In this paper, we study an analytical approach to selecting expansion locations for retailers selling add-on products whose demand is derived from the demand for a separate base product. Demand for the add-on product ...
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In this paper, we study an analytical approach to selecting expansion locations for retailers selling add-on products whose demand is derived from the demand for a separate base product. Demand for the add-on product is realized only as a supplement to the demand for the base product. In our context, either of the two products could be subject to spatial autocorrelation where demand at a given location is impacted by demand at other locations. Using data from an industrial partner selling add-on products, we build predictive models for understanding the derived demand of the add-on product and establish an optimization framework for automating expansion decisions to maximize expected sales. Interestingly, spatial autocorrelation and the complexity of the predictive model impact the complexity and the structure of the prescriptive optimization model. Our results indicate that the formulated models are highly effective in predicting add-on-product sales, and that using the optimization framework built on the predictive model can result in substantial increases in expected sales over baseline policies.
Local delay predictions are crucial for optimizing airport capacity management, enhancing overall resilience, efficiency, and effectiveness of airport operations. This paper delves into the development and comparison ...
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Local delay predictions are crucial for optimizing airport capacity management, enhancing overall resilience, efficiency, and effectiveness of airport operations. This paper delves into the development and comparison of state-of-the-art predictiveanalytics techniques-spanning rule- based simulations, queuing models, and data-driven approaches-and demonstrates how they can empower informed decision-making toward mitigating the impact of potential delays across the whole spectrum of capacity management initiatives-from long-term strategic capacity planning to near real-time air traffic flow management. Using real-world data for four major airports in Southeast Asia, we comprehensively assess the performance of different methods and highlight the improved predictive capabilities achievable through data-driven methods and the incorporation of sophisticated features. Results show that (i) embedding queuing model features into machine learning models effectively captures congestion dynamics and nonlinear patterns, resulting in an improvement in predictive accuracy;(ii) incorporating advanced day-of features - lightning strikes, wind conditions, and propagated delays from prior hours - further enhances prediction accuracy, yielding MAE gains ranging from 15% to 30%, contingent on the specific airport;(iii) in cases where limited information is available (years to months in advance of operations), conventional simulation and queuing models emerge as robust alternatives. Ultimately, we conceptualize and validate a delay prediction framework for airport capacity management, characterizing the different planning phases based on their specific delay prediction requirements and identifying appropriate methods accordingly. This framework offers practical guidance to airport authorities, enabling them to effectively leverage delay predictions into their airport capacity management practices.
We review and analyze the farming (upstream agribusiness supply chain) research literature since 1965 to identify farming research opportunities for operations management (OM) researchers. A majority of reviewed paper...
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We review and analyze the farming (upstream agribusiness supply chain) research literature since 1965 to identify farming research opportunities for operations management (OM) researchers. A majority of reviewed papers in our corpus, until the turn of the 21st century, primarily focus on improving operational efficiency and effectiveness of farming using optimization techniques. However, during the last two decades, farmers' welfare and the interests of other stakeholders have drawn OM researchers' attention. This expanded focus on farming research has become possible due to the proliferation of mobile communication devices and the Internet as well as advancements in information technology platforms and social media. Our review also shows that there is a paucity of OM literature that leverages increased data availability from the emergence of precision agriculture and blockchain to address major challenges for the farming sector emanating from climate change, natural disasters, food security, and sustainable and equitable agriculture, among others. Big data, in conjunction with opportunities for field-based experimentation, artificial intelligence and machine learning, and integration of predictive and prescriptive analytics, can be leveraged by OM scholars engaged in farming research. We zero in on specific questions, issues, and opportunities for research in farming.
What-if analysis (WIA) is essential for data-driven decision-making, allowing users to assess how changes in variables impact outcomes and explore alternative scenarios. Existing WIA research primarily supports the wo...
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For extrapolation, climate change and other meteorological analysis, a study of past and current weather events is a prerequisite. NASA (National Aeronautics and Space Administration) has been able to develop a model ...
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For extrapolation, climate change and other meteorological analysis, a study of past and current weather events is a prerequisite. NASA (National Aeronautics and Space Administration) has been able to develop a model capable of predicting various weather data for any location on the Earth, including locations lacking weather stations, weather satellite coverage, and other weather measuring instruments. This paper evaluates the prediction accuracy of the NASA temperature data with respect to NiMet (Nigerian Meteorological Agency) ground truth measurement, using Akwa Ibom Airport as a case study. Exploratory data analysis (descriptive and diagnostic analyses) of temperature retrieved from NiMet and NASA was performed to give a clear path to follow for predictive and prescriptive analyses. Using 2783 days of weather data retrieved from NiMet as ground truth, the accuracy of NASA predictions with the corresponding resolution was calculated. Mean absolute error (MAE) of 2.184 degrees C and root mean square error (RMSE) of 2.579 degrees C, with a coefficient of determination (R-2) of 0.710 for maximum temperature, then MAE of 0.876 degrees C, RMSE of 1.225 degrees C with a coefficient of determination (R-2) of 0.620 for minimum temperature was discovered. There is a good correlation between the two datasets;hence, a model can be developed to generate more accurate predictions, using the NASA data as input. predictive and prescriptive analyses were performed by employing five prediction algorithms: decision tree regression, XGBoost regression and MLP (multilayer perceptron) with LBFGS (limited-memory Broyden-Fletcher-Goldfarb-Shanno) optimizer, MLP with SGD (stochastic gradient) optimizer and MLP with Adam optimizer. The MLP LBFGS algorithm performed best, by significantly reducing the MAE by 35.35% and RMSE by 31.06% for maximum temperature, accordingly, MAE by 10.05% and RMSE by 8.00% for minimum temperature. Results obtained show that given sufficient data, plugging NAS
Recently, the term "Industry 4.0" has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital pro...
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Recently, the term "Industry 4.0" has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive analytics, predictiveanalytics and prescriptiveanalytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.
While many organizations use business intelligence and analytics in business functions including Supply Chain, Finance, Accounting and Marketing, they have taken little advantage of this in the Human Resources (HR) ma...
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While many organizations use business intelligence and analytics in business functions including Supply Chain, Finance, Accounting and Marketing, they have taken little advantage of this in the Human Resources (HR) management area. Seeing tremendous opportunities in the use of analytics, businesses are taking big measures, such as creating a culture of making critical decisions validated by data driven approaches and hiring analytics professionals in areas that promises high rates of return. Experts continue to emphasize the importance of analytics for HR to transform itself into a more effective resource for the organization. In this paper the authors study the current and the near future states of analytics in Human Resources area. With information collected from leading job search engines, *** and ***, the authors have modeled trends in hiring analytics professionals in different functional areas of business. The authors compared the HR analytics trend with trends in hiring analytics professionals in Supply Chain, Finance, Accounting and Marketing functions. The extent to which companies are hiring analytics professionals now should be a good indication of analytics adoptions in the future.
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