Innovating in data-driven production environments: simulation analysis of Net-CONWIP priority rule
| DOI | https://doi.org/10.1108/IMDS-10-2022-0629 |
| Published date | 30 March 2023 |
| Date | 30 March 2023 |
| Pages | 1569-1598 |
| Subject Matter | Information & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems |
| Author | Rafael Diaz,Ali Ardalan |
Innovating in data-driven
production environments:
simulation analysis
of Net-CONWIP priority rule
Rafael Diaz
Virginia Modeling, Analysis, and Simulation Center (VMASC),
Old Dominion University, Suffolk, Virginia, USA, and
Ali Ardalan
Department of Information Technology & Decision Sciences,
Strome College of Business, Old Dominion University, Norfolk, Virginia, USA
Abstract
Purpose –Motivated by recent research indicating that the operational performance of an enterprise can be
enhanced by building a supporting data-driven environment in which to operate, this paper presents a
simulation framework that enables an examinationof the effects of applying smart manufacturing principles to
conventional production systems, intending to transition to digital platforms.
Design/methodology/approach –To investigate the extent to which conventional production systems can
be transformed into novel data-driven environments, the well-known constant work-in-process (CONWIP)
production systemsand consideredproduction sequencing assignments in flowshops were studied. As a result,
a novel data-driven priority heuristic, Net-CONWIP was designed and studied, based on the ability to collect
real-time information about customer demand and work-in-process inventory, which was applied as part of a
distributed and decentralised production sequencing analysis. Application of heuristics like the Net-CONWIP
is only possible through the ability to collect and use real-time data offered by a data-driven system.
A four-stage application framework to assist practitioners in applying the proposed model was created.
Findings –To assess the robustness of the Net-CONWIP heuristic under the simultaneous effects of different
levels of demand, its different levels of variability and the presence of bottlenecks, the performance of Net-CONWIP
with conventional CONWIP systems that use first come, firstservedpriorityrulewascompared.The results show
that the Net-CONWIP priority rule significantly reduced customer wait time in all cases relative to FCFS.
Originality/value –Previous research suggests there is considerable value in creating data-driven
environments. This study provides a simulation framework that guides the construction of a digital
transformation environment. The suggested framework facilitates the inclusion and analysis of relevant smart
manufacturing principles in production systems and enables the design and testing of new heuristics that
employ real-time data to improve operational performance.An approach that can guide the structuring of data-
driven environments in production systems is currently lacking. This paper bridges this gap by proposing a
framework to facilitate the design of digital transformation activities, explore their impact on production
systems and improve their operational performance.
Keywords Innovation, Scheduling, Industry 4.0, Production systems, Simulation framework, CONWIP
Paper type Research paper
1. Introduction
As many industrial systems are being transitioned to digital transformation structures,
businesses are embracing different dimensions of the Industry 4.0 revolution, which is
centred on the extensive use of data to support decision-making and to improve operational
performance. In the context of operations management, digital transformation seeks to
convert slow, outdated and ill-defined operations with a high degree of uncertainty into data-
driven, agile processes based on the application of Industry 4.0 technology (Matt et al., 2015;
Ustundag and Cevikcan, 2017;Diaz et al., 2021). In these new data-driven environments,
innovation and productivity can be spurred, while waste can be drastically reduced by
The Net-
CONWIP
priority rule
1569
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0263-5577.htm
Received 21 October 2022
Revised 7 February 2023
Accepted 3 March 2023
Industrial Management & Data
Systems
Vol. 123 No. 5, 2023
pp. 1569-1598
© Emerald Publishing Limited
0263-5577
DOI 10.1108/IMDS-10-2022-0629
developingthe ability to dynamicallyallocate scarce resources(Sarkis et al., 2021). The resultis
an increase in industrial capabilities through enhanced efficiency, flexibility and heightened
responsivenesswhile improving process and product integrity (Dallasega et al., 2022).
Digitally-oriented environments are largely based on data capturing, parsing, sharing and
automation, resulting in a paradigm shift in how firms plan and execute their decision-
making processes (Provost and Fawcett, 2013;Lasi et al., 2014). Thus, many firms are
transitioning to data-driven organisations (Elgendy et al., 2022) in which decision making is
shifted to an environment that enables substantial information visibility (Mubarik et al., 2021)
and an understanding of customer requirements and operational intricacies (Wang et al.,
2015). In this case, data-driven environments can reflect the actual market and operational
conditions in real-time, which is becoming critical in terms of enhancing opportunities to
improve the supply and demand matching process and hence the firm’s performance (Gupta
et al., 2021).
Data have become one of the essential commodities of the 21st century (Parkins, 2017),
partly since they enable the capturing of demand behaviour, and their use in concert with
supply chain principles to improve operational performance. It has also been shown to be
critical to innovation (Hahn, 2020). In complex industrial ecosystems, production systems are
changing to adapt to the new informational landscape, in which data-driven strategies are
gaining traction and becoming ubiquitous, particularly in terms of enabling the sensing,
integration and use of different sources of information (Sartal and V
azquez, 2017;Alc
acer and
Cruz-Machado, 2019). These new levels of access and data integration are providing
opportunities for firms to develop their capabilities to better synchronise their resource
assignment and production priorities and to accurately align them with customer demand,
thus effectively reducing waste without jeopardising industrial growth in a sustainable
environment (Ghobakhloo et al., 2021). In this quest, a firm may explore novel ways to
leverage data in the decision-making process, to streamline their procedures and to conduct
effective planning and execution (Qu et al., 2019). In this way, the firm can create and nurture
an environment that promotes and enables creativity and innovation (Reischauer, 2018), in
which numerous competing opportunities for engaging in digital transformation projects
(Ramos et al., 2020) become apparent (e.g. customer relationship management (CRM)
systems).
The emergence of digital project prospects is driving companies to strategically select
critical systems that can maximise the benefits of this new data-driven environment (Diaz
et al., 2020b). Studies in the literature indicate that companies tend to favour projects that
provide customer visibil ity, and which offer cues abo ut customer preferences an d
requirements (Xu et al., 2017;Nam et al., 2019), such as business intelligence and analytics
(Jimenez-Marquez et al., 2019). This priority is understandable given the relatively easy
access to customer information, as firms seek to fulfil customers’needs and look for
opportunities to improve product offerings, for example. bundles (Rao et al., 2018). However,
clarification of how a firm can further innovate in this data-driven environment and
quantifying these benefits depend on the extent to which the firm can identify the interactions
between new and existing data flows in the decision-making process for key selected
systems. One important and attractive dimension of the enterprise that requires careful
consideration in a manufacturing firm is its production systems.
In general, manufacturing production systems are characterised by the conversion of
material into products via production factors (e.g. labour, energy and equipment), according
to a production philosophy that is primarily dictated by the nature of the product to be
manufactured (Vollmann et al., 2004;Hopp and Spearman, 2011). One of the gains from
digitisation in this setting, and in particular from the use of real-time information, may stem
from workers focusing on performing tasks that are prioritised adequately, with minimised
execution gaps (e.g. Zhao et al. (2019)). This also may promote production flexibility,
IMDS
123,5
1570
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