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Equipment performance helps the manufacturing sector achieve operational and financial improvements despite process variations. However, the literature lacks a clear index or metric to quantify the monetary advantages of enhanced equipment performance. Thus, the paper presents two innovative monetary performance measures to estimate the financial advantages of enhancing equipment performance by isolating the effect of manufacturing fluctuations such as product mix price, direct and indirect characteristics, and cost changes.

The research provides two measures, ISB (Improvement Saving Benefits) and IEB (Improvement Earning Benefits), to assess equipment performance improvements. The effectiveness of the metrics is validated through a three stages approach, namely (1) experts' binary opinion, (2) sample, and (3) actual cases. The relevant data may be collected through accounting systems, purpose-built software, or electronic spreadsheets.

The findings suggest that both measures provide an effective cost–benefit analysis of equipment performance enhancement. The measure ISB indicates savings from performance increases when equipment capacity is greater than product demand. IEB is utilised when equipment capacity is less than product demand. Both measurements may replace the unitary cost variation, which is subject to manufacturing changes.

Manufacturing businesses may utilise the ISB and IEB metrics to conduct a systematic analysis of equipment performance and to appreciate the financial savings perspective in order to emphasise profitability in the short and long term.

The study introduces two novel financial equipment performance improvement indicators that distinguish the effects of manufacturing variations. Manufacturing variations cause cost advantages from operational improvements to be misrepresented. There is currently no approach for manufacturing organisations to calculate the financial advantages of enhancing equipment performance while isolating production irregularities.

This paper aims to present a highly accessible and affordable tracking model for earthmoving operations in an attempt to overcome some of the limitations of current tracking models.

The proposed methodology involves four main processes: acquiring onsite terrestrial images, processing the images into 3D scaled cloud data, extracting volumetric measurements and crew productivity estimations from multiple point clouds using Delaunay triangulation and conducting earned value/schedule analysis and forecasting the remaining scope of work based on the estimated performance. For validation, the tracking model was compared with an observation-based tracking approach for a backfilling site. It was also used for tracking a coarse base aggregate inventory for a road construction project.

The presented model has proved to be a practical and accurate tracking approach that algorithmically estimates and forecasts all performance parameters from the captured data.

The proposed model is unique in extracting accurate volumetric measurements directly from multiple point clouds in a developed code using Delaunay triangulation instead of extracting them from textured models in modelling software which is neither automated nor time-effective. Furthermore, the presented model uses a self-calibration approach aiming to eliminate the pre-calibration procedure required before image capturing for each camera intended to be used. Thus, any worker onsite can directly capture the required images with an easily accessible camera (e.g. handheld camera or a smartphone) and can be sent to any processing device via e-mail, cloud-based storage or any communication application (e.g. WhatsApp).

New maintenance hypotheses such as lean smart maintenance emphasized internal integration. Since the maintenance process is not fully integrated with other business processes, it indicates that some of the problems in the maintenance process are caused by other departments. Additionally, nothing can be managed or improved without first measuring it. In order to enhance internal integration, this study developed a model that makes use of information systems data to examine synchronization and collaboration across departments engaged in maintenance operations.

This research connects maintenance management and business process management through information systems. A conceptual module model based on CMMS is proposed that will use data which are already available in CMMS and, using process mining, will assess the level of synchronization between departments within an organization.

This conceptual model will serve as a roadmap for creating better value-added CMMS software. This system operates as a performance measurement tool in three majors, including organizational analysis, workflow analysis and eventually, a future simulation of maintenance processes. This module will serve as a decision support system, highlighting opportunities for improvement in maintenance processes.

A practical guideline is provided for the future development of CMMSs and their enhancement to intelligence. All assumptions are based on maintenance theories, techniques for measuring maintenance performance and business process management and process mining.

The purpose of this paper is to provide a comprehensive review of a non-contact full-field optical measurement technique known as digital image correlation (DIC).

The approach of this review paper is to introduce the research pertaining to DIC. It comprehensively covers crucial facets including its principles, historical development, core challenges, current research status and practical applications. Additionally, it delves into unresolved issues and outlines future research objectives.

The findings of this review encompass essential aspects of DIC, including core issues like the subpixel registration algorithm, camera calibration, measurement of surface deformation in 3D complex structures and applications in ultra-high-temperature settings. Additionally, the review presents the prevailing strategies for addressing these challenges, the most recent advancements in DIC applications across quasi-static, dynamic, ultra-high-temperature, large-scale and micro-scale engineering domains, along with key directions for future research endeavors.

This review holds a substantial value as it furnishes a comprehensive and in-depth introduction to DIC, while also spotlighting its prospective applications.

This paper proposes a progressive, multi-level framework for diagnosing maintenance performance: rapid performance health checks of key performance for different equipment groups and end-to-end process diagnostics to further locate potential performance issues. A question-based performance evaluation approach is introduced to support the selection and derivation of case-specific indicators based on diagnostic aspects.

The case research method is used to develop the proposed framework. The generic parts of the framework are built on existing maintenance performance measurement theories through a literature review. In the case study, empirical maintenance data of 196 emergency shutdown valves (ESDVs) are collected over a two-year period to support the development and validation of the proposed approach.

To improve processes, companies need a separate performance measurement structure. This paper suggests a hierarchical model in four layers (objective, domain, aspect and performance measurement) to facilitate the selection and derivation of indicators, which could potentially reduce management complexity and help prioritize continuous performance improvement. Examples of new indicators are derived from a case study that includes 196 ESDVs at an offshore oil and gas production plant.

Methodological approaches to deriving various performance indicators have rarely been addressed in the maintenance field. The proposed diagnostic framework provides a structured way to identify and locate process performance issues by creating indicators that can bridge generic evaluation aspects and maintenance data. The framework is highly adaptive as data availability functions are used as inputs to generate indicators instead of passively filtering out non-applicable existing indicators.

Construction activities conducted in urban areas are often a source of significant noise disturbances, which cause psychological and health issues for residents as well as long-term auditory impairments for construction workers. The limited effectiveness of passive noise control measures due to the close proximity of the construction site to surrounding neighborhoods often results in complaints and eventually lawsuits. These can then lead to delays and cost overruns for the construction projects.

The paper proposes a novel approach to integrating construction noise as an additional dimension into scheduling construction works. To achieve this, a building information model, including the three-dimensional construction site layout object geometry, resource allocation and schedule information, is utilized. The developed method explores further project data that are typically available, such as the assigned equipment to a task, its precise location, and the estimated duration of noisy tasks. This results in a noise prediction model by using noise mapping techniques and suggesting less noisy alternative ways of construction. Finally, noise data obtained from sensors in a case study contribute real values for validating the proposed approach, which can be used later to suggest solutions for noise mitigation.

The results of this study indicate that the proposed approach can accurately predict construction noise given a few available parameters from digital project planning and sensors installed on a construction site. Proactively integrating construction noise control measures into the planning process has benefits for both residents and construction managers, as it reduces construction noise-related disturbances, prevents unexpected legal issues and ensures the health and well-being of the workforce.

While previous research has concentrated on real-time data collection using sensors, a more effective solution would also involve addressing and mitigating construction noise during the pre-construction work planning phase.

This research aims to investigate critical success factors, barriers and initiatives of total productive maintenance (TPM) implementation in selected manufacturing industries in Addis Ababa, Ethiopia.

This study built and looked into a conceptual research framework. The potential barriers and success factors to TPM implementation have been highlighted. The primary study techniques used to collect relevant data were a closed-ended questionnaire and semi-structured interview questions. With the use of SPSS version 23 and SmartPLS 3.0 software, the data were examined using descriptive statistics and the inferential Partial Least Square Structural Equation Modeling (PLS-SEM) techniques.

According to the results of descriptive statistics and multivariate analysis using PLS-SEM, the case manufacturing industries' TPM implementation initiative is in its infancy; break down maintenance is the most widely used maintenance policy; top managers are not dedicated to the implementation of TPM; and there are TPM pillars that have been weakly and strongly addressed by the case manufacturing companies.

The small sample size is a limitation to this study. It is therefore challenging to extrapolate the research findings to other industries. The only manufacturing KPI utilized in this study is overall equipment effectiveness (OEE). It is possible to add more parameters to the manufacturing performance measurement KPI. The relationships between TPM and other lean production methods may differ from those observed in this cross-sectional study. Longitudinal experimental studies and in-depth analyses of TPM implementations may shed further light on this.

Defining crucial success factors and barriers to TPM adoption, as well as identifying the weak and strong TPM pillars, will help companies in allocating their scarce resources exclusively to the most important areas. TPM is not a quick solution. It necessitates a change in both the company's and employees' attitude and their values, which takes time to bring about. Hence, it entails a long-term planning. The commitment of top managers is very important in the initiatives of TPM implementation.

This study is unique in that, it uses a new conceptual research model and the PLS-SEM technique to analyze relationships between TPM pillars and OEE in depth.

This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.

The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.

The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.

The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.

Facility management gained profound importance due to the increasing complexity of different systems and the cost of operation and maintenance. However, due to the increasing complexity of different systems, facility managers may suffer from a lack of information. The purpose of this paper is to propose a new facility management approach that links segmented assets to the vital data required for managing facilities.

Automatic point cloud segmentation is one of the most crucial processes required for modelling building facilities. In this research, laser scanning is used for point cloud acquisition. The research utilises region growing algorithm, colour-based region-growing algorithm and Euclidean cluster algorithm.

A case study is worked out to test the accuracy of the considered point cloud segmentation algorithms utilising metrics precision, recall and F-score. The results indicate that Euclidean cluster extraction and region growing algorithm revealed high accuracy for segmentation.

The research presents a comparative approach for selecting the most appropriate segmentation approach required for accurate modelling. As such, the segmented assets can be linked easily with the data required for facility management.

The heterogeneous character of Industry 4.0 opens opportunities for studies to understand the difficulties and challenges found in the transformation process of manufacturers. This article aims to present a critical analysis of the modernization process of an Industry 3.0 automated cell into a fully autonomous cell of Industry 4.0. The objective is to elucidate the difficulties found in this transition process and the possible ways to overcome the challenges, focusing on the management perspective.

For this, the needed steps for the technology transition were defined and the main I4.0 enabling technologies were applied, such as the application of machine learning algorithms to control quality parameters in milling.

The main challenges found were related to the obsolescence of the equipment present in the cell, challenges in data integration and communication protocols, in addition to the training of people who work actively in the project team. The difficulties faced were discussed based on similar studies in the literature and possible solutions for each challenge.

This understanding of possible barriers in the modernization process, and the step-by-step defined for this transition, can be important references for professionals working in manufacturing industries and researchers who aim to deepen their studies in this important and disruptive stage of world industrialization.