Dynamization of learning for sustainability in the electronics industry: studies in electromedical equipment manufacturers

Silveira, Marco Antônio; Kikuchi, Leticia Sayuri; Policeno, Cristiani Aparecida; Gardim, Nanci

doi:10.3182/20130911-3-br-3021.00076

Cited by 2 publications

(1 citation statement)

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“…Considering the organizational learning effect as the learning effect of a larger group of individuals within the same organization is with great influence on "converging" sustainability goals, by enhancing the efficiency and competence of individuals. Employees at all levels within the organization are continuously encouraged to develop their abilities and technical knowledge (Silveira et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Aggregate Production Planning with Organizational Learning and Social Sustainability Considerations – Current Gaps and Future Opportunities

Mostafa¹,

Elhabashy²,

Fors³

et al. 2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Enhancing industrial organizations' responsiveness to market fluctuations is essential to survive current global supply chain challenges. Better responsiveness can be attained through robust and real-time production planning, especially on the tactical level through Aggregate Production Planning (APP) activities. A key variable in APP is workforce productivity enhancement. However, the dynamic nature of the workforce raises concerns about productivity improvement initiatives. To address this issue, the incorporation of organizational learning and social sustainability into APP has received significant attention from megacorps looking to increase competitiveness. This literature review assesses the prevalence of workforce productivity and social sustainability considerations in the APP problem. The review found that there is a limited number of studies that consider organizational learning in APP, and more research is needed to address the resilience of organizational learning evaluation models. Additionally, there is a lack of studies that focus on social sustainability measures in APP. Finally, it was observed that metaheuristic approaches are commonly used to solve complex APP problems, with genetic algorithms, harmony search algorithms, and simulated annealing being the most frequently employed methods. More research is needed to explore the use of other metaheuristic approaches and to assess their effectiveness in solving APP problems.

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Section: Introductionmentioning

confidence: 99%