected. Data exchange standards are playing an increasingly important role to reduce risks associated with investments in these Industrial Internet of Things (IIoT) and adoptions of those emerging manufacturing paradigms. This paper looks back into the history of the standards for carrying the semantics of data across systems (or things), how they are developed, maintained, and represented, and then presents an insight into the current trends. In particular, the paper discusses the emerging move in data exchange standards practices toward model-based development and usage. We present functional requirements for a system supporting the model-based approach and conclude with implications and future directions.

An Investigation to Manufacturing Analytical Services Composition using the Analytical Target Cascading Method.

As cloud computing is increasingly adopted, the trend is to offer software functions as modular services and compose them into larger, more meaningful ones. The trend is attractive to analytical problems in the manufacturing system design and performance improvement domain because 1) finding a global optimization for the system is a complex problem; and 2) sub-problems are typically compartmentalized by the organizational structure. However, solving sub-problems by independent services can result in a sub-optimal solution at the system level. This paper investigates the technique called Analytical Target Cascading (ATC) to coordinate the optimization of loosely-coupled sub-problems, each may be modularly formulated by differing departments and be solved by modular analytical services. The result demonstrates that ATC is a promising method in that it offers system-level optimal solutions that can scale up by exploiting distributed and modular executions while allowing easier management of the problem formulation.

An Overview of a Smart Manufacturing System Readiness Assessment.

Smart manufacturing, today, is the ability to continuously maintain and improve performance, with intensive use of information, in response to the changing environments. Technologies for creating smart manufacturing systems or factories are becoming increasingly abundant. Consequently, manufacturers, large and small, need to correctly select and prioritize these technologies correctly. In addition, other improvements may be necessary to receive the greatest benefit from the selected technology. This paper proposes a method for assessing a factory for its readiness to implement those technologies. The proposed readiness levels provide users with an indication of their current factory state when compared against a reference model. Knowing this state, users can develop a plan to increase their readiness. Through validation analysis, we show that the assessment has a positive correlation with the operational performance.

On architecting and composing engineering information services to enable smart manufacturing.

Engineering information systems play an important role in the current era of digitization of manufacturing, which is a key component to enable smart manufacturing. Traditionally, these engineering information systems spanned the lifecycle of a product by providing interoperability of software subsystems through a combination of open and proprietary exchange of data. But research and development efforts are underway to replace this paradigm with engineering information services that can be composed dynamically to meet changing needs in the operation of smart manufacturing systems. This paper describes the opportunities and challenges in architecting such engineering information services and composing them to enable smarter manufacturing.

An Analysis of Technologies and Standards for Designing Smart Manufacturing Systems.

Smart manufacturing is defined by high degrees of automation. Automation, in turn, is defined by clearly defined processes. The use of standards in this environment is not just commonplace, but essential to creating repeatable processes and reliable systems. As with the rest of society, manufacturing systems are becoming more tightly connected through advances in information and communication technologies (ICT). As a result, manufacturers are able to receive information from their business partners and operational units much more quickly and are expected to respond quickly as well. Quick responses to changes in a manufacturing system are much more challenging than the responses that we have come to expect in other aspects of our lives. Manufacturing revolves around heavy capital investments to rapidly produce large amounts of product in anticipation of steady streams of commerce. Changes under these conditions not only disrupt the operations, slowing the production of goods, but also create difficulties with managing the capital investments. These are challenges manufacturers face daily. A large part of these challenges is understanding how best to refit manufacturing facilities to respond to variability, and how to plan new production facilities. By analyzing the information that is available in a manufacturing system, manufacturers can make more informed decisions as to how to respond to change. Advances in the technological infrastructure underlying manufacturing systems are enabling more reliable and timely flow of information across all levels of the manufacturing operation. We propose that effective utilization of such operational information will enable more automated, agile responses to the changing conditions, i.e. Smart Manufacturing. In this paper, we analyze the sources and the standards supporting the flow of that information throughout the enterprise. The analysis is based an intersection of two reference models: the Factory Design and Improvement (FDI) process and the ISA88 hierarchical model of manufacturing operations. The FDI process consists of a set of high-level activities for designing and improving manufacturing operations. The ISA88 hierarchical model specifies seven levels of control within a manufacturing enterprise.