Management accounting tools for failure prevention and risk management in the context of Indian innovative start-ups: a contingency theory approach

Purpose This paper aims to analyse various failures that Indian innovative start-ups (ISs) are exposed to and proposes interventions from management accounting tools (MATs) that can tackle their failure-causing risks. This paper justifies the applicability of contingency theory (CT) for applying MATs for failure prevention and risk management. Design/methodology/approach This paper uses multimethod research while undertaking two sequential studies. The methods include a Survey via semi-structured interviews of 51 specialists and media reports and the Delphi method. Findings Reasons for the failures of Indian ISs have been identified and grouped based on eight broad underlying risk factors. Appropriate MATs relevant to ISs have been identified and examined by relating them with the risk factors underlying failure. Applicability of CT is shown while using the MATs for failure prevention and risk management of ISs. Research limitations/implications This study is limited to the Indian context. Empirical validation of the applicability of MATs for each type of failure along the lifecycle stages of ISs needs to be undertaken. Practical implications Founders/owners of ISs can use this conceptual framework to tackle the risks underlying the failure of their firms. Policymakers can introduce appropriate policies to enhance the survival of ISs. Researchers can further explore the application of CT for failure prevention and risk management of ISs. Originality/value A conceptual framework has been developed relating failure-causing risk factors relevant to ISs and appropriate MATs, which justifies the applicability of CT.

Ensemble Neuroevolution-Based Approach for Multivariate Time Series Anomaly Detection

Multivariate time series anomaly detection is a widespread problem in the field of failure prevention. Fast prevention means lower repair costs and losses. The amount of sensors in novel industry systems makes the anomaly detection process quite difficult for humans. Algorithms that automate the process of detecting anomalies are crucial in modern failure prevention systems. Therefore, many machine learning models have been designed to address this problem. Mostly, they are autoencoder-based architectures with some generative adversarial elements. This work shows a framework that incorporates neuroevolution methods to boost the anomaly detection scores of new and already known models. The presented approach adapts evolution strategies for evolving an ensemble model, in which every single model works on a subgroup of data sensors. The next goal of neuroevolution is to optimize the architecture and hyperparameters such as the window size, the number of layers, and the layer depths. The proposed framework shows that it is possible to boost most anomaly detection deep learning models in a reasonable time and a fully automated mode. We ran tests on the SWAT and WADI datasets. To the best of our knowledge, this is the first approach in which an ensemble deep learning anomaly detection model is built in a fully automatic way using a neuroevolution strategy.

Torsion—Resistant Structures: A Nature Addressed Solution

The complexity of torsional load, its three-dimensional nature, its combination with other stresses, and its disruptive impact make torsional failure prevention an ambitious goal. However, even if the problem has been addressed for decades, a deep and organized treatment is still lacking in the actual research landscape. For this reason, this review aims at presenting a methodical approach to address torsional issues starting from a punctual problem definition. Accidents and breaks due to torsion, which often occur in different engineering fields such as mechanical, biomedical, and civil industry are considered and critically compared. More in depth, the limitations of common-designed torsion-resistant structures (i.e., high complexity and increased weight) are highlighted, and emerge as a crucial point for a deeper nature-driven analysis of novel solutions. In this context, an accurate screening of torsion-resistant bio-inspired unit cells is presented, taking inspiration specifically from plants, that are often subjected to the torsional effect of winds. As future insights, the actual state of technology suggests an innovative transposition to the industry: these unit cells could be prominently implied to develop novel metamaterials that could be able to address the torsional issue with a multi-scale and tailored arrangement.

Materials Selection for Failure Prevention

Materials selection is closely related to the objectives of failure analysis and prevention. This article briefly reviews the general aspects of materials selection as a concern in both proactive failure prevention during design and as a possible root cause of failed parts. Coverage is more conceptual, with general discussions on the following topics: design and failure prevention, materials selection in design, materials selection for failure prevention, and materials selection and failure analysis. Because materials selection is just one part of the design process, the overall concept of design is discussed. The article also describes the role of the materials engineer in the design and materials selection process. It provides information on the significance of materials selection in both the prevention and analysis of failures.