The increasing availability of data and advancements in computational intelligence have accelerated the adoption of data-driven methods (DDMs) in product development. However, their integration into product development remains fragmented. This fragmentation stems from uncertainty, particularly the lack of clarity on what types of DDMs to use and when to employ them across the product development lifecycle. To address this, a necessary first step is to investigate the usage of DDM in engineering design by identifying which methods are being used, at which development stages, and for what application. This paper presents a PRISMA systematic literature review. The V-model as a product development framework was adopted and simplified into four stages: system design, system implementation, system integration, and validation. A structured search across Scopus, Web of Science, and IEEE Xplore (2014--2024) retrieved 1{,}689 records. After screening, 114 publications underwent full-text analysis. Findings show that machine learning (ML) and statistical methods dominate current practice, whereas deep learning (DL), though still less common, exhibits a clear upward trend in adoption. Additionally, supervised learning, clustering, regression analysis, and surrogate modeling are prevalent in design, implementation, and integration system stages but contributions to validation remain limited. Key challenges in existing applications include limited model interpretability, poor cross-stage traceability, and insufficient validation under real-world conditions. Additionally, it highlights key limitations and opportunities such as the need for interpretable hybrid models. This review is a first step toward design-stage guidelines; a follow-up synthesis should map computer science algorithms to engineering design problems and activities.

HP has announced a lower-than-expected profit outlook for the coming year. HP has announced a lower-than-expected profit outlook for the coming year. Up to 6,000 jobs are to go at HP worldwide in the next three years as the US computer and printer maker increasingly adopts AI to speed up product development. Announcing a lower-than-expected profit outlook for the coming year, HP said it would cut between 4,000 and 6,000 jobs by the end of October 2028. It has about 56,000 employees.

Customer feedback in industrial forums reflect a rich but underexplored source of insight into real-world product experience. These publicly shared discussions offer an organic view of user expectations, frustrations, and success stories shaped by the specific contexts of use. Yet, harnessing this information for systematic analysis remains challenging due to the unstructured and domain-specific nature of the content. The lack of structure and specialized vocabulary makes it difficult for traditional data analysis techniques to accurately interpret, categorize, and quantify the feedback, thereby limiting its potential to inform product development and support strategies. To address these challenges, this paper presents the User eXperience Perception Insights Dataset (UXPID), a collection of 7130 artificially synthesized and anonymized user feedback branches extracted from a public industrial automation forum. Each JavaScript object notation (JSON) record contains multi-post comments related to specific hardware and software products, enriched with metadata and contextual conversation data. Leveraging a large language model (LLM), each branch is systematically analyzed and annotated for UX insights, user expectations, severity and sentiment ratings, and topic classifications. The UXPID dataset is designed to facilitate research in user requirements, user experience (UX) analysis, and AI-driven feedback processing, particularly where privacy and licensing restrictions limit access to real-world data. UXPID supports the training and evaluation of transformer-based models for tasks such as issue detection, sentiment analysis, and requirements extraction in the context of technical forums.

Humanity is progressing towards automated product development, a trend that promises faster creation of better products and thus the acceleration of technological progress. However, increasing reliance on non-human agents for this process introduces many risks. This perspective aims to initiate a discussion on these risks and appropriate mitigation strategies. To this end, we outline a set of principles for safer AI-driven product development which emphasize human oversight, accountability, and explainable design, among others. The risk assessment covers both technical risks which affect product quality and safety, and sociotechnical risks which affect society. While AI-driven product development is still in its early stages, this discussion will help balance its opportunities and risks without delaying essential progress in understanding, norm-setting, and regulation.

Online customer data provides valuable information for product design and marketing research, as it can reveal the preferences of customers. However, analyzing these data using artificial intelligence (AI) for data-driven design is a challenging task due to potential concealed patterns. Moreover, in these research areas, most studies are only limited to finding customers' needs. In this study, we propose a game theory machine learning (ML) method that extracts comprehensive design implications for product development. The method first uses a genetic algorithm to select, rank, and combine product features that can maximize customer satisfaction based on online ratings. Then, we use SHAP (SHapley Additive exPlanations), a game theory method that assigns a value to each feature based on its contribution to the prediction, to provide a guideline for assessing the importance of each feature for the total satisfaction. We apply our method to a real-world dataset of laptops from Kaggle, and derive design implications based on the results. Our approach tackles a major challenge in the field of multi-criteria decision making and can help product designers and marketers, to understand customer preferences better with less data and effort. The proposed method outperforms benchmark methods in terms of relevant performance metrics.

The Engineering, Procurement and Construction (EPC) businesses operating within the energy sector are recognizing the increasing importance of Artificial Intelligence (AI). Many EPC companies and their clients have realized the benefits of applying AI to their businesses in order to reduce manual work, drive productivity, and streamline future operations of engineered installations in a highly competitive industry. The current AI market offers various solutions and services to support this industry, but organizations must understand how to acquire AI technology in the most beneficial way based on their business strategy and available resources. This paper presents a framework for EPC companies in their transformation towards AI. Our work is based on examples of project execution of AI-based products development at one of the biggest EPC contractors worldwide and on insights from EPC vendor companies already integrating AI into their engineering solutions. The paper covers the entire life cycle of building AI solutions, from initial business understanding to deployment and further evolution. The framework identifies how various factors influence the choice of approach toward AI project development within large international engineering corporations. By presenting a practical guide for optimal approach selection, this paper contributes to the research in AI project management and organizational strategies for integrating AI technology into businesses. The framework might also help engineering companies choose the optimum AI approach to create business value.

As a family company, we serve people and communities. When you work at Meijer, you’re provided with career and community opportunities centered around leadership, personal growth and development. Consider joining our family – take care of your career and your community! Meijer Rewards * Weekly pay * Scheduling flexibility * Paid parental leave * Paid education assistance * Care.com/back-up care assistance * Team member discount * Development programs for advancement and career growth Please review the job profile below and apply today! The Data Science team at Meijer leads the strategy, development and integration of Machine Learning and Artificial Intelligence at Meijer. Data Scientists on the team will drive customer loyalty, digital conversion, and system efficiencies by delivering innovative data driven solutions. This role works directly with product development, merchandising, marketing, operations, ITS, ecommerce, and vendor partners. **Position follows hybrid schedule: Monday-Wednesday in office, Thursday-Friday remote.** What You'll Be Doing * Deliver against the overall data science strategy to drive in-store and digital merchandising, marketing, customer loyalty, and operational performance * Partner with product development to define requirements which meet system and customer experience needs for data science projects * Partner with Merchandising, Supply Chain, Operations and customer insights to understand the journey that will be improved with the data science deliverables * Deliver end-to-end data science pipelines including custom algorithms, statistical models, machine learning and artificial intelligence functions to meet partner needs * Partner with product development and technology teams to deploy pipelines into production MLOps environment following Safe Agile methodology * Drive adoption and usage of analytical products and models * Anticipate future business needs and identify opportunities that make substantial difference for organization, and lead the development of analytics initiatives * Responsible to define, document and follow best practices for ML/AI development at Meijer * Own communication with data consumers (internal and external) to ensure they understand the data science products, have the proper training, and are following the best practices in application of data science products * Define and analyze Key Performance Indicators to monitor the overall health and measure value of the data products * Identify and scope, in conjunction with IT, the architecture of systems and environment needs to ensure that data science systems can deliver against strategy * Build and maintain relationships with key partners, suppliers and industry associations and continue to advance data science capabilities, knowledge and impact * This job profile is not meant to be all inclusive of the responsibilities of this position; may perform other duties as assigned or required What You'll Bring With You * Advanced Degree (MA/MS, PhD) in Mathematics, Statistics, Economics, or related quantitative field * Certifications: Azure Data Science Associate, Azure AI, Safe Agile * 4+ years of relevant data science experience in an applied role – preferable w/in retail, logistics, supply chain or CPG * Demonstrated strength in using: Python, Databricks, Azure ML, Azure Cognitive Service, SAS, R, SQL, PySpark, Numpy, Pandas, Scikit Learn, TensorFlow, PyTorch, AutoTS, Prophet, NLTK * Experience with Azure Cloud technologies including Azure DevOps, Azure Synapse, MLOps, GitHub * Experience working with large datasets and developing ML/AI systems such as: natural language processing, speech/text/image recognition, supervised and unsupervised learning models, forecasting and/or econometric time series models * Proactive and action oriented * Ability to collaborate with, and present to internal and external partners * Able to learn company systems, processes and tools, and identify opportunities to improve * Detail oriented and organized * Ability to meet production deadlines * Strong communications, interpersonal and organizational skills * Excellent written and verbal communication skills * Understanding of intellectual property rights, compliance and enforcement

A brand that is successful not only pays attention to its workers and the manufacturing process but also the product development is a success. There are two types of product development cycles: pre-launch and after-launch. This blog will examine the role of machine learning and artificial intelligence in product development. When a product is launched in the market, The brand's R&D department continues to look for ways to improve. It includes all stages involved in product development, from idea or concept to launch on the market. However, product development is different for different brands and products.

As the calendar turns to 2023, Intuit's leading technologists share the trends they'll be watching closely. Ranging from the generative AI phenomenon, the rise in data protection regulations, and implications of Web3 for the fintech landscape, to how "thinking like criminals" will pay off for companies looking to stave off bad actors, Intuit leaders weigh in on what the future holds. Opportunities to catalyze innovation abound for tech companies. Generative AI is rapidly becoming more powerful and more prominent, popularized by chatbots and apps such as ChatGPT and Lensa, but it still needs to develop and mature before it can safely be used in industries where the accuracy of statements are critical, such as finance or medicine. Within the next several years, generative AI will likely play a pivotal role in helping create personalized conversational systems to provide financial or medical advice and guidance directly to customers.

CIOs must keep an eye on emerging technologies. The following emerging technology innovations could impact businesses in 2023. This decade of IT is significant as innovations are responsive to the turbulent economy. So, to provide a glimpse of how emerging technology will impact businesses in 2023, here is a compilation of leading technologies that hold the potential for enterprises to embark on a journey of innovation and growth. The following technologies are also a recap for leaders to experience the advancements they grabbed in 2022.