Abstract
PurposeThe aim of the paper is to assess the role and effectiveness of a proposed novel strategy for Life Cycle Inventory (LCI) data collection in the food sector and associated supply chains. The study represents one of the first of its type and provides answers to some of the key questions regarding the data collection process developed, managed and implemented by a multinational food company across the supply chain.MethodsAn integrated LCI data collection process for confectionery products was developed and implemented by Nestlé, a multinational food company. Some of the key features includes (1) management and implementation by a multinational food company; (2) types of roles to manage, provide and facilitate data exchange; (3) procedures to identify key products, suppliers and customers; (4) LCI questionnaire and cover letter and (5) data quality management based on the pedigree matrix. Overall, the combined features in an integrated framework provide a new way of thinking about the collection of LCI data from the perspective of a multinational food company.Results and discussionThe integrated LCI collection framework spanned across 5 months and resulted in 87 new LCI datasets for confectionery products from raw material, primary resource use, emission and waste release data collected from suppliers across 19 countries. The data collected was found to be of medium to high quality compared with secondary data. However, for retailers and waste service companies, only partially completed questionnaires were returned. Some of the key challenges encountered during the collection and creation of data included lack of experience, identifying key actors, communication and technical language, commercial compromise, confidentiality protection and complexity of multi-tiered supplier systems. A range of recommendations are proposed to reconcile these challenges which include standardisation of environmental data from suppliers, concise and targeted LCI questionnaires and visualising complexity through drawings.ConclusionsThe integrated LCI data collection process and strategy has demonstrated the potential role of a multinational company to quickly engage and act as a strong enabler to unlock latent data for various aspects of the confectionery supply chain. Overall, it is recommended that the research findings serve as the foundations to transition towards a standardised procedure which can practically guide other multinational companies to considerably increase the availability of LCI data.
Highlights
From the early days of life cycle assessment (LCA) over 40 years ago, the availability of Life Cycle Inventory (LCI) data has been a continuing major problem—a bottleneck—for the wide application of LCA (Testa et al 2016; Ang et al 2014; Finnveden et al 2009; Pennington et al 2007)
The integrated LCI collection framework spanned across 5 months and resulted in 87 new LCI datasets for confectionery products from raw material, primary resource use, emission and waste release data collected from suppliers across 19 countries
It is recommended that the research findings serve as the foundations to transition towards a standardised procedure which can practically guide other multinational companies to considerably increase the availability of LCI data
Summary
From the early days of life cycle assessment (LCA) over 40 years ago, the availability of Life Cycle Inventory (LCI) data has been a continuing major problem—a bottleneck—for the wide application of LCA (Testa et al 2016; Ang et al 2014; Finnveden et al 2009; Pennington et al 2007). Secondary data are defined as Bdata that is not directly collected, measured, or estimated, but rather sourced from a third-party life-cycleinventory database^ (European Commission 2013). This can include data from publications and reports. Some of the major LCI databases (DB) include Ecoinvent DB (Ecoinvent 2016), US LCI DB (NREL 2014), World Food LCA DB (WFLDB) (Nemecek et al 2014) and Plastics Europe DB (PlasticsEurope 2015) For both primary and secondary data, there are guidelines available to ensure completeness, quality and transparency (Weidema et al 2013; PEF World Forum 2013; UNEP 2011). A considerable amount of time and cost is required by an LCA practitioner to physically collect primary data and rationalise and interpret LCI data as defined by the goal and scope of the LCA study (Testa et al 2016; Jolliet et al 2015; Ang et al 2014)
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