Distinguishing Organisational Profiles of Food Loss Management in Logistics

Julia Kleineidam ()
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Julia Kleineidam: Institute for Technology and Management, Berlin University of Technology, 10623 Berlin, Germany

Logistics, 2022, vol. 6, issue 3, 1-23

Abstract: Background : Food loss management (FLM), which is discussed at length in the literature, lacks a scientific basis on which to determine the current engagement of actors in the food value chain and what is relevant to derive appropriate measures according to the circumstances in the organisations concerned. Therefore, this paper aims to derive patterns by which the engagement of actors can be distinguished and, on this basis, to make recommendations for further action. Methods : Based on an online survey of 40 participants, a clustering analysis was conducted using the unsupervised learning method and hierarchical clustering (R and R Studio). Results : Five clusters representing different profiles were derived, showing how actors in the food value chain have addressed FLM in the past. The derived profiles do not represent stages of development but rather characteristics of organisations that have addressed FLM in a certain way in the past. Conclusions : For the five organisational profiles, recommendations for action were given for further engagement with FLM. As the level of engagement with FLM increases, organisations should tackle increasingly complex measures to reduce food losses. At the same time, a shift in measures from the tactical to the strategic planning level was derived.

Keywords: food loss management; logistics and supply chain management; SMEs; clustering; online survey (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlogis:v:6:y:2022:i:3:p:61-:d:890302

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