Demand Forecasting Techniques for Build-to-Order Lean Manufacturing Supply Chains

Rivera-Castro, Rodrigo; Nazarov, Ivan; Xiang, Yuke; Pletneev, Alexander; Maksimov, Ivan; Burnaev, Evgeny

doi:10.1007/978-3-030-22796-8_23

Cited by 6 publications

(5 citation statements)

References 22 publications

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“…Furthermore, in an era where just-intime production and lean manufacturing principles are paramount, the ability to accurately forecast demand and supply dynamics becomes indispensable. It aids in reducing inventory costs, improving cash flow, and ensuring timely delivery to customers, thereby enhancing overall customer satisfaction [7].…”

Section: Significance Of Time-series Forecasting In Industrial Manufa...mentioning

confidence: 99%

A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems

Fatima,

Rahimi

2024

Machines

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Time-series forecasting is crucial in the efficient operation and decision-making processes of various industrial systems. Accurately predicting future trends is essential for optimizing resources, production scheduling, and overall system performance. This comprehensive review examines time-series forecasting models and their applications across diverse industries. We discuss the fundamental principles, strengths, and weaknesses of traditional statistical methods such as Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ES), which are widely used due to their simplicity and interpretability. However, these models often struggle with the complex, non-linear, and high-dimensional data commonly found in industrial systems. To address these challenges, we explore Machine Learning techniques, including Support Vector Machine (SVM) and Artificial Neural Network (ANN). These models offer more flexibility and adaptability, often outperforming traditional statistical methods. Furthermore, we investigate the potential of hybrid models, which combine the strengths of different methods to achieve improved prediction performance. These hybrid models result in more accurate and robust forecasts. Finally, we discuss the potential of newly developed generative models such as Generative Adversarial Network (GAN) for time-series forecasting. This review emphasizes the importance of carefully selecting the appropriate model based on specific industry requirements, data characteristics, and forecasting objectives.

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Section: Significance Of Time-series Forecasting In Industrial Manufa...mentioning

confidence: 99%

A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems

Fatima,

Rahimi

2024

Machines

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“…Hart et al [20] introduced effective manufacturing systems for supply chains based on demand forecasting. Rivera-Castro et al [21] presented diagonal feeding, a useful technique for forecasting build-to-order lean manufacturing supply chains. A review of the literature regarding sustainable manufacturing strategies can be found in the article by Garetti and Taisch [18].…”

Section: Sustainable Manufacturingmentioning

confidence: 99%

“…The extended modified discordance matrix d is is obtained using Equation ( 20), as presented in Table 9. The extended modified total matrix can be obtained using Equation (21), as listed in Table 10. The closeness coefficients can then be obtained using Equation (22), as presented in Table 11.…”

Section: As Presented Inmentioning

confidence: 99%

Extension of Fuzzy ELECTRE I for Evaluating Demand Forecasting Methods in Sustainable Manufacturing

Chu,

Nghiem

2023

Axioms

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The selection of a demand forecasting method is critical for companies aiming to avoid manufacturing overproduction or shortages in pursuit of sustainable development. Various qualitative and quantitative criteria with different weights must be considered during the evaluation of a forecasting method. The qualitative criteria and criteria weights are usually assessed in linguistic terms. Aggregating these various criteria and linguistic weights for evaluating and selecting demand forecasting methods in sustainable manufacturing is a major challenge. This paper proposes an extension of fuzzy elimination and choice translating reality (ELECTRE) I to resolve this problem. In the proposed method, fuzzy weighted ratings are defuzzified with the signed distance to develop a crisp ELECTRE I model. Moreover, an extension to ELECTRE I is developed by suggesting an extended modified discordance matrix and a closeness coefficient for ranking alternatives. The proposed extension can overcome the problem of information loss, which can lead to incorrect ranking results when using the Hadamard product to combine concordance and modified discordance matrices. A comparison is conducted to show the advantage of the proposed extension. Finally, a numerical example is used to demonstrate the feasibility of the proposed method. Furthermore, a numerical comparison is made to display the advantage of the proposed method.

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“…Indeed, agility is a set of capabilities for assembling extensively numerous client necessities in phrases of price, specification, quantity, quality, and delivery [47]. Hence, agile production pertains to businesses coping with risky market demand by permitting changes to be generated in an economically possible and timely way [48]. Although a great quantity of study has been carried out in the location of supply chain planning (SCP), the context is basically missing in presenting an analytical version to solve the issue of agile production operating in a BTO (Build-To-Order) environment.…”

Section: Agility and Risk In Scmmentioning

confidence: 99%

A Study on Risk and Expense Evaluation of Agility Supply Management of Machinery

Heydari

Lai

2020

Discrete Dynamics in Nature and Society

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We provide a technical assessment and critique of the existing literature via highlighting problems associated with conceptualization and technical improvement of agility practices. By presenting a narrative evaluation of the technical review, we underscore the nature of the SC hazard in addition to agility practices and furnish a synthesis for future studies. The purpose is to examine the relationship between identifying and ranking the elements affecting supply management for SC agility in compressed natural gas pressure vessels. This study is applied research and data are collected based on descriptive survey. The populace of the investigation is milled compressed natural gas pressure vessels. Survey data are amassed through interviews and questionnaires. Cronbach’s alpha has been utilized to evaluate the reliability of the values received 0.778, which shows that the questionnaire is of excellent reliability and the questionnaire has 27 questions. Also, because the volume of the statistical population is 270 people, the consequences show that there is a positive connection among the empowerment of SC agility. Driving agility is on a positive supply chain (SC) agility. The expense has a poagilitysitive impact on SC agility.

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Demand Forecasting Techniques for Build-to-Order Lean Manufacturing Supply Chains

Cited by 6 publications

References 22 publications

A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems

A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems

Extension of Fuzzy ELECTRE I for Evaluating Demand Forecasting Methods in Sustainable Manufacturing

A Study on Risk and Expense Evaluation of Agility Supply Management of Machinery

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