A study on decision-making of food supply chain based on big data

Ji, Guojun; Hu, Limei; Tan, Kim Hua

doi:10.1007/s11518-016-5320-6

Cited by 42 publications

(24 citation statements)

References 19 publications

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“…This is also the article with second highest values for in-degree (12) and betweenness centrality (764). Ji et al (2017) present an example of how BD in the food chain can be combined with Bayesian network and deduction graph models to guide production decisions.…”

Section: Analytics and Data Managementmentioning

confidence: 99%

Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis

et al. 2019

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Digital transformation (DT) has become a buzzword, triggering different disciplines in research and influencing practice, which leads to independent research streams. Scholars investigate the antecedents, contingencies, and consequences of these disruptive technologies by examining the use of single technologies or of digitization, in general. Approaches are often very specialized and restricted to their domains. Thus, the immense breadth of technologies and their possible applications conditions a fragmentation of research, impeding a holistic view. With this systematic literature review, we aim to fill this gap in providing an overview of the different disciplines of DT research from a holistic business perspective. We identified the major research streams and clustered them with co-citation network analysis in nine main areas. Our research shows the main fields of interest in digital transformation research, overlaps of the research areas and fields that are still underrepresented. Within the business research areas, we identified three dominant areas in literature: finance, marketing, and innovation management. However, research streams also arise in terms of single branches like manufacturing or tourism. This study highlights these diverse research streams with the aim of deepening the understanding of digital transformation in research. Yet, research on DT still lacks in the areas of accounting, human resource management, and sustainability. The findings were distilled into a framework of the nine main areas for assisting the implications on potential research gaps on DT from a business perspective.

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Section: Analytics and Data Managementmentioning

confidence: 99%

Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis

et al. 2019

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“…Most relevant technology is based on data analysis, which means data collection and analysis are at the centre of improving efficiency and consistency. In this respect, [19] presents a case study focusing on understanding the use of big data in new food product development; [20] presents some big data models to make more informed production decisions in the food supply chain; [40] develops a new model for production management to minimise the reconfiguration of the production lines for different products; and [4] utilises big data to improve sustainability management in food supply chain design.…”

Section: State Of the Art And Research Gapmentioning

confidence: 99%

Towards design and implementation of Industry 4.0 for food manufacturing

Konur

Lan

Thakker

et al. 2021

Neural Comput & Applic

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Today’s factories are considered as smart ecosystems with humans, machines and devices interacting with each other for efficient manufacturing of products. Industry 4.0 is a suite of enabler technologies for such smart ecosystems that allow transformation of industrial processes. When implemented, Industry 4.0 technologies have a huge impact on efficiency, productivity and profitability of businesses. The adoption and implementation of Industry 4.0, however, require to overcome a number of practical challenges, in most cases, due to the lack of modernisation and automation in place with traditional manufacturers. This paper presents a first of its kind case study for moving a traditional food manufacturer, still using the machinery more than one hundred years old, a common occurrence for small- and medium-sized businesses, to adopt the Industry 4.0 technologies. The paper reports the challenges we have encountered during the transformation process and in the development stage. The paper also presents a smart production control system that we have developed by utilising AI, machine learning, Internet of things, big data analytics, cyber-physical systems and cloud computing technologies. The system provides novel data collection, information extraction and intelligent monitoring services, enabling improved efficiency and consistency as well as reduced operational cost. The platform has been developed in real-world settings offered by an Innovate UK-funded project and has been integrated into the company’s existing production facilities. In this way, the company has not been required to replace old machinery outright, but rather adapted the existing machinery to an entirely new way of operating. The proposed approach and the lessons outlined can benefit similar food manufacturing industries and other SME industries.

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“…In quantitative aspects, Ji et al proposed a decisionmaking model of food supply chain using Big Data as input to predict market demand [23]. Badiezadeh et al proposed a method based on Big Data approach to evaluate the sustainability of supply chain; meanwhile, the double frontier network DEA was also used [24].…”

Section: Literature Reviewmentioning

confidence: 99%

Pricing Strategies of a Three-Stage Supply Chain: A New Research in the Big Data Era

Liu¹

2017

Discrete Dynamics in Nature and Society

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In the Big Data era, Data Company as the Big Data information (BDI) supplier should be included in a supply chain. In the new situation, to research the pricing strategies of supply chain, a three-stage supply chain with one manufacturer, one retailer, and one Data Company was chosen. Meanwhile, considering the manufacturer contained the internal and external BDI, four benefit models about BDI investment were proposed and analyzed in both decentralized and centralized supply chain using Stackelberg game. Meanwhile, the optimal retail price and benefits in the four models were compared. Findings are as follows. (1) The industry cost improvement coefficient, the internal BDI investment cost of the manufacturer, and the added cost of the Data Company on using Big Data technology have different relationships with the optimal prices of supply chain members in different models. (2) In the retailer-dominated supply chain model, the optimal benefits of the retailer and the manufacturer are the same, and the optimal benefits of the Data Company are biggest in all the members.

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A study on decision-making of food supply chain based on big data

Abstract: Abstract

Cited by 42 publications

References 19 publications

Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis

Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis

Towards design and implementation of Industry 4.0 for food manufacturing

Pricing Strategies of a Three-Stage Supply Chain: A New Research in the Big Data Era

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