Towards a learning curve for electric motors production under organizational learning via shop floor data

Attia, El-Awady; Megahed, Ashraf; Duquenne, Philippe

doi:10.1016/j.ifacol.2016.07.587

Cited by 5 publications

(4 citation statements)

References 31 publications

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“…Los autores Yang, D., Rhee, M. (2017) [3] y Attia, E. A.,Megahed, A., Duquenne, P. (2016) [4] evalúan el aprendizaje de los operarios en empresas dedicadas a la fabricación de motores para automóviles aplicando un modelo de curva de aprendizaje. En función a los resultados obtenidos, se reforzaban los temas asociados a producción.…”

Section: B Gestión Del Conocimiento 1) Curva De Aprendizajeunclassified

Modelo de medición del rendimiento operacional basado en la gestión del conocimiento para reducir pedidos devueltos para una empresa distribuidora

Burga¹,

Jimenez²,

Sotelo³

et al. 2019

Proceedings of the 17th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Industry, Innovation

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Small and medium-sized enterprises (SMEs) in Peru present problems such as access to the national market due to limited capital or access to technology compared to large companies. However, they also lack an adequate definition and conceptualization of their processes, as well as a low level of business information transfer, which causes an absence in the measurement, control of processes and low operational performance. Given this situation, we propose the application and implementation of knowledge management tools in a company dedicated to the marketing and distribution of school supplies, which represents an SME in Peru. The tools used as the map of processes and flowcharts of the different processes were used to make the participants of the processes known. Through the realization of training, performance evaluations and internal audit, the learning of the operator is analyzed. As a result, there was a 57.63% decrease in errors in armed orders during picking, as well as a decrease of 7.98% and 8.59% of times in the execution of the processes under study for small orders and larger orders, respectively. Similarly, 96.56% of dispatches generated correctly were obtained.

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Section: B Gestión Del Conocimiento 1) Curva De Aprendizajeunclassified

Modelo de medición del rendimiento operacional basado en la gestión del conocimiento para reducir pedidos devueltos para una empresa distribuidora

Burga¹,

Jimenez²,

Sotelo³

et al. 2019

Proceedings of the 17th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Industry, Innovation

View full text Add to dashboard Cite

show abstract

“…month) should be less than 15% of the normal hours worked in that period. This industrial restriction also was considered in the proposed model as shown by equation (15). In which the total overtime hours per period is less than a specified percentage (KO.T) from the total normal hours worked by the permanent workers during such period.…”

Section: Overtime Constraintsmentioning

confidence: 99%

“…lean, six-sigma, etc. Relying on the work of some of the current authors in [15], the log-linear model of Wright [7] can be used efficiently. This model can be represented by equation (21).…”

Section: Inventory Constraintsmentioning

confidence: 99%

Aggregate Production Planning Considering Performance Evolution: A Case Study

Attia¹,

Megahed²,

Baioumy³

et al. 2016

The International Conference on Applied Mechanics and Mechanica

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Due to the violent market competition, organizations should respond quickly to customer needs. This strategic objective can be reached through the development of robust production planning. One of the most important factors in production planning is the workforce productivity which is a dynamic manufacturing property, i.e. the workforce productivity increases thanks to in-job training. This phenomenon is known as production progress function or work-based-learning. Considering this phenomenon in industrial planning can lead to robust manufacturing plans. The current study introduces a novel model for a medium term production planning, which used to find the yearly optimum aggregate production plan in order to minimize the total production costs in respecting the operational constraints and considering the production progress function. The resultant model is a linear mixed integer program that can be solved optimally. The data used in validating and running the model was taken from an Egyptian factory that is dedicated to produce electric motors. The model was solved optimally using ILOG CPLEX Software. By comparing the results of this study against the adopted approach in the factory; one can find that the model succeeded to minimize the production costs by about 5.43 % for first year, 2.66% for the second, and 1.86% for the third one. In monetary units these percentages can be translated respectively to 11.7 million L.E., 6.3 million L.E., and 4.7 million L.E.

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“…In addition, it was observed from both queries that the learning curve models could be classified into single-variable or multi-variable models, where multi-variable models can accommodate factors that would influence how fast, how far, and how well a worker learns within a specified horizon (Attia et al 2016). Future research in this area could be enriched by distinguishing appropriate learning curve evaluation variables and studying the impact of these variables on production rates, inventory levels, and workforce requirements.…”

mentioning

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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Towards a learning curve for electric motors production under organizational learning via shop floor data

Cited by 5 publications

References 31 publications

Modelo de medición del rendimiento operacional basado en la gestión del conocimiento para reducir pedidos devueltos para una empresa distribuidora

Modelo de medición del rendimiento operacional basado en la gestión del conocimiento para reducir pedidos devueltos para una empresa distribuidora

Aggregate Production Planning Considering Performance Evolution: A Case Study

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

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