IT integration, operations flexibility and performance: an empirical study

Purpose: This study examines the relationship between IT implementation and performance with manufacturing flexibility based on a sample drawn from a set of manufacturing firms. Design/methodology/approach: The relationships were analyzed using structural equations modelling (SEM) using EQS 6.2 software. Previously, an explanatory factor analysis confirmed one-dimensionality of the scales, Cronbach’s alpha was calculated to evaluate its internal consistency and a confirmatory factor analysis was run to observe scales’ validity. Findings: This research proves a significant positive and direct effect of IT implementation on operations performance with 4 out of 6 flexibility dimensions (Machine, Labour, Material handling and Volume). Mix and Routing flexibility dimensions show no significant impact on firm performance. Research limitations/implications: It is necessary to be cautious when generalizing this findings these findings, as service firms were not part of the sample even when statistical results prove robustness suggesting that the findings are quite reliable. Some flexibility dimensions show no significant impact in performance (Routing and Mix flexibility). This is consistent with the fact that these flexibility dimensions act as variability absorbers within the manufacturing process. Future research lines: Future studies can focus on determining further internal and environmental factors that affect operations flexibility according to specific sector characteristics. Originality/value: This research proves a significant positive and direct effect of IT implementation on operations performance. Results show not only the links between IT implementation and operations performance, but also the magnitude of every impact. The model considers IT integration as the degree of alignment that existing technology resources in a firm have with the business strategy, in terms of importance and support for this strategyPeer Reviewe

Contributions of anterior cingulate cortex and basolateral amygdala to decision confidence and learning under uncertainty.

The subjective sense of certainty, or confidence, in ambiguous sensory cues can alter the interpretation of reward feedback and facilitate learning. We trained rats to report the orientation of ambiguous visual stimuli according to a spatial stimulus-response rule that must be learned. Following choice, rats could wait a self-timed delay for reward or initiate a new trial. Waiting times increase with discrimination accuracy, demonstrating that this measure can be used as a proxy for confidence. Chemogenetic silencing of BLA shortens waiting times overall whereas ACC inhibition renders waiting times insensitive to confidence-modulating attributes of visual stimuli, suggesting contribution of ACC but not BLA to confidence computations. Subsequent reversal learning is enhanced by confidence. Both ACC and BLA inhibition block this enhancement but via differential adjustments in learning strategies and consistent use of learned rules. Altogether, we demonstrate dissociable roles for ACC and BLA in transmitting confidence and learning under uncertainty

Sourcing of Internal Auditing: An Empirical Study

In recent years, the scope of internal auditing has broadened considerably, increasing the importance of internal auditing as part of the organization’s management control structure. This expanding role has changed the demands being put on internal auditors. Their new role requires different skills and competencies, and many organizations now need to face the choice whether to develop these broader competencies internally or to outsource internalauditing to outside service providers.This paper studies the factors associated with organizations’ internal audit sourcing decisions, building from a previous study by Widener & Selto (1999; henceforth W&S). In their study, W&S used Transaction Cost Economics (TCE) to explain the organization of internal auditing. Our study seeks to replicate their results, using newly collected data from 66 companies headquartered in the Netherlands. Our findings are supportive of W&S. Like W&S, we find asset specificity and frequency (both individually and in interaction) to be significantly associated with sourcing decisions in a regression model that explains 65% (adjusted R2 = 0.63) of the variance in outsourced internal auditing. Additional analyses reinforce the importance of these TCE variables in explaining organizations’ internal auditing sourcing behaviour.transaction cost economics;internal auditing;make-or-buy decision

Adaptive Online Sequential ELM for Concept Drift Tackling

A machine learning method needs to adapt to over time changes in the environment. Such changes are known as concept drift. In this paper, we propose concept drift tackling method as an enhancement of Online Sequential Extreme Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by adding adaptive capability for classification and regression problem. The scheme is named as adaptive OS-ELM (AOS-ELM). It is a single classifier scheme that works well to handle real drift, virtual drift, and hybrid drift. The AOS-ELM also works well for sudden drift and recurrent context change type. The scheme is a simple unified method implemented in simple lines of code. We evaluated AOS-ELM on regression and classification problem by using concept drift public data set (SEA and STAGGER) and other public data sets such as MNIST, USPS, and IDS. Experiments show that our method gives higher kappa value compared to the multiclassifier ELM ensemble. Even though AOS-ELM in practice does not need hidden nodes increase, we address some issues related to the increasing of the hidden nodes such as error condition and rank values. We propose taking the rank of the pseudoinverse matrix as an indicator parameter to detect underfitting condition.Comment: Hindawi Publishing. Computational Intelligence and Neuroscience Volume 2016 (2016), Article ID 8091267, 17 pages Received 29 January 2016, Accepted 17 May 2016. Special Issue on "Advances in Neural Networks and Hybrid-Metaheuristics: Theory, Algorithms, and Novel Engineering Applications". Academic Editor: Stefan Hauf

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

A functional link neural network with modified cuckoo search for prediction tasks

The impact of temperature, relative humidity and ozone changes bring a sharp warming climate. These changes can cause extreme consequences such as floods, hurricanes, heat waves and droughts. Therefore, prediction of temperature and relative humidity is an important factor to measure the environmental changes. Neural network, especially the Multi-Layer Perceptron (MLP) which uses Back Propagation algorithm (BP) as a supervised learning method, has been successfully applied in various problems for meteorological prediction tasks. However, this architecture has still been facing problems which the convergence rate is very low due to the multi layering topology of the network. Thus, this research proposed an implementation of Functional Link Neural Network (FLNN) which composed of a single layer of tunable weight trained with the Modified Cuckoo Search algorithm (MCS). The proposed approach was used to predict the daily temperatures, relative humidity and ozone data. Extensive simulation results have been compared with standard MLP trained with the BP, FLNN with BP and FLNN with CS. Promising results have shown that the proposed model has successfully out performed 14% percentage compared to other network models with reduced prediction error and fast convergence rate