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Artificial intelligence (AI) can enhance, enable, or replace traditional open innovation (OI) practices, changing the scope and efficiency of both outside-in and inside-out OI. This article provides a comprehensive framework to analyze AI's influence on OI, supported by illustrative examples, and outlines the key implications for organizations and researchers. The co-evolutionary relationship between AI and OI will be a central focus in both research and practice moving forward. [ABSTRACT FROM AUTHOR]

Predictive analytics is the approach to business analytics that answers the question of what might happen in the future. Although predictive information is critical for making forward-looking decisions, traditional approaches struggle to cope with the increasing uncertainty and complexity that characterise modern supply chains. Simulation is limited by insufficient timeliness, while machine learning is constrained by poor interpretability and data scarcity. Inspired by the complementary nature of simulation and machine learning, an integrated predictive analytics approach is proposed and applied to a humanitarian supply chain. By coupling simulation and machine learning, predictive models can be developed with limited historical data, and pre-crisis performance assessment can be performed to facilitate timely and informed decisions. The proposed approach enables managers to gain valuable insights into the complex evolution of the uncertain future, which also opens up the possibility of further integration with optimisation and digital twins. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) and prescriptive analytics are increasingly being reported as having transformative powers to enable resilient supply chains (SC). Despite such a benefit, and the increase in popularity of AI and analytics in general, research is largely fragmented into streams based on different types of AI technologies across several SC contexts and through varying disciplinary perspectives. In response, we curate and synthesise this fragmented body of knowledge by conducting a systematic literature review of AI research in supply chains that have been published in 3* and 4* Chartered Association of Business Schools (CABS) ranked journals between 2000 and 2023. The search strategy retrieved 5, 293 studies, of which 76 were identified as primary papers relevant to this study. The study contributes to the accumulative building of knowledge by extending theoretical discourse about the specificities of AI for prescriptive analytics to enable SC resilience. This study proposes a strategic AI resilience framework to support SC decision-makers enhance the use and value of prescriptive analytics as an enabler to developing resilient SC. We make the call to action for an orchestrated effort within and between academic disciplines and organisations that are guided by a research agenda to guide future research initiatives. [ABSTRACT FROM AUTHOR]

Sales managers are unlikely to reap the benefits of implementing predictive analytics applications when salespeople show aversion to or lack understanding of these applications. For managers, it is essential to understand which factors mitigate or exacerbate these challenges. This article investigates these factors by studying the implementation of an application that predicts customer churn. Using 9.7 million transactions from a business-to-business company, the authors develop a predictive model of customer churn, implement it in a field experiment, and study its treatment effects using causal forests. Furthermore, the authors manipulate one specific mitigation strategy proposed by prior literature: the fostering of users' realistic expectations regarding the accuracy of an algorithm. The results show that the effectiveness of the churn prediction application strongly depends on customer characteristics (most importantly the predicted churn probability and prior revenue) and salesperson characteristics (technology perceptions, abilities, and selling orientations). Fostering realistic expectations improves the effectiveness of the churn prediction only under very specific circumstances. Two follow-up stimuli-based experiments conceptually replicate key results of the field study. Therefore, this article helps build theory on predictive sales analytics and provides specific guidance to managers aiming to increase their return on analytics investments. [ABSTRACT FROM AUTHOR]

Project managers need reliable predictive analytics tools to make effective project intervention decisions throughout the project life cycle. This study uses Machine learning (ML) to enhance the reliability in project cost forecasting. A XGBoost forecasting model is developed and computational experiments are conducted using real data of 110 projects representing 1268 cost data points. The developed model performs better than some Earned value management (EVM), ML (Random forest, Support vector regression, LightGBM, and CatBoost), and non-linear growth (Gompertz and Logistic) models. The model produces more accurate estimates at the early, middle, and late stages of the project execution, allowing for early warning signals for more effective cost control. In addition, it shows more accurate estimates in most projects tested, suggesting consistency when repeatedly used in practice. Project forecasting studies mainly used ML to estimate the project duration; a few ML studies estimated the project cost at the project's conceptual stage. This study uses real data and EVM metrics, proposing an effective XGBoost model for forecasting the cost throughout the project life cycle. [ABSTRACT FROM AUTHOR]

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The train stowage planning problem (TSPP) of the steel industry aims to select steel coils and allocate them to trains cost-effectively. It is a key component in the transportation of steel products. This study focuses on a multiobjective train stowage planning problem (MoTSPP) that maximises both the loading efficiency of the crane and the loading rate of the train. The MoTSPP also considers operation constraints related to steel coils, train wagons, and stowage modes in real-life railway transportation. An integer programming model is established to mathematically describe this problem. To obtain an efficient solution, a multiobjective analytical evolutionary algorithm (MAEA) that combines evolutionary algorithm (EA) with machine learning (ML) is presented. The EA part is a multiobjective differential evolution that introduces guided evolution and parameter adaptation to produce promising individuals and parameters, respectively. ML part adopts clustering algorithm and surrogate model to accelerate the search. Extensive comparisons and insight analyses are conducted from various perspectives to demonstrate the effectiveness and efficiency of the MAEA for solving the MoTSPP. [ABSTRACT FROM AUTHOR]

The article shares views on opportunities and challenges posed by big data (BD), mobile health and artificial intelligence to dentistry and craniofacial research. Topics discussed include concerns about privacy, security and data sharing in relation to the use of BD analysis, the Dental, Oral, Medical Epidemiological (DOME) electronic records-based BD study, and the role of combining electronic health record with mobile health tools in improving data sets and in advancing BD research.

Production systems have to be adapted continuously to changing circumstances. This means that the responsible production planner has to frequently make decisions about reconfiguring complex systems under uncertainty whose outcome greatly affects the company's business success. Discrete-event simulation is one powerful tool to support the necessary analysis and scenario evaluation, but still remains time-consuming and tricky to set up and to maintain. When implemented as a digital twin of the production system, the simulation model can maintain a high degree of accuracy over a long time period. An approach to realise this potential is presented and illustrated in this paper with a use case from the automotive industry. This paper contributes several new methods and findings to the development of digital twins of production systems: Firstly, it demonstrates how exceptional events in the validation of the digital twin can be handled. Secondly, it shows how structural changes in the system can be discovered using data on machine activity and process mining. Thirdly, the paper introduces a possibility on how to assess the accuracy of the digital twin. Furthermore, it demonstrates how to assess the robustness of the digital twin to estimation errors in machine processing times. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape, digitising the operations and facilities in a supply chain network is essential to make the system autonomous and develop strategies for enhancing resilience, transparency, and efficiency. The COVID-19 pandemic highlights the necessity of sustainable solutions for the hybrid mode of operations. To overcome several challenges, including price optimisation, demand forecasting under uncertainty, supply-demand gap reduction, take into account vulnerability, competitive business environment and risk, the supply chain needs to be streamlined with technology-driven infrastructures incorporating physical and information flow into overall supply chain processes. The digitisation aspect encompasses adopting cutting-edge technologies such as enterprise resource planning (ERP) for supply chain visibility, e-hailing platforms, real-time data analytics, the Internet of Things (IoT) and Internet of Behaviour (IoB), blockchain-driven technology, as well as additive manufacturing, enabling seamless connectivity and communication among diverse stakeholders. This revolution enables strategic integration of various entities and state of the art data-driven decision-making, providing real-time insights into logistics movements, demand forecasting, production planning and inventory levels. Supply chain digitisation and management emphasises collaboration with supply chain partners to identify important factors, optimise costs and enhance overall supply chain resilience. Digitisation and management are technological evolutions and strategic shifts integrating analytical tools, allowing businesses to formulate models to improve performance. The implementation of blockchain-driven technology solidifies trust and safety transactions by creating an immutable and transparent log, mitigating threats and enhancing traceability. Digitisation and management exemplify a transformative journey towards a more connected, data-driven, and agile global supply chain ecosystem. [ABSTRACT FROM AUTHOR]

While sport fandom can be assumed to be inclusive, a deeper exploration of discourses around fandom exposes alternative perspectives. Using the frameworks of cultural boundaries and critical discourse studies, we explored how sport fans use Twitter to create, maintain, and transform cultural boundaries of sport fandom. We used tweets from a season of the Toronto Blue Jays baseball team as a case. Data were collected via Visual Twitter Analytics software focusing on tweets containing #LetsRise and #BlueJays. From the larger data set, we selected 172 tweets to examine using critical discourse analysis and ideological structures of discourse. Findings demonstrate that discourses of loyalty, consumption, and unity have plural meanings and are used to draw boundaries that are simultaneously fluid and rigid. Thus, we argue that fans engage in an active process of determining who is and is not included in fan cultures through Twitter use. [ABSTRACT FROM AUTHOR]

Supply chain decarbonisation has become a strategic requirement in the era of a net-zero economy. Despite the significant role of Big Data Analytics (BDA) in decarbonising the supply chain (SC), no prior study has evaluated it systematically. The present study aims to provide a systematic literature review on the applications and outcomes of big data analytics in SC decarbonisation. A total of 69 papers on applying BDA technology for supply chain decarbonisation published between 2016 and 2021 have been selected following the PRISMA protocol. The findings show that the topic is evolving. Studies employed methods such as surveys (30), case studies (11), and conceptual research designs (8). Thematic analysis reveals that 65% of the studies are grounded in resource-advantage theories, organisational theories, and system theories. Studies from India and China (35%) dominate the topic, while most studies have been conducted on the food and manufacturing industries. Further, this study applied the Antecedent-Decision-Outcomes (ADO) framework in BDA-based SC decarbonisation. Antecedents include BDA resources and capabilities, workforce skills, and supplier capabilities. Decisions refer to improving decision-making across the supply chain. Outcomes refer to improving decarbonisation, sustainable growth, and sustainable innovativeness. Future research directions and questions are provided using the Theory-Context-Methodology (TCM) framework. [ABSTRACT FROM AUTHOR]

In public libraries, especially those in rural settings, it is important that every dime of library funding is leveraged effectively into serving the community. As part of a year-long project beginning in January 2023, we are evaluating item-level cost-effectiveness for each circulating item housed at the public library in Lakeville, Indiana. Through the use of big(ish) data, some custom Python scripting, and machine learning algorithms we hope to answer: How much money is saved by library patrons through their use of the public library's physical collection? How much money is saved by the community through the operation of a public library based on the use of the circulating collection? And are there any non-obvious traits which make an item or title a more or less cost-effective circulating asset? In this column, I will describe the scripts, share initial findings, discuss challenges, and investigate next steps. [ABSTRACT FROM AUTHOR]

In the U.S., multihospital systems (MHSs) charge significantly higher prices for hospital services than stand-alone hospitals. Rivalry restraint theory suggests that MHS with multimarket contact (MMC) can tacitly collude and mutually forebear from price competition to keep their prices above competitive levels. We posit that the success of such MMC-induced rivalry restraints (the truce) is affected by two conflicting roles of IT at the corporate level and market unit levels, respectively. The corporate parent seeks to standardize IT applications enterprise-wide to coordinate market units as a means of jointly implementing the rivalry restraint strategy and keeping prices high enterprise-wide. However, market units, i.e., the member hospitals of MHS clustered in geographic patient markets, face competitive pressures to reduce their service costs. Market units seek to use differentiated IT applications to achieve cost reductions, which then fuel price competition in local markets, jeopardize the sustainability of the truce, and weaken the enterprise-wide price effects of the corporate parent's rivalry restraint strategy. In a longitudinal study of 195 multihospital systems in the U.S. in the 2005-2013 time period, we found support for these ideas. The corporate-wide standardization of the operational IT of MHS complements the rivalry restraint strategy to increase enterprise-wide prices. Market units' use of differentiated analytical IT reduces costs in local markets and weakens the price effects of the rivalry restraint strategy. The study advances IS research and practice by theorizing how the corporate-level and the market unit-level IT of a multi-unit, multimarket (MUMM) organization can have opposing moderating effects on the link between MMC and the average prices charged by the MUMM organization. [ABSTRACT FROM AUTHOR]

The Industry 4.0 revolution is changing the manufacturing landscape. A broad set of new technologies emerged (including software and connected equipment) that digitise manufacturing systems. These technologies bring new vitality and opportunities to the manufacturing industry, but they also bring new challenges. This paper focuses on the impact of Industry 4.0 on production planning approaches and software. We first propose a digital twin framework that integrates production planning systems and frontier technologies. The frontier technologies that may impact production planning software are the internet of things, cloud manufacturing, blockchain, and big data analytics. Second, we provide a state-of-the-art on the application of each technology in the production planning, as well as a detailed analysis of the benefit and application status. Finally, this paper discusses the future research and application directions in the production planning. We conclude that Industry 4.0 will lead to the construction of data-driven models for production planning software. These tools will include models built accurately from data, account for uncertainty, and partially actuate the decision autonomously. [ABSTRACT FROM AUTHOR]

In the realm of neuroscience, mapping the three-dimensional (3D) neural circuitry and architecture of the brain is important for advancing our understanding of neural circuit organization and function. This study presents a novel pipeline that transforms mouse brain samples into detailed 3D brain models using a collaborative data analytics platform called Texera. The user-friendly Texera platform allows for effective interdisciplinary collaboration between team members in neuroscience, computer vision, and data processing. Our pipeline utilizes the tile images from a serial two-photon tomography/TissueCyte system, then stitches tile images into brain section images, and constructs 3D whole-brain image datasets. The resulting 3D data supports downstream analyses, including 3D whole-brain registration, atlas-based segmentation, cell counting, and high-resolution volumetric visualization. Using this platform, we implemented specialized optimization methods and obtained significant performance enhancement in workflow operations. We expect the neuroscience community can adopt our approach for large-scale image-based data processing and analysis.

The training workflow of neural networks can be quite complex, potentially time‐consuming, and require specific hardware to accomplish operation needs. This study presents a novel analytical video‐based approach for vehicle tracking and vehicle volume estimation at nighttime using a monocular traffic surveillance camera installed over the road. To build this approach, we employ computer vision‐based algorithms to detect vehicle objects, perform vehicle tracking, and vehicle counting in a predefined detection zone. To address low‐illumination conditions, we adapt and employ image noise reduction techniques, image binary conversion, image projective transformation, and a set of heuristic reasoning rules to extract the headlights of each vehicle, pair them belonging to the same vehicle, and track moving candidate vehicle objects continuously across a sequence of video frames. The robustness of the proposed method was tested in various scenarios and environmental conditions using a publicly available vehicle dataset as well as own labeled video data. [ABSTRACT FROM AUTHOR]