EconPapers    
Economics at your fingertips  
 

AN APPROACH TO MODELING THE PROBABLE CONSUMERS DEMAND OF FOOD PRODUCTS USING PEARSON DISTRIBUTION SYSTEM AND JOHNSON DISTRIBUTION SYSTEM

Julieta Mihaylova ()
Additional contact information
Julieta Mihaylova: Department of Statistics and Applied Mathematics, Univercity of Economics - Varna, Bulgaria

Business & Management Compass, 2023, issue 3, 213-223

Abstract: To meet the random consumers demand the distributors maintain inventory. For optimal inventory control under random demand it is necessary to know the cumulative distribution function (CDF). The practical determination of CDF is related with a number of difficulties. This paper proposes a way to construct a probability distribution function of demand. Data on weekly sales of over 400 types of food products over a period of five years in a small distribution company were analyzed. The ARIMA model was used for primary analysis of the consumption data. Random variables are modeled using Pearson Distribution System and Johnson Distribution System and can be used to determine inventory management strategies.

Keywords: Food distribution; ARIMA model; Pearson distribution system; Johnson distribution system (search for similar items in EconPapers)
JEL-codes: C02 L81 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journal.ue-varna.bg/uploads/20231024113258_6184927586537ab6aa4c10.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrn:journl:y:2023:i:3:p:213-223

Access Statistics for this article

Business & Management Compass is currently edited by Julian Vasilev

More articles in Business & Management Compass from University of Economics Varna Contact information at EDIRC.
Bibliographic data for series maintained by Yana Doneva ().

 
Page updated 2025-03-20
Handle: RePEc:vrn:journl:y:2023:i:3:p:213-223