Asset Intensive Industries Research Articles

Fuzzy FMEA-Resilience Approach for Maintenance Planning in a Plastics Industry ‎

The productivity and efficiency of industrial systems are highly affected by failures and machine breakdowns. Further, in asset-intensive industries, unexpected failures are considered the primary source of operational risk. In response, the maintenance department strives to calculate reliable estimates of the risk levels associated with such failures and develop resilient maintenance strategies that enable it to respond effectively to equipment failures. The research developed a framework for integrating fuzzy failure mode and effects analysis (FFMEA) with resilience engineering (RE) concepts for maintenance planning. The framework consists of four main stages: FFMEA, Risk iso-surface (RI), resilience assessment, and maintenance planning. In FFMEA, multiple sub-factors were considered for each main risk factor and evaluated using fuzzy logic. Then, in the RI stage, the risk priority number (RPN) was calculated through a fuzzy approach that considered the order of the importance of the main three risk factors. The fuzzy resilience assessment was applied through a survey of fifty-one questions related to the main four RE potentials to determine the need for resilient maintenance strategies. Finally, the RPN-Resilience diagram was employed to classify maintenance activities into six main maintenance strategies. A case study from a production line of plastic bags was used for illustration. The main advantage of the proposed FFMEA is that it divides the main risk criteria into sub-criteria to increase the accuracy of risk assessment and evaluate resilience potentials under fuzziness. In conclusion, the integration of the risk-resilience evaluation is a valuable tool for effectively planning maintenance activities.

Accountability audit of natural resources and total factor productivity—Evidence based on Chinese listed companies

Based on a natural event, namely a pilot accountability audit of natural resources conducted by local officials in 2014, this study empirically investigates the impact of the pilot on the total factor productivity (TFP) of enterprises. The study utilizes the Differences-in-Differences model with an observation window spanning from 2012 to 2015. The findings indicate a significant reduction in the total factor productivity of enterprises in the pilot area due to the implementation of the pilot program. The study identifies that this impact is primarily driven by increased production costs and decreased investment. Further analysis reveals heterogeneity in the effects, with regions characterized by low levels of economic development, distortions in the production element market, low competition in industries, heavy asset-intensive industries, large enterprises, and absolute holding enterprises experiencing a more pronounced impact of the audit on total factor productivity. Overall, this study sheds light on the influence of accountability audits of natural resources on the real economy and offers valuable insights for policymakers.

Adopting New Machine Learning Approaches on Cox’s Partial Likelihood Parameter Estimation for Predictive Maintenance Decisions

The Proportional Hazards Model (PHM) under a Condition-Based Maintenance (CBM) policy is used by asset-intensive industries to predict failure rate, reliability function, and maintenance decisions based on vital covariates data. Cox’s partial likelihood optimization is a method to assess the weight of time and conditions into the hazard rate; however, parameter estimation with diverse covariates problem could have multiple and feasible solutions. Therefore, the boundary assessment and the initial value strategy are critical matters to consider. This paper analyzes innovative non/semi-parametric approaches to address this problem. Specifically, we incorporate IPCRidge for defining boundaries and use Gradient Boosting and Random Forest for estimating seed values for covariates weighting. When applied to a real case study, the integration of data scaling streamlines the handling of condition data with diverse orders of magnitude and units. This enhancement simplifies the modeling process and ensures a more comprehensive and accurate underlying data analysis. Finally, the proposed method shows an innovative path for assessing condition weights and Weibull parameters with data-driven approaches and advanced algorithms, increasing the robustness of non-convex log-likelihood optimization, and strengthening the PHM model with multiple covariates by easing its interpretation for predictive maintenance purposes.

Integrating business impact analysis and risk assessment for physical asset criticality analysis: A framework for sustainable operations in process industries

The paper tackles the issue of managing physical assets in asset-intensive industries, where continuity plans are crucial for business operations during disruptions. However, resource constraints often hinder companies from implementing these plans for all their assets. Moreover, efforts to maximize resource utilization efficiency and minimize environmental impacts are limited to isolated disciplines, necessitating an integrated approach across natural, social, and technological systems. To address these challenges, this study proposes a framework based on a hybrid fuzzy BWM-TOPSIS methodology for identifying critical physical assets. The framework incorporates Business Impact Analysis (BIA) to determine essential products, a physical asset risk assessment matrix (PARAM) to assess critical assets based on likelihood and impact, and a mathematical model to estimate asset continuity parameters. The framework is applied to a petrochemical company in a case study, offering a guide for estimating the probability and impact of physical asset failure while accounting for inherent uncertainties. By adopting this framework, companies can efficiently manage their physical assets, prioritize continuity plans, and address resource constraints and uncertainties. This research contributes to practical tools and approaches that assist industries in sustaining operations and ensuring business continuity during disruptions.

Optimizing Predictive Maintenance Decisions: Use of Non-Arbitrary Multi-Covariate Bands in a Novel Condition Assessment under a Machine Learning Approach

Jointing Condition-Based Maintenance (CBM) with the Proportional Hazards Model (PHM), asset-intensive industries often monitor vital covariates to predict failure rate, the reliability function, and maintenance decisions. This analysis requires defining the transition probabilities of asset conditions evolving among states over time. When only one covariate is assessed, the model’s parameters are commonly obtained from expert opinions to provide state bands directly. However, the challenge lies within multiple covariate problems, where arbitrary judgment can be difficult and debatable, since the composite measurement does not represent any physical magnitude. In addition, selecting covariates lacks procedures to prioritize the most relevant ones. Therefore, the present work aimed to determine multiple covariate bands for the transition probability matrix via supervised classification and unsupervised clustering. We used Machine Learning (ML) to strengthen the PHM model and to complement expert knowledge. This paper allows obtaining the number of covariate bands and the optimal limits of each one when dealing with predictive maintenance decisions. This novel proposal of an ML condition assessment is a robust alternative to the expert criterion to provide accurate results, increasing the expectation of the remaining useful life for critical assets. Finally, this research has built an enriched bridge between the decision areas of predictive maintenance and Data Science.

A BIA-Based Quantitative Framework for Built Physical Asset Criticality Analysis under Sustainability and Resilience

Asset-intensive industries, such as the construction industry, have experienced major catastrophes that have led to significant operational disruptions. Physical asset failure has been the primary cause of these disruptions. Therefore, implementing proper asset management plans, including continuity plans, is crucial for the business continuity of companies active in these industries. However, companies often face severe resource limitations when implementing these plans for all of their physical assets. Therefore, those critical physical assets that are vital for providing their key products should be identified. Moreover, sustainability and resilience are inseparable parts of organizations’ strategies, including strategic asset management plans. Therefore, any proposed ranking methodology for physical asset prioritization should encompass sustainability and resilience measures to ensure its practicality. This paper proposes a novel framework for physical asset criticality analysis based on the so-called business impact analysis to ensure the continuity of providing products/services through the continuity of physical assets. A hybrid fuzzy BWM-TOPSIS method is first applied to identify the key products. Then, a hybrid fuzzy DEMATEL-Bayesian network is applied based on proper sustainability and resilience factors to determine the critical physical assets, while interdependencies among these factors are well captured. The normalized expected asset criticality index is defined to guide managers in taking appropriate directions while developing asset management plans. A case study of a gas company is provided to show the applicability of the proposed decision model. The data needed for each step of the framework is gathered through experts’ judgments, historical data available on the sites, or quantitative risk assessment scenarios.

Digital maintenance and the functional blocks for sustainable asset maintenance service – A case study

Rapid advances in technology, data, and analytics have helped maintenance services to evolve from a mere reactive process to a fully digital solution. Digital maintenance services exhibit a variety of functionalities to every adopting industry, with monitoring, diagnosing, troubleshooting, predicting, and optimizing being the common influencer. The presence of digital maintenance is slowly growing in the asset-intensive industries; however, the early adopters have been benefitting the most. This research tries to showcase a digital and sustainable maintenance service approach from both a scientific study and a practice perspective. It explains essential blocks of digital maintenance from a proposed model and provides a basis for new and in-depth research in digitally enabled reliability. Using a case study approach, it further aims to validate a functionality model for digital and sustainable maintenance services; explains different blocks as digital offerings; highlights the importance of digitalization and digital maintenance, and presents the results achieved based on the implementation. Metso Outotec being one of the early adopters of digital maintenance, their Connected Service is studied as a case study from a practical perspective for this research. The outcome observes positive sustainable impacts such as economic savings, reduced production loss, time saved, reduced travel to the site, and ensuring safety through fault prevention.

Using Big Data to Improve Safety Performance: An Application of Process Mining to Enhance Data Visualisation

Abstract The management of health and safety plays an important role in safety performance, and is therefore an important foundational element in an organisation's overall sustainable development. Many organisations are now able to collect vast amounts of data to shed light on the underlying causes behind accidents and safety-related incidents, and to spot patterns that can lead to solutions. Despite these well-intentioned Big Data collection efforts, however, accident statistics in asset-intensive industries remain stubbornly high as the data frequently fails to reveal actionable insights. In this paper, we answer Wang and Wu's (2020) [60] , [60] and Wang et al.'s (2019) [61] , [61] calls for the application of Big Data science to the safety domain by exploring the potential of applying tools and techniques from process mining, a research area concerned with analysing process execution data, to derive novel insights from and improve the visualisation of safety process data. We demonstrate how these tools can yield useful insights in the occupational health and safety domain by analysing process execution data from a Permit to Work system in an Australian energy company. Specifically, the analysis presented here highlights the underlying complexity of the organisation's Permit to Work process, reveals conformance and performance issues, and uncovers resources associated with conformance issues and changes in the frequency of such issues over time, thereby underlining the need to simplify the system. Encouraged by these fresh perspectives and insights delivered by process mining, we hope that this novel application will be a catalyst for further research at the interface between these research disciplines.

Decision frameworks for restoration & adaptation investment-Applying lessons from asset-intensive industries to the Great Barrier Reef.

Asset-intensive industries (including water and power utilities, mineral resources and energy) are those which require significant levels of capital investment in their assets in order to operate. These industries face challenges from uncertainty in resource availability and demand for end products, the intricate and complicated nature of their assets, and the complexity of the economic, ecological and social settings in which they operate. In these industries, the application of decision frameworks that account for this uncertainty and complexity in guiding asset investment and development is standard practice. Lessons from asset-intensive industries were applied during the concept feasibility phase of the Reef Restoration and Adaptation Program (RRAP) to establish the investment case for research and development into interventions to help the Great Barrier Reef (GBR) resist, adapt to, and recover from the impacts of climate change. The authors worked with RRAP partners to define a decision framework that included structured decision-making processes (SDM), a cost-benefit analysis (CBA), and a value of information (VoI) analysis, to establish the investment case for intervening on the GBR which led to success in securing Australian Government commitment for the next phase of the Program. With climate change expected to drive increased demand for significant levels of restoration and adaptation investment in large integrated social, ecological and economic assets (such as the GBR), the lessons from RRAP offer insights for the application of decision frameworks to inform public and private investment priorities.

Predictive Maintenance for Infrastructure Asset Management

Optimal maintenance is one of the key concerns for asset-intensive industries in terms of reducing downtime and occurring costs. The advancement of data-driven technologies, affordable computing powers, and growing amounts of data introduced a paradigm with the name of predictive maintenance (PdM). PdM seeks to find out an optimal moment for the maintenance of an asset, where no early intervention leads to undue extra cost, and no late maintenance activity poses a safety risk. With the instrumentation of the cyber-physical system on assets, PdM transforms a typical structure into a smart structure that can send warnings in cases of near failure states. However, several practical challenges hamper the adoption of PdM solutions within industries. This article outlines a typical PdM modeling framework and its key components. Additionally, the adoption challenges, along with alternatives for implementation of the PdM solution are provided. This article concludes by offering several research directions that can accelerate the PdM adoption procedure.

Enterprise modeling and operational technologies (OT) application in the oil and gas industry

Abstract When modeling enterprises, it is necessary to capture, simplify, abstract, and organize a vast number of key organizational elements. These elements (e.g., organizational units, human resources, production processes, and IT systems) are frequently organized and modeled in the business and IT domains. The focus on modeling these domains casts aside other important domains for asset-intensive industries such as operational technologies (OT), which is directly responsible for producing and delivering a company's goods and services. The information produced with OT can be used to generate insight into the production performance, which allows a more accurate decision-making process regarding productivity, its strategy, and within the business. With the rise of trends such as industrial Internet of Things (IoT) and cloud manufacturing, that seek convergence of IT technologies in OT networks, OT and IT analysis are highly sought after in today's industries striving for real-time analysis of data. To analyze the data in the OT and IT domain it is necessary to use models, which not only focus on describing both domains but can also show the relationship between them. In this paper, we present a technique that uses the operational data, produced in an organization, in order to model OT, with the purpose of applying analysis methods as those applied to enterprise modeling. To do so, the technique classifies, structures, and characterizes the information, measurements, and indicators, in a catalog, which is then used to complement an OT metamodel. We illustrate this technique for the oil and gas sector, for which we built an OT indicator catalog, based on an OT metamodel, and created corresponding analysis methods.

A DESIGN SUPPORT TOOL FOR IMPROVING MAINTENANCE SCHEDULING PROCESS

AbstractThe challenge of user requirements for maintenance scheduling design in large asset-intensive industries suffers from lack of academic and empirical studies. Therefore, using a representative case study, this paper aims to: (1) identify the current practices and complex scheduling requirements; (2) propose a design support tool to optimize the maintenance scheduling process; and (3) report the gained benefits. The results reveal that the proposed tool can decrease the resource requirements, increase the capacity utilization, and reduce the cost while addressing the complex user requirements.

A novel performance evaluation model for MRO management indicators of high-end equipment

High-end equipment oriented maintenance, repair and operation (MRO) management is crucial for asset intensive industries. The existing works mainly focus on providing the best possible joint optimisation for production and maintenance management without aiming at the complicated relationships among them. In the intelligence-connected era, the rapid development of Internet of things and big data technologies enables us to access, collect, and store the industrial big data, which is especially necessary for MRO management indicator evaluation, and so we try to apply big data analysis to visualise the system structure of complicated relationships among MRO indicators at different management levels. In this paper, the decision-making trial and evaluation laboratory (DEMATEL) and improved analytical network process (ANP) are applied to build the performance evaluation model for MRO management indicators, in which DEMATEL is utilised to quantify the system structure of different management levels, and the improved ANP is introduced to calculate relative weights of corresponding indicators. The results point out to managers which indicators should deserve more attention in MRO management decision-making as well as joint optimisation for production and maintenance management. A case study illustrates the feasibility and practicality of the proposed model.

An asset-management oriented methodology for mine haul-fleet usage scheduling

Different complexities force mining companies to find efficient ways to respond to demand challenges and ensure long-term sustainability. It explains, in part, the increase in the use of prescriptive analytics to optimize physical-asset life-cycle costs and decrease greenhouse gas (GHG) emissions. Mining, being an asset-intensive industry, provides huge improvement opportunities. This is especially true for scheduling practices of mine haulage fleet usage in long term planning. Fleet aging implies important cost increases in maintenance and repair (M&R), and overhauls. Fleets are often heterogeneous in term of truck performance, fuel consumption and GHG emissions. Sub-optimal scheduling decisions may induce severe cost over-runs and increased emissions. This paper proposes an original mixed integer programming formulation to optimize mine haulage equipment scheduling in the long term. The model considers the effects of equipment aging, fuel consumption, payload capacity and cycle times. Our formulation handles different aspects that according to author’s knowledge have not been considered in the literature as a whole: (i) joint minimization of fuel, M&R, and overhaul costs, (ii) reduction of GHG emissions, (iii) heterogeneous equipment performance metrics, (iv) increase in cycle times due to mine aging. The case study shows a cost reduction of 13% in the discounted flows associated with fuel, M&R, and overhauls in a time horizon of 10 years. This figure translates into an NPV gain of 13.1 million USD. Additionally, GHG emissions are reduced by an average of 3470 t/year or 11% overall.

Evolving BIS Usage in Asset-Intensive Industries: What to Look For As an Investor

Providers of Business Information Systems solutions to asset-intensive industries such as industrial/manufacturing, transportation/logistics, and energy have a lot of allure for private equity and other investors these days. Companies in these industries have been slow relative to many others in adopting BIS solutions, but they're racing to catch up as they've had to fight harder for every bit of incremental profit. Once buyers do pick solutions, however, they tend not to change out often. As attractive as this kind of rising demand and brand loyalty may sound from an investment perspective, the solutions these companies are buying have changed markedly over the past several years. It's important for investors to understand the shift in spending trends to determine whether a potential BIS investment is in the right place in an evolving market.

Molding the asset management system to PAS 55-1/ISO 55001 in one Scandinavian water and wastewater works

Good practice and standards set strict requirements for optimization and continuous improvement of physical asset management. The new international standard for asset management provides a framework for asset intensive industries to develop and implement sound asset management systems. Unlike the largest and most global industries, there are very few, if any, providers of services in the water sector that have developed and implemented a full set of best practices, in compliance with specifications created by the standard. Oslo Water and Sewerage Works (Oslo VAV) has worked systematically to evolve its asset management system by standardizing work processes and operating procedures in its Business Process Management System, implementing good practice in infrastructure asset management, and by improved management and control of the private contractor that operates the wastewater treatment plant's processes and activities. This paper provides insight into Oslo VAV's asset management system, assessment of current practice vs. best practice for complying with the standard and high priority areas for improvement.

Tire demand planning based on reliability and operating environment

Tires represent a critical spare part in mines. There is a shortage of medium and large tires. In addition, with increased mining activities and the creation of new mines, the demand for tires has increased significantly. Thus, it is particularly important for mining engineers to identify tire characteristics and correctly manage the spare part inventory. Spare parts management is critical from an operational perspective, especially in asset intensive industries, such as mining, as well as in organizations owning and operating costly assets. A knowledge of the tires’ behavior (historical data) must be considered together with the operating environment conditions (covariates). This study uses multiple regression analysis based on Cox’s regression model to incorporate machine operating environment information into systems reliability analysis to estimate spare parts. It considers a proportional hazard model and a stratified Cox regression model for time independent and dependent covariates. Based on the results, the study develops a mathematical model for spare parts estimation at the component level for non-repairable parts (tires). It validates the outcomes using a case study of loader tires in Sungun mine in Iran. There is a significant difference in the results of spare parts forecasting and inventory management when considering and not considering covariates.

Technology foresight in asset intensive industries: The case of Russian shipbuilding

Developing competitive national shipbuilding industries is a strategic priority for many countries. Shipbuilding has evolved into a high tech industry over the last few decades that is strongly driven by customer needs and wishes. Consequently economic development of the industry is now far more complex than previously.International competition in the shipbuilding industry is very strong in all segments of the industry. The article assesses the future development of the shipbuilding industry globally and evaluates the position and opportunities for Russian shipbuilding. International experience of estimating industries' future development shows that a necessary condition of success is building a vision of the industry's long-term future in the context of social and economic development. One way to create such a long-term vision is through developing scenarios based on factors including wild card events, global challenges, trends, threats, drivers, barriers, and limitations.One of the most effective approaches to enhance competitiveness of the industry is Technology Foresight. The paper presents results of Foresight for civil shipbuilding in Russia on the basis of benchmarking, expert procedures and scenario analysis. It demonstrates how Technology Foresight was adjusted to the special conditions of Russia as an emerging country and how the special features of strategic industries which are in the national interest of countries can be included in Technology Foresight studies. Finally the article derives strategies for policy making to set priorities for revitalizing the industry.

A Discrete Stress–Strength Interference Theory-Based Dynamic Supplier Selection Model for Maintenance Service Outsourcing

Maintenance service outsourcing is a strategic driver for asset intensive industries pursuing the enhancement of supply chain performance. Maintenance service supplier selection plays a relevant role in this premise since its significant impact on equipment availability, and hence, on business success. To periodically review suppliers’ performances and update the outsourcing contract, a discrete stress–strength interference (DSSI) theory-based dynamic supplier selection model is presented for maintenance service outsourcing process. Taken account of the influence of randomness and uncertainty, a novel and universal evaluation criterion, demand fulfillment level (DFL) is introduced based on the DSSI theory. DFL is relevant to two random variables, which are random user's service order quantity (stress) and random supplier's service fulfillment quantity (strength), and DFL is defined as the probability that the latter (strength) is larger than the former (stress). Based on DFL, the proposed model can help users outsource the corresponding maintenance service to the most suitable supplier (maximum supplier reliability) at different periods. The decision rule can be described as a dynamic 3-D diagram, according to which decision makers can periodically review suppliers’ performances and update the outsourcing contract. A case study on maintenance service supplier selection problem for a steel company illustrates the effectiveness of the proposed model.

Asset health management using predictive and prescriptive analytics for the electric power grid

Electric utilities make up an asset-intensive industry with a broad geographical spread of assets, such as poles, transformers, cables, and switchgear. The utilities face a backlog of aging assets that are pending replacement. Increasingly, a consensus has been reached on moving away from time-based maintenance planning of assets to developing a proactive and smarter asset health management program to meet the competing constraints of reducing customer downtimes, meeting regulatory standards, and managing ever-expanding infrastructure within budget. Incomplete information, fragmented data, and a diversity of asset classes collectively make a holistic assessment of the grid extremely challenging. Working with DTE Energy and Alliander N.V., IBM Research has developed advanced analytics to model asset health and network reliability by predicting the aging of assets, identifying the remaining lifecycle, and computing the network robustness. The analytics exploit data from multiple systems such as enterprise asset management, work management, geographic information systems, supervisory control and data acquisition systems, advanced metering infrastructure, weather systems, and outage management systems. The algorithms systematically evaluate asset health and prioritize preventive, proactive, and corrective maintenance strategies for all asset classes in the electrical network. We describe outcomes, summarizing an overall health score and risk ranking along with a suggested optimal maintenance strategy considering budgetary constraints.