Artificial Intelligence (AI) is poised to revolutionize all aspects of business, particularly new-product development (NPD). Currently, our approach to NPD has remained largely unchanged for decades, yielding stubbornly poor results: only 30% of NP development projects become commercial successes. However, the Artificial Intelligence revolution is set to alter this landscape significantly!. Leading early-adopter firms demonstrate that AI not only finds many applications in NPD, but also offers substantial payoffs, such as 50% reductions in development times. Thes article provides an outline of the diverse and powerful applications of AI in NPD, offering numerous examples from leading companies. Examples include GE's use of digital models and twins to quickly test product design time in turbine development; BASF's use of AI to identify new molecules for use in customer's formulations; and AI to generate new-product ideas, identify new product opportunities, and even create new product concepts. Our exploratory journey begins at the idea stage and traverses the entire new-product process to the post-launch period. While AI might still resemble science fiction to many, that future is no longer fiction – it's here now. AI has arrived in full force! With an adoption window of about 13 years, the time is now to embrace AI in NPD in your business: AI will become a major milestone in NPD, perhaps the most important, within the decade (Figure 1)

In our globalized and digitalized post-COVID world, Knowledge and Technology Transfer Offices and Commercialization Companies (collectively hereafter TTOs) strive to increase and diversify their research impact portfolio. To achieve this target, TTOs should clearly redefine “research impact” and communicate it effectively, engage strategically based on an intersubjective understanding and mutual benefits with their stakeholders to be able to identify the market's unmet needs, and proactively foster an innovation pipeline aligned with their socio-economical innovation ecosystem. Drawing on the author's lived experience, this article explains the key characteristics of an impactful TTO and suggests approaches to improve its performance. In addition, this article discusses the role of such a TTO in an ever-evolving research commercialization ecosystem. Also, it proposes a practical model for a TTO to maximize its opportunity to create impact.

The consumer-driven fast fashion (FF) approach has become a formidable business strategy to create, boost, and sustain competitive advantage in the global fashion industry. FF firms in China have adopted this approach, together with its supporting supply chain management (SCM) practices, to yield superior supply chain (SC) performance to compete with their global rivals. We seek to identify the critical success factors (CSFs) of SCM and their SC performance implications focusing on FF in China. We conducted a large-scale survey of FF firms in China and analyzed the data. We identify six CSFs of SCM that are significantly and positively associated with six SC performance measures of FF in China. Specifically, we find that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Consumer Orientation</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Production Management</i> are the two most prominent CSFs of SCM of FF in China, as they are significantly and positively associated with all the six SC performance measures identified. In addition, we conducted a case on a prominent FF firm in China to validate and triangulate the empirical findings. We provide practical insights to FF firms in China, enabling them to prioritize and allocate necessary resources to develop, maintain, and enhance the most relevant and appropriate CSFs of SCM to deliver desirable SC performance outcomes.

For management purposes, visualizations are not merely for reporting, they can be strategically deployed to support decision-making. If properly designed, and designed well, visualizations can be used to trigger and structure the necessary conversations between stakeholders, so enabling much needed discussions and even healthy debates. As a practical demonstration of designing and developing a purposeful visualization, an illustrative industrial example is provided which depicts a portfolio of science and technology projects within a product pipeline. This new visualization has a novel format and logic-driven layout that exemplifies a richness of relevant information for managers and executives. The visualization brings together and shows: total budget and contributions from strategic business units; current fiscal year spend; key performance indicators for each project and an overall assessment of the domain; decision gates; degrees of difficulty to advance along the steps in technology readiness; confidence of delivery; and, initial market applications.

As data-driven decision making becomes prevalent, research needs to provide more evidence to direct user decision making, particularly concerning transportation systems. In concurrence, ridesharing has been touted to reduce driving-while-intoxicated fatalities, albeit prior studies have provided inconsistent findings. A limitation of prior research on this topic is lacking adequate experimental controls while addressing the impact of potential confounds. This issue may affect potential assumptions and conclusions on whether the deployment of ridesharing services has led to a considerable reduction in driving-while-intoxicated fatalities. The present article leverages statistical modeling to control age, education, vehicle miles traveled, and metropolitan size. It reveals that ridesharing represented a 13.8% decline in driving-while-intoxicated fatalities among youths’ ages 17–34, but without significantly affecting drivers’ ages 35–65. Also, the results suggest that city population, vehicle miles traveled, and educational attainment can affect younger adults, whereas the same features were not significant for older adults. Furthermore, the article suggests that the initiation of UberX can serve as a ride-planning option to reduce driving-while-intoxicated fatalities among younger rather than older drivers. Based on the analysis results, multiple implications for transportation platform and software engineering managers are provided, especially in the areas of dispatch algorithms, requirement analysis, and ridesharing security and safety.

Incorporating artificial intelligence (AI) into engineering practice is becoming increasingly prominent, driven by the demand for more efficient and reliable solutions to engineering challenges. Despite numerous frameworks and methodologies that showcase how AI techniques can enhance engineering workflows, there is still a notable absence of simple and generic decision-making paradigms that comprehensively facilitate the adoption of AI in engineering settings. Lateral thinking, a widely used problem-solving approach rooted in reasoning, has been extensively employed in management spheres. This article introduces a new approach by drawing inferences from generic perspectives on the pros and cons of AI adoption, broadly classified under the six lateral thinking hats to assist engineering managers in navigating AI adoption decisions. The recommended actions inferred from these perspectives are then posited as guidance notes for engineering managers to leverage the benefits and mitigate the risks of AI adoption.

This paper introduces an innovative PTSQM model for the software development process of automotive powertrain electronic control systems. This model solves the problems that are faced in the research and development process of modern automotive powertrain systems by integrating the QFD quality method with the ASPICE standard model. This paper uses questionnaires and data statistics methods to analyze the requirements of the automotive electronics software R&D team. The model design is carried out in three different directions: demand management, change management, and quality management. The PTSQM model combines big data technology with the QFD method to efficiently and accurately realize the technical transformation from customer requirements to detailed module design. Furthermore, the practical application of the model in a powertrain development project is analyzed in this paper. The results show that the PTSQM model has achieved all the quality assurance activities in the effective development stage of the project. It also fulfils all the quality objectives. As a result, the overall quality of the software has been effectively improved. The practical data demonstrate the effectiveness and adaptability of the PTSQM model, indicating that the model is suitable for the powertrain software development process, especially for those direct-to-customer companies.

Interested in publishing in an IEEE journal, but do not have the time to write a long and in-depth manuscript? <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Engineering Management Review</small> (EMR) offers authors the opportunity to submit Technology Manager's Notebook (TMN) articles to share their insights in management of engineering and technology. This is not only particularly designed for industry practitioners but also for researchers who are experts in a field who want to summarize their key insights. We call these evidence-based articles where authors reflect upon their personal experiences on a certain topic. Experience is not necessarily a matter of age but of interesting and useful insights.