A Data Science Approach to Cost Estimation Decision Making - Big Data and Machine Learning

Cost estimation may become increasingly difficult, slow, and resource-consuming when it cannot be performed analytically. If traditional cost estimation techniques are usable at all under those circumstances, they have important limitations. This article analyses the potential applications of data science to management accounting, through the case of a cost estimation task posted on Kaggle, a Google data science and machine learning website. When extensive data exist, machine learning techniques can overcome some of those limitations. Applying machine learning to the data reveals non-obvious patterns and relationships that can be used to predict costs of new assemblies with acceptable accuracy. This article discusses the advantages and limitations of this approach and its potential to transform cost estimation, and more widely management accounting. The multinational company Caterpillar posted a contest on Kaggle to estimate the price that a supplier would quote for manufacturing a number of industrial assemblies, given historical quotes for similar assemblies. Hitherto, this problem would have required reverse-engineering the supplier’s accounting structure to establish the cost structure of each assembly, identifying non-obvious relationships among variables. This complex and tedious task is usually performed by human experts, adding subjectivity to the process. La estimación de costes puede resultar cada vez más difícil, lenta y consumidora de recursos cuando no puede realizarse de forma analítica. Cuando las técnicas tradicionales de estimación de costes son utilizadas en esas circunstancias se presentan importantes limitaciones. Este artículo analiza las posibles aplicaciones de la ciencia de datos a la contabilidad de gestión, a través del caso de una tarea de estimación de costes publicada en Kaggle, un sitio web de ciencia de datos y aprendizaje automático de Google. Cuando existen muchos datos, las técnicas de aprendizaje automático pueden superar algunas de esas limitaciones. La aplicación del aprendizaje automático a los datos revela patrones y relaciones no evidentes que pueden utilizarse para predecir los costes de nuevos montajes con una precisión aceptable. En nuestra investigación se analizan las ventajas y limitaciones de este enfoque y su potencial para transformar la estimación de costes y, más ampliamente, la contabilidad de gestión. La multinacional Caterpillar publicó un concurso en Kaggle para estimar el precio que un proveedor ofrecería por la fabricación de una serie de conjuntos industriales, dados los presupuestos históricos de conjuntos similares. Hasta ahora, este problema habría requerido una ingeniería inversa de la estructura contable del proveedor para establecer la estructura de costes de cada ensamblaje, identificando relaciones no obvias entre las variables. Esta compleja y tediosa tarea suele ser realizada por expertos humanos, lo que añade subjetividad al proceso.

Food for Thought: Connecting Zooplankton Science to Management in the San Francisco Estuary

[Abstracts are not associated with Essays. - the SFEWS Editors]

Servqual dalam Pelayanan Kelas pada Laboratorium Manajemen

This study aims to determine how the quality service class of Laboratory School of Business Management (SoBM), Bina Nusantara University to students majoring in management science to management courses (Quantitative Business Analysis). SoBM has 3 campuses spreading across West Jakarta and Tangerang, which are as much as 2 campuses in West Jakarta and 1 campus in Alam Sutra area (Tangerang). The research was only conducted on campus Alam Sutra (Tangerang) which is relatively new, consisted only 1 class (42 students) that the specialization is entrepreneurship management science courses. This study applied Servqual method which is a measure of customer satisfaction through gap analysis, developed by Parasuraman, Zeithaml, and Berry. Respondents in this study consisted of 42 students who all as population. The results showed that all Servqual dimensions have an unsatisfactory quality. This can be seen from all the negative gaps in dimension tangibles, reliability, responsiveness, assurance and empathy. From all the negative values, reliability dimension(-6.58) has a gap with the highest negative value or the least satisfactory quality.