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Copyright of Revista Producao Online is the property of Associacao Brasileira de Engenharia de Producao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Producao Online is the property of Associacao Brasileira de Engenharia de Producao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Shipping is an indispensable tool for the sustainable global supply chain, and seafarers play a key role in safe navigation. Maritime autonomous surface ships (MASS) have been expected to reduce marine accidents by human error of the seafarers. On the other hand, MASS may have adverse effects on operators' mental workload (MWL) and increase safety risks in some cases. This research aims to provide a scheme for identifying the relationship between MWL and MASS in the maritime that can be utilised for rulemaking and technological development. The provided scheme identifies the factors that affect the MWL of operators and sub-elements of MWL through gap analysis. Five factors related to MASS operation were defined, in addition to general factors. The case study was carried out by utilising the scheme on typical cases focusing on the normal navigational situation. The NASA task load index method was used to measure MWL. Ten deck officers with various ranks, including the third officer and captain, participated in the case study. The results suggested that various causes such as conflicted situations, machine–human interfaces, mechanical-style movements of the ship, reliability of MASS, and visibility constraints affect the MWL of operators. It also confirmed the verification of the identification scheme. [ABSTRACT FROM AUTHOR]

Cities play a crucial role in the implementation of carbon emission mitigation policies since most CO 2 emissions are related to energy consumption in urban areas. Recent years, environmentally extended multi-regional input-output tables (EEMRIO) are emerged to reveal regional environment impact considering upstream supply chains crossing regional boundaries. However, city-scale MRIO has not been fully addressed since more focus has been located on national or prefectural level evaluation. For quantifying city-scale emission considering regional energy flow, we apply the input-output table of Tokyo to evaluate energy consumption at the city scale, and the results provides one of the the most detailed sector classification from a wide range of city emission studies. In Japan, Tokyo is considered to be representative of the contemporary Japanese urban energy structure, and its energy consumption and associated CO 2 emissions are of major importance when making city-scale carbon mitigation policies. Here, domestic production for Tokyo and other regions is used to quantify the direct and indirect emissions arising from Tokyo final consumption. Our results are divided into sector-specific and fossil fuel source-specific (coal, crude oil, and natural gas) for both direct and indirect emissions. The major findings are as follows: (1) The transportation sector accounts for the largest share of direct emissions in Tokyo. (2) The energy supply, construction, private service sectors account for the largest share of indirect emissions in Tokyo. (3) There are three scopes of emission responsibility evaluated in this study: direct emission before allocation (BE) as 12.88 MtC, after allocation (AE) as 18.91 MtC, and the total including indirect emissions is 27.24 MtC, which is more than twice the direct emissions before allocation. The detailed and energy consumption and emission inventory may be essential for emission responsibility allocation to overcome geographical boundary, therefore, contributes to varied scales of carbon reduction policies. [ABSTRACT FROM AUTHOR]

Managing uncertainty is the way to secure stability of the supply chain. Uncertainty within chipping operation and chip transportation causes production loss. In the wood chip supply chain for bioenergy, operational uncertainty mainly appears in the moisture content of the material, chipping productivity, and the interval of truck arrival. This study theoretically quantified the loss in wood chip production by applying queuing theory and stochastic modelling. As well as the loss in production, the inefficiency was identified as the idling time of chipper and the queuing time of trucks. The aim of this study is to quantify the influence of three uncertainties on wood chip production. This study simulated the daily chip production using a mobile chipper by applying queuing theory and stochastic modelling of three uncertainties. The result was compared with the result of deterministic simulation which did not consider uncertainty. Uncertainty reduced the production by 14% to 27% compared to the production of deterministic simulation. There were trucks scheduled but not used. The cases using small trucks show the largest daily production amount, but their lead time was the longest. The large truck was sensitive to the moisture content of material because of the balance between payload and volumetric capacity. This simulation method can present a possible loss in production amount and enables to evaluate some ways for the loss compensation quantitatively such as outsourcing or storing buffer. For further development, the data about the interval of truck arrival should be collected from fields and analyzed. We must include the other uncertainties causing technical and operator delays. [ABSTRACT FROM AUTHOR]

• Carbon footprints embodied in Japan's food consumption are evaluated. • Six types of transport-related carbon footprint are given out separately. • Sea and coastal water transportation services account for 75% of food-related carbon emissions. • Food trade with some Asia and Pacific countries and the United States accounts for 51% of food-related emissions from overseas countries. • Transportation of processed food holds large potential in food supply chain decarbonization. Based on the trade boom in the globalization wave, nowadays, food products travel long distances from producers to consumers. However, the embodied environmental impact associated with food consumption is still under discussion, which demands an in-depth analysis of its upstream environmental impact. Therefore, in this study, we consider Japan as a case study and apply an environmentally extended input-output table to tackle its embodied carbon footprint across sectors and regions. To further investigate the contribution of food transport, we extracted transport-related carbon footprints embodied in Japanese food consumption according to six transport types. The results indicate that 34% of the carbon footprints from Japan's food consumption were induced overseas, mostly being leaked in some Asian and Pacific countries (4536 kilo tonCO 2 eq in China and 4758 kilo tonCO 2 eq in other Asian and Pacific countries' group) and the United States (4444 kilo tonCO 2 eq). The implications of this study reveal the key role of food consumption in Japan's future decarbonization and carbon neutrality, and provide potential mitigation measures to reduce transport emissions from the perspective of food supply chain. [ABSTRACT FROM AUTHOR]

• The paper uses both enterprise demographic data and financing behavioral data for credit risk prediction of SMEs in SCF. • The paper proposes the DeepRisk approach by using multi-modal deep learning to fuse the two different sources of data. • Experiments on SCF dataset demonstrate that the DeepRisk approach outperforms the baseline methods in terms of precision, recall, F1-score, AUC and economic loss. • The fusion of the two different sources of data is superior to the existing studies on credit risk prediction of SMEs in SCF. The credit risk of small and medium-sized enterprises (SMEs) in supply chain finance (SCF) is defined as the probability that the SME would default on loans derived from financing for the SCF platform. Traditional models make use of merely the static data of SMEs, such as enterprise demographic data and financial statement data, to predict the credit risk of SMEs in SCF. Nevertheless, behavioral data, which reflect the dynamic financing behavior of SMEs in SCF, are overlooked by these models, which limits the performance of credit risk prediction. To address this problem, a novel approach is proposed called DeepRisk to fuse enterprise demographic data and financing behavioral data to predict the credit risk of SMEs in SCF. We adopt the multi-modal learning strategy to fuse the two different sources of data. The concatenated vectors derived from data fusion are then used as the input of the feed forward neural network to predict the credit risk of SMEs. Experiments on a real SCF dataset demonstrate that the proposed DeepRisk approach outperforms the baseline methods in credit risk prediction in terms of precision, recall, F1-score, area under curve (AUC), and economic loss. The fusion of the two different sources of data is superior to the existing approaches to the credit risk prediction of SMEs in SCF. Both the static enterprise demographic data and the dynamic financing behavioral data are crucial to improve the credit risk prediction of SMEs. Nevertheless, the variables derived from the financing behavioral data have a better predictability than those from the enterprise demographic data. Managerial implications have been identified for decision makers involved in SCF in utilizing the benefits of SCF and in managing their credit risks. [ABSTRACT FROM AUTHOR]