Abstract
The rapid changes in the design and development of new food products in marketplaces increase the pressure and growing demand on Supply Chain Systems (SCS), necessitating a focus on reducing operational costs and enhancing response time efficiency to meet consumers' demand in New Product Development (NPD). Current research and development approaches involve a substantial volume of information concerning the desired attributes for product quality that align with consumer expectations. This introduces high uncertainty and potential risks within supply chain networks. In order to address these challenges, it is necessary to integrate modern communication methods and
advanced manufacturing technologies. This integration takes the form of innovative tools, techniques, and models designed for the preprocessing, selection, and dimensionality reduction of the extensive amount of information and data associated with product quality attributes. Therefore, this research
proposes a new structural quality model for designing and developing food products to reduce the independent variables affecting newly formulated food products and all other possible quality factors.
As a methodology, Principal Component Analysis (PCA), combined with multivariate statistical techniques, is employed to enable quick and flexible responses to consumer demand through advanced food processing technologies. This assists in determining the optimum number of quality dimensions that affect the final formulations of new products produced at decentralised local markets. A real-life case study on formulated beverages was conducted in the UK, focusing on food-grade applications, and it aimed to identify strategies for reducing waste, uncertainty, and risk in food NPD through an optimised manufacturing process. The results indicate that the developed model offers a better option for management, enabling the integration of supply chain actors with formulation processes to achieve economic and sustainable benefits for a resilient supply chain.
advanced manufacturing technologies. This integration takes the form of innovative tools, techniques, and models designed for the preprocessing, selection, and dimensionality reduction of the extensive amount of information and data associated with product quality attributes. Therefore, this research
proposes a new structural quality model for designing and developing food products to reduce the independent variables affecting newly formulated food products and all other possible quality factors.
As a methodology, Principal Component Analysis (PCA), combined with multivariate statistical techniques, is employed to enable quick and flexible responses to consumer demand through advanced food processing technologies. This assists in determining the optimum number of quality dimensions that affect the final formulations of new products produced at decentralised local markets. A real-life case study on formulated beverages was conducted in the UK, focusing on food-grade applications, and it aimed to identify strategies for reducing waste, uncertainty, and risk in food NPD through an optimised manufacturing process. The results indicate that the developed model offers a better option for management, enabling the integration of supply chain actors with formulation processes to achieve economic and sustainable benefits for a resilient supply chain.
| Original language | English |
|---|---|
| Pages (from-to) | 671-689 |
| Number of pages | 19 |
| Journal | Jordan Journal of Mechanical and Industrial Engineering |
| Volume | 18 |
| Issue number | 4 |
| Early online date | 1 Dec 2024 |
| DOIs | |
| Publication status | Published - 1 Dec 2024 |
Bibliographical note
Copyright © 2024, Jordan Journal of Mechanical and Industrial Engineering. This is an accepted manuscript of an article published in the Jordan Journal of Mechanical and Industrial Engineering. The published version is available at: https://doi.org/10.59038/jjmie/180404Funding
This work was fully funded by the UK Engineering and Physical Sciences Research Council (EPSRC) primarily by grant number EP/K014234/2, with supplementary grant number EP/M017567/1. The authors fully acknowledge and wish to thank EPSRC for their financial support.
Keywords
- Food industry
- Formulated beverages
- Modelling
- Multivariate statistical analysis
- New product development
- Supply chain systems
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