The development of a hierarchical rough-cut capacity planning model and demand management system for fishing-net manufacturing

Hsieh, Chiung Hsi; Yang, Taho; Su, Chao; Lin, Cheng Tao

doi:10.1504/ejie.2012.047664

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…The fishing-net industry has unique characteristics and has not been discussed in the literature with regard to supply network flexibility. Hsieh et al (2012) introduced the fishing-net manufacturing process in detail and then developed a hierarchical RCCP model and demand management system. This section addresses material flow of a fishing-net manufacturing process to show the importance of supply network flexibility.…”

Section: Case Studymentioning

confidence: 99%

“…Mesh size is a special unit of a net; different mesh depth and mesh length will result in a different shape and area of fishing net. Readers can refer to Hsieh et al (2012) for details. Customers' orders consist of any raw material type, twine size, mesh size, mesh depth and mesh length based on their business needs.…”

Section: Customer and Supplier Flexibilitymentioning

confidence: 99%

“…Moreover, for the case company, the net knitting workstation is a bottleneck. For details of each workstation, readers can refer to Hsieh et al (2012).…”

Section: Customer and Supplier Flexibilitymentioning

confidence: 99%

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Integration of customer and supplier flexibility in a make-to-order industry

Kuo

Yang

Parker

et al. 2016

Industr Mngmnt & Data Systems

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Purpose -The purpose of this paper is to solve an integration of customer and supplier flexibility problem in a make-to-order (MTO) industry. The flexible strategies, where delivery leadtime and unit price (or raw material cost) can be negotiated, are provided by customers and suppliers. Its effectiveness is illustrated by a practical application. Design/methodology/approach -The present study is a rolling decision-making problem and is solved by a proposed combined mixed integer program (MIP) and simulation approach. A simulation model was developed for evaluating solutions of the MIP and will serve as the virtual factory to provide the initial work-in-process status for a new incoming order evaluation. Findings -The experimental results show that when either customers or suppliers provide flexible strategies to the manufacturer, total profits can be increased. Moreover, when both customers and suppliers provide flexibility strategies to the manufacturer simultaneously, total profits can be significantly increased. Research limitations/implications -An expanded experiment would be of help in realizing the relationship between the flexibility and profit. Moreover, there are other price-sensitivity functions for both customers and suppliers. Practical implications -A fishing-net manufacturing company was used for the case study to illustrate the effectiveness and the feasibility of the proposed methodology and its application to industry. Originality/value -The proposed methodology innovatively solved a practical application. The customer and supplier flexibility was investigated in a MTO production system that has no inventory of raw material. The experimental results are promising.

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Section: Case Studymentioning

confidence: 99%

Section: Customer and Supplier Flexibilitymentioning

confidence: 99%

See 1 more Smart Citation

Integration of customer and supplier flexibility in a make-to-order industry

Kuo

Yang

Parker

et al. 2016

Industr Mngmnt & Data Systems

Self Cite

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Lean production system design for fishing net manufacturing using lean principles and simulation optimization

Yang

Kuo

et al. 2015

Journal of Manufacturing Systems

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Demand Forecasting for Aqua Tech Technical Textiles

Chaithanya L,

Dr. Chitra K

2024

IJARSCT

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Demand forecasting plays a significant role in the Aquatech industry, where innovation integration with aquacultural practices drives efficiency and effectiveness. This abstract outline the importance, challenges, techniques, and implications of demand forecasting in the sector. Demand forecasting is essential for businesses to anticipate trends, optimize resource allocation, and enhance operational efficiency. By accurately predicting future demand for aquacultural products, services, and technological outcomes, companies can make informed decisions regarding production, operations, and marketing strategies. However, demand forecasting in this industry faces several challenges, including seasonal variations, data availability, technological complexity, and uncertainties. Aquaculture demand is influenced by factors such as precipitation patterns, regulatory policies, and consumer preferences, making it difficult to develop precise forecasting models. Various techniques are used for demand forecasting, including statistical methods, data analytics, and machine learning algorithms. Time series analysis, regression analysis, and hybrid forecasting approaches are commonly employed to analyze historical data and predict future demand patterns. Accurate demand forecasting has significant implications for businesses, including improved resource planning, supply chain management, and customer satisfaction. By aligning production capacities with anticipated demand and optimizing supply levels, companies can minimize costs, reduce waste, and enhance competitiveness in the market. Demand forecasting is a critical aspect of strategic planning and decision-making in the specialized industry. By utilizing advanced techniques and effectively addressing challenges, businesses can enhance their ability to anticipate trends, meet customer demands, and drive sustainable growth in an increasingly dynamic and competitive environment

show abstract

The development of a hierarchical rough-cut capacity planning model and demand management system for fishing-net manufacturing

Cited by 3 publications

References 10 publications

Integration of customer and supplier flexibility in a make-to-order industry

Integration of customer and supplier flexibility in a make-to-order industry

Lean production system design for fishing net manufacturing using lean principles and simulation optimization

Demand Forecasting for Aqua Tech Technical Textiles

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