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Adapting to a changing environment: inspiration for planetary health from east African communities
(2022)
Enterprise Resource Planning (ERP) system customization is often necessary because companies have unique processes that provide their competitive advantage. Despite new technological advances such as cloud computing or model-driven development, technical ERP customization options are either outdated or ambiguously formulated in the scientific literature. Using a systematic literature review (SLR) that analyzes 137 definitions from 26 papers, the result is an analysis and aggregation of technical customization types by providing clearance and aligning with future organizational needs. The results show a shift from ERP code modification in on-premises systems to interface and integration customization in cloud ERP systems, as well as emerging technological opportunities as a way for customers and key users to perform system customization. The study contributes by providing a clear understanding of given customization types and assisting ERP users and vendors in making customization decisions.
Food preferences are crucial for diet-related decisions, which substantially impact individual health and global climate. However, the persistence of unfavorable food preferences is a significant obstacle to changing eating behavior.
Here we explored the effects of posthypnotic suggestions (PHS) on food-related decisions by measuring food choices, subjective ratings, and indifference points. In Session 1, demographic data and hypnotic susceptibility of participants were assessed. In Session 2, following hypnosis induction, PHS aiming to increase the desirability of healthy food was delivered.
Afterward, a task set was administrated twice, once when PHS was activated and once deactivated. The order of PHS activation was counterbalanced across participants. The task set included a liking-rating task for 170 pictures of different food items, followed by an online supermarket where participants were instructed to select enough food for a fictitious week of quarantining from the same item pool. After 1 week, Session 3 repeated Session 2 without hypnosis induction in order to assess the persistence of PHS.
The crucial dependent measures were food choices, subjective ratings, and the indifference points as a function of time and PHS condition.
Algorithmic management
(2022)
Algorithmic management
(2022)
The organisation of legislative chambers and the consequences of parliamentary procedures have been among the most prominent research questions in legislative studies. Even though democratic elections not only lead to the formation of a government but also result in an opposition, the literature has mostly neglected oppositions and their role in legislative chambers. This paper proposes to fill this gap by looking at the legislative organisation from the perspective of opposition players. The paper focuses on the potential influence of opposition players in the policy-making process and presents data on more than 50 legislative chambers. The paper shows considerable variance of the formal power granted to opposition players. Furthermore, the degree of institutionalisation of opposition rights is connected to electoral systems and not necessarily correlated with other institutional characteristics such as regime type or the size of legislative chambers.
This article merges theoretical literature on non-controlling minority shareholdings (NCMS) in a coherent model to study the effects of NCMS on competition and collusion. The model encompasses both the case of a common owner holding shares of rival firms as well as the case of cross ownership among rivals. We find that by softening competition, NCMS weaken the sustainability of collusion under a greater variety of situations than was indicated by earlier literature. Such effects exist, in particular, in the presence of an effective competition authority.
Volatile supply and sales markets, coupled with increasing product individualization and complex production processes, present significant challenges for manufacturing companies. These must navigate and adapt to ever-shifting external and internal factors while ensuring robustness against process variabilities and unforeseen events. This has a pronounced impact on production control, which serves as the operational intersection between production planning and the shop- floor resources, and necessitates the capability to manage intricate process interdependencies effectively. Considering the increasing dynamics and product diversification, alongside the need to maintain constant production performances, the implementation of innovative control strategies becomes crucial.
In recent years, the integration of Industry 4.0 technologies and machine learning methods has gained prominence in addressing emerging challenges in production applications. Within this context, this cumulative thesis analyzes deep learning based production systems based on five publications. Particular attention is paid to the applications of deep reinforcement learning, aiming to explore its potential in dynamic control contexts. Analysis reveal that deep reinforcement learning excels in various applications, especially in dynamic production control tasks. Its efficacy can be attributed to its interactive learning and real-time operational model. However, despite its evident utility, there are notable structural, organizational, and algorithmic gaps in the prevailing research. A predominant portion of deep reinforcement learning based approaches is limited to specific job shop scenarios and often overlooks the potential synergies in combined resources. Furthermore, it highlights the rare implementation of multi-agent systems and semi-heterarchical systems in practical settings. A notable gap remains in the integration of deep reinforcement learning into a hyper-heuristic.
To bridge these research gaps, this thesis introduces a deep reinforcement learning based hyper- heuristic for the control of modular production systems, developed in accordance with the design science research methodology. Implemented within a semi-heterarchical multi-agent framework, this approach achieves a threefold reduction in control and optimisation complexity while ensuring high scalability, adaptability, and robustness of the system. In comparative benchmarks, this control methodology outperforms rule-based heuristics, reducing throughput times and tardiness, and effectively incorporates customer and order-centric metrics. The control artifact facilitates a rapid scenario generation, motivating for further research efforts and bridging the gap to real-world applications. The overarching goal is to foster a synergy between theoretical insights and practical solutions, thereby enriching scientific discourse and addressing current industrial challenges.