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

2022 ◽  
Vol 25 (1) ◽  
pp. 45-57
Author(s):  
Luis Fernández-Revuelta Pérez ◽  
Álvaro Romero Blasco
Keyword(s):  
Machine Learning ◽  
Cost Estimation ◽  
Data Science ◽  
Management Accounting ◽  
Machine Learning Techniques ◽  
Estimation Task ◽  
Learning Techniques ◽  
Potential Applications ◽  
The Cost ◽  
Science To Management

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.

scholarly journals Early Cost Estimation in Customized Furniture Manufacturing Using Machine Learning

2021 ◽  
Vol 11 (1) ◽  
pp. 28-33
Author(s):  
O. Kurasova ◽  
◽  
V. Marcinkevičius ◽  
V. Medvedev ◽  
B. Mikulskienė
Keyword(s):  
Machine Learning ◽  
Cost Estimation ◽  
Early Stage ◽  
Machine Learning Techniques ◽  
Design Stage ◽  
Early Design Stage ◽  
Furniture Manufacturing ◽  
Learning Techniques ◽  
Key Factor ◽  
The Cost

Accurate cost estimation at the early stage of a construction project is a key factor in the success of most projects. Many difficulties arise when estimating the cost during the early design stage in customized furniture manufacturing. It is important to estimate the product cost in the earlier manufacturing phase. The cost estimation is related to the prediction of the cost, which commonly includes calculation of the materials, labor, sales, overhead, and other costs. Historical data of the previously manufactured products can be used in the cost estimation process of the new products. In this paper, we propose an early cost estimation approach, which is based on machine learning techniques. The experimental investigation based on the real customized furniture manufacturing data is performed, results are presented, and insights are given.

Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

2020 ◽  
Vol 13 ◽  
Author(s):  
Ritu Khandelwal ◽  
Hemlata Goyal ◽  
Rajveer Singh Shekhawat
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Data Science ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Future Trends ◽  
Data Set ◽  
Learning Stage ◽  
Learning Techniques ◽  
Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

scholarly journals Statistical and Machine Learning-Driven Optimization of Mechanical Properties in Designing Durable HDPE Nanobiocomposites

Polymers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 3100
Author(s):  
Anusha Mairpady ◽  
Abdel-Hamid I. Mourad ◽  
Mohammad Sayem Mozumder
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Genetic Algorithm ◽  
Machine Learning Techniques ◽  
Hybrid Technique ◽  
Learning Techniques ◽  
Artificial Neural Network Ann ◽  
Major Factors ◽  
The Cost ◽  
Design Factors

The selection of nanofillers and compatibilizing agents, and their size and concentration, are always considered to be crucial in the design of durable nanobiocomposites with maximized mechanical properties (i.e., fracture strength (FS), yield strength (YS), Young’s modulus (YM), etc). Therefore, the statistical optimization of the key design factors has become extremely important to minimize the experimental runs and the cost involved. In this study, both statistical (i.e., analysis of variance (ANOVA) and response surface methodology (RSM)) and machine learning techniques (i.e., artificial intelligence-based techniques (i.e., artificial neural network (ANN) and genetic algorithm (GA)) were used to optimize the concentrations of nanofillers and compatibilizing agents of the injection-molded HDPE nanocomposites. Initially, through ANOVA, the concentrations of TiO2 and cellulose nanocrystals (CNCs) and their combinations were found to be the major factors in improving the durability of the HDPE nanocomposites. Further, the data were modeled and predicted using RSM, ANN, and their combination with a genetic algorithm (i.e., RSM-GA and ANN-GA). Later, to minimize the risk of local optimization, an ANN-GA hybrid technique was implemented in this study to optimize multiple responses, to develop the nonlinear relationship between the factors (i.e., the concentration of TiO2 and CNCs) and responses (i.e., FS, YS, and YM), with minimum error and with regression values above 95%.

Machine Learning Techniques for Internet of Things

2019 ◽  
pp. 160-180
Author(s):  
P. Priakanth ◽  
S. Gopikrishnan
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Independent Learning ◽  
Machine Learning Techniques ◽  
Analytical Models ◽  
Guided Learning ◽  
Learning Techniques ◽  
Learning Machine

The idea of an intelligent, independent learning machine has fascinated humans for decades. The philosophy behind machine learning is to automate the creation of analytical models in order to enable algorithms to learn continuously with the help of available data. Since IoT will be among the major sources of new data, data science will make a great contribution to make IoT applications more intelligent. Machine learning can be applied in cases where the desired outcome is known (guided learning) or the data is not known beforehand (unguided learning) or the learning is the result of interaction between a model and the environment (reinforcement learning). This chapter answers the questions: How could machine learning algorithms be applied to IoT smart data? What is the taxonomy of machine learning algorithms that can be adopted in IoT? And what are IoT data characteristics in real-world which requires data analytics?

Machine Learning Techniques for Internet of Things

2021 ◽  
pp. 1490-1506
Author(s):  
P. Priakanth ◽  
S. Gopikrishnan
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Independent Learning ◽  
Machine Learning Techniques ◽  
Analytical Models ◽  
Guided Learning ◽  
Learning Techniques ◽  
Learning Machine

The idea of an intelligent, independent learning machine has fascinated humans for decades. The philosophy behind machine learning is to automate the creation of analytical models in order to enable algorithms to learn continuously with the help of available data. Since IoT will be among the major sources of new data, data science will make a great contribution to make IoT applications more intelligent. Machine learning can be applied in cases where the desired outcome is known (guided learning) or the data is not known beforehand (unguided learning) or the learning is the result of interaction between a model and the environment (reinforcement learning). This chapter answers the questions: How could machine learning algorithms be applied to IoT smart data? What is the taxonomy of machine learning algorithms that can be adopted in IoT? And what are IoT data characteristics in real-world which requires data analytics?

scholarly journals Evaluation of the Risk of Recurrence in Patients with Local Advanced Rectal Tumours by Different Radiomic Analysis Approaches

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Alaa Khadidos ◽  
Adil Khadidos ◽  
Olfat M. Mirza ◽  
Tawfiq Hasanin ◽  
Wegayehu Enbeyle ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Locally Advanced ◽  
Textural Analysis ◽  
Machine Learning Techniques ◽  
Response To Treatment ◽  
Learning Techniques ◽  
Potential Applications ◽  
Analysis Models

The word radiomics, like all domains of type omics, assumes the existence of a large amount of data. Using artificial intelligence, in particular, different machine learning techniques, is a necessary step for better data exploitation. Classically, researchers in this field of radiomics have used conventional machine learning techniques (random forest, for example). More recently, deep learning, a subdomain of machine learning, has emerged. Its applications are increasing, and the results obtained so far have demonstrated their remarkable effectiveness. Several previous studies have explored the potential applications of radiomics in colorectal cancer. These potential applications can be grouped into several categories like evaluation of the reproducibility of texture data, prediction of response to treatment, prediction of the occurrence of metastases, and prediction of survival. Few studies, however, have explored the potential of radiomics in predicting recurrence-free survival. In this study, we evaluated and compared six conventional learning models and a deep learning model, based on MRI textural analysis of patients with locally advanced rectal tumours, correlated with the risk of recidivism; in traditional learning, we compared 2D image analysis models vs. 3D image analysis models, models based on a textural analysis of the tumour versus models taking into account the peritumoural environment in addition to the tumour itself. In deep learning, we built a 16-layer convolutional neural network model, driven by a 2D MRI image database comprising both the native images and the bounding box corresponding to each image.

Application of Machine Learning Techniques As a Means of Mooring Integrity Monitoring

2019 ◽  
Author(s):  
Jonathan M. Gumley ◽  
Hayden Marcollo ◽  
Stuart Wales ◽  
Andrew E. Potts ◽  
Christopher J. Carra
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Single Point ◽  
Original System ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Mooring Line ◽  
Artificial Noise ◽  
Data Set ◽  
Learning Techniques

Abstract There is growing importance in the offshore floating production sector to develop reliable and robust means of continuously monitoring the integrity of mooring systems for FPSOs and FPUs, particularly in light of the upcoming introduction of API-RP-2MIM. Here, the limitations of the current range of monitoring techniques are discussed, including well established technologies such as load cells, sonar, or visual inspection, within the context of the growing mainstream acceptance of data science and machine learning. Due to the large fleet of floating production platforms currently in service, there is a need for a readily deployable solution that can be retrofitted to existing platforms to passively monitor the performance of floating assets on their moorings, for which machine learning based systems have particular advantages. An earlier investigation conducted in 2016 on a shallow water, single point moored FPSO employed host facility data from in-service field measurements before and after a single mooring line failure event. This paper presents how the same machine learning techniques were applied to a deep water, semi taut, spread moored system where there was no host facility data available, therefore requiring a calibrated hydrodynamic numerical model to be used as the basis for the training data set. The machine learning techniques applied to both real and synthetically generated data were successful in replicating the response of the original system, even with the latter subjected to different variations of artificial noise. Furthermore, utilizing a probability-based approach, it was demonstrated that replicating the response of the underlying system was a powerful technique for predicting changes in the mooring system.

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