Critical chain and risk analysis applied to high-risk industry maintenance: A case study

Research agenda on turnaround strategies beyond systemic disruptions

Purpose

Since the Great Financial Crisis (GFC), the shocks are getting deeper and deeper on the economy, sectors and companies. In these years, turnaround strategies have evolved and contribute to improving the agility and audacity of managers. This article studies the change in the research agenda and in the academic discourse as systemic disruptions become widespread and provides evidence on collaboration networks and publication opportunities.

Design/methodology/approach

This research uses a comparative bibliometric analysis to understand the changes in the academic debate as of 2008. The core collection of Web of Science (WoS) is used and 198 articles on turnaround strategies published in journals indexed in Journal Citation Reports® (JCR) – Social Sciences Citation Index (SSCI) in areas like business, management, economics and finance during 1965–2022.

Findings

This research reveals an important intertemporal evolution between periods, both in the collaborative networks of researchers and in the journals that dominate the impact discourse. In addition, it provides evidence of the change in academic discourse, through the evolution of the topics of interest after the GFC. The results suggest publication opportunities around gaps not yet closed by the academic literature.

Practical implications

This article allows researchers to be guided in identifying gaps that have not yet been closed. In addition, this research has important managerial implications, since it guides and advises journal editors on new emerging issues.

Originality/value

This document offers a global vision on the subject of study and an understanding of the development of the discourse of the academy.

Knowledge Criticality Assessment and Codification Framework for Major Maintenance Activities: A Case Study of Cement Rotary Kiln Plant

Maintenance experts involved in managing major maintenance activities such as; Major overhauls, outages, shutdowns and turnarounds (MoOSTs) are constantly faced with uncertainties during the planning and/or execution phases, which often stretches beyond the organisation’s standard operating procedures and require the intervention of staff expertise. This underpins a need to complement and sustain existing efforts in managing uncertainties in MoOSTs through the transformation of knowledgeable actions generated from experts’ tacit-based knowledge. However, a vital approach to achieve such transformation is by prioritising maintenance activities during MoOSTs. Two methods for prioritising maintenance activities were adopted in this study; one involved a traditional qualitative method for task criticality assessment. The other, a quantitative method, utilised a Fuzzy inference system, mapping membership functions of two crisp inputs and output accompanied by If-Then rules specifically developed for this study. Prior information from a 5-year quantitative dataset was obtained from a case study with appreciable frequency for performing MoOSTs; in this case, a Rotary Kiln system (RKS) was utilised in demonstrating practical applicability. The selection of the two methods was informed by their perceived suitability to adequately analyse the available dataset. Results and analysis of the two methods indicated that the obtained Fuzzy criticality numbers were more sensitive and capable of examining the degree of changes to membership functions. However, the usefulness of the traditional qualitative method as a complementary approach lies in its ability to provide a baseline for informing expert opinions, which are critical in developing specific If-Then rules for the Fuzzy inference system.

Machine criticality assessment for productivity improvement

Purpose

The purpose of this paper is to increase productivity through smart maintenance planning by including productivity as one of the objectives of the maintenance organization. Therefore, the goals of the paper are to investigate existing machine criticality assessment and identify components of the criticality assessment tool to increase productivity.

Design/methodology/approach

An embedded multiple case study research design was adopted in this paper. Six different cases were chosen from six different production sites operated by three multi-national manufacturing companies. Data collection was carried out in the form of interviews, focus groups and archival records. More than one source of data was collected in each of the cases. The cases included different production layouts such as machining, assembly and foundry, which ensured data variety.

Findings

The main finding of the paper is a deeper understanding of how manufacturing companies assess machine criticality and plan maintenance activities. The empirical findings showed that there is a lack of trust regarding existing criticality assessment tools. As a result, necessary changes within the maintenance organizations in order to increase productivity were identified. These are technological advancements, i.e. a dynamic and data-driven approach and organizational changes, i.e. approaching with a systems perspective when performing maintenance prioritization.

Originality/value

Machine criticality assessment studies are rare, especially empirical research. The originality of this paper lies in the empirical research conducted on smart maintenance planning for productivity improvement. In addition, identifying the components for machine criticality assessment is equally important for research and industries to efficient planning of maintenance activities.

Using Text Mining to Estimate Schedule Delay Risk of 13 Offshore Oil and Gas EPC Case Studies During the Bidding Process

Korean offshore oil and gas (O&G) mega project contractors have recently suffered massive deficits due to the challenges and risks inherent to the offshore engineering, procurement, and construction (EPC) of megaprojects. This has resulted in frequent prolonged projects, schedule delay, and consequently significant cost overruns. Existing literature has identified one of the major causes of project delays to be the lack of adequate tools or techniques to diagnose the appropriateness and sufficiency of the contract deadline proposed by project owners prior to signing the contract in the bid. As such, this paper seeks to propose appropriate or correct project durations using the research methodology of text mining for bid documents. With the emergence of ‘big data’ research, text mining has become an acceptable research strategy, having already been utilized in various industries including medicine, legal, and securities. In this study the scope of work (SOW), as a main part of EPC contracts is analyzed using text mining processes in a sequence of pre-processing, structuring, and normalizing. Lessons learned, collected from 13 executed off shore EPC projects, are then used to reinforce the findings from said process. For this study, critical terms (CT), representing the root of past problems, are selected from the reports of lessons learned. The occurrence of the CT in the SOW are then counted and converted to a schedule delay risk index (SDRI) for the sample projects. The measured SDRI of each sample project are then correlated to the project’s actual schedule delay via regression analysis. The resultant regression model is entitled the schedule delay estimate model (SDEM) for this paper based on the case studies. Finally, the developed SDEM’s accuracy is validated through its use to predict schedule delays on recently executed projects with the findings being compared with actual schedule performance. This study found the relationship between the SDRI, frequency of CTs in the SOW, and delays to be represented by the regression formula. Through assessing its performance with respect to the 13th project, said formula was found to have an accuracy of 81%. As can be seen, this study found that more CTs in the SOW leads to a higher tendency for a schedule delay. Therefore, a higher project SDRI implies that there are more issues on projects which required more time to resolve them. While the low number of projects used to develop the model reduces its generalizability, the text mining research methodology used to quantitatively estimate project schedule delay can be generalized and applied to other industries where contractual documents and information regarding lessons learned are available.

Machine criticality based maintenance prioritization

Purpose

The purpose of this paper is to identify the productivity improvement potentials from maintenance planning practices in manufacturing companies. In particular, the paper aims at understanding the connection between machine criticality assessment and maintenance prioritization in industrial practice, as well as providing the improvement potentials.

Design/methodology/approach

An explanatory mixed method research design was used in this study. Data from literature analysis, a web-based questionnaire survey, and semi-structured interviews were gathered and triangulated. Additionally, simulation experimentation was used to evaluate the productivity potential.

Findings

The connection between machine criticality and maintenance prioritization is assessed in an industrial set-up. The empirical findings show that maintenance prioritization is not based on machine criticality, as criticality assessment is non-factual, static, and lacks system view. It is with respect to these finding that the ways to increase system productivity and future directions are charted.

Originality/value

In addition to the empirical results showing productivity improvement potentials, the paper emphasizes on the need for a systems view for solving maintenance problems, i.e. solving maintenance problems for the whole factory. This contribution is equally important for both industry and academics, as the maintenance organization needs to solve this problem with the help of the right decision support.

Improving performance in project-based management: synthesizing strategic theories

Purpose

– The purpose of this paper is to explore benefits and issues of integrating the theory of constraints (TOC), resource-based view (RBV) and resource-dependence theory (RDT) with conventional project-based management frameworks.

Design/methodology/approach

– Extant literature is used to develop a conceptual framework of an integrated model that will be tested for applied robustness. The model has been applied to published projects to identify its strengths and weaknesses.

Findings

– The work shows important implications for improved success of projects from the use of TOC, RBV and RDT.

Research limitations/implications

– While TOC, RBV and RDT are well established in the context of organization theory, there is limited application in project management. Moreover, the model has yet to be applied in the field. The hypotheses identified in this research are currently being tested using empirical investigation.

Practical implications

– The research falls short in addressing some resources, e.g. innovation, tacit knowledge and decision-making methods in traditional project management context. Therefore, identifying these critical resources in future work and exploiting them as the means of improving project performance would enhance the success of project-based management.

Social implications

– Project management is an emergent discipline and a project is temporary in nature. Therefore, new ideas and development of theories for project management practices are required. This innovative research, for example, may change the way projects are executed in future.

Originality/value

– This paper examines the components of a successful project according to the iron triangle, i.e. scope, quality, time and cost. However, through the application of TOC, RBV and RDT into an integrated project-based management framework gives new insights to resources management.

Critical Chain Method in Traditional Project and Portfolio Management Situations

Critical Path (CP) method has been under scrutiny in recent years as the next evolution of project schedule development, the Critical Chain (CC) project management is gaining attention. Advocates of the Critical Chain method cite the Critical Path method's failure to address uncertainty properly. The purpose of this paper is to apply some of the features of the Critical Chain concepts to traditional approach of Critical Path for projects. More importantly, this research effort aims to demonstrate the applicability of CCPM to managing a portfolio of projects. The analysis, based on a critical review of past studies, experiments in both Critical Path and Critical Chain techniques, and a case study, presents recommendations to gain benefits of Critical Chain in a traditional Critical Path scheduling environment and to manage portfolio of projects or programs using some of the concepts of the Critical Chain Method.