Benchmarking the failure assessment of perishable product supply chain using fault tree approach: insights from apple case study of India

PurposeThe purpose of this paper is to facilitate perishable product supply chain (PPSC) managers and practitioners to assess PPSC failure events. The paper proposed fault tree methodology for assessing failures associated with PPSC for evaluating the performance in terms of effective PPSC management adoption.Design/methodology/approachInitially, different failure events were identified from literature and semi-structured interviews from experts. Fault tree model was developed from the identified failure events. Probability of failure events was calculated using Poisson distribution based on the annual reports and interviews conducted from experts. Further, qualitative analysis – minimum cut sets (MCSs), structural importance coefficient (SIC) – and quantitative analysis – Birnbaum importance measure (BIM), criticality importance factor (CIF) and diagnosis importance factor (DIF) – were performed for ranking of failure events. In this study, fault tree development and analysis were conducted on apple supply chain to present the authenticity of this method for failure analysis.FindingsThe findings indicate that the failure events, given as failure at production and procurement (A2), that is, involvement of middleman (BE3), handling and packaging failure (BE4) and transportation failure (A3), hold the highest-ranking scores in analysis of PPSC using fault tree approach.Originality/valueThis research uses the modularization approach for evaluation of failure events of PPSC. This paper explores failures related to PPSC for efficient management initiatives in apple supply chain context. The paper also provides suggestion from managerial perspective with respect to each failure event.

The inventory as an importance factor in the administration of micro-businesses

Inventory is one that represents an investment of great importance for MICRO-BUSINESSES, this according to the relationship it has with other assets, and are of great importance for sales, as well as essential to achieve profit optimization. The objective given in this research study is to analyze the importance of inventories by means of a bibliographic investigation predicting the effectiveness of their administration in Micro-businesses. In the following investigation the methodology of documentary analysis was used, where the purpose is to be able to arrive at an analysis of the administration techniques to carry out an inventory and recognize the importance that should be given to it in Micro-businesses, theoretical foundations on the administration of inventories in Micro-businesses will be detailed, factors that are used to carry out inventories. It was concluded as the techniques to carry out the inventory were known and attached, the probabilities that are given to have successful operation, profit and growth in companies, which allows generating profits for micro-businesses.

Opening Area Effect of Core Type Shear Wall in Hospital Building with Highest Importance Factor

Multi-storey buildings have fascinated mankind from the beginning of civilization, their construction being initially for defence and subsequently for ecclesiastical purposes. These tall buildings because of its height, is affected by lateral forces due to wind or earthquake actions tends to snap the building in shear and push it over in bending. In general, the rigidity (i.e. Resistance to lateral deflection) and stability (i.e. Resistance to overturning moments) requirement become more important. Shear walls (Structural walls) contribute significant lateral stiffness, strength, and overall ductility and energy dissipation capacity. In many structural walls a regular pattern of openings has to be provided due to various functional requirements such as to accommodate doors, windows and service ducts. Such type of openings reduces the stiffness of the shear wall to some extent depending on the shape and size of the opening. In the present parametric study, efforts are made to investigate and critically assess the effects of various size of openings in shear walls on the responses and behaviours of multi-storey buildings also Opening Area Effect of Core Type Shear Wall In Hospital Building with Highest Importance Factor. Many G+20 storey prototype buildings with different types of openings in shear wall with and without incorporating the volume of shear wall reduced in the boundary elements are analysed using software Staad-Pro using Response spectrum method (1893-2016). Overall analysis shows that the most efficient case for this study has been HIF5. The hospital building can be survived with highest importance with the value of I = 1.5 as per IS 1893:2016 for opening area effect of core type shear wall. It can also be recommended that upto 25% opening will be possible without any seismic damage. Keywords: Shear wall, Opening Criteria, Highest Importance Factor, Multi-storey Hospital Building

Effect of Internal Audit on Expenditure Control in Mubi North Local Government Area

This study examines the effect of internal audit on expenditure control in Mubi North Local Government Area. This study employed the survey research design. From the population of the study 50 staffs were selected as a sample for this study. The instrument used for collecting data from the respondents is the questionnaire. The data gathered from the respondents was analyzed using simple percentage statistics and hypothesis was tested using chi-square. The finding shows internal audit in Mubi North LGA is very ineffective. This is because internal audit is not carry out effectively, internal audit has significant effect on expenditure control and among the factors militating affecting the effectiveness of internal audit in Mubi North LGA, low quality of staff is the most importance factor. The study therefore, recommends that there is need for top management of LGA to give high priority to internal audit, by putting in place strategies that will lead to effective practice of internal audit among the staff of the LGA which in turn will lead to effective expenditure control. Keywords: Audit, control, effect, expenditure & internal.

Roll Angle Estimation of a Motorcycle through Inertial Measurements

Currently, the interest in creating autonomous driving vehicles and progressively more sophisticated active safety systems is growing enormously, being a prevailing importance factor for the end user when choosing between either one or another commercial vehicle model. While four-wheelers are ahead in the adoption of these systems, the development for two-wheelers is beginning to gain importance within the sector. This makes sense, since the vulnerability for the driver is much higher in these vehicles compared to traditional four-wheelers. The particular dynamics and stability that govern the behavior of single-track vehicles (STVs) make the task of designing active control systems, such as Anti-lock Braking System (ABS) systems or active or semi-active suspension systems, particularly challenging. The roll angle can achieve high values, which greatly affects the general behavior of the vehicle. Therefore, it is a magnitude of the utmost importance; however, its accurate measurement or estimation is far from trivial. This work is based on a previous paper, in which a roll angle estimator based on the Kalman filter was presented and tested on an instrumented bicycle. In this work, a further refinement of the method is proposed, and it is tested in more challenging situations using the multibody model of a motorcycle. Moreover, an extension of the method is also presented to improve the way noise is modeled within this Kalman filter.

Decision-Level Fusion with a Pluginable Importance Factor Generator for Remote Sensing Image Scene Classification

Remote sensing image scene classification acts as an important task in remote sensing image applications, which benefits from the pleasing performance brought by deep convolution neural networks (CNNs). When applying deep models in this task, the challenges are, on one hand, that the targets with highly different scales may exist in the image simultaneously and the small targets could be lost in the deep feature maps of CNNs; and on the other hand, the remote sensing image data exhibits the properties of high inter-class similarity and high intra-class variance. Both factors could limit the performance of the deep models, which motivates us to develop an adaptive decision-level information fusion framework that can incorporate with any CNN backbones. Specifically, given a CNN backbone that predicts multiple classification scores based on the feature maps of different layers, we develop a pluginable importance factor generator that aims at predicting a factor for each score. The factors measure how confident the scores in different layers are with respect to the final output. Formally, the final score is obtained by a class-wise and weighted summation based on the scores and the corresponding factors. To reduce the co-adaptation effect among the scores of different layers, we propose a stochastic decision-level fusion training strategy that enables each classification score to randomly participate in the decision-level fusion. Experiments on four popular datasets including the UC Merced Land-Use dataset, the RSSCN 7 dataset, the AID dataset, and the NWPU-RESISC 45 dataset demonstrate the superiority of the proposed method over other state-of-the-art methods.

Stratified Particle Filter Monocular SLAM

This paper presents a solution to the problem of simultaneous localization and mapping (SLAM), developed from a particle filter, utilizing a monocular camera as its main sensor. It implements a novel sample-weighting idea, based on the of sorting of particles into sets and separating those sets with an importance-factor offset. The grouping criteria for samples is the number of landmarks correctly matched by a given particle. This results in the stratification of samples and amplifies weighted differences. The proposed system is designed for a UAV, navigating outdoors, with a downward-pointed camera. To evaluate the proposed method, it is compared with different samples-weighting approaches, using simulated and real-world data. The conducted experiments show that the developed SLAM solution is more accurate and robust than other particle-filter methods, as it allows the employment of a smaller number of particles, lowering the overall computational complexity.

Evaluation of the Floor Acceleration Amplification Demand of Instrumented Buildings

The floor acceleration amplification (FAA) factor is one of the most critical parameters in computing the equivalent seismic force of nonstructural component (NC). To evaluate the heightwise FAA distribution profile, the recorded acceleration response of the instrumented buildings was analyzed using the California Strong Motion Instrumentation Program (CSMIP) database. The FAA demands for three groups of buildings consisting of reinforced concrete, steel, and masonry buildings were analyzed. In each group, the buildings were classified into four subgroups according to their heights. Parabolic distribution profiles were suggested that could envelop most of the FAA data, as demonstrated by the processed results. An earthquake experience-based importance factor was suggested in terms of the percentage of the enveloped records. The obtained FAAs at the roof were generally larger than those in other levels. The percentile distributions of the roof acceleration amplification (RAA) were computed. The results showed that the roof FAA was underestimated in ASCE 7-16. The magnitudes of the FAA and the RAA correlated to the fundamental period of the building, which was considered by classifying the buildings according to the period ranges. The RAA profile against the period was obtained from a regression analysis. The developed FAA profile is expected to be useful in the seismic design of NCs, and it is expected to be adopted in future code provisions.