<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Optimal production planning based on accurate market demand forecasting is crucial for cost control, inventory management, and market response in the multi-variety and small-batch production modes. Considering factors such as market demand, unit selling price, market demand trends, and product saturation within the attention cycle, an attention degree model for multi-variety and small-batch materials is constructed using historical market demand data from an electronic product manufacturing enterprise. A method for prioritizing and screening small-batch materials based on attention degree to formulate production plans is proposed. The demand for prioritized small-batch materials is predicted using the autoregressive integrated moving average model, multilayer perceptron, and bidirectional long short-term memory network. The optimal prediction results are selected to calculate the attention degree, which is then used to formulate the production plan for the next attention cycle to achieve orderly production. Taking the electronic product manufacturing enterprise as an example, the effectiveness and feasibility of the proposed model and method are verified by applying the prioritized production of small-batch materials screened based on attention degree.</p>
</abstract>

Optimal production planning based on accurate market demand forecasting is crucial for cost control, inventory management, and market response in the multi-variety and small-batch production modes. Considering factors such as market demand, unit selling price, market demand trends, and product saturation within the attention cycle, an attention degree model for multi-variety and small-batch materials is constructed using historical market demand data from an electronic product manufacturing enterprise. A method for prioritizing and screening small-batch materials based on attention degree to formulate production plans is proposed. The demand for prioritized small-batch materials is predicted using the autoregressive integrated moving average model, multilayer perceptron, and bidirectional long short-term memory network. The optimal prediction results are selected to calculate the attention degree, which is then used to formulate the production plan for the next attention cycle to achieve orderly production. Taking the electronic product manufacturing enterprise as an example, the effectiveness and feasibility of the proposed model and method are verified by applying the prioritized production of small-batch materials screened based on attention degree.

Demand Forecasting for Multi-Variety and Small-Batch Materials Based on Attention to Degree

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Optimal production planning based on accurate market demand forecasting is crucial for cost control, inventory management, and market response in the...

Demand Forecasting for Multi-Variety and Small-Batch Materials Based on Attention to Degree

Pubblicato online: 10 lug 2024

Pagine: -

Ricevuto: 22 mar 2024

Accettato: 20 giu 2024

DOI: https://doi.org/10.2478/amns-2024-1829

Parole chiave
Small-batch materials, Multi-variety and small-batch production mode, Attention degree, Market demand forecasting

© 2024 Xiaoyong Yuan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Demand Forecasting for Multi-Variety and Small-Batch Materials Based on Attention to Degree

Pubblicato online: 10 lug 2024

Pagine: -

Ricevuto: 22 mar 2024

Accettato: 20 giu 2024

DOI: https://doi.org/10.2478/amns-2024-1829

Parole chiaveSmall-batch materials, Multi-variety and small-batch production mode, Attention degree, Market demand forecasting

© 2024 Xiaoyong Yuan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Small-batch materials, Multi-variety and small-batch production mode, Attention degree, Market demand forecasting