Antonio Acernese, Carmen Del Vecchio, Massimo Tipaldi, Nicola Battilani and Luigi Glielmo
The purpose of this paper is to describe a model for the design and development of a condition-based maintenance (CBM) strategy for the cutting group of a labeling machine. The…
Abstract
Purpose
The purpose of this paper is to describe a model for the design and development of a condition-based maintenance (CBM) strategy for the cutting group of a labeling machine. The CBM aims to ensure the quality of labels' cut and overall machine performances.
Design/methodology/approach
In developing a complete CBM strategy, two main difficulties have to be overcome: (1) appropriately dealing with incomplete and low-quality production database and (2) selecting the most promising predictive model. The first issue has been addressed applying data cleansing operations and creating ad hoc methodology to enlarge the training data. The second issue has been handled developing and comparing an empirical model with a machine learning (ML)-based model; the comparison has been performed assessing capabilities thereof in predicting erroneous label cuts on data obtained from an operating plant located in Italy.
Findings
Research results showed that both empirical and ML-based approaches exhibit good performances in detecting the operating conditions of the cutting machine. The advantage of adopting an ML-based model is that it can be used not only as a condition indicator (i.e. a model able to continuously provide the health status of an asset) but also in predictive maintenance policies (i.e. a CBM carried out following a forecast of the degradation of the item).
Research limitations/implications
The study described in this manuscript has been developed on the practices of a labeling machine developed by an international company manufacturing bottling lines for beverage industry. The proposed approach might need some customization in case it is applied to other industries. Future researches can validate the applicability of such models on different rotary machines in other companies and similar industries.
Originality/value
The main contribution of this paper lies in the empirical demonstration of the benefits of CBM and predictive maintenance in manufacturing, through the overcoming of a specific production issue. The large number of variables involved in thin label cutting lines (film thickness between 30 and 38 µm), the high throughput and the high costs due to production interruptions render the prediction of non-conforming labels an economically relevant, albeit challenging, goal. Moreover, despite the large scientific literature on CBM in rolling bearing and face cutting movements, papers dealing with rotary labeling machines are very unusual and unique.
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Mpho Trinity Manenzhe, Arnesh Telukdarie and Megashnee Munsamy
The purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.
Abstract
Purpose
The purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.
Design/methodology/approach
The extant literature in physical assets maintenance depicts that poor maintenance management is predominantly because of a lack of a clearly defined maintenance work management process model, resulting in poor management of maintenance work. This paper solves this complex phenomenon using a combination of conceptual process modeling and system dynamics simulation incorporating 4IR technologies. A process for maintenance work management and its control actions on scheduled maintenance tasks versus unscheduled maintenance tasks is modeled, replicating real-world scenarios with a digital lens (4IR technologies) for predictive maintenance strategy.
Findings
A process for maintenance work management is thus modeled and simulated as a dynamic system. Post-model validation, this study reveals that the real-world maintenance work management process can be replicated using system dynamics modeling. The impact analysis of 4IR technologies on maintenance work management systems reveals that the implementation of 4IR technologies intensifies asset performance with an overall gain of 27.46%, yielding the best maintenance index. This study further reveals that the benefits of 4IR technologies positively impact equipment defect predictability before failure, thereby yielding a predictive maintenance strategy.
Research limitations/implications
The study focused on maintenance work management system without the consideration of other subsystems such as cost of maintenance, production dynamics, and supply chain management.
Practical implications
The maintenance real-world quantitative data is retrieved from two maintenance departments from company A, for a period of 24 months, representing years 2017 and 2018. The maintenance quantitative data retrieved represent six various types of equipment used at underground Mines. The maintenance management qualitative data (Organizational documents) in maintenance management are retrieved from company A and company B. Company A is a global mining industry, and company B is a global manufacturing industry. The reliability of the data used in the model validation have practical implications on how maintenance work management system behaves with the benefit of 4IR technologies' implementation.
Social implications
This research study yields an overall benefit in asset management, thereby intensifying asset performance. The expected learnings are intended to benefit future research in the physical asset management field of study and most important to the industry practitioners in physical asset management.
Originality/value
This paper provides for a model in which maintenance work and its dynamics is systematically managed. Uncontrollable corrective maintenance work increases the complexity of the overall maintenance work management. The use of a system dynamic model and simulation incorporating 4IR technologies adds value on the maintenance work management effectiveness.
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Joklan Imelda Camelia Goni and Amy Van Looy
Building process innovation capability (PIC) is becoming increasingly important to keep pace with changing business environments and emerging technological opportunities. However…
Abstract
Purpose
Building process innovation capability (PIC) is becoming increasingly important to keep pace with changing business environments and emerging technological opportunities. However, the literature on process innovation capability (PIC) is still very broad and does not consistently reflect today's reality that is becoming increasingly complicated and knowledge-intensive, leading to more organizational needs for supporting less-structured business processes (LSBP).
Design/methodology/approach
Based on a systematic literature review (SLR), the authors provide evidence for this under-investigated subject by consolidating organizational capabilities for managing PIC in LSBP. The authors screened 1,083 and identified only 26 peer-reviewed articles that simultaneously encompass process innovation and LSBP.
Findings
The authors explain which LSBP types are typically addressed, and in which sectors. The authors categorize research avenues, best practices and a framework that links PIC to performance outcomes by also considering business environments. Three theories (i.e. resource-based view, dynamic capabilities and sociotechnical theory) help to underpin the six empirically observed capabilities along three pillars (i.e. people, process and technology).
Research limitations/implications
Besides a research agenda, the authors offer a conceptual framework for PIC in LSBP as a reference to guide scholars and practitioners.
Practical implications
The authors offer best practices, as derived from the literature.
Originality/value
This is the first SLR for PIC in LSBP, consolidating and categorizing the PIC-LSBP characteristics. Due to few studies on the subject, this work contributes to a deeper understanding of the PICs needed for LSBP to obtain the desired performance outcomes.