Citation: Sbiti, M.; Beladjine, D.;
Beddiar, K.; Mazari, B. Leveraging
Quantity Surveying Data and BIM to
Automate Mechanical and Electrical
(M & E) Construction Planning. Appl.
Sci. 2022, 12, 4546. https://doi.org/
10.3390/app12094546
Academic Editor: Paolo Renna
Received: 6 April 2022
Accepted: 27 April 2022
Published: 29 April 2022
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Article
Leveraging Quantity Surveying Data and BIM to Automate
Mechanical and Electrical (M & E) Construction Planning
Maroua Sbiti
1,2,3,
* , Djaoued Beladjine
1
, Karim Beddiar
1
and Bélahcène Mazari
4
1
Laboratoire d’Innovation Numérique pour les Entreprises et les Apprentissages au service de la Compétitivité
des Territoires LINEACT CESI, 8 Rue Isabelle Autissier, 17140 Lagord, France; dbeladjine@cesi.fr (D.B.);
kbeddiar@cesi.fr (K.B.)
2
EQUANS, 15 Rue Nina Simone, 44000 Nantes, France
3
Ecole Nationale Supérieure des Arts et Métiers ENSAM, 151 Boulevard de l’Hôpital, 75013 Paris, France
4
Laboratoire d’Innovation Numérique pour les Entreprises et les Apprentissages au service de la Compétitivité
des Territoires LINEACT CESI, la Défense FR, 1 Avenue du Président Wilson, 92074 Paris, France;
bmazari@cesi.fr
* Correspondence: maroua.sbiti@viacesi.fr; Tel.: +33-0605916788
Abstract:
Despite the great potential of LPS and BIM to improve construction project productivity,
the full integration of these modern production and information management systems at the data
processing level is not yet achieved. After matching the literature to empirical studies in a Construc-
tive Research Approach, it emerged that very few studies have investigated how buildings’ data
could be preserved and continuously evolve during the project lifecycle. Accordingly, we underline
the potential role of data warehousing in rendering operational data as a strategic asset for decision
making. These findings motivate the present research, which aims to capitalize on quantity surveying
data in order to automate the generation of M & E installation schedules. This paper first introduces
the system functional requirements. Then, it proposes a conceptual scheme for the planning data
mart (a data warehouse subset dedicated to planning subject area). Furthermore, we shed light on
the M & E fragnet standardization procedure and how data have been processed. Finally, we present
the current software developments to demonstrate the feasibility of this concept.
Keywords: lean; LPS
®
; BIM; data warehouse; information management; automation
1. Introduction
Schedule slippages and massive cost overruns are typical failures of construction
projects across the globe. Professionals underline the correlation between project failures
and labor productivity that can be measured by time on tool indicators [
1
]. The latter
consist of the effective working time of crews excluding anything that prevent laborers
from working, such as waiting times, movements, planning, etc. Time on tool analysis
has shown that fewer than 4 out of 10 h in a day are productive [
1
]. Thus, removing the
bulk of distractions and constraints that are known to impact labor productivity is the
key for improving construction project performances. Installation crews must, however,
have all the information and resources needed to complete their work. This is the aim of
Workface Planning (WP), which refers to the definition, creation, execution and tracking
of Installation Work Packages (IWPs) by Workface Planners. IWPs are small groupings
of work tasks for execution by a single crew in a short time period [
2
]. There are many
similarities between Workface Planning and the Last Planner System
®
(LPS). On one hand,
LPS is a collaborative planning process that follows Lean Construction (LC) principles.
It involves trades foremen and design team leaders in planning in greater and greater
detail as the work deadline gets closer. It encompasses multiple components: master
planning and phase planning to define what SHOULD be done, lookahead planning based
on constraint identification and removal to establish what CAN be done, weekly work
Appl. Sci. 2022, 12, 4546. https://doi.org/10.3390/app12094546 https://www.mdpi.com/journal/applsci
