Intermittent demand forecasting in the Enterprise: Empirical verification

• DOI: http://doi.org/10.1002/for.2575
• Published date: 01 August 2019
• Author: Mariusz Doszyń
• Date: 01 August 2019

RESEARCH ARTICLE

Intermittent demand forecasting in the Enterprise:

Empirical verification

Mariusz Doszyń

Econometrics and Statistics Institute,

University of Szczecin, 71‐101

Szczecin Mickiewicza 64, room 320,

Poland

Correspondence

University of Szczecin, Econometrics and

Statistics Institute, 71‐101 Szczecin,

Mickiewicza 64, room 320, Poland.

Email: mariusz.doszyn@usz.edu.pl

Abstract

Forecasting methods are often valued by means of simulation studies. For

intermittent demand items there are often very few non–zero observations,

so it is hard to check any assumptions, because statistical information is often

too weak to determine, for example, distribution of a variable. Therefore, it

seems important to verify the forecasting methods on the basis of real data.

The main aim of the article is an empirical verification of several forecasting

methods applicable in case of intermittent demand. Some items are sold only

in specific subperiods (in given month in each year, for example), but most

forecasting methods (such as Croston's method) give non–zero forecasts for

all periods. For example, summer work clothes should have non–zero forecasts

only for summer months and many methods will usually provide non–zero

forecasts for all months under consideration. This was the motivation for

proposing and testing a new forecasting technique which can be applicable

to seasonal items. In the article six methods were applied to construct separate

forecasting systems: Croston's, SBA (Syntetos–Boylan Approximation), TSB

(Teunter, Syntetos, Babai), MA (Moving Average), SES (Simple Exponential

Smoothing) and SESAP (Simple Exponential Smoothing for Analogous

subPeriods). The latter method (SESAP) is an author's proposal dedicated for

companies facing the problem of seasonal items. By analogous subperiods the

same subperiods in each year are understood, for example, the same months

in each year. A data set from the real company was used to apply all the above

forecasting procedures. That data set contained monthly time series for about

nine thousand products. The forecasts accuracy was tested by means of both

parametric and non–parametric measures. The scaled mean and the scaled root

mean squared error were used to check biasedness and efficiency. Also, the

mean absolute scaled error and the shares of best forecasts were estimated.

The general conclusion is that in the analyzed company a forecasting system

should be based on two forecasting methods: TSB and SESAP, but the latter

method should be applied only to seasonal items (products sold only in specific

subperiods). It also turned out that Croston's and SBA methods work worse

than much simpler methods, such as SES or MA. The presented analysis might

be helpful for enterprises facing the problem of forecasting intermittent items

(and seasonal intermittent items as well).

Received: 14 March 2018 Revised: 7 November 2018 Accepted: 31 January 2019

DOI: 10.1002/for.2575

Journal of Forecasting. 2019;38:459–469. © 2019 John Wiley & Sons, Ltd.wileyonlinelibrary.com/journal/for 459