In cloud-based manufacturing environments, there is a trade-off between the extent of information sharing and decision-making among participating companies and overall production efficiency. However, there appear to be no previous studies focusing on this trade-off. The present study first categorizes typical cloud-based manufacturing environments into five classes from the perspectives of information sharing and decision-making among participants. Next, production planning mechanisms are formulated for each class by extending the scheduling auction mechanism. The characteristics of the five mechanisms are also discussed and then compared through numerical experiments. The experimental results show the existence of the aforementioned trade-off relationship. Giving the participants the authority to partially re-schedule their production plans does not lead to them submitting false information, even if the mechanism does not satisfy strategyproofness. This is because it is difficult for participants to predict in advance how lying can improve their profits. Misreporting is not a problem in practice.

The diverse needs of customers have led to a demand for production systems capable of handling both make-to-stock (MTS) and make-to-order (MTO) products. A dynamic hybrid MTS/MTO production system addresses this demand. This system includes hybrid machines for MTS and MTO operations, as well as dedicated MTS machines for MTS products. A hybrid machine can produce both MTS and MTO products by adjusting its setup accordingly. By dynamically controlling the setup of each hybrid machine based on current conditions, the system can achieve high utilization with fewer machines. However, frequent switching of machines might occur due to the switching decisions based on only the MTS inventory level and stepwise switching. This study proposes a partial switch-restriction model for hybrid machines in a dynamic hybrid MTS/MTO production system in order to suppress excessive switching. The numerical experiments clarified that the proposed model can suppress the frequency of machine switches, and that the system operating costs can also be reduced when MTS demand arrives more than MTO demand. For the cost minimization problem, a relation was found between the number of hybrid machines and the number of switch-restricted machines so as to obtain a lower cost. Based on this result, a heuristic search method is also proposed in order to quickly achieve effective operation rules. Then, it was shown that the proposed search heuristic could find approximately optimal solutions with less errors at fewer searches by comparing the exhaustive search.

The purpose of this study is to clarify the meanings and differences that are inherent in various measurement scales of tone—an indicator of positive and negative polarity—which have not been thoroughly examined in the accounting field. Specifically, Multiple regression analyses were conducted on transcripts of financial results briefings held by firms listed on the Tokyo Stock Exchange between 2018 and 2021. The aim of the present study is to explore the relationship between tone and business performance as measured by several scales, and therefore, these results have been compared and discussed. To facilitate a more nuanced analysis, previously employed tone measurement scales have been reclassified into two categories: tone and sentiment, each of which representing indicators of positive and negative polarity. A key finding of this study is that tone demonstrates a positive relationship with financial performance, whereas positive sentiment shows no significant association with ROA. This suggests that managers may be manipulating impressions to their advantage at financial results briefings. In addition, the results also suggest that the differences in the frequency of occurrence of positive and negative words can be used not only as a measure of tone, but also as a measure of sentiment, which may lead to the identification of clues for strategic disclosure behavior by managers.

Pairwise comparison-based evaluation methods are effective when it is difficult to perform a direct evaluation of multiple items. Traditionally, businesses have conducted analyses on a small number of products. However, with the expansion of e-commerce, comparing and selecting from among dozens of options has become increasingly common. The number of required pairwise comparisons increases quadratically with the number of items. When evaluations are conducted manually, practical applications are generally limited to around 1,000 comparisons. Thus, conventional methods that rely on human raters to provide pairwise comparison data for all possible combinations become infeasible. Some methods have been proposed to reduce the required number of comparisons by leveraging the structure of the pairwise comparison matrix to impute missing data. However, these approaches typically achieve only a modest reduction, and their applicability becomes limited when a large proportion of data is missing. As a result, they remain insufficient as a solution to the increasing number of evaluation targets. To address this issue, this study proposes a subjective evaluation method based on pairwise comparisons that remain applicable even when the number of evaluation targets is large. This approach utilizes machine learning to impute missing entries in the pairwise comparison matrix. Specifically, the present study focuses on the relationship between item features and subjective evaluation scores, and training a machine learning model to estimate missing pairwise comparisons based on auxiliary information. Through experiments using synthetic and real-world datasets, the present paper demonstrates the proposed evaluation model's effectiveness and practical applicability for analysis.