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AbstractBased on the practice of a Chinese food manufacturer, this article develops a predictive and prescriptive analytics research on a multiperiod risk‐averse newsvendor problem with nonstationary demands. We start with a predictive analysis that aims to transform the nonstationary demand series into a predictable stationary one, and then develop inventory models for deriving prescriptive decisions based on transformed stationary series. When transforming the nonstationary demand series, we consider three commonly used methods—detrending (DET), differencing (DIT), and percentage change transformations (PCT)—which all effectively convert nonstationary demand series into stationary ones. These methods not only have desired simplicity and interpretability but also provide better predictive performance than the autoregressive integrated moving (ARIMA) process. Moreover, we develop an ensemble of the three transformation methods following the model averaging approach, which provides comparable predictions as the machine learning approaches. When developing prescriptive inventory decisions, we construct dynamic risk‐averse newsvendor models for the three methods having different structures, and find that the optimal order quantities under DET and DIT monotonically change as the newsvendor becomes more risk‐averse, but the optimal order quantity under PCT may not. Similarly, we also develop a heuristic ensemble of the inventory decisions under the three methods, which can lead to better profit performance. An extensive numerical simulation based on the manufacturer's historical data set shows that the heuristic ensemble inventory decision outperforms the sole decision generated by every transformation method and achieves an average performance improvement up to 92.25%. Several extensions are also considered to confirm the robustness of our findings.

ABSTRACTProblem statement: We present a data‐driven analytics study of a Chinese fashion retailer. The retailer fulfills cross‐border orders using online platforms, but faces inventory problems in its overseas warehouses, owing to operational complexities, such as extensive product offerings, high demand risks, and tax risks in cross‐border trade. Traditional approaches (e.g., model‐driven approaches) often fail to provide effective solutions. Therefore, this study proposes a new data‐driven approach to manage inventory in overseas warehouses.Methodology: A two‐stage predictive analytics approach is implemented, as follows: (i) all items are classified into one of two classes, where ‐items are profitable to store in overseas warehouses, but ‐items are not; (ii) the demand levels of SKUs of ‐items are predicted. In the subsequent prescriptive analytics, models are proposed for optimizing inventory decisions related to ‐items. These include a deterministic model that uses the predicted demand as the true demand, and a stochastic model that treats the true demand as a random variable.Results: (i) Using a variety of machine learning techniques in the predictive analytics phase, we find the random forest outperforms other methods. (ii) The deterministic model can be solved as a linear program, and the stochastic model with maximum entropy distributions can be solved using Karush–Kuhn–Tucker conditions. (iii) An application of our results shows that the predictive classification reduces costs (an average cost reduction of up to 20%) by avoid shipping unprofitable items to overseas warehouses. Furthermore, the stochastic model provides near‐optimal solutions (the smallest performance loss is just 0.00%).

Building Information Modelling (BIM) has been adopted as the main technology in the construction industry in many developed countries due to its notable advantages. However, its applications in developing countries are limited. This paper aims to investigate factors which impact on BIM adoption in the construction industry. Twelve external variables were identified by an integrated TAM (Technology Acceptance Model) and TOE (Technology Organization Environment) framework and a systematic review of past studies. A survey was conducted in development, construction, design and consulting companies to investigate the impacts of these 12 external variables on BIM adoption. Using the interval Decision Making Trial and Evaluation Laboratory (DEMATEL) method, retrieved 120 completed questionnaires were analysed. The "Requirements from national policies" was found to be the most significant driving variable of BIM adoption by investigated companies. A further simulation analysis revealed that the "Intention to Use" BIM varied significantly with the change of "Requirements from national policies", "Standardization of BIM", and "Popularity of BIM in the industry". The results lead to the conclusion that government incentives play critical roles in BIM adoption in China. Policy makers could put more efforts into motivation strategies, standardization measures, and BIM culture cultivation to promote BIM applications in the construction industry.

Building Information Modelling (BIM) has been adopted as the main technology in the construction industry in many developed countries due to its notable advantages. However, its applications in developing countries are limited. This paper aims to investigate factors which impact on BIM adoption in the construction industry. Twelve external variables were identified by an integrated TAM (Technology Acceptance Model) and TOE (Technology Organization Environment) framework and a systematic review of past studies. A survey was conducted in development, construction, design and consulting companies to investigate the impacts of these 12 external variables on BIM adoption. Using the interval Decision Making Trial and Evaluation Laboratory (DEMATEL) method, retrieved 120 completed questionnaires were analysed. The "Requirements from national policies" was found to be the most significant driving variable of BIM adoption by investigated companies. A further simulation analysis revealed that the "Intention to Use" BIM varied significantly with the change of "Requirements from national policies", "Standardization of BIM", and "Popularity of BIM in the industry". The results lead to the conclusion that government incentives play critical roles in BIM adoption in China. Policy makers could put more efforts into motivation strategies, standardization measures, and BIM culture cultivation to promote BIM applications in the construction industry.

Child pedestrians are vulnerable to traffic-related injuries and fatalities. Though a decrease in the number of road injuries and deaths has been found in recent years, child pedestrian safety remains a major public health burden in Australia. Traffic risk has been recognized as one of the leading impediments of children's active transport to school, which can be an important contributor to child health, as well as help manage traffic congestion at peak travel times. There has been extensive research on the effects of the built environment on school travel mode. Research on the links between built environment factors and school travel safety, however, is limited. The key aim of this research was to explore the relationships between the built environment and child pedestrian safety around schools in order to provide implications for future school neighbourhood and campus designing and planning to improve school travel safety for child pedestrians. The premise of this thesis is that appropriate planning and design in school neighbourhoods could help to reduce child pedestrian collisions during school trips.Literature related to pedestrian safety, child pedestrian safety and the built environment was systematically searched and reviewed. An ecological conceptual framework was developed to integrate four elements - sociodemographics, built environment of school neighbourhoods, school location and school campus design, and school context - for predicting the risk of child pedestrian collisions around schools. The study involved a two-phase research approach to understand first the distribution of child pedestrian collisions around schools, then the effects of built environment attributes on child pedestrian collisions at the school neighbourhood level. Data were collected from various sources and by different methods. Police-recorded crash data (2001-2010) was requested from the Transport for NSW CrashLink database. Built environment and sociodemographic data were obtained from NSW Land and Property Information DTDB database, the TfNSW Transport Data Exchange and the Australian Bureau of Statistics (ABS) Census 2006. Site locations and polygon maps of 564 public primary schools and 180 secondary schools were extracted and geo-coded. School portfolios were retrieved from the Government MySchool website, the Centre for Education Statistics and Evaluation (CESE) and by visiting official school websites. School campus layouts and designs were manually retrieved and geo-coded from Google Maps. At the first stage, a retrospective analysis of child pedestrian collision data in the Sydney Metropolitan Area from 2001 to 2010 was conducted to analyse the temporal patterns, location characteristics and geographic distributions of collisions around schools. A total of 1,155 child pedestrian collisions and 2,533 adult pedestrian collisions at school travel times (STT) were identified for further study. In total, 76% of collisions involving young primary-school-aged (5-11 years) pedestrians and 63% of collisions involving older secondary-school-aged (12-17 years) pedestrians occurred in STT; whereas a majority of adult pedestrian collisions occurred at Non-STT. During STT, the mean distance from primary-school-aged pedestrian collisions to the nearest schools was 280m, with more than 90% within a 10-minute walking distance from a school, compared to a mean of 544m for collisions involving older children. Consistent with previous studies, it was found that most child pedestrian collisions occurred when a child was attempting to cross a road. And compared to adults, children were more often injured when stepping out from between parked cars. This study also found that the locations of secondary-school-aged pedestrian collisions were similar to those of adult collisions. Compared to secondary-school-aged (12-17 years) and adult pedestrian collisions, those of primary-school-aged (5-11 years) pedestrians were more likely to occur on 2-way undivided roads and locations without traffic signals.At the second stage, a cross-sectional study across the Sydney Metropolitan Area was conducted by using a sample of 564 public primary schools and 180 secondary schools from 38 local government areas. Generalised linear regression models were introduced to analyse the relationships between various variables relating to the four framework elements and child/adult pedestrian collision frequencies at three spatial levels of school neighbourhoods (200m, 400m and 800m buffer around each school polygon). The results confirmed that the effects of built environment factors on collision risk of child pedestrians at STT varied between primary and secondary school neighbourhoods, and across different spatial scales of school neighbourhoods.The findings revealed that higher socioeconomic level, proportions of other educational land use and arterial roads and bus stop density were associated with increased young child collisions around primary schools; whereas more older child collisions occurred with greater proportions of commercial land and primary roads around secondary schools. Both young and older child collisions were more likely around older school campuses. Young child collisions increased around primary schools with larger proportions of non-English speaking background students and closer to a railway station and decreased with the presence of a school bus bay or a school ground. Collisions involving older children decreased with a greater number of school entrances. The results across different scales of school neighbourhoods revealed that some factors (such as land use development and school campus facilities) are significant in areas close to schools and some (such as road network planning) are significant in wider areas. Overall, compared to young child pedestrian collisions, the findings for older children were closer to those for adult pedestrians, particularly in 400m and 800m school buffers. Young child pedestrian collisions were more strongly associated with relevant school campus facilities rather than built environment features in 200m school buffers. Differences in the spatial distribution and location characteristics of collisions around schools by age group, and the varying effects of built environment factors and school factors on collision risk of child pedestrians between primary schools and secondary schools, call the attention of researchers and practitioners, to apply different planning and designing countermeasures to improve road safety around schools. In addition, the varying results across the three spatial scales of school neighbourhoods reveal the different emphasis in relevant planning and retrofitting stages. The results of this thesis provide new insights to planning practitioners for selecting the location and designing the configuration of a school site in the district structure plans in advance of a new development. The area level models also give practitioners the chance to identify and prioritise candidate locations for subsequent investment decisions (such as lower SES neighbourhoods, non-English speaking groups, older school campuses and schools which are near to major traffic generators). Meanwhile, traffic engineers and school district practitioners should realize the importance of improved design of school campus (such as bus bays, school grounds and others) on school travel safety. Only pedestrian safety in terms of collision with a vehicle was considered within the scope of this study. The effects of factors on injury severity and other collision types were not investigated. Future research attention could be focused on extending the framework and models to explore injury severity as well as student cyclist safety.This work draws together the fields of planning and road safety to increase our understanding of how to improve child pedestrian safety with identified pathways for further advances in research, policies and practices. It extends the built environment and road safety framework and applies it in the context of school neighbourhoods, which have thus far received little attention. It has significant public health implications as its results could be synthesized with currently available evidence on the active school travel of children to produce useful policy and practice initiatives for improved health and safety.