Effective sales prediction for e-commerce would assist retailers in developing accurate production and inventory control plans, which would further help them to reduce inventory costs and overdue losses. This paper develops a systematic method for e-commerce sales prediction, with a particular focus on predicting the sales of products with short shelf lives. The short-shelf-life product sales prediction problem is poorly addressed in the existing literature. Unlike products with long shelf lives, short-shelf-life products such as fresh milk exhibit significant fluctuations in sales volume and incur high inventory costs. Therefore, accurate prediction is crucial for short-shelf-life products. To solve these issues, a stacking method for prediction is developed based on the integration of GRU and LightGBM. The proposed method not only inherits the ability of the GRU model to capture timing features accurately but also acquires the ability of LightGBM to solve multivariable problems. A case study is applied to examine the accuracy and efficiency of the GRU-LightGBM model. Comparisons among other sales prediction methods such as ARIMA and SVR are also presented. The comparative results show that the GRU-LightGBM model is able to predict the sales of short-shelf-life products with higher accuracy and efficiency. The selected features of the GRU-LightGBM model are also useful due to their interpretability while developing sales strategies.

Exposure to chronic harm is difficult to manage and prevent in industry. There is a need to better understand the state of mind when workers disregard safety processes and expose themselves to this type of risk. This paper develops a theoretical model of the reason why workers voluntarily expose themselves to occupational health and safety (OHS) hazards. This Risk, Agency, and Safety & Health (RASH) model proposes that people willingly expose themselves to chronic injuries via a series of risk-taking processes. This causal chain starts with personal motivation and over-alignment with organisational purpose (including impression management). Ideally, that motivation would be moderated by an ability to predict future harm consequences from the task at hand, but that mechanism is weak because it is difficult to predict cause and effect, the consequences are too far in the future, and the opportunities for vicarious learning are few. The motivation then causes misdirected creativity, hence the development of personally novel ways of solving the problem, albeit with greater risk of harm. Perverse agency then sustains actions that exposure the person to harm. Original contributions are the provision of a detailed explanation for risk-taking, and the integration of multiple well-established psychological constructs.

Urban pickup and delivery (PUD) activities are important for logistics operations. Real operations for general freight involve a high degree of complexity due to daily variability. Discrete-event simulation (DES) is a method that can mimic real operations and include stochastic parameters. However, realistic vehicle routing is difficult to build in DES models. The objective is to create a DES model for realistic freight routing, which considers the driver’s routing decisions. Realistic models need to predict the delivery route (including time and distance) for variable consignment address and backhaul pickup. Geographic information systems (GIS) and DES were combined to develop freight PUD models. GIS was used to process geographical data. Two DES models were developed and compared. The first was a simple suburb model, and the second an intersection-based model. Real industrial data were applied including one-year consignment data and global positioning system (GPS) data. A case study of one delivery tour is shown, with results validated with actual GPS data. The DES results were also compared with conventional GIS models. The result shows the intersection-based model is adequate to mimic actual PUD routing. This work provides a method for combining GIS and DES to build freight operation models for urban PUD. This has the potential to help industry logistics practitioners better understand their current operations and experiment with different scenarios.

Small and medium enterprises (SMEs) in the manufacturing industry normally arise as a result of product innovations. Transiting and growing into large organizations is a critical process for the sustainable growth of SMEs, and this requires developing appropriate production systems. Such development focuses on production economics and the optimization of facility layout, production capacity, and machine utilization. These optimizations are usually achieved using discrete event simulation (DES). However, the DES primarily focuses on production optimization and is not formulated to give concurrent attention to occupational health and safety risks, where the workers’ occupational health and safety are also important to production systems; hence, sustainability issues arise. Until now, the production economics and workers’ health and safety are usually treated separately, and the health and safety aspects are often managed after the system has been developed. This brings increasing hazards in the production environment since adding new facilities to the system may introduce new hazards and cause further safety issues. The present paper proposes a methodology to solve the above concerns. Its key features are the use of a quality-of-life metric for determining the occupational health and safety risks of a particular work activity and the embedding thereof as a routine in DES for manufacturing plant simulation. One of the contributions of the proposed integration method is that it helps to enhance the development of production systems that support financial due diligence, as well as occupational health and safety (OHS) due diligence. This is particularly important to SMEs in the manufacturing industries, where growth comes with particular sustainability challenges related to the need to develop more systematic operational and safety management systems.