Wasting less food: smart mass customisation of food provision

Woolley, Elliot; Jellil, Aicha; Simeone, Alessandro

doi:10.1016/j.procir.2021.01.073

Cited by 9 publications

(3 citation statements)

References 16 publications

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“…The study adopted different approaches to fund low-carbon policy considering the household expenditure survey data. A product service system was developed to cater the concerns of supply-demand balancing at the consumer level (Woolley et al, 2021). A case study is proposed to highlight the implementation of the model in modern commerce systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Examining the interactions among smart supply chains and carbon reduction strategies: To attain carbon neutrality

Patil,

Shardeo,

Dwivedi

et al. 2023

Bus Strat Env

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Rapid industrialization and global supply chains have magnified industrial activities' carbon footprint, resulting in environmental hazards. Several countries have magnified their efforts to reach carbon neutrality goals. The increased attention to carbon‐neutral goals has been enabled by United Nations (UN) calls for a carbon‐neutral economy. In this context, supply chains will have greater responsibilities to reduce the network's carbon footprint. To achieve carbon neutrality goals, decision‐makers must be well‐informed and facilitated with suitable theoretical frameworks. In literature, the adoption of intelligent technologies is suggested to construct efficient and smart supply chains. However, there are uncertainties regarding the impact of this transformation on the carbon neutrality goal. The present study caters to the uncertainties by discussing its potential impact on carbon neutrality goals and how to use adopted technologies to achieve the same. A list of 11 potential strategies is identified and modelled to provide a strategic roadmap for smart supply chains' adoption. The proposed framework aims to facilitate carbon neutrality goals. The findings indicate that decision‐makers must pay significant attention to circular economy practices. Additionally, green transportation is an impactful area with significant potential to support carbon neutrality goals. Critical insights have been extracted regarding stakeholders' training and process innovation. The study findings can help decision‐makers achieve carbon neutrality goals.

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Section: Literature Reviewmentioning

confidence: 99%

Examining the interactions among smart supply chains and carbon reduction strategies: To attain carbon neutrality

Patil,

Shardeo,

Dwivedi

et al. 2023

Bus Strat Env

View full text Add to dashboard Cite

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“…More than The United Nations Environment Program reports that Turkey, in particular, discards over 7,7 Mt of food each year, positioning it as one of the foremost global contributors to food waste [6]. The incidence of food waste is not confined to specific geographic regions but is closely intertwined with a country's level of development [7]. Food waste gives rise to economic, environmental, and social challenges in numerous countries, underscoring the imperative of implementing strategies to curtail its occurrence [8].…”

Section: Introductionmentioning

confidence: 99%

Demand Forecasting for Food Production Using Machine Learning Algorithms: A Case Study of University Refectory

2023

Teh. vjesn.

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Accurate food demand forecasting is one of the critical aspects of successfully managing restaurants, cafeterias, canteens, and refectories. This paper aims to develop demand forecasting models for a university refectory. Our study focused on the development of Machine Learning-based forecasting models which take into account the calendar effect and meal ingredients to predict the heavy demand for food within a limited timeframe (e.g., lunch) and without pre-booking. We have developed eighteen prediction models gathered under five main techniques. Three Artificial Neural Network models (i.e.

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“…The prevention of food waste is one of the most important issues today worldwide, especially in the context of sustainable development. The amount of food wasted is not geographically specific but correlates with the country's development [5]. Food waste is responsible for economic, environmental, and social problems in many countries, and the strategies for its reduction are crucial [2], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Food Demand Prediction Using the Nonlinear Autoregressive Exogenous Neural Network

Lutosławski

Hernes²,

Radomska

et al. 2021

IEEE Access

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Food demand prediction is a significant issue for both businesses processes improvement and sustainable development issues. The data science methods, including artificial intelligence methods, are often used for this purpose. The aim of this research is to develop the models for food demand prediction based on the Nonlinear Autoregressive Exogenous Neural Network. The research focuses on the processed food, such as bread or butter. Developed models' architectures differing in the number of hidden layers and the number of neurons in the hidden layers, as well as with different sizes of the delay-line, were tested for a given product. Results of the research show that depending on the type of product, prediction performance slightly differed. The results of the R 2 measure ranged from 96,2399 to 99,6477 depending on particular products. The proposed models can be used in a company's intelligent management system for rational control of inventories and food production. It can also lead to reducing food waste.INDEX TERMS food industry, sustainable development, neural networks, machine learning, demand forecasting.

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Wasting less food: smart mass customisation of food provision

Cited by 9 publications

References 16 publications

Examining the interactions among smart supply chains and carbon reduction strategies: To attain carbon neutrality

Examining the interactions among smart supply chains and carbon reduction strategies: To attain carbon neutrality

Demand Forecasting for Food Production Using Machine Learning Algorithms: A Case Study of University Refectory

Food Demand Prediction Using the Nonlinear Autoregressive Exogenous Neural Network

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