Classifying healthcare warehouses according to their performance. A Cluster Analysis-based approach

• DOI: https://doi.org/10.1108/IJLM-02-2020-0110
• Published date: 15 November 2021
• Date: 15 November 2021
• Pages: 311-338
• Subject Matter: Management science & operations,Logistics
• Author: Anna Corinna Cagliano,Giulio Mangano,Carlo Rafele,Sabrina Grimaldi

Classifying healthcare warehouses

according to their performance.

A Cluster Analysis-based approach

Anna Corinna Cagliano, Giulio Mangano, Carlo Rafele and

Sabrina Grimaldi

Department of Management and Production Engineering, Politecnico di Torino,

Turin, Italy

Abstract

Purpose –The objective of this paper is to propose an approach to comparatively analyze the performance of

drugs and consumable products warehouses belonging to different healthcare institutions.

Design/methodology/approach –A Cluster Analysis is completed in order to classify warehouses and

identify common patterns based on similarorganizational characteristics. The variables taken into account are

associated with inventory levels, the number of SKUs, and incoming and outgoing flows.

Findings –The outcomes of the empirical analysis are confirmed by additional indicators reflecting the

demand level and the associated logistics flows faced by the warehouses at issue. Also, the warehouses

belonging to the same cluster show similar behaviors for all the indicators considered, meaning that the

performed Cluster Analysis can be considered as coherent.

Research limitations/implications –The study proposes an approach aimed at grouping healthcare

warehouses based on relevant logistics aspects. Thus, it can foster the application of statistical analysis in the

healthcare Supply Chain Management. The present work is associated with only one regional healthcare

system.

Practical implications –The approach might support healthcare agencies in comparing the performance of

their warehouses more accurately. Consequently, it could facilitate comprehensive investigations of the

managerial similarities and differences that could be a first step toward warehouse aggregation in

homogeneous logistics units.

Originality/value –This analysisputs forward an approach based on a consolidated statistical tool, to assess

the logistics performances in a set of warehouses and, in turn to deepen the related understanding as well as the

factors determining them.

Keywords Healthcare, Logistics, Performance management, Warehouses, Cluster Analysis

Paper type Research paper

1. Introduction

In the last twenty years healthcare providers in industrialized countries have faced a growing

aging of population, with a consequent increase in the need for healthcare services, together

with shrinking budgets, especially for those systems that are largely public funded. Thus,

they have been subjected to the challenge of providing high-quality treatments while cutting

operations costs (Feibert and Jacobsen, 2019). Among such costs, material management and

logistics play a significant role since it has been proved that they account for around 38% of

the total expense, when this ratio is limited to 5% in the retail industry and to 2% in the

electronics sector (Johnson, 2015).

In such a context, although some decades later than the manufacturing industry, supply

chain management (SCM) has become a key lever to contain expenditures and improve

competitiveness in the light of steadily increasing costs. The most popular SCM topics span

different fields, from SC configuration, to procurement management, warehouse and

inventory management, and drugs and other materials delivery to the patient beds, together

with their administration (Mustaffa and Potter, 2009;de Vries and Huijsman, 2011).

Among them, warehouses and inventory management have been largely neglected by

researchers and practitioners and only recently have gained momentum as main drivers of

Classifying

healthcare

warehouses

311

The current issue and full text archive of this journal is available on Emerald Insight at:

https://www.emerald.com/insight/0957-4093.htm

Received 27 February 2020

Revised 29 September 2020

29 March 2021

21 September 2021

Accepted 11 October 2021

The International Journal of

Logistics Management

Vol. 33 No. 1, 2022

pp. 311-338

© Emerald Publishing Limited

0957-4093

DOI 10.1108/IJLM-02-2020-0110

efficiency without compromising the level of patient care (Volland et al., 2017). However,

actions to improve warehouse processes also require ways of checking whether they are

successful. To this end, a performance management system, evaluating a set of appropriately

defined Key Performance Indicators (KPIs), should be adopted. Based on Smith’s work

(Smith, 2002), performance management in the healthcare sector has three roles, namely

guidance, monitoring and response. The guidance function aims to convey strategies and

objectives to policymakers, intermediate managers and front-line staff. The monitoring

function verifies whether guidance has been followed and the associated targets achieved.

Finally, the response function fosters actions to correct performance problems and to

stimulate improvement.

Relatively few literature contributions assess lo gistics performance in healthcare

organizations and in their warehouses (Gonul Kochan et al., 2018;Leksono et al., 2019;

Moons et al., 2019). Such works usually focus on measuring the performance of single

healthcare systems and there is a substantial lack of methodologies to numerically contrast

and compare the logistics outcomes of multiple warehouses. Thus, given the relevance of

warehouses in SCM in general and in the healthcare sector in particular, this is a research

stream that deserves further attention, also because nowadays very often policymakers look

at the redesign of healthcare warehouses and their operations as the key to reduce

inefficiencies and unnecessary costs.

In order to contribute to close such a research gap this work deals with healthcare

performance by taking a guidance perspective. Compared to the other two performance

management perspectives suggested by Smith (2002), the guidance one is deemed to be of

paramount importance by the authors because, by enabling setting goals, it constitutes an

unavoidable first step toward measuring the achievement of such objectives through KPIs

(monitoring perspective) and then addressing possible criticalities (response perspective).

The present research puts forward an approach based on a consolidated statistical tool,

namely Cluster Analysis, to comparatively study the logistics performance in a set of

warehouses and, thus, deepening their understanding as well as the factors determining

them. To reach the purpose, warehouses are classified in homogeneous groups sharing

common organizational features in terms of size of stocks and logistics flows. The approach

has been then applied to a regional healthcare system in Italy. Finding commonalities and

differences in warehouse performance in the various clusters through the proposed

methodology supports decision-makers in setting appropriate healthcare logistics strategies

for each of them, hence the guidance perspective function, based on the actual organizational

behavior of the warehouses they manage.

The reminder of the paper is organized as follows. Section 2 performs a literature review

on the major topics in which the research is framed. Section 3 presents the methodology and

discusses the development of the approach, while Section 4 analyses the outcomes of its

application. Finally, Section 5 conveys research implications and conclusions.

2. Literature review

2.1 Logistics and warehouse in healthcare sector

SCM concerns the optimal functioning of various logistics activities, with the aim of

controlling their performance and improving their efficiency. SCM was developed initially in

the context of manufacturing but its introduction is also beneficial to the healthcare sector,

where it shows an important impact on hospital performance (Parnaby and Towill, 2009).

In such a context SCM has the potential to reduce waste, prevent medical errors, increase

productivity, improve quality of care, service and operational efficiencies (Cagliano et al.,

2011a;Doerner and Reimann, 2007;Ford and Scanlon, 2007). Therefore, it becomes

increasingly important to intervene in the healthcare SCM, and in particular in the healthcare

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