Reducing Plastic Packaging and Food Waste Through Product Innovation Simulation: Household Simulation Model for Chicken Fillets, 2021-2023

DOI

This data collection presents the new Household Simulation Model (HHSM) specifically developed for chicken fillets, using Arena software version 16.2. The primary aim of the HHSM is to offer insights into the impact of various market and consumer behavior interventions on the amount of food and packaging waste generated in households. By simulating diverse scenarios, the model enables researchers and stakeholders to understand the potential effects of different interventions on household decision-making related to chicken fillet consumption and waste generation. The data collection contains three components: the Arena simulation model (HHSM) (.doe file), an accompanying input/output data file developed in Microsoft Excel (.xlsm file), which allows users to modify input parameters and retrieve the outputs generated by the HHSM and an detailed user manual of the model (.pdf file). This data collection is a valuable resource for researchers, policy-makers, and industry professionals interested in understanding the dynamics of household consumption and waste generation related to chicken fillets, offering a powerful tool for investigating potential interventions, promoting sustainable consumption patterns, and informing future policies in the context of food waste reduction and resource optimisation.THE PROBLEM Plastic packaging waste is a major issue that has recently entered public consciousness, with the British government committing to a 25-year plan that would phase out disposable packaging by 2042. Around 41% of plastic packaging is used for food, with the UK generating 1 million tonnes per year of packaging waste. Food packaging has had a 1844% increase in recycling since 2007, yet still only one third of food packaging is currently recycled [3]. Currently many consumers are boycotting plastic packaging. However, this is leading to a rise in food waste (and foodborne illness risk) due to decreased shelf life. Up to a third of the resources used to produce food could be saved by eliminating food waste [1]. In the UK, approximately 10 million tonnes of food are wasted every year, with the average family (i.e. a household containing children) spending £700 a year on food that is wasted. 31% of avoidable household food waste (1.3 million tonnes), is caused by a mismatch of packaging, pack, and portion size, and household food habits [2]. Plastic pollution and food waste can be reduced through product re-design and other household interventions. However, there is little evidence to determine the best solutions to reduce plastic pollution and food waste. The food industry and consumers have a variety of possible solutions, but no way of knowing the impacts and unintended consequences (without costly, time consuming trials and measurement). This is a major barrier to empowering the food system to enable the rapid reduction of plastic waste. THE VISION This project reduces plastic pollution (and food waste) by providing a decision support tool to trigger action in the food industry and by consumers. Evidence concerning plastic and food waste reduction (and trade-offs with cost, and environmental impacts) will be generated by updating the Household Simulation Model (HHSM). The HHSM was piloted by the University of Sheffield and WRAP (the Waste & Resources Action Programme) to model the impacts of food product innovation quickly, to enable manufacturers to select the best innovations and interventions, and to prioritise their development and deployment. This project will incorporate into the current HHSM, data on 1) plastic packaging options and composition (from Valpak/WRAP), 2) household behavioural insights around packaging (single and reuse options) and food (provided by UoS/WRAP), with specific fresh produce data (from Greenwich) 3) plastic in the supply chain and environmental impacts (via SCEnATi- a big data analytics tool of the food supply chain processes (provided by Sheffield). The updated HHSM will enable the quantification of plastic and food waste reduction, and the environmental and monetary trade-offs of various solutions. This will be done by developing an optimization engine and integrating it with the updated HHSM which will further the simulation optimization methodology with the findings from applying developed meta-heuristic algorithms to this problem. Possible solutions include offering consumers different pack sizes, or changing packaging type/shape/reusability/durability. The most successful solutions will be translated into consumer and industry guidance focusing on the top 30 foods linked to the highest waste and tradeoff potential. This will enable rapid product and food system redesign. This guidance will be open access, and deployed through WRAP and global industry networks, and open access web tools. WRAP is coordinating the voluntary agreements UK Plastics Pact and the Courtauld Commitment 2025 (focused on food waste and carbon reduction). This allows rapid scaling of the HHSM outputs throughout the UK. References: [1] Institution of Mechanical Engineers, "Global food - Waste not, want not" London, 2013 [2] Quested, T. E., et al. "Spaghetti soup: The complex world of food waste behaviours." RCR 79 (2013): 43-51. [3] Recoup 2018, UK Household Plastics Collection

The data collection methods for the chicken fillets' Household Simulation Model (HHSM) focused on obtaining relevant information through a combination of primary and secondary sources, ensuring the data's accuracy and reliability. By adopting a multi-faceted and multidisciplinary data collection approach, this research ensured the robustness and validity of the Household Simulation Model, providing a reliable foundation for exploring the impacts of various market and consumer behavior interventions on food and packaging waste generated in households. The data collection methods included: 1. Literature Review. A comprehensive literature review was conducted to identify key factors influencing household consumption patterns and waste generation related to chicken fillets. This review included academic articles, industry reports, and WRAP reports. The information gathered through the literature review was utilised to develop the conceptual framework for the HHSM and to inform the model's structure and parameters. 2. Expert Consultations. Input from industry professionals, academics, and other experts in food waste reduction and consumer behavior was sought to refine the model and validate its assumptions. These consultations also provided valuable insights into the potential market and consumer behavior interventions to be explored using the HHSM. 3. Qualitative research with households. Primary data on household consumption patterns, waste generation, and packaging use related to chicken fillets was collected through structured online interviews following practice theory and approaches to household waste and consumption. In addition, the households filled diaries, including photos about routines and practices, such as purchasing behavior, storage conditions preference, and consumption practices. 4. Data Processing and Analysis. The collected data was analysed to generate input parameters for the HHSM. The model incorporated these findings to ensure its accuracy and relevance to real-world scenarios.

Identifier
DOI https://doi.org/10.5255/UKDA-SN-856471
Metadata Access https://datacatalogue.cessda.eu/oai-pmh/v0/oai?verb=GetRecord&metadataPrefix=oai_ddi25&identifier=44d5a95d3195b3bcb96bc508be58ca090bb83609e53ed48ae6150ce0ae09ca86
Provenance
Creator Reynolds, C, City, University of London; Fayad, R, The University of Sheffield
Publisher UK Data Service
Publication Year 2023
Funding Reference NERC
Rights Christian Reynolds, City, University of London. Ramzi Fayad, The University of Sheffield; The UK Data Archive has granted a dissemination embargo. The embargo will end on 24 October 2024 and the data will then be available in accordance with the access level selected.
OpenAccess true
Representation
Language English
Resource Type Software; Other
Discipline Social Sciences
Spatial Coverage Sheffield, London; United Kingdom