In recent years multiple countries have witnessed the dramatic diffusion of the so-called “on-demand food delivery”, i.e., a model based on online platforms offering the delivery of freshly prepared meals from restaurants to the customers’ home. In these ecosystems, novel solutions referred to as “Kitchens for Delivery” are being created, which are aimed to fulfil these orders. Differently from traditional restaurants, these are kitchens dedicated to the preparation of online orders only, with no offline customers. This being the context, the present research has a twofold goal. First, identifying and describing the major different models existing in the field (i.e., Dark, Cloud and Ghost Kitchens). Second, estimating their performances from a logistics perspective, by means of an evaluation of their impact on the on-demand food delivery logistics problem. The implemented approach is multi-method, as it combines: (i) the analysis of (black, grey and white) literature, to understand the state of art and map the main solutions; (ii) a simulation study, to assess the changes implied by introducing Ghost Kitchens into the network in terms of delivery performances; (iii) interviews with practitioners, to validate and interpret the results. The research is expected to have both academic and managerial implications. Considering academia, it sheds light on a field that is under-investigated in literature, proposing a classification of extant models, as well as a model to estimate their logistics implications. Considering industry, it provides an estimation of the impact that implementing Ghost Kitchens could have on the most significant logistics performances.

Dark, cloud and ghost kitchens: a logistics perspective

A. Seghezzi;C. Siragusa;R. Mangiaracina;A. Perego;A. Tumino
2023-01-01

Abstract

In recent years multiple countries have witnessed the dramatic diffusion of the so-called “on-demand food delivery”, i.e., a model based on online platforms offering the delivery of freshly prepared meals from restaurants to the customers’ home. In these ecosystems, novel solutions referred to as “Kitchens for Delivery” are being created, which are aimed to fulfil these orders. Differently from traditional restaurants, these are kitchens dedicated to the preparation of online orders only, with no offline customers. This being the context, the present research has a twofold goal. First, identifying and describing the major different models existing in the field (i.e., Dark, Cloud and Ghost Kitchens). Second, estimating their performances from a logistics perspective, by means of an evaluation of their impact on the on-demand food delivery logistics problem. The implemented approach is multi-method, as it combines: (i) the analysis of (black, grey and white) literature, to understand the state of art and map the main solutions; (ii) a simulation study, to assess the changes implied by introducing Ghost Kitchens into the network in terms of delivery performances; (iii) interviews with practitioners, to validate and interpret the results. The research is expected to have both academic and managerial implications. Considering academia, it sheds light on a field that is under-investigated in literature, proposing a classification of extant models, as well as a model to estimate their logistics implications. Considering industry, it provides an estimation of the impact that implementing Ghost Kitchens could have on the most significant logistics performances.
2023
Proceedings of the XXVIII Summer School “Francesco Turco”
on-demand food delivery, logistics, e-commerce, ghost kitchens
File in questo prodotto:
File Dimensione Formato  
C1_Dark, cloud and ghost kitchens- a logistics perspective.pdf

accesso aperto

Dimensione 2.19 MB
Formato Adobe PDF
2.19 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1263136
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact