Balanced readiness level assessment (BRLa): A tool for exploring new and emerging technologies.

Vik, Jostein; Melås, Anders Mahlum; Stræte, Egil Petter; Søraa, Roger Andre

doi:10.1016/j.techfore.2021.120854

Cited by 60 publications

(36 citation statements)

References 25 publications

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“…In more recent works, the general concept of technological maturity has also been applied to other technology-related subjects such as systems, integration, manufacturing, regulations, and markets. These efforts have resulted in additional readiness levels, such as manufacturing readiness level (MRL) [71], system (SRL) [72], integration (IRL) [73], regulatory (RRL), market (MRL) [74], acceptance (ARL), and organizational (ORL) [75]. Some of these have been used in combination and discussed for the inclusion of expected benefits in technology assessment.…”

Section: Technology Selection In the Context Of Emerging Technologies...mentioning

confidence: 99%

Future in Battery Production: An Extensive Benchmarking of Novel Production Technologies as Guidance for Decision Making in Engineering

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Krätzig

2024

IEEE Trans. Eng. Manage.

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Due to the rising interest in electric vehicles, the demand for more efficient battery cells is increasing rapidly. To support this trend, battery cells must become much cheaper and "greener." Energy consumption during production is a major driver of cost and CO 2 emissions. The drying production step is one of the major energy consumers and cost drivers. The technological approach of "dry coating" allows the energy-intensive drying step to be eliminated for significant energy and cost savings. However, there are numerous emerging dry coating technologies that differ significantly in physics, chemistry, and readiness levels. Moreover, typical methodological procedures for technology selection remain less applicable to the early stages of technological development. Both issues raise the questions, "What is the most promising dry coating technology?" and "How do we identify it?" To answer these questions, a comprehensive, systematic technology benchmark was conducted. Following a four-step analytical approach, based on the nominal group technique, qualitative content analysis, and multicriteria decision analysis, different dry coating technologies were identified, analyzed, and cross-compared. This was performed qualitatively and quantitatively. We also forecast which factor will impact the application of the most promising technologies for CO 2 emission rate reductions and cost savings in 2030. In summary, four different technologies were identified with a high chance of technological breakthrough within the next 3-5 years. By applying these technologies, 4.76 million tons of CO 2 could be saved per year by 2030.

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Section: Technology Selection In the Context Of Emerging Technologies...mentioning

confidence: 99%

Future in Battery Production: An Extensive Benchmarking of Novel Production Technologies as Guidance for Decision Making in Engineering

Degen

Krätzig

2024

IEEE Trans. Eng. Manage.

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“…Technology assessment models tend to be generic or focused. Generic technology acceptance models assess a firm's general readiness to accept new technology and can be used for a specific technology, but a particular context is not needed [37,38]. Focused assessment models may center on a particular technology, like cloud services, or for more specific applications, like cloud services in manufacturing [23,39].…”

Section: Cloud Assessment Models or Frameworkmentioning

confidence: 99%

Creation of a Unified Cloud Readiness Assessment Model to Improve Digital Transformation Strategy

Smith¹

2022

IJDSA

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Digital transformation can disrupt any organization in any industry, but few organizations have successfully transformed. For an organization to transform digitally, a firm must adopt new technologies that enable it to change how it creates value. One of the most crucial new technologies organizations need to facilitate digital transformation is Cloud services. Cloud-based technologies are necessary for digital transformation because they allow a firm to cost-effectively obtain needed infrastructure capacity, processing, and developmental flexibility to support advanced analytical tools and methods. Implementing and adopting cloud services can be challenging and requires firm leaders and transformation teams to have an effective strategy that requires understanding a company's cloud readiness. Multiple organizational factors can impact cloud readiness, and depending on a firm's strengths or weaknesses, each element will support or hinder the adoption of cloud services. Understanding a firm's cloud readiness has been shown to improve an organization's adoption of cloud services, but assessing readiness can also be challenging since different assessment models determine readiness in varying ways. Readiness assessment models range from generic technology adoption models to specific cloud services readiness models, and a unified approach is needed that combines the strengths of each model to create a more comprehensive assessment model. A meta-analysis of current readiness assessment models was conducted to identify what crucial factors of an organization need to be assessed. The findings show that there seems to be significant agreement that a company's strategy, current technology, existing operations, and external factors are crucial readiness factors. More recent assessment models also identify gaps in past models, especially on human capital capabilities, system flexibility needs, and security. A more unified cloud assessment model is proposed based on the analysis showing that a firm's readiness should be based on seven crucial factors: strategy, technology, current operations, external requirements, human capital, system flexibility, and security. The new proposed assessment model provides a more comprehensive assessment of a firm's cloud readiness and enables organizations to create an improved adoptions strategy that will better support a company's digital transformation.

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“…In this section, STAR Levels are presented as orthogonal axes of readiness: autonomous capability and trust. The need to balance readiness levels, while important to any program [60], becomes acute in this two-dimensional representation of readiness. As depicted in Figure 3, high trust and low readiness is associated with over-confidence of the autonomous operations, while low trust and high readiness is associated with under-confidence.…”

Section: Space Trusted Autonomy Readiness Levelsmentioning

confidence: 99%

Space Trusted Autonomy Readiness Levels

Hobbs¹,

Lyons²,

Feather³

et al. 2022

Preprint

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Technology Readiness Levels are a mainstay for organizations that fund, develop, test, acquire, or use technologies. Technology Readiness Levels provide a standardized assessment of a technology's maturity and enable consistent comparison among technologies. They inform decisions throughout a technology's development life cycle, from concept, through development, to use. A variety of alternative Readiness Levels have been developed, including Algorithm Readiness Levels, Manufacturing Readiness Levels, Human Readiness Levels, Commercialization Readiness Levels, Machine Learning Readiness Levels, and Technology Commitment Levels. However, while Technology Readiness Levels have been increasingly applied to emerging disciplines, there are unique challenges to assessing the rapidly developing capabilities of autonomy. This paper adopts the moniker of Space Trusted Autonomy Readiness Levels to identify a two-dimensional scale of readiness and trust appropriate for the special challenges of assessing autonomy technologies that seek space use. It draws inspiration from other readiness levels' definitions, and from the rich field of trust and trustworthiness.

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Balanced readiness level assessment (BRLa): A tool for exploring new and emerging technologies.

Cited by 60 publications

References 25 publications

Future in Battery Production: An Extensive Benchmarking of Novel Production Technologies as Guidance for Decision Making in Engineering

Future in Battery Production: An Extensive Benchmarking of Novel Production Technologies as Guidance for Decision Making in Engineering

Creation of a Unified Cloud Readiness Assessment Model to Improve Digital Transformation Strategy

Space Trusted Autonomy Readiness Levels

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