A Bayesian Approach for Predicting Food and Beverage Sales in Staff Canteens and Restaurants
Clicks: 95
ID: 282968
2020
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
28.2
/100
95 views
30 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #129 of 803 articles by views in arXiv
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 803 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Accurate demand forecasting is one of the key aspects for successfully
managing restaurants and staff canteens. In particular, properly predicting
future sales of menu items allows a precise ordering of food stock. From an
environmental point of view, this ensures maintaining a low level of
pre-consumer food waste, while from the managerial point of view, this is
critical to guarantee the profitability of the restaurant. Hence, we are
interested in predicting future values of the daily sold quantities of given
menu items. The corresponding time series show multiple strong seasonalities,
trend changes, data gaps, and outliers. We propose a forecasting approach that
is solely based on the data retrieved from Point of Sales systems and allows
for a straightforward human interpretation. Therefore, we propose two
generalized additive models for predicting the future sales. In an extensive
evaluation, we consider two data sets collected at a casual restaurant and a
large staff canteen consisting of multiple time series, that cover a period of
20 months, respectively. We show that the proposed models fit the features of
the considered restaurant data. Moreover, we compare the predictive performance
of our method against the performance of other well-established forecasting
approaches.
| Reference Key |
pilz2020a
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Konstantin Posch; Christian Truden; Philipp Hungerländer; Jürgen Pilz |
| Journal | arXiv |
| Year | 2020 |
| DOI |
DOI not found
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.