Analyzing environmental sustainability methods for use earlier in the product lifecycle.

Environmental sustainability information in the manufacturing industry is not easily shared between stages in the product lifecycle. In particular, reliable manufacturing-related information for assessing the sustainability of a product is often unavailable at the design stage. Instead, designers rely on aggregated, often outdated information or make decisions by analogy (e.g., a similar manufacturing process for a similar product yielded X and Y results). However, smart manufacturing and the Internet of Things have potential to bridge the gap between design and manufacturing through data and knowledge sharing. This paper analyzes environmental sustainability assessment methods to enable more accurate decisions earlier in design. The techniques and methods are categorized based on the stage they apply to in the product lifecycle, as described by the Systems Integration of Manufacturing Applications (SIMA) reference architecture. Furthermore, opportunities for aligning standard data representation to promote sustainability assessment during design are identified.

Methods and Tools for Performance Assurance of Smart Manufacturing Systems.

The emerging concept of smart manufacturing systems is defined in part by the introduction of new technologies that are promoting rapid and widespread information flow within the manufacturing system and surrounding its control. These systems can deliver unprecedented awareness, agility, productivity, and resilience within the production process by exploiting the ever-increasing availability of real-time manufacturing data. Optimized collection and analysis of this voluminous data to guide decision-making is, however, a complex and dynamic process. To establish and maintain confidence that smart manufacturing systems function as intended, performance assurance measures will be vital. The activities for performance assurance span manufacturing system design, operation, performance assessment, evaluation, analysis, decision making, and control. Changes may be needed for traditional approaches in these activities to address smart manufacturing systems. This paper reviews the current methods and tools used for establishing and maintaining required system performance. It then identifies trends in data and information systems, integration, performance measurement, analysis, and performance improvement that will be vital for assured performance of smart manufacturing systems. Finally, we analyze how those trends apply to the methods studied and propose future research for assessing and improving manufacturing performance in the uncertain, multi-objective operating environment.

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.

Using formal methods to scope performance challenges for Smart Manufacturing Systems: focus on agility.

Smart Manufacturing Systems (SMS) need to be agile to adapt to new situations by using detailed, precise, and appropriate data for intelligent decision-making. The intricacy of the relationship of strategic goals to operational performance across the many levels of a manufacturing system inhibits the realization of SMS. This paper proposes a method for identifying what aspects of a manufacturing system should be addressed to respond to changing strategic goals. The method uses standard modeling techniques in specifying a manufacturing system and the relationship between strategic goals and operational performance metrics. Two existing reference models related to manufacturing operations are represented formally and harmonized to support the proposed method. The method is illustrated for a single scenario using agility as a strategic goal. By replicating the proposed method for other strategic goals and with multiple scenarios, a comprehensive set of performance challenges can be identified.

Realizing Environmentally-Conscious Manufacturing in the Post-COVID-19 Era.

The unique and unprecedented challenges of the COVID-19 pandemic have resulted in significant disruptions to diverse manufacturing supply chains across the globe. The negative economic impacts of these unexpected and rapid changes in demand and available supplies have been severe, and the economic sustainability of many businesses has been revealed as being highly sensitive to such changes. COVID-19 will inevitably change manufacturing, and potentially in a way that is not sustainable unless we factor sustainability into our "redesign." Otherwise, the industry will remain overwhelmed in a reactionary cycle when the next major problem emerges, such as a lack of resources during a natural or man-made disaster. In this article, we present strategies for addressing three sustainability challenges relevant to manufacturing introduced by the COVID-19 pandemic: 1) an increase in waste generation, 2) uncertainty in life cycle impacts, and 3) navigating new modes of operation for manufacturing. To mitigate the sustainability challenges of COVID-19 and create a more resilient industrial sector, we need to assess the potential of each risk to product development and production processes. We envision a systematic integration of sustainable manufacturing principles and metrics into the business practices of manufacturing enterprises, including the products they produce and the processes used to create them. Realizing this vision will require greater availability and transparency of key data related to environmental and social sustainability factors, to create a clean and sustainable future in which pandemic and disaster readiness is realized through sustainable manufacturing.

Measuring Manufacturing's Significance in the USA.

Economic value added is a primary metric for measuring manufacturing activity; however, this metric and others exclude approximately half of the economic activity necessary for producing manufactured goods. With the recent disruption in the supply of goods and services by the COVID-19 pandemic, the criticality of these supply chains to production has become more apparent. Measuring and understanding these additional activities is foundational to reducing the effect of supply chain disruption. Additionally, manufacturing supply chains are fundamental to any response to the virus, including the production of masks, tests, and eventually a vaccine. When looked at closely, manufacturing stands out as a key driver of our economy. New manufacturing technologies can be leveraged to differentiate products in multiple ways resulting in a greater variety of products made more efficiently, with less environmental impacts, and higher quality. In addition, the digitization of manufacturing supports supply chains that are more connected, anticipatory, and agile. Metrics are needed that better reflect the role manufacturing plays in society, that better identify the social gains manufacturing produces, and that better establish the total economic activity that drives production. In this paper we propose a macro-economic metric to better measure the influence of manufacturing on our economy as an example of one such measure. We argue a need for solidifying similar radical changes to our current ways of measuring manufacturing's relevance and emphasizing the impact of new technologies that support the manufacturing economic sector.

Foundations of information governance for smart manufacturing.

The manufacturing systems of the future will be even more dependent on data than they are today. More and more data and information are being collected and communicated throughout product development lifecycles and across manufacturing value chains. To enable smarter manufacturing operations, new equipment often includes built-in data collection capabilities. Older equipment can be retrofitted inexpensively with sensors to collect a wide variety of data. Many manufacturers are in a quandary as to what to do with increasing quantities of data. Much hype currently surrounds the use of AI to process large data sets, but manufacturers struggle to understand how AI can be applied to improve manufacturing system performance. The gap lies in the lack of good information governance practices for manufacturing. This paper defines information governance in the manufacturing context as the set of principles that allow for consistent, repeatable, and trustworthy processing and use of data. The paper identifies three foundations for good information governance that are needed in the manufacturing environment-data quality, semantic context, and system context-and reviews the surrounding and evolving body of work. The work includes a broad base of standard methods that combines to create reusable information from raw data formats. An example from an additive manufacturing case study is used to show how those detailed specifications create the governance needed to build trust in the systems.

Defining Near-Term to Long-Term Research Opportunities to Advance Metrics, Models, and Methods for Smart and Sustainable Manufacturing.

Over the past century, research has focused on continuously improving the performance of manufacturing processes and systems-often measured in terms of cost, quality, productivity, and material and energy efficiency. With the advent of smart manufacturing technologies-better production equipment, sensing technologies, computational methods, and data analytics applied from the process to enterprise levels-the potential for sustainability performance improvement is tremendous. Sustainable manufacturing seeks the best balance of a variety of performance measures to satisfy and optimize the goals of all stakeholders. Accurate measures of performance are the foundation on which sustainability objectives can be pursued. Historically, operational and information technologies have undergone disparate development, with little convergence across the domains. To focus future research efforts in advanced manufacturing, the authors organized a one-day workshop, sponsored by the U.S. National Science Foundation, at the joint manufacturing research conferences of the American Society of Mechanical Engineers and Society of Manufacturing Engineers. Research needs were identified to help harmonize disparate manufacturing metrics, models, and methods from across conventional manufacturing, nanomanufacturing, and additive/hybrid manufacturing processes and systems. Experts from academia and government labs presented invited lightning talks to discuss their perspectives on current advanced manufacturing research challenges. Workshop participants also provided their perspectives in facilitated brainstorming breakouts and a reflection activity. The aim was to define advanced manufacturing research and educational needs for improving manufacturing process performance through improved sustainability metrics, modeling approaches, and decision support methods. In addition to these workshop outcomes, a review of the recent literature is presented, which identifies research opportunities across several advanced manufacturing domains. Recommendations for future research describe the short-, mid-, and long-term needs of the advanced manufacturing community for enabling smart and sustainable manufacturing.

ENABLING SMART MANUFACTURING TECHNOLOGIES FOR DECISION-MAKING SUPPORT.

Smart manufacturing combines advanced manufacturing capabilities and digital technologies throughout the product lifecycle. These technologies can provide decision-making support to manufacturers through improved monitoring, analysis, modeling, and simulation that generate more and better intelligence about manufacturing systems. However, challenges and barriers have impeded the adoption of smart manufacturing technologies. To begin to address this need, this paper defines requirements for data-driven decision making in manufacturing based on a generalized description of decision making. Using these requirements, we then focus on identifying key barriers that prevent the development and use of data-driven decision making in industry as well as examples of technologies and standards that have the potential to overcome these barriers. The goal of this research is to promote a common understanding among the manufacturing community that can enable standardization efforts and innovation needed to continue adoption and use of smart manufacturing technologies.

Psychological distress in carers of head injured individuals: the provision of written information.

Since the early 1970s, researchers have expressed concern about the emotional well-being of family members after traumatic brain injury (TBI), and it is now widely acknowledged that TBI has long-term effects on the patient and relatives alike. Researchers have found a substantial number of relatives caring for head injured patients to show significant levels of anxiety and depression, and have emphasized the need for information for relatives on the prognosis of head injury. There are, however, very few studies that have investigated the usefulness of giving literature to relatives. Using a longitudinal, mixed variable, within- and between-subject design, the present study investigated the effect of an information booklet on levels of distress in a group of 34 carers of individuals with TBI. These results are discussed, and the proposal made that an information booklet such as the one used in the present study should become an integral part of the discharge procedure for relatives of individuals who have sustained a head injury.

Antisense fosB RNA inhibits thrombin-induced hypertrophy in cultured pulmonary arterial smooth muscle cells.

BACKGROUND: We have previously reported that fosB mRNA is induced by hypertrophic stimuli (thrombin, angiotensin II) but not proliferative stimuli (platelet-derived growth factor, basic fibroblast growth factor) in pulmonary arterial smooth muscle cells (PASMCs) (J Biol Chem. 1994;9:6399-6404). Our aim in the present study was to investigate the potential role of FosB in PASMC hypertrophy. METHODS AND RESULTS: Adenoviruses carrying sense or antisense fosB RNA expression cassettes were used to infect cultured PASMCs with the aim of increasing or inhibiting fosB expression, respectively. We examined whether fosB expression modification affected the growth of quiescent PASMCs, thrombin-induced hypertrophy, or platelet-derived growth factor-induced proliferation. PASMC growth was assessed by daily cell number count, determination of [3H]leucine incorporation, and quantification of total cellular protein. Neither an increase nor a decrease in FosB protein expression caused a significant change in the growth of quiescent PASMCs over a period of 96 hours, indicating that FosB alone is not sufficient to induce hypertrophy. Modification of FosB levels did not affect platelet-derived growth factor-induced PASMC proliferation. An increase in FosB expression did not augment thrombin-induced hypertrophy; however, inhibition of FosB expression resulted in a diminution of thrombin-induced hypertrophy by 58+/-6% (P<0.005). CONCLUSIONS: These results suggest that FosB is necessary but not sufficient for thrombin-induced hypertrophy in cultured PASMCs.

Predictiveness of sonographic fetal weight estimation as a function of prior probability of intrauterine growth retardation.

We evaluated the predictiveness of sonographically estimated fetal weight as a function of the estimation of probability of having intrauterine growth retardation (IUGR) before obtaining an ultrasound scan (prior probability). The value of the estimated fetal weight resided more in its high specificity than in its sensitivity, hence in its ability to confirm that the fetus is normal. The predictiveness of the method was further enhanced when the fetal weight estimation was placed in the context of the prior probability of IUGR. In particular, the positive predictive value of the test as well as the likelihood of having a growth-retarded infant in spite of an estimated fetal weight within the normal range were considerably higher as the prior probability of IUGR increased. Since the obstetrician using all available evidence is likely to form a rather good estimate of the possibility of IUGR before ordering a scan, this improvement in the predictiveness of estimated fetal weight through a Bayesian approach can be advantageously applied to ultrasound analysis and can effectively support clinical decision making.