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Open Access
Article
Publication date: 12 December 2023

Laura Lucantoni, Sara Antomarioni, Filippo Emanuele Ciarapica and Maurizio Bevilacqua

The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely…

Abstract

Purpose

The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.

Design/methodology/approach

Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.

Findings

The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.

Originality/value

The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

Details

International Journal of Quality & Reliability Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0265-671X

Keywords

Abstract

Details

Rutgers Studies in Accounting Analytics: Audit Analytics in the Financial Industry
Type: Book
ISBN: 978-1-78743-086-0

Book part
Publication date: 21 October 2019

Jordan French

This chapter used empirical data from five developed markets and five emerging markets to perform an examination of anomalies using common financial economic approaches along with…

Abstract

This chapter used empirical data from five developed markets and five emerging markets to perform an examination of anomalies using common financial economic approaches along with more innovative econometric models. Of the methodologies used to test for anomalies, the data-driven panel and quantile regressions were empirically found to be better suited over the traditionally common approaches to describe the non-linear, switching behavior of the anomalies. In the developed markets, the statistically significant small firms (size) had the highest average returns. In the developing markets, the lower price-to-earnings (P/E) ratios (value) had the highest average returns. In addition, the research found (1) a small country effect, (2) sales had a negative relationship with returns, and (3) a lower (higher) book-to-market (B/M) was associated with higher returns in the developed (developing) markets, indicating investors received a higher premium for growth (value) equities. The semi-strong form of the efficient market hypothesis was also found to be violated. The anomalies’ behavior varied between sorted portfolios, industries, and developed to emerging markets; though it was found to be consistent through time (not disrupted by bear or bull markets).

Details

Disruptive Innovation in Business and Finance in the Digital World
Type: Book
ISBN: 978-1-78973-381-5

Keywords

Article
Publication date: 13 March 2023

Anagha Vaidya and Sarika Sharma

Course evaluations are formative and are used to evaluate learnings of the students for a course. Anomalies in the evaluation process can lead to a faulty educational outcome…

Abstract

Purpose

Course evaluations are formative and are used to evaluate learnings of the students for a course. Anomalies in the evaluation process can lead to a faulty educational outcome. Learning analytics and educational data mining provide a set of techniques that can be conveniently applied to extensive data collected as part of the evaluation process to ensure remedial actions. This study aims to conduct an experimental research to detect anomalies in the evaluation methods.

Design/methodology/approach

Experimental research is conducted with scientific approach and design. The researchers categorized anomaly into three categories, namely, an anomaly in criteria assessment, subject anomaly and anomaly in subject marks allocation. The different anomaly detection algorithms are used to educate data through the software R, and the results are summarized in the tables.

Findings

The data points occurring in all algorithms are finally detected as an anomaly. The anomaly identifies the data points that deviate from the data set’s normal behavior. The subject which is consistently identified as anomalous by the different techniques is marked as an anomaly in evaluation. After identification, one can drill down to more details into the title of anomalies in the evaluation criteria.

Originality/value

This paper proposes an analytical model for the course evaluation process and demonstrates the use of actionable analytics to detect anomalies in the evaluation process.

Details

Interactive Technology and Smart Education, vol. 21 no. 1
Type: Research Article
ISSN: 1741-5659

Keywords

Open Access
Article
Publication date: 15 August 2022

Zhuo (June) Cheng and Jing (Bob) Fang

This study examines the effect of stock liquidity on the magnitude of the accrual anomaly.

1079

Abstract

Purpose

This study examines the effect of stock liquidity on the magnitude of the accrual anomaly.

Design/methodology/approach

This paper examines the relation—both time-series and cross-sectional—between stock liquidity and the magnitude of the accrual anomaly and use the 2001 minimum tick size decimalization as a quasi-experiment to establish causality.

Findings

There is both cross-sectional and time-series evidence that stock liquidity is negatively related to the magnitude of the accrual anomaly. Moreover, the extent to which investors overestimate the persistence of accruals decreases with stock liquidity. Results from a difference-in-differences analysis conducted using the 2001 minimum tick size decimalization as a quasi-experiment suggest that the effect of stock liquidity on the accrual anomaly is causal. The findings of this study are consistent with the enhancing effect of stock liquidity on pricing efficiency.

Originality/value

The study's findings are well aligned with the mispricing-based explanation for the accrual anomaly, suggesting that the improvement in market-wide stock liquidity drives the contemporaneous decline in the magnitude of the accrual anomaly, at least to a great extent.

Details

China Accounting and Finance Review, vol. 25 no. 1
Type: Research Article
ISSN: 1029-807X

Keywords

Article
Publication date: 3 August 2015

Saumya Ranjan Dash and Jitendra Mahakud

This paper aims to investigate whether the use of conditional and unconditional Fama and French (1993) three-factor and Carhart (1997) four-factor asset pricing models (APMs…

1671

Abstract

Purpose

This paper aims to investigate whether the use of conditional and unconditional Fama and French (1993) three-factor and Carhart (1997) four-factor asset pricing models (APMs) captures the role of asset pricing anomalies in the context of emerging stock market like India.

Design/methodology/approach

The first step time series regression approach has been used to drive the risk-adjusted returns of individual securities. For examining the predictability of firm characteristics or asset pricing anomalies on the risk-adjusted returns of individual securities, the panel data estimation technique has been used.

Findings

Fama and French (1993) three-factor and Carhart (1997) four-factor model in their unconditional specifications capture the impact of book-to-market price and liquidity effects completely. When alternative APMs in their conditional specifications are tested, the importance of medium- and long-term momentum effects has been captured to a greater extent. The size, market leverage and short-term momentum effects still persist even in the case of alternative unconditional and conditional APMs.

Research limitations/implications

The empirical analysis does not extend for different market scenarios like high and low volatile market or good and bad macroeconomic environment. Because of the constraint of data availability, the authors could not include certain important anomalies like net operating assets, change in gross profit margin, external equity and debt financing and idiosyncratic risk.

Practical implications

Although the active investment approach in stock market shares a common ground of semi-strong form of market efficiency hypothesis which also supports the presence of asset pricing anomalies, less empirical evidence has been explored in this regard to support or repute such belief of practitioners. Our empirical findings make an attempt in this regard to suggest certain anomaly-based trading strategy that can be followed for active portfolio management.

Originality/value

From an emerging market perspective, this paper provides out-of-sample empirical evidence toward the use of conditional Fama and French three-factor and Carhart four-factor APMs for the complete explanation of market anomalies. This approach retains its importance with respect to the comprehensiveness of analysis considering alternative APMs for capturing unique effects of market anomalies.

Details

Journal of Asia Business Studies, vol. 9 no. 3
Type: Research Article
ISSN: 1558-7894

Keywords

Article
Publication date: 7 January 2021

Wang Jianhong and Wang Yanxiang

The purpose of this paper is to deal with the anomaly detection problem in multi-unmanned aerial vehicles (UAVs) formation that can be transformed to identify some unknown…

Abstract

Purpose

The purpose of this paper is to deal with the anomaly detection problem in multi-unmanned aerial vehicles (UAVs) formation that can be transformed to identify some unknown parameters; a more general nonlinear dynamical model for each UAV is considered to include two terms. Due to an unknown parameter corresponding to the normal or abnormal state for each UAV, the bias-compensated approach is proposed to obtain the unbiased parameter estimation. Meanwhile, the biased error and accuracy analysis are also given in case of strict statistical description of the uncertainty or white noise. To relax this strict statistical description on external noise, an analytic center approach is proposed to identify the unknown parameters in presence of bounded noise, such that two inner and outer ellipsoidal approximations are constructed to include the membership set. To be precise, this paper is regarded as one extension and summary for the author’s previous research on the anomaly detection in multi-UAV formation. Finally, one simulation example is given to confirm the theoretical results.

Design/methodology/approach

Firstly, one extended nonlinear relation is constructed to embody the mutual relationship of UAVs. Secondly, to obtain the unbiased parameter estimations, the bias-compensated approach is applied to achieve it under the condition of white noise. Thirdly, in case of unknown but bounded noise, an analytic center approach is proposed to deal with this special case. Without loss of generality, the author thinks this paper can be used as one extension and summary for research on multi-UAVs formation anomaly detection.

Findings

An anomaly detection problem in multi-UAVs formation can be transformed into a problem of nonlinear system identification, and in modeling the nonlinear dynamical model for each UAV, two terms are considered simultaneously to embody the mutual relationships with other nearest UAV.

Originality/value

To the best knowledge of the authors, this problem of the anomaly detection problem in multi-UAVs formation is proposed by the authors’ previous work, and the problem of multi-UAVs formation anomaly detection can be transferred into one problem of parameter identification. In case of unknown but bounded noise, an analytic center approach is proposed to identify the unknown parameters, which correspond to achieve the goal of the anomaly detection.

Details

Aircraft Engineering and Aerospace Technology, vol. 93 no. 1
Type: Research Article
ISSN: 1748-8842

Keywords

Article
Publication date: 1 May 2018

Meher Shiva Tadepalli and Ravi Kumar Jain

Market efficiency suggests that price of the security must reflect its intrinsic value by impounding all the available and accessible information. Asset pricing in capital markets…

Abstract

Purpose

Market efficiency suggests that price of the security must reflect its intrinsic value by impounding all the available and accessible information. Asset pricing in capital markets has been an exceptionally dynamic area of scholarly research and is considered as a barometer for assessing market efficiency. This phenomenon was very well explained by several market pricing models and theories over the last few decades. However, several anomalies, which cannot be explained by the traditional asset pricing models due to seasonal and psychological factors, were observed historically. The same has been studied by several researchers over the years and is well captured in the literature pertaining to market asset pricing. The purpose of this paper is to revisit the research studies related to a few asset pricing anomalies, collectively referred to as “calendar anomalies”, such as – day-of-the-week, turn-of-the-month, turn-of-the-year and the holiday effects. In this pursuit, a thorough survey of literature in this area, published over the last 80 years (from 1934 to 2016) across 24 prominent journals, has been made and presented in a comprehensive, structured and chronologically arranged major findings and learnings. This literature survey reveals that the existing literature do provide a great depth of understanding around these calendar anomalies often with reference to specific markets, the size of the firm and investor type. The paper also highlights a few aspects where the existing literature is silent or provides little support leaving a gap that needs to be addressed with further research in this area.

Design/methodology/approach

The goal of the study requires a comprehensive review of the past literature related to calendar anomalies. As a consequence, to identify papers which sufficiently represent the area of study, the authors examined the full text of articles within EBSCOHost, Elsevier-Science direct, Emerald insight and JSTOR databases with calendar anomalies related keywords for articles published since inception. Further, each article was classified based on the anomaly discussed and the factors used to sub-categorize the anomaly. Once all the identified fields were populated, we passed through another article by constantly updating the master list till all the 99 articles were populated.

Findings

It is also important to understand at this juncture that most of the papers surveyed discuss the persistence of the asset pricing anomalies with reference to the developed markets with a very few offering evidences from emerging markets. Thus leaving a huge scope for further research to study the persistence of asset pricing anomalies, the degree and direction of the effect on asset pricing among emerging markets such as India, Russia, Brazil vis-a-vis the developed markets. Further, regardless of the markets with reference to which the study is conducted, the research so far appears to have laid focus only on the overall market returns derived from aggregate market indices to explain the asset pricing anomalies. Thus leaving enough scope for further research to study and understand the persistence of these anomalies with reference to various strategic, thematic and sectoral indices in various markets (developed, emerging and underdeveloped countries) across different time periods. It will be also interesting to understand how, some or all of, these established asset pricing anomalies behave over a certain time period when markets move across the efficiency maturity model (from weak form to semi-strong to strong form of efficiency).

Originality/value

The main purpose of the study entails a detailed review of all the past literature pertinent to the calendar anomalies. In order to explore the prior literature that sufficiently captures the research area, various renowned databases were examined with keywords related to the calendar anomalies under scope of current study. Furthermore, based on the finalized articles, a comprehensive summary table was populated and provided in the Appendix which gives a snapshot of all the articles under the current assessment. This helps the readers of the article to directly relate the findings of each article with its background information.

Details

American Journal of Business, vol. 33 no. 1/2
Type: Research Article
ISSN: 1935-5181

Keywords

Article
Publication date: 5 March 2018

Xu Kang and Dechang Pi

The purpose of this paper is to detect the occurrence of anomaly and fault in a spacecraft, investigate various tendencies of telemetry parameters and evaluate the operation state…

Abstract

Purpose

The purpose of this paper is to detect the occurrence of anomaly and fault in a spacecraft, investigate various tendencies of telemetry parameters and evaluate the operation state of the spacecraft to monitor the health of the spacecraft.

Design/methodology/approach

This paper proposes a data-driven method (empirical mode decomposition-sample entropy-principal component analysis [EMD-SE-PCA]) for monitoring the health of the spacecraft, where EMD is used to decompose telemetry data and obtain the trend items, SE is utilised to calculate the sample entropies of trend items and extract the characteristic data and squared prediction error and statistic contribution rate are analysed using PCA to monitor the health of the spacecraft.

Findings

Experimental results indicate that the EMD-SE-PCA method could detect characteristic parameters that appear abnormally before the anomaly or fault occurring, could provide an abnormal early warning time before anomaly or fault appearing and summarise the contribution of each parameter more accurately than other fault detection methods.

Practical implications

The proposed EMD-SE-PCA method has high level of accuracy and efficiency. It can be used in monitoring the health of a spacecraft, detecting the anomaly and fault, avoiding them timely and efficiently. Also, the EMD-SE-PCA method could be further applied for monitoring the health of other equipment (e.g. attitude control and orbit control system) in spacecraft and satellites.

Originality/value

The paper provides a data-driven method EMD-SE-PCA to be applied in the field of practical health monitoring, which could discover the occurrence of anomaly or fault timely and efficiently and is very useful for spacecraft health diagnosis.

Details

Aircraft Engineering and Aerospace Technology, vol. 90 no. 2
Type: Research Article
ISSN: 1748-8842

Keywords

Article
Publication date: 1 April 2006

Chein‐I Chang and Mingkai Hsueh

The paper aims to characterize anomaly detection in hyperspectral imagery.

Abstract

Purpose

The paper aims to characterize anomaly detection in hyperspectral imagery.

Design/methodology/approach

This paper develops an adaptive causal anomaly detector (ACAD) to investigate several issues encountered in hyperspectral image analysis which have not been addressed in the past. It also designs extensive synthetic image‐based computer simulations and real image experiments to substantiate the work proposed in this paper.

Findings

This paper developed an ACAD and custom‐designed computer simulations and real image experiments to successfully address several issues in characterizing anomalies for detection, which are – first, how large size for a target to be considered as an anomaly? Second, how an anomaly responds to its proximity? Third, how sensitive for an anomaly to noise? Finally, how different anomalies to be detected? Additionally, it also demonstrated that the proposed ACAD can be implemented in real time processing and implementation.

Originality/value

This paper is the first work on investigation of several issues related to anomaly detection in hyperspectral imagery via extensive synthetic image‐based computer simulations and real image experiments. In addition, it also develops a new developed an ACAD to address these issues and substantiate its performance.

Details

Sensor Review, vol. 26 no. 2
Type: Research Article
ISSN: 0260-2288

Keywords

1 – 10 of over 10000