Inventory holding costs measurement: a multi-case study

• Date: 06 May 2014
• Published date: 06 May 2014
• DOI: https://doi.org/10.1108/IJLM-01-2012-0004
• Pages: 109-132
• Author: Anna Azzi,Daria Battini,Maurizio Faccio,Alessandro Persona,Fabio Sgarbossa
• Subject Matter: Management science & operations,Logistics

Inventory holding costs

measurement: a multi-case study

Anna Azzi, Daria Battini, Maurizio Faccio,

Alessandro Persona and Fabio Sgarbossa

Department of Management and Engineering,

University of Padova, Vicenza, Italy

Abstract

Purpose – Logisticians in the worldwide industry are frequently faced with the problem of measuring

the total cost of holding inventories with simple and easy-to-use methodologies. The purpose of this

paper is to look at the problem, and in particular illustrate the inventory holding cost rate computation,

when different kind of warehousing systems are applied.

Design/methodology/approach – A multiple case study analysis is here developed and supported

by a methodological framework directly derived from the working group discussions and

brainstorming activities. Two different field of application are considered: one related to five

companies with manual warehousing systems operating with traditional fork lift tr ucks; the other

is among five companies operating with automated storage/retrieval systems (AS/RS) to store

inventories.

Findings – The multi-case study helps to understand how the holding cost parameter is currently

computed by industrial managers and how much the difference between manual and automated/

automatic warehousing systems impacts on the inventory cost structure definition. The insights from

the ten case studies provide evidence that the kind of storage system adopted inside the factory can

impact on the holding cost rate computation and permit to derive important considerations.

Practical implications – The final aim of this work is to help industrial engine ers and logisticians

in correctly understanding the inventory costs involved in their systems and their cost structure.

In addition, the multi-case analysis leads to considerations, to be applied in different industrial

contexts. As other industrial applications are identified, they may be analyzed by using the presented

methodology, and with aid from the data from this paper.

Originality/value – The relevance of this work is to help industrial engineers and logisticians in

understanding correctly the inventory costs involved in their logistics systems and their cost str ucture.

In addition, the multi-case analysis lead to interesting final considerations, easily to be applied in

different industrial contexts. As other industrial applications are identified, they may be analyzed by

using the methodology and extrapolating the data from this paper.

Keywords Inventory management, Total cost, Warehousing

Paper type Case study

1. Introduction

Knowledge of holding costs constitutes a vital part of any industrial logistics systems

management, as they are widely used in well-established inventory management

models such as Economic Batch Quantity (i.e. Harris, 1990; Wilson, 1934), Joint

Economic Lot Size Analysis (Hill, 1997; Goyal, 2000) and in all recent studies where an

inventory model is developed and an inventory holding cost needs to be computed

(i.e. Choi and Noble, 2000; Thiagarajan and Rajendranz, 2003; Persona et al., 2007;

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0957-4093.htm

Received 10 November 2011

Revised 24 October 2012

24 January 2012

Accepted 17 December 2012

The International Journal of Logistics

Management

Vol.25 No. 1, 2014

pp. 109-132

rEmeraldGroup Publishing Limited

0957-4093

DOI 10.1108/IJLM-01-2012-0004

This work and all data reported would not have been possible without the essential and gracious

support of the industrial managers involved in the two working groups. For commercial reasons

and privacy issues, the identity of the companies that participated in the study is kept

confidential.

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Inventory

holding costs

measurement

Battini et al., 2009, 2010). Generally speaking, any industrial system has the need

to compile solid inventory cost parameters in order to drive logisticians decision

making, both for logistics and mana gement purposes (Daugherty et al., 2011; Defe e

et al., 2010; Randall et al., 2011). This work focusse s on total inventory carrying cost

computation, which is a relevant facto r in several situations, i.e. in warehouse

investment decisions and in any situation in which efficient inventory management

becomes a priority (i.e. the centralized/decentralized control of inventories, Bregman

et al., 1989; Cattani et al., 2011).

An intuitive choice of this index could lead to misunderstanding and errors in the

inventory management models application. As recently underlined by Berling (2008)

it is surprising that “little effort has been put into finding ways fo r accurately

determining the cost parameters used for inventory control, i.e. the holding cost rate

although such costs are used as inputs in nearly all existing methods and heuristics.”

Traditionally,it is a common assumption that the holding cost parameter is a constant

and is expressed as a percentage of the product value. This approach is based on the

assumption that capital cost makes up most of the cost (Singhal and Raturi, 1990;

Berling, 2008). Decades of research are summed up in several studies (Plossl,

1967/1985), yet it is still difficult to find detailed results of efficient applications in

industrial contexts, able to guide managers in the correct determination of the holding

cost rate. Existing liter ature offers only general guidelines: textbooks published in 1990

report widely that, while the carrying costs will vary with specific situations, they can

be estimated at 20-50 percent above the inventory value (e.g. Schonberger and Knod,

1997; Pyke and Cohen, 1994), while Stock and Lambert (1993) suggests a value ranging

between 12 and 34 percent and closes the range with a 18 to 25 percent above

inventory value, depending on the industrial field; and Clendenen and Rinks (1996)

assume an holding cost equal to the 30 percent of the product cost in their

pull inventory model. Consequently, holding costs are often not precisely known and

usually approximated by managers, according to different rules of thumb depending

on the industry.

Among both inventory theorists and practitioners, it is common practice to includ e

in the holding cost rate an opportunity cost rate by applying the discounted cash flow

approach (Grubbstrom, 1980). Such approach allows roughly incorp orated the cost of

capital in an average cost (AC) inventory model. This AC approach in single source

models with only forward logistics is straightforward and usually produces good

results (Teunter et al., 2000), thus, the opportunity cost of inventory investment are

usually included in the traditional holding cost parameter computation model. It is

important to recognize that inventory costs extend well beyond the capital cost of

materials, including both evident and hidden costs, which means out-of-pocket holding

costs (Schonberger and Knod, 1997). In a recent study, Berling and Rosling (2005) show

that the capital cost of goods varies considerably due to the effect of different financial

risks on inventory policy. Berling and Rosling (2005) suggests a computation method

based on an activity-based costing concept, where cost drivers are the amount of

activity used to store one unit of product. Only in few of these studies, the holding cost

is assumed to be variable over time: Giri et al. (1996) develope d an Economic Order

Quantity (EOQ) model in which the holding cost is a continuous function of time, while

Goh’s (1992) model provides holding cost variations expressed as non-linear functions

of storage time or level; VujoBevi et al. (1996) handle imprecise holding cost parameters

in EOQ inventory model by using fuzzy sets; and Beltran and Krass (2002) analyze the

dynamic lot sizing problem considering concave holding costs. Corbacy

`oglu et al. (2007)

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