Paper Example. Data Analyzing

Organizations have been created to provide different kinds of services as well as products to their target market. However, for these organizations to maintain a high number of the client, they need to put in much focus on various issues. One of the most important aspects that an organization can rely on is data. Data provides an insight into the activities going on within an organization. Therefore, data analysis must be regularly conducted to inform the organization on various steps that it should undertake (Agrawal, Deshpande & Cecen, 2014). The process of data analysis involves various data science techniques. This paper will investigate an opportunity that calls for the use of a specific data science technique within the organization to address a concrete managerial problem.

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The organization focuses on the production of steel products. The organization has put in place various strategies so that it can succeed in its operations. One of the methods that the company has employed is to be divided into two divisions (Provost, & Fawcett, 2013). One of the divisions that have been developed within the organization deals with the production of products meant to ensure road safety. At the same time, the other one focuses on fabrication, which consists of the production of trailers and crane construction. These two divisions produce the products that should be developed in a series by the factory in less than 62 different machines. However, the products are not the same, meaning different machines will have to be used to ensure that all these products are developed. This element demands a lot of experience to provide the products that are designed effectively. In this case, the expertise and planning that is in need will be channeled into various parts to enable the production of different products.

The main problem within the organization is a lot of production data involved in the process. The process initially starts with customers or prospects request for a product, after getting the required information estimation is made by the calculator. This process is known as pre-calculation, which involves evaluation and calculations of the time needed to process the metal into the finished product. The required time and machines to be used determines the cost sales and sales price. When the product has become an order, the generation of the production order is done, and steel is processed into a product. These steps are clocked by the operator(s), ensuring that the finished product can be tracked. When the product is ready, the essential data is taken and stored in an ERP system, also known as MKG for analysis.

Various resources may be involved when solving a problem that has been experienced within the organization (Chen & Zhang, 2014). These resources have to be effectively utilized in the organization so that the goals will be achieved within the required time (Chen & Zhang, 2014). In this case, one of the resources that may be needed when developing the problem is the processing time (Chen & Zhang, 2014). The process time is the time that is required to carry out all the activities needed to solve the problem (Chen & Zhang, 2014). Therefore, the process time should be adequate so that all the planned activities are carried out effectively (Chen & Zhang, 2014).

Moreover, every activity should be allocated time that it should be accomplished, which will translate to the entire problem. Quality materials are essential as they are useful in solving specific issues in the organization (Albashrawi, & Lowell, 2016). In this case, quality materials have to be used when solving the problem so that the goals and objectives may be achieved easily. Finally, time is one of the precious resources required whenever an issue is being addressed (Chen & Zhang, 2014). Therefore, there has to be a calculated time that will be used to make sure that every action is carried out within the stipulated time to meet the goals and objectives (Chen & Zhang, 2014).

The feasibility related to solving this problem can be accomplished by using various methods. According to Albashrawi, & Lowell (2016), one of these methods is the Enterprise Resource Planning (ERP). ERP is a tool that involves the integration of the primary management business processes. In most cases, the ERP tool is dependable on the software as well as the technology used (Rossi, Walker and Musolesi, 2015). The rise of technology has come along with various impacts, and one of them is that it makes work easier (Albashrawi, & Lowell, 2016). Technology has made it easy to develop software. The presence of this software within the organization will help in solving the problem that has been experienced. This software will be able to handle various activities within the organization effectively (Albashrawi, & Lowell, 2016). In the process, this software, as they are being used within the organization, will enhance productivity by solving that had been identified (Albashrawi, & Lowell, 2016).

The ultimate goal is to optimize the production by the use of data we gather in the ERP system. The three types of optimization include; linear programming, integer linear programming and, nonlinear programming. Linear programming the sums of multiples of decision variables are the formulas for objectives and constraints. And integer linear programming is ideal when the decision variable takes only integer values, and finally, nonlinear programming is flexible and is solvable if sufficient regular status. Data analysis methods are distinguished by either being supervised or unsupervised. The approach to be used in the data analysis is determined by the question that we want to answer when the decision is made an appropriate supervised or unsupervised method for the data, research question, and analysis. Supervised methods have a specific target and require kinds of techniques, while unsupervised methods do not have a particular goal and do not assure essential results. Supervised methods solve regression, classification, and casual modeling, whereas unsupervised methods explain clustering, co-currence grouping, and profiling. Link prediction, data reduction, and similarity matching is solved by both ways.

Concluding, it is evident that many organizations have been created with the sole purpose of serving their customers. However, these organizations may fail to honor the needs of these customers due to the problems that may occur while discharging their duties and services. Some of these problems may provide an opportunity for the organization to use data science techniques. The organization relies on different forms of data for it to succeed in most of its operations. Therefore, data has to be analyzed effectively to ensure that specific problems within the organization are solved. In the case of Laura Metals, an organization that deals with the development of various products are experiencing particular issues. Solving this problem will require the action of all the stakeholders within the company so that their collective effort provides the required solution. Apart from the stakeholders, various resources will be needed to solve the problem. Some of these resources include the process time, quality materials, as well as estimated time. Also, data has a significant impact on human behavior in the sense that it will help people to registers that are important to the organization. Moreover, the personnel working within the organization should ensure that they enter the correct data because wrong data can lead to wrong decisions. Therefore, it is recommended that organizations should put the adequate focus on its data so because it will help it in carrying out various processes.

References

Agrawal, A., Deshpande, P. D., Cecen, A., Basavarsu, G. P., Choudhary, A. N., & Kalidindi, S.R. (2014). Exploration of data science techniques to predict fatigue strength of steel fromcomposition and processing parameters. Integrating Materials and ManufacturingInnovation, 3(1), 8. https://link.springer.com/article/10.1186/2193-9772-3-8

Albashrawi, M., & Lowell, M. (2016). Detecting financial fraud using data mining techniques: adecade review from 2004 to 2015. Journal of Data Science, 14(3), 553-569.https://www.researchgate.net/profile/Mousa_Albashrawi/publication/311557850_Detecting_Financial_Fraud_Using_Data_Mining_Techniques_A_Decade_Review_from_2004_to_2015/links/584c2d3908aecb6bd8c2e28e/Detecting-Financial-Fraud-Using-Data-Mining-Techniques-A-Decade-Review-from-2004-to-2015.pdf

Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques, andtechnologies: A survey on Big Data. Information sciences, 275, 314-347.https://www.sciencedirect.com/science/article/pii/S0020025514000346

Erl, T., Khattak, W., & Buhler, P. (2016). Big data fundamentals: concepts, drivers &techniques. Prentice-Hall Press.

Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about datamining and data-analytic thinking. "O'Reilly Media, Inc.."

Rossi, L., Walker, J., & Musolesi, M. (2015). Spatio-temporal techniques for user identificationby means of GPS mobility data. EPJ Data Science, 4(1), 1-16.https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-015-0049-x