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Maintenance management is a vital strategic task given the increasing demand on sustained availability of machines. Machine performance depends primarily on frequency and downtime; therefore, ranking critical machines based on these two criteria is important to determine the appropriate maintenance strategy. The purpose of this paper is to compare two methods, using case studies, to allocate maintenance strategies while prioritising performance based on frequency and downtime or Mean Time to Repair: the Decision Making Grid (DMG) and Jack-Knife Diagram (JKD).

The literature indicates the need for an approach able to integrate maintenance performance and strategy in order to adapt existing data on equipment failures and to routinely adjust preventive measures. Maintenance strategies are incomparable; one strategy should not be applied to all machines, nor all strategies to the same machine.

Compared to the Pareto histogram, the DMG and JKD provide visual representations of the performance of the worst machines with respect to frequency and downtime, thus allowing maintenance technicians to apply the appropriate maintenance strategy. Each method has its own merits.

This work compares only two methods based on their original conceptualisation. This is due to their similarities in using same input data and their main features. However, there is a scope to compare to other methods or variations of these methods.

This paper highlights how the DMG and JKD can be incorporated in industrial applications to allocate appropriate maintenance strategy and track machine performance over time.

Neither DMG nor JKD have been compared in the literature. Currently, the JKD has been used to rank machines, and the DMG has been used to determine maintenance strategies.

The main purpose is to provide a demonstration of the utility of the DMG as a streamlined approach of a prescriptive-type model that can facilitate strategic and dynamic methodology to the decision-maker with respect to what policies, strategies or actions should be carried out.

This paper proposes a framework for an agile criticality assessment process using decision-making grid (DMG) to accommodate the needs of this dynamic environment.

The proposed new approach has delivered better results with more consistency when applied by different teams and achieved better distribution of assets over the criticality scale.

The proposed approach has been validated through an industrial case study related to a steam generation system (SGS).

The implementation of the proposed approach in a petroleum refinery to assess the criticality of steam generation system (SGS) has shown positive results in terms of time and effort optimization. This research contributes to production planning and control literature with respect to one of its core activities of maintenance, through an innovative, systematic and practical approach.

Decision-Making Grid (DMG) is used for determining maintenance tactics and is associated with the reliability and risk management of assets. In this grid, decision making is performed based on two indicators of Mean Time to Repair (MTTR) and frequency of failures. The purpose of this paper is to improve DMG by recognizing interdependence among failures.

Fault Tree Analysis and Reliability Block Diagram have been applied for improving DMG. The proposed approach has been examined on eight equipment of the steel making and continuous casting plant of Mobarakeh Steel Company.

Findings indicate different positions of equipment in the cells of the new grid compared to the basic grid.

DMG is limited to two criteria of frequency of failures and MTTR values. In both basic and new DMGs, cost analysis has not been performed. The application of the proposed approach will help the reliability/maintenance engineers/analysts/managers to allocate more suitable maintenance tactics to equipment. This, in turn, will enhance the equipment life cycle and availability as the main objectives of physical asset management.

A major limitation of basic DMG is that the determined tactic based on these two indicators might not be an appropriate solution in all conditions, particularly when failures are interdependent. This has been resolved in this paper.

The aim of this study is to develop a 3D model of decision- making grid (DMG) considering failure detection rate.

In a comparison between DMG and failure modes and effects analysis (FMEA), severity has been assumed as time to repair and occurrence as the frequency of failure. Detection rate has been added as the third dimension of DMG. Nine months data of 21 equipment of casting unit of Mobarakeh Steel Company (MSC) has been analyzed. Then, appropriate condition monitoring (CM) techniques and maintenance tactics have been suggested. While in 2D DMG, CM is used when downtime is high and frequency is low; its application has been developed for other maintenance tactics in a 3D DMG.

Findings indicate that the results obtained from the developed DMG are different from conventional grid results, and it is more capable in suggesting maintenance tactics according to the operating conditions of equipment.

In failure detection, the influence of CM techniques is different. In this paper, CM techniques have been suggested based on their maximum influence on failure detection.

In conventional DMG, failure detection rate is not included. The developed 3D DMG provides this advantage by considering a new axis of detection rate in addition to mean time to repair (MTTR) and failure frequency, and it enhances maintenance decision-making by simultaneous selection of suitable maintenance tactics and condition-monitoring techniques.

The purpose of this paper is to propose a model for assisting in the decision-making process for acquiring a condition monitoring (CM) system for an oil-immersed power transformer in order to improve its maintainability.

The proposed model is based on the analytic hierarchy process. The assessment was performed by pairwise comparisons, and a sensitivity analysis (what-if analysis) was used to identify the implications of changing the criteria weights. In order to select the criteria and alternatives, a search was conducted for the power transformer failure modes, monitored parameters and CM technologies.

The proposed model provides a structured solution for a complex problem: deciding the best combination of technologies for CM of power transformers.

Because the pairwise comparisons were done only by the author, the results may need to be improved with the assessment of more experts. Also, it was done for a specific type of transformer; it might be necessary to customise the alternatives for other cases. Finally, as a future consideration, more levels can be added to the hierarchy to improve the accuracy of the model.

The power transformer is an asset where the most appropriate maintenance strategy for it is condition-based maintenance. In order to improve its maintainability, it is recommendable to improve its testability and diagnosability. For achieving this goal, the maintenance personnel have to decide the best combination of technologies for CM. The methodology developed can assist the decision makers to select the most appropriate cost-benefit strategy.

The paper presents a structured and generic method of selecting the most appropriate CM system for power transformers.

Disturbances in terms of major crises such as pandemics, fluctuations in demand and oil price, energy consumption and supply chain can significantly impair the maintenance programs effectiveness and efficiency. Hence, there is an urgent need for an agile asset performance management (AAPM) framework.

This paper's main objective is to design a comprehensive framework for an AAPM system that sustains the desired asset performance by reacting efficiently, quickly and intelligently to the changes in the operating context parameters and asset health conditions. Such a framework is adaptive to changes in scenarios and aims to systematise the decision support process, considering different objectives.

The development of the proposed framework has led to identifying an innovative way of seamless integration between crucial reliability and asset management tools. Also, the methodology implementation is expected to promote the practical use of its reliability tools and enable asset stakeholders to break silo working for clear communication around asset performance.

The implementation of the AAPM framework follows a new approach developed during this research and coined by the authors as the “8S approach.”

Regular surveys by the Malta Hotels & Restaurants Association indicate a substantial improvement in the financial performance of five-star hotels in Malta in recent years. Therefore, the purpose of this paper is to investigate if this positive performance is primarily due to customer centricity by management.

The assessment is based on the findings of a quantitative study that compared the results of a demand side (customer survey) with those of a supply side (management survey). In total, 24 decision-choice criteria were framed within the four perspectives (financial, customer, internal process and employee learning/growth) of the balanced scorecard. Actual and potential customers of five-star hotels and hotel managers were asked to rank the decision-choice criteria and the balanced scorecard perspectives. The multi-criteria decision analysis was carried out by means of the AHP.

The study showed that managers in the five-star hospitality sector in Malta are in harmony with customer expectations given the strong positive correlation between the results of the customer and management surveys.

This research is limited to the island of Malta but can be easily replicated for other touristic destinations.

This study has implication for hospitality customers, hotel managers and policy makers to help them to identify weak areas of hotel performance and improve them.

The paper has developed a Prioritised scorecard, a new hybrid balanced scorecard and AHP. Targets are therefore prioritised, which allow a better allocation of scarce resources.

The purpose of this paper is to develop a conceptual auditing framework (CAF), which can help the organizations that experience difficulties with current ISO 9001 auditing to achieve their desired benefits with regard to the auditing of conformance, performance, risk management and improvement collectively.

A literature review was undertaken to identify the audit limitations and the program strategies that were reported in literature to overcome these limitations. The relevant audit standard (ISO 9001:2015) and guidelines (ISO 19011:2011 and ISO 19011:2018) have also been reviewed to determine the gap in relation to this purpose.

Most of the previous suggestions to overcome the difficulties of the current ISO 9001 auditing were to focus on processes in addition to ISO 9001 clauses and to apply the two principles of “Process Approach” and “Improvement” of ISO 9001, by having the manner of integration of their risk-based thinking concept and Lean Six Sigma (LSS) approaches, respectively, within audit. However, this integration also has limitations such as the desire for implementing risk management program and the necessity for applying LSS approach. Therefore, a CAF was developed to enhance the ISO 9001 quality audit. It suggests the integration of 12 management tools that are connected to the seven principles of ISO 9001:2015 severally and collectively. The selected tools ought to be linked to the audit limitations that have been previously reported in the literature, and connected with the complaints of the organizations that experience difficulties with current ISO 9001 auditing. Auditing of conformance/performance, risk management and improvement in combination can be assured by upgrading, testing and validating this CAF.

Utilizing the ISO 9001 quality audit in a way that helps organizations to audit performance, risk management and improvement in combination by changing the auditing approach from ISO 9001 elements to ISO 9001 principles and by learning from the experience of business excellence models implementers in development of the Performance measurement frameworks and in converting the concepts and principles into practice.

The purpose of this paper is to review literature about the applications of artificial intelligence (AI) in strategic situations and identify the research that is needed in the area of applying AI to strategic marketing decisions.

The approach was to carry out a literature review and to consult with marketing experts who were invited to contribute to the paper.

There is little research into applying AI to strategic marketing decision-making. This research is needed, as the frontier of AI application to decision-making is moving in many management areas from operational to strategic. Given the competitive nature of such decisions and the insights from applying AI to defence and similar areas, it is time to focus on applying AI to strategic marketing decisions.

The application of AI to strategic marketing decision-making is known to be taking place, but as it is commercially sensitive, data is not available to the authors.

There are strong implications for all businesses, particularly large businesses in competitive industries, where failure to deploy AI in the face of competition from firms, who have deployed AI to improve their decision-making could be dangerous.

The public sector is a very important marketing decision maker. Although in most cases it does not operate competitively, it must make decisions about making different services available to different citizens and identify the risks of not providing services to certain citizens; so, this paper is relevant to the public sector.

To the best of the authors’ knowledge, this is one of the first papers to probe deployment of AI in strategic marketing decision-making.

The purpose of this paper is to construct a fuzzy logic model that acts as a decision support system to minimize inventory-related costs in the field of industrial maintenance. Achieving a balance between the unavailability and over-storage of spare parts is a problem with potentially significant consequences. That significance increases proportionally with the ever-increasing challenge of reducing overall cost. Either scenario can result in substantial financial losses because of the interruption of production or the costs of tied-up capital, also called the “solidification of capital.” Moreover, there is that additional problem of the expiry of parts on the shelf.

The proposed approach relies on inputs from experts with consideration for incompleteness and inaccuracy. Two levels of decision are considered simultaneously. The first is whether a part should be stored or ordered when needed. The second involves comparing suppliers with their batch-size offers based on user-determined criteria. A mathematical model is developed in parallel for validation.

The results indicate that the fuzzy logic approach is accurate and satisfactory for this application and that it is advantageous because of its limited sensitivity to the inaccuracy and/or incompleteness of data. In addition, the approach is practical because it requires minimal user effort.

To the best of the authors’ knowledge, the exploitation of fuzzy-logic altogether with limited sensitivity experts' inputs were never combined for the solution of this particular problem; however, this approach's positive impact is expected to be highly significant in solving a chronic problem in industry.