From Waste to Value: A Practical Framework for Waste Identification and Mitigation Using Lean Management Principles

In the complex and fast-changing marketing environment, there is a constant need to reduce costs and enhance the performance of production systems. The cost-cutting strategies need to consider the long-term effect on the company. For example, the layoff may reduce the cost in the short term, but in the long term, it may significantly affect employees' psychological safety and increase human error. Hence, any changes in the company must be based on a clear management philosophy. Lean management focuses on continuously improving processes by eliminating non-value-adding activities. It tries to create more value for customers with fewer resources, increasing efficiency, quality, and customer satisfaction. Lean philosophy considers these non-value activities in three general categories: overburden, unevenness, and waste, and tries to remove them from the value production cycle through the continuous development process. Although the lean management style is a well-known approach style, there is much difficulty in implementing this approach. One of the main reasons is the organization's culture and habits, daily routine, and approach, which may not be aligned with lean thinking. Furthermore, for lean thinking to be effectively applied throughout the organization, it must be comprehensible and straightforward. In some cases, the Lean tools need to modify based on the already well-developed approach of the company. The main goal of this paper is to present a practical approach for implementing Lean thinking in identifying and prioritizing non-value activities for the industry. Here, "Waste walking" and "Value-stream mapping" lean tools and the FMECA principle are used to develop "waste ranking criteria" for the identification and prioritization of non-value activities

An overall performance index for wind farms: a case study in Norway Arctic region

Wind farms (WFs) experience various challenges that affect their performance. Mostly, designers focus on the technical side of WFs performance, mainly increasing the power production of WFs, through improving their manufacturing and design quality, wind turbines capacity, their availability, reliability, maintainability, and supportability. On the other hand, WFs induce impacts on their surroundings, these impacts can be classified as environmental, social, and economic, and can be described as the sustainability performance of WFs. A comprehensive tool that combines both sides of performance, i.e. the technical and the sustainability performance, is useful to indicate the overall performance of WFs. An overall performance index (OPI) can help operators and stakeholders rate the performance of WFs, more comprehensively and locate the weaknesses in their performance. The performance model for WFs, proposed in this study, arranges a set of technical and sustainability performance indicators in a hierarchical structure. Due to lack of historical data in certain regions where WFs are located, such as the Arctic, expert judgement technique is used to determine the relative weight of each performance indicator. In addition, scoring criteria are predefined qualitatively for each performance indicator. The weighted sum method makes use of the relative weights and the predefined scoring criteria to calculate the OPI of a specific WF. The application of the tool is illustrated by a case study of a WF located in the Norwegian Arctic. Moreover, the Arctic WF is compared to another WF located outside the Arctic to illustrate the effects of Arctic operating conditions on the OPI

Resilience Assessment: A Performance‐Based Importance Measure

The resilience of a system can be considered as a function of its reliability and recoverability. Hence, for effective resilience management, the reliability and recoverability of all components which build up the system need to be identified. After that, their importance should be identified using an appropriate model for future resource allocation. The critical infrastructures are under dynamic stress due to operational conditions. Such stress can significantly affect the recoverability and reliability of a system‘s components, the system configuration, and consequently, the importance of components. Hence, their effect on the developed importance measure needs to be identified and then quantified appropriately. The dynamic operational condition can be modeled using the risk factors. However, in most of the available importance measures, the effect of risk factors has not been addressed properly. In this paper, a reliability importance measure has been used to determine the critical components considering the effect of risk factors. The application of the model has been shown through a case study

Observed and unobserved heterogeneity in failure data analysis

In reality, failure data are often collected under diffract operational conditions (covariates), leading to heterogeneity among the data. Heterogeneity can be classified as observed and unobserved heterogeneity. Un-observed heterogeneity is the effect of unknown, unrecorded, or missing covariates. In most reliability studies, the effect of unobserved covariates is neglected. This may lead to inaccurate reliability modeling, and consequently, wrong operation and maintenance decisions. There is a lack of a systematic approach to model the unobserved covariate in reliability analysis. This paper aims to present a framework for reliability analysis in the presence of unobserved and observed covariates. Here, the unobserved covariates will be analyzed using frailty models. A case study will illustrate the application of the framework

Industrial Equipment’s Throughput Capacity Analysis

Throughput capacity (TC) is defined as the total amount of material processed or produced by the system in the given time. In practice, full capacity performance for industrial equipment is impossible because the failures are affected and cause a reduction. Therefore, failure interruptions, especially critical ones (bottlenecks), must be detected and considered in production management. From the point of production view, the bottleneck has the lowest production or performance. Most of the previous works used the availability and related importance measures as performance indicators and prioritization of subsystems. However, these measures cannot consider system production in their prioritization. This paper presents a bottleneck detection framework based on system performance and production capacity integration. The integrated approach is used to assess the loading and hauling subsystems of Golgohar Iron Mine, Iran. As a result of the analysis, the hauling subsystem identifies the system’s bottleneck

The Effect of Risk Factors on the Resilience of Industrial Equipmen

Recently, to evaluate the response of systems against disruptive events, the application of the resilience concept has been increased. Resilience depicts the system’s ability to return to its normal operational status after the disruption. Various studies in the field of engineering and non-engineering systems have only considered systems’ performance indicators to estimate resilience. Therefore, the impact of operating and environmental factors (risk factors) has been neglected. In this paper, the influence of the risk factors (rock type), as well as the system’s performance indicators, are considered in the resilience estimation of the excavator system of Gol-E-Gohar Iron mine

Production Performance Analysis : Reliability, Maintainability and Operational Conditions

PhD thesis in Offshore technologyThis thesis is based on the following papers, not yet available in UiS Brage due to copyright:PAPER 1: Barabadi, A. and Markeset, T. (2011). Reliability and maintainability performance under Arctic conditions, International Journal of Systems Assurance Engineering and Management, DOI 10.1007/s13198-011-0071-8.PAPER 2: Barabadi, A., Barabady, J. and Markeset, T. (2011). A methodology for throughput capacity analysis of a production facility considering environment condition, Reliability Engineering and System Safety, Vol. 96, No. 12, pp. 1637-1646. http://www.sciencedirect.com/science/article/pii/S0951832011001736PAPER 3: Barabadi, A., Barabady, J. and Markeset, T. (2011). Maintainability analysis considering time-dependent and timeindependent covariates, Reliability Engineering and System Safety, Vol. 96, No. 1, pp. 210-217. http://www.sciencedirect.com/science/article/pii/S0951832010001924PAPER 4: Kayrbekova, D., Barabadi, A. and Markeset, T. (2011). Maintenance cost evaluation of a system to be used in Arctic conditions: A case study, Journal of Quality in Maintenance Engineering, Vol. 17, No. 4, pp. 320-336. http://www.emeraldinsight.com/journals.htm?issn=1355-2511&volume=17&issue=4&articleid=1958855&show=abstractPAPER 5: Barabadi, A. (2012). Reliability and spare part provision considering operational environment: A case study, To appear in International Journal of Performability Engineering, Vol. 8, No. 4, pp. 417-426.With the increasing demand for energy over recent decades, the Arctic region has become an interesting area for future exploration and development for the oil and gas industry. The Arctic region is known to have a harsh climate and a sensitive environment in a remote location. The severe and complex operational conditions in the Arctic can significantly affect the lifetime of a system, the repair processes and the support activities. Hence, it is important to consider the effect of the operational conditions on the performance of the production facility/systems/equipment and machines, and the related reliability and maintainability characteristics. The aim of this thesis is to study, analyze and suggest a methodology for production performance analysis considering operational conditions. Furthermore, the study focuses on developing and modifying the available statistical approach for prediction of maintainability performance and spare part provision considering the effect of time-dependent and time-independent covariates (influence factors). In this research study, firstly a brief survey of technological and operational challenges in the Arctic region from a maintainability and reliability performance point of view is presented. Then, available statistical approaches for reliability and maintainability performance analysis considering the effect of covariates are reviewed. Thereafter, a methodology is developed and proposed for production performance analysis considering time-dependent and time-independent covariates. The methodology is based on the concept of the proportional hazard model (PHM) and the proportional repair model (PRM), as well as their extensions. A case study from the mining industry is presented to demonstrate how the proposed methodology can be applied. In the second part of this research study, the application of the extension of PHM is developed and discussed in order to predict the maintainability performance considering time-dependent covariates. Furthermore, the existing methods for calculating the number of spare parts on the basis of the reliability characteristics, without the consideration of time-dependent iv ABBAS BARABADI covariates, is modified and improved to enhance their application in the presence of time-dependent covariates. The applications of these methods are demonstrated and discussed using a case study. The result of the study shows that the operational conditions may have a significant effect on the reliability and maintainability performance of a component. This also consequently affects the number of the required spare parts for a given operational condition. The result also shows that considering time-dependent covariates as time-independent covariates may lead to wrong results in the prediction of reliability and maintainability performance as well as the required spare parts. Therefore, before any analysis, the timedependency of covariates must be checked. Thereafter, based on the result of the analysis, the appropriate statistical approach must be selected

On Context, Issues, and Pitfalls of Expert Judgement Process in Risk Assessment of Arctic Offshore Installations and Operations

Decisions to be made in the Arctic offshore operations rely extensively on risk assessment outputs, which require a great deal of historical data and information. However, at the current stage of operating in the Arctic offshore – compared to normal-climate regions – such data is scarce due to the limited industrial activities to date. Lack of data on the probability of the occurrence of an unwanted event and, given severe Arctic environmental conditions, the extent of potential severe consequences pose a great deal of challenges and issues for decision-makers. A widely acceptable alternative is the use of expert judgement process. However, this is faced with some issues and pitfalls, which may raise questions regarding the objectivity and level of uncertainty of risk assessment outputs. In this paper, we discuss such issues and pitfalls associated with expert judgement application in risk assessment of Arctic offshore operations

Resilience Assessment of Wind Farms in the Arctic with the Application of Bayesian Networks

Infrastructure systems, such as wind farms, are prone to various human-induced and natural disruptions such as extreme weather conditions. There is growing concern among decision makers about the ability of wind farms to withstand and regain their performance when facing disruptions, in terms of resilience-enhanced strategies. This paper proposes a probabilistic model to calculate the resilience of wind farms facing disruptive weather conditions. In this study, the resilience of wind farms is considered to be a function of their reliability, maintainability, supportability, and organizational resilience. The relationships between these resilience variables can be structured using Bayesian network models. The use of Bayesian networks allows for analyzing different resilience scenarios. Moreover, Bayesian networks can be used to quantify resilience, which is demonstrated in this paper with a case study of a wind farm in Arctic Norway. The results of the case study show that the wind farm is highly resilient under normal operating conditions, and slightly degraded under Arctic operating conditions. Moreover, the case study introduced the calculation of wind farm resilience under Arctic black swan conditions. A black swan scenario is an unknowable unknown scenario that can affect a system with low probability and very high extreme consequences. The results of the analysis show that the resilience of the wind farm is significantly degraded when operating under Arctic black swan conditions. In addition, a backward propagation of the Bayesian network illustrates the percentage of improvement required in each resilience factor in order to attain a certain level of resilience of the wind farm under Arctic black swan conditions

A practical guideline for human error assessment: A causal model

Abstract: To meet the availability target and reduce system downtime, effective maintenance have a great importance. However, maintenance performance is greatly affected in complex ways by human factors. Hence, to have an effective maintenance operation, these factors needs to be assessed and quantified. To avoid the inadequacies of traditional human error assessment (HEA) approaches, the application of Bayesian Networks (BN) is gaining popularity. The main purpose of this paper is to propose a HEA framework based on the BN for maintenance operation. The proposed framework aids for assessing the effects of human performance influencing factors on the likelihood of human error during maintenance activities. Further, the paper investigates how operational issues must be considered in system failure-rate analysis, maintenance planning, and prediction of human error in pre- and post-maintenance operations. The goal is to assess how performance monitoring and evaluation of human factors can effect better operation and maintenance