Problem-solving approaches in maintenance cost management: a literature review

2016 ◽  
Vol 22 (4) ◽  
pp. 334-352 ◽  
Author(s):  
Fazel Ansari ◽  
Madjid Fathi ◽  
Ulrich Seidenberg
Keyword(s):  
Problem Solving ◽  
Morphological Analysis ◽  
Cost Management ◽  
Literature Survey ◽  
Maintenance Cost ◽  
Cost Models ◽  
Content Type ◽  
Scope Of Application ◽  
Past Experiences

Purpose The purpose of this paper is to investigate the use of problem-solving approaches in maintenance cost management (MCM). In particular, the paper aims to examine characteristics of MCM models and to identify patterns for classification of problem-solving approaches. Design/methodology/approach This paper reflects an extensive and detailed literature survey of 68 (quantitative or qualitative) cost models within the scope of MCM published in the period from 1969 to 2013. The reviewed papers have been critically examined and classified based on implementing a morphological analysis which employs eight criteria and associated expressions. In addition, the survey identified two main perspectives of problem solving: first, synoptic/incremental and second, heuristics/meta-heuristics. Findings The literature survey revealed the patterns for classification of the MCM models, especially the characteristics of the models for problem-solving in association with the type of modeling, focus of purpose, extent and scope of application, and reaction and dynamics of parameters. Majority of the surveyed approaches is mathematical, respectively, synoptic. Incremental approaches are much less and only few are combined (i.e. synoptic and incremental). A set of features is identified for proper classification, selection, and coexistence of the two approaches. Research limitations/implications This paper provides a basis for further study of heuristic and meta-heuristic approaches to problem-solving. Especially the coexistence of heuristic, synoptic, and incremental approaches needs to be further investigated. Practical implications The detected dominance of synoptic approaches in literature – especially in the case of specific application areas – contrasts to some extent to the needs of maintenance managers in practice. Hence the findings of this paper particularly address the need for further investigation on combining problem-solving approaches for improving planning, monitoring, and controlling phases of MCM. Continuous improvement of MCM, especially problem-solving and decision-making activities, is tailored to the use of maintenance knowledge assets. In particular, maintenance management systems and processes are knowledge driven. Thus, combining problem-solving approaches with knowledge management methods is of interest, especially for continuous learning from past experiences in MCM. Originality/value This paper provides a unique study of 68 problem-solving approaches in MCM, based on a morphological analysis. Hence suitable criteria and their expressions are provided. The paper reveals the opportunities for further interdisciplinary research in the maintenance cost life cycle.

Expected maintenance waste reduction benefits after implementation of Just in Time (JIT) philosophy in maintenance (a statistical analysis)

2019 ◽  
Vol 25 (1) ◽  
pp. 25-40 ◽  
Author(s):  
Sandeep Phogat ◽  
Anil Kumar Gupta
Keyword(s):  
Data Interpretation ◽  
General Information ◽  
Spare Parts ◽  
Waste Reduction ◽  
Maintenance Cost ◽  
Just In Time ◽  
Content Type ◽  
Great Saving ◽  
Research Findings ◽  
Structured Questionnaire

Purpose The maintenance department of today, like many other departments, is under sustained pressure to slash costs, show outcome and support the assignment of the organization, as it is a commonsensical prospect from the business perspective. The purpose of this paper is to examine expected maintenance waste reduction benefits in the maintenance of organizations after the implementation of just-in-time (JIT) managerial philosophy. For this, a structured questionnaire was designed and sent to the 421 industries in India. Design/methodology/approach The designed questionnaire was divided into two sections A and B to assist data interpretation. The aim of the section A was to build general information of participants, type of organization, number of employees, annual turnover of the organization, etc. Section B was also a structured questionnaire developed based on a five-point Likert scale. The identified critical elements of the JIT were included in the questionnaire to identify the maintenance waste reduction benefits in the maintenance of organizations. Findings On the basis of the 133 responses, hypothesis testing was done with the help of Z-test, and it was found out that in maintenance, we can reduce a large inventory of spare parts and also shorten the excessive maintenance activities due to the implementation of JIT philosophy. All the four wastes: waste of processing; waste of rejects/rework/scrap in case of poor maintenance; waste of the transport of spares, and waste of motion, have approximately equal weightage in their reduction. Waste of waiting for spares got the last rank, which showed that there are little bit chances in the reduction of waiting for spares after the implementation of JIT philosophy in maintenance. Practical implications The implication of the research findings for maintenance of organizations is that if maintenance practitioners implement elements of JIT philosophy in maintenance then there will be a great reduction in the maintenance wastes. Originality/value This paper will be abundantly useful for the maintenance professionals, researchers and others concerned with maintenance to understand the significance of JIT philosophy implementation to get the expected reduction benefits in maintenance wastes of organizations which will be helpful in the great saving of maintenance cost and time side by side great increment in the availability of machines.

Cost management for concrete batch plant using stochastic mathematical models

2006 ◽  
Vol 33 (8) ◽  
pp. 1065-1074 ◽  
Author(s):  
Tarek M Zayed ◽  
Ibrahim A Nosair
Keyword(s):  
Mathematical Models ◽  
Production Efficiency ◽  
Cost Management ◽  
Unit Cost ◽  
Expected Value ◽  
Cost Models ◽  
Productivity Cost ◽  
Batch Plant ◽  
Factor Share ◽  
Construction Operation

Assessing productivity, cost, and delays are essential to manage any construction operation, particularly the concrete batch plant (CBP) operation. This paper focuses on assessing the above-mentioned items for the CBP using stochastic mathematical models. It aims at (i) identifying the potential sources of delay in the CBP operation; (ii) assessing their influence on production, efficiency, time, and cost; and (iii) determining each factor share in inflating the CBP concrete unit expense. Stochastic mathematical models were designed to accomplish the aforementioned objectives. Data were collected from five CBP sites in Indiana, USA, to implement and verify the designed models. Results show that delays due to management conditions have the highest probability of occurrence (0.43), expected value of delay percent (62.54% out of total delays), and relative delay percent. The expected value of efficiency for all plants is 86.53%; however, the average total expense is US$15.56/m3 (all currency are in US$). In addition, the expected value of effective expenses (EE) is $18.03/m3, resulting in extra expenses (XE) of $2.47/m3. This research is relevant to both industry practitioners and researchers. It develops models to determine the effect of delays on concrete unit cost. They are also beneficial to the CBP management.Key words: concrete batch plant, delays, management conditions, cost models, cost management, stochastic mathematical models.

Additional Protocol II: Elevating the minimum threshold of intensity?

2021 ◽  
pp. 1-28
Author(s):  
Martha M. Bradley
Keyword(s):  
Case Law ◽  
Armed Conflicts ◽  
Military Operations ◽  
Minimum Threshold ◽  
Additional Protocol ◽  
Geneva Conventions ◽  
Scope Of Application ◽  
Treaty Interpretation ◽  
Armed Violence

Abstract This paper examines the notion of intensity in the context of common Article 3 and Additional Protocol II (AP II) to the Geneva Conventions in order to establish whether AP II demands a different intensity threshold from the minimum threshold of intensity contemplated in common Article 3. The paper considers the question of whether the inclusion of the term “sustained” in the phrase “sustained and concerted military operations” intrinsic to the threshold in Article 1(1) of AP II introduces a temporal requirement in addition to mere protracted armed violence. The paper argues that the inclusion of the term “sustained” in Article 1(1) of AP II potentially demands prolonged protracted armed violence. The research aims to contribute to the existing literature on the notion of intensity demanded by the scope of application inherent in AP II through an interrogation of the phrase “sustained” military operations by employing the rules of treaty interpretation and by examining relevant case law and scholarly debate. In this way, the author hopes to contribute towards filling a lacuna with regard to the minimum threshold for intensity in the context of treaty law concerned with the classification of non-international armed conflicts.

Good problem solvers? Leveraging knowledge sharing mechanisms and management support

2019 ◽  
Vol 23 (6) ◽  
pp. 1017-1038 ◽  
Author(s):  
Ambra Galeazzo ◽  
Andrea Furlan
Keyword(s):  
Transformational Leadership ◽  
Problem Solving ◽  
Knowledge Sharing ◽  
Hierarchical Linear Modeling ◽  
Shop Floor ◽  
Management Support ◽  
Linear Modeling ◽  
Content Type ◽  
Problem Solvers ◽  
Good Problem

Purpose Organizational learning relies on problem-solving as a way to generate new knowledge. Good problem solvers should adopt a problem-solving orientation (PSO) that analyzes the causes of problems to arrive at an effective solution. The purpose of this paper is to investigate this relevant, though underexplored, topic by examining two important antecedents of PSO: knowledge sharing mechanisms and transformational leaders’ support. Design/methodology/approach Hierarchical linear modeling analyses were performed on a sample of 131 workers in 12 plants. A questionnaire was designed to collect data from shop-floor employees. Knowledge sharing was measured using the mechanisms of participative practices and standardized practices. Management support was assessed based on the extent to which supervisors engaged in transformational leadership. Findings Knowledge sharing mechanisms are an antecedent of PSO behavior, but management support measured in terms of transformational leadership is not. However, transformational leadership affects the use of knowledge sharing mechanisms that, in turn, is positively related to PSO behavior. Practical implications The research provides practical guidance for practitioners to understand how to manage knowledge in the workplace to promote employees’ PSO behaviors. Originality/value Though problem-solving activities are intrinsic in any working context, PSO is still very much underrepresented and scarcely understood in knowledge management studies. This study fills this gap by investigating the antecedents of PSO behavior.

Study of a discrete grey forecasting model based on the quality cost characteristic curve

2017 ◽  
Vol 7 (3) ◽  
pp. 376-384 ◽  
Author(s):  
Wenjie Dong ◽  
Sifeng Liu ◽  
Zhigeng Fang ◽  
Xiaoyu Yang ◽  
Qian Hu ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Characteristic Curve ◽  
Grey System Theory ◽  
Forecasting Model ◽  
Quality Level ◽  
Cost Models ◽  
Quality Cost ◽  
Content Type ◽  
Grey Forecasting ◽  
Grey Forecasting Model

Purpose The purpose of this paper is to clarify several commonly used quality cost models based on Juran’s characteristic curve. Through mathematical deduction, the lowest point of quality cost and the lowest level of quality level (often depicted by qualification rate) can be obtained. This paper also aims to introduce a new prediction model, namely discrete grey model (DGM), to forecast the changing trend of quality cost. Design/methodology/approach This paper comes to the conclusion by means of mathematical deduction. To make it more clear, the authors get the lowest quality level and the lowest quality cost by taking the derivative of the equation of quality cost and quality level. By introducing the weakening buffer operator, the authors can significantly improve the prediction accuracy of DGM. Findings This paper demonstrates that DGM can be used to forecast quality cost based on Juran’s cost characteristic curve, especially when the authors do not have much information or the sample capacity is rather small. When operated by practical weakening buffer operator, the randomness of time series can be obviously weakened and the prediction accuracy can be significantly improved. Practical implications This paper uses a real case from a literature to verify the validity of discrete grey forecasting model, getting the conclusion that there is a certain degree of feasibility and rationality of DGM to forecast the variation tendency of quality cost. Originality/value This paper perfects the theory of quality cost based on Juran’s characteristic curve and expands the scope of application of grey system theory.

Split spinal cord malformations in children

1998 ◽  
Vol 88 (1) ◽  
pp. 57-65 ◽  
Author(s):  
Yusuf Ersşahin ◽  
Saffet Mutluer ◽  
Sevgül Kocaman ◽  
Eren Demirtasş
Keyword(s):  
Spinal Cord ◽  
Neurological Deficits ◽  
Single Type ◽  
Type I ◽  
Type Ii ◽  
Content Type ◽  
Spinal Lesion ◽  
The Mean ◽  
Fine Print

Object. The authors reviewed and analyzed information on 74 patients with split spinal cord malformations (SSCMs) treated between January 1, 1980 and December 31, 1996 at their institution with the aim of defining and classifying the malformations according to the method of Pang, et al. Methods. Computerized tomography myelography was superior to other radiological tools in defining the type of SSCM. There were 46 girls (62%) and 28 boys (38%) ranging in age from less than 1 day to 12 years (mean 33.08 months). The mean age (43.2 months) of the patients who exhibited neurological deficits and orthopedic deformities was significantly older than those (8.2 months) without deficits (p = 0.003). Fifty-two patients had a single Type I and 18 patients a single Type II SSCM; four patients had composite SSCMs. Sixty-two patients had at least one associated spinal lesion that could lead to spinal cord tethering. After surgery, the majority of the patients remained stable and clinical improvement was observed in 18 patients. Conclusions. The classification of SSCMs proposed by Pang, et al., will eliminate the current chaos in terminology. In all SSCMs, either a rigid or a fibrous septum was found to transfix the spinal cord. There was at least one unrelated lesion that caused tethering of the spinal cord in 85% of the patients. The risk of neurological deficits resulting from SSCMs increases with the age of the patient; therefore, all patients should be surgically treated when diagnosed, especially before the development of orthopedic and neurological manifestations.

scholarly journals A surrogate-based generic classifier for Chinese TV series reviews

2017 ◽  
Vol 45 (2) ◽  
pp. 66-74
Author(s):  
Yufeng Ma ◽  
Long Xia ◽  
Wenqi Shen ◽  
Mi Zhou ◽  
Weiguo Fan
Keyword(s):  
Design Methodology ◽  
Experimental Result ◽  
Digital Information ◽  
Data Set ◽  
Content Type ◽  
Category Labels ◽  
Information Supply Chain ◽  
Feature Selection Techniques ◽  
Practical Implications

Purpose The purpose of this paper is automatic classification of TV series reviews based on generic categories. Design/methodology/approach What the authors mainly applied is using surrogate instead of specific roles or actors’ name in reviews to make reviews more generic. Besides, feature selection techniques and different kinds of classifiers are incorporated. Findings With roles’ and actors’ names replaced by generic tags, the experimental result showed that it can generalize well to agnostic TV series as compared with reviews keeping the original names. Research limitations/implications The model presented in this paper must be built on top of an already existed knowledge base like Baidu Encyclopedia. Such database takes lots of work. Practical implications Like in digital information supply chain, if reviews are part of the information to be transported or exchanged, then the model presented in this paper can help automatically identify individual review according to different requirements and help the information sharing. Originality/value One originality is that the authors proposed the surrogate-based approach to make reviews more generic. Besides, they also built a review data set of hot Chinese TV series, which includes eight generic category labels for each review.

Detection of COVID-19 cases through X-ray images using hybrid deep neural network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajit Nair ◽  
Santosh Vishwakarma ◽  
Mukesh Soni ◽  
Tejas Patel ◽  
Shubham Joshi
Keyword(s):  
Real Time ◽  
Binary Classification ◽  
Data Set ◽  
Content Type ◽  
X Ray ◽  
Internal Parameters ◽  
Average Accuracy ◽  
Chest X Ray ◽  
Multi Class Classification

Purpose The latest 2019 coronavirus (COVID-2019), which first appeared in December 2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It has had a devastating impact on daily lives, the public's health and the global economy. The positive cases must be identified as soon as possible to avoid further dissemination of this disease and swift care of patients affected. The need for supportive diagnostic instruments increased, as no specific automated toolkits are available. The latest results from radiology imaging techniques indicate that these photos provide valuable details on the virus COVID-19. User advanced artificial intelligence (AI) technologies and radiological imagery can help diagnose this condition accurately and help resolve the lack of specialist doctors in isolated areas. In this research, a new paradigm for automatic detection of COVID-19 with bare chest X-ray images is displayed. Images are presented. The proposed model DarkCovidNet is designed to provide correct binary classification diagnostics (COVID vs no detection) and multi-class (COVID vs no results vs pneumonia) classification. The implemented model computed the average precision for the binary and multi-class classification of 98.46% and 91.352%, respectively, and an average accuracy of 98.97% and 87.868%. The DarkNet model was used in this research as a classifier for a real-time object detection method only once. A total of 17 convolutionary layers and different filters on each layer have been implemented. This platform can be used by the radiologists to verify their initial application screening and can also be used for screening patients through the cloud. Design/methodology/approach This study also uses the CNN-based model named Darknet-19 model, and this model will act as a platform for the real-time object detection system. The architecture of this system is designed in such a way that they can be able to detect real-time objects. This study has developed the DarkCovidNet model based on Darknet architecture with few layers and filters. So before discussing the DarkCovidNet model, look at the concept of Darknet architecture with their functionality. Typically, the DarkNet architecture consists of 5 pool layers though the max pool and 19 convolution layers. Assume as a convolution layer, and as a pooling layer. Findings The work discussed in this paper is used to diagnose the various radiology images and to develop a model that can accurately predict or classify the disease. The data set used in this work is the images bases on COVID-19 and non-COVID-19 taken from the various sources. The deep learning model named DarkCovidNet is applied to the data set, and these have shown signification performance in the case of binary classification and multi-class classification. During the multi-class classification, the model has shown an average accuracy 98.97% for the detection of COVID-19, whereas in a multi-class classification model has achieved an average accuracy of 87.868% during the classification of COVID-19, no detection and Pneumonia. Research limitations/implications One of the significant limitations of this work is that a limited number of chest X-ray images were used. It is observed that patients related to COVID-19 are increasing rapidly. In the future, the model on the larger data set which can be generated from the local hospitals will be implemented, and how the model is performing on the same will be checked. Originality/value Deep learning technology has made significant changes in the field of AI by generating good results, especially in pattern recognition. A conventional CNN structure includes a convolution layer that extracts characteristics from the input using the filters it applies, a pooling layer that reduces calculation efficiency and the neural network's completely connected layer. A CNN model is created by integrating one or more of these layers, and its internal parameters are modified to accomplish a specific mission, such as classification or object recognition. A typical CNN structure has a convolution layer that extracts features from the input with the filters it applies, a pooling layer to reduce the size for computational performance and a fully connected layer, which is a neural network. A CNN model is created by combining one or more such layers, and its internal parameters are adjusted to accomplish a particular task, such as classification or object recognition.

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