Use of Data Mining For Inventory Forecasting

Data Analysis

In order to focus on the data analysis section for this research study, the survey questionnaire are made so that there could be analysis of the use of data mining for inventory forecasting. However, the questionnaire is designed and the survey is taken from 50 marketing managers of the companies using data mining software or tool for their inventory forecasting and management. The interviews are also taken so that there could be better analysis of the companies that how they are getting benefits through the use of data mining tool (Lavrač et al., 2001).

For the data analysis process, the rapid miner tool is used in the data mining because it could predict the values and do the analysis in the better way. Thus, the efficient tool example, data mining is done in the Microsoft excel, so that there could be appropriate discussion of using the by using the appropriate instrument and there could be the evaluation of the people responses that what they feel about the tool. For the data analysis, it is important to notice that primary data is collected for the primary analysis, which is also based on the quantitative method. Therefore, the data is analyzed by using the statistical method of the data mining, using Microsoft Excel to analyze the quantitative data (Waller and Fawcett, 2013).

In order to analyze the data, five questions are made on the five-likert-scale so that there could be better evaluation of the responses. Close-ended questions are made that focus on the marketing managers responses. All five questions are related to the data mining software or tool for inventory forecasting. There are five questions, which have five options, including agree, disagree, neutral, disagree, and strongly disagree.

First question is “do you believe that use of data mining tool is helpful tool for inventory forecasting in organizations?”, second question is “Do you believe that there are some limitations of using data mining tool for inventory forecasting?. However, third question is “Do you believe that data mining tool for inventory forecasting may have some errors in the analysis?”, forth question is “Do you believe through using data mining tool for inventory forecasting, can help organizations to focus on analyze data, notice patterns and devising rules etc.?”. Consequently, fifth question is “Do you believe through data mining tool for inventory forecasting, organizations can do better predictions about inventory process in future?”

Several data mining applications focused on the supply chain inventory management; however, for the analysis of this thesis, the researchers have used the rapid miner tool so that there could be the better use of the business intelligence and the data warehouse. The companies are getting benefits from the mining technology because they can manage the inventory processes in the better way. Thus, through the analysis or evaluation of the research question, it is anticipated that the companies are getting better information through using the software, as there can be the up-to-date information and better inventory management decisions by the companies or the managers (Kusiak and Smith, 2007).

Through the evaluation of the managerial thinking, it is known that there are several managers who believe that the evaluation can be successful through the data mining tool. For this research, the methodology is designed in an effective way, as though the qualitative analysis of the data it is known that companies or organizations can get the better results, in order to know the current situation of the investors and in order to know that if the inventory is out-of-stock forecasts. There are also evaluation of the store and product level, moreover, the managers can make effective decision for the aggregate sales patterns and there can be the quality store-cluster.

Through analyzing the results it is clear that through analysis of the mining tool there are benefits to the companies because the decision trees are given the chance to make the better decision for the companies and the management, which are also based on the accuracy measurement. Through the mining tool, there could be better analysis of the reporting and the managers or the decision makers can also focus on the customization and collaboration with the companies (Brandenburg et al., 2014). Consequently, there are neural network mining algorithms that their companies are applying and through which there can be benefits from the past two decades because companies are getting the better environment of financial and there are more powerful or the environmental decisions in order to support the powerful communication, through the effective selection for investors (Elmaghraby and Keskinocak., 2003).

Moreover, through the interview with the managers or the marketing managers of the companies, it is analyzed that there are benefits to the companies because for the inventory and other supply chain decisions companies are making better capital market theory and there are more benefits of the financial analysis. The mining tool and its strategies giving the companies to make better decisions so through focused on the important financial subject. There is also the fundamental information in regards to the future stock returns (Koh and Tan, 2011).

Moreover, the companies are getting benefits of the data mining not only in the companies but there are better benefits in the market because the stakeholders can be given better interest rates and exchange rates. Consequently, the results have shown that there are growth rates in the industrial production because the consumers are given better prices in the market, in order to increase the market efficiency (Sandborn, Mauro and Knox, 2007).

Regarding focus on the results, it is evaluated that companies are satisfied through data mining process because they are getting maximum benefits. Consequently, companies and managers seem to be satisfied through the result of the data mining in the companies, as they show the positive attitude towards or gave the positive responses on the questionnaires that are given to them. There were five questions in the questionnaires, as in the response to the first question that is, “do you believe that use of data mining tool is the helpful tool for inventory forecasting in organizations?” More than 60% of the marketing managers said that they are satisfied from the result or use of data mining for inventory forecasting in the company because it helps to tell them about the future of the inventory management. Most of the employees and managers believe that it is the helpful tool as it focuses on the aspects that are related to the future (Rygielski, Wang and Yen, 2002).

Managers in the companies believe that it is the computing process, which helps to discover the patterns in the large data sets. There can be other value able result, which could be obtained by the extract data patterns through the data mining process. Data mining is the very helpful tool because it helps to anticipate the results that can be there in the future as it saves the company overall goal for the future, through telling that what going to be wrong in the future. The data mining process provides the valuable and the extract information in the different managerial or management aspects because it is not easy for the companies to manage the data pre-processing according to the decision support system. Data mining process is also helpful in the business intelligence because it also supports the aspects of the machine learning (Vadhavkar and Gupta, 1998).

There could be other benefits, which the managers in the companies have analyzed. Business or data mining could be explained as the artificial intelligence that can be very supportive for the companies because its focuses on the aspects of the semi-automatic and the other relevant database techniques. Moreover, managers told that there could be larger population data set of the companies that can be only managed or benefited by the aspects that are related to the business or data mining process. There can be several challenges related to the inventory management; however, if the management or the data is not focused in an effective way, then there could be the issues while managing the processes and companies can face the losses or issues in the future. The processes related to data mining need to be focused in the effective ways so that there could be the better decision making regarding the business or inventory management system. Moreover, the result that is obtained by the marketing manager, tell clearly that most of the manager appreciate the system of data mining process (Wu and Olson, 2013).

In the response of the second question that is, “do you believe that there are some limitations of using data mining tool for inventory forecasting?” More than 40% of the marketing managers said, that they agree because they think that the process also have some of the limitations from the result or use of data mining for inventory forecasting in the company. Because, There can be several challenges related to the data mining processes, as mangers regarding this question said that there are challenges because they may be facing the issues of the data mining. However, the companies may face the challenges of the inventory forecasting, there could be-be extracting information because of the large volumes of data. There could be issues in the real-world data or there could be real-world problems as known through the results.  Moreover, many of the marketing managers believe that the data could be incomplete and noisy if there is issue in the data. Example there could be issues in the heterogeneous data, if the data is of the large quantities, there could be issues relating to the inaccurate or unreliable results.

In the response of the third question that is, “do you believe that data mining tool for inventory forecasting may have some errors in the analysis?” More than 30% of the marketing managers said that they are agree because there can be human and other errors. in the use of data mining for inventory forecasting in the company. There could be the errors of the instruments or there can be other error, which can be known as the human errors. However, for the data mining in the inventory process, the mining data may not be successful in the retail chain, as it could be the incorrect data. The manager often faces the issues in the data mining process if the data is incomplete or there can be human error. In this way, the results that are obtained are said to be the results, which are noisy. Data mining could be explained as the really challenging, especially in the computing environments because there can be issues that are relating to the store. Moreover, all the data need to be analyzed according to the mining demands so that there could be better results in the development of tools and the algorithms should enable mining. The managers believe that the distributed data may not provide the effective results because there can be different errors, regarding human or machine errors (Renesse, Birman and Vogels, 2003).

In the response of the fourth question that is, “do you believe through using data mining tool for inventory forecasting, can help organizations to focus on analyze data, notice patterns and devising rules etc.?” More than 60% of the marketing managers said that they agree from the result or use of data mining for inventory forecasting in the company because, they think that there could be prediction regarding the future and there could be anticipation of the better results (Jacobs and Bendoly, 2003).

The general perception as focused on the market hypothesis and through the understanding of the interview with the general public and marketing managers, it is analyzed that there are companies or the managers that are hoping to do the forecast future returns in the better way with the help of the process of data mining. Moreover, there could be other empirical issues, that need to be focused on the managers, so that the information can be obtained in a correct manner. The managers have analyzed that there are benefits of future stock returns if the data mining is done in an effective way for the benefits of the management of the inventory and in order to, focus on the interest rates. There are expectations to increase the monetary growth rates through the better data analysis in order to support the assumption (Sánchez and Pérez, 2005).

In the response of the fifth question that is, “do you believe through data mining tool for inventory forecasting, organizations can do better predictions about inventory process in future?” More than 60% of the marketing managers said that they are satisfied from the result or use of data mining for inventory forecasting in the company because, they think that there could be better decision making through the analysis of this tool. Managers can make better strategies in future. Consequently, companies have analyzed the importance of the data mining because it could tell the companies that what going to be wrong in the future.. Data mining process is also helpful in the business intelligence because it also supports the aspects of the machine learning (Min and Zhou, 2002).

As a result, it is analyzed through the survey, which is done from 50 marketing managers that the data mining is the better analysis of the companies that how they are getting benefits through the use of data mining tool. Moreover, the quantitative method or the statistical method of the data mining is used by the companies, and this research used the Microsoft Excel to analyze the quantitative data to analyze that the data-mining tool is helpful tool for inventory forecasting in organizations (Liao, Chu and Hsiao, 2012).

Companies are using the several data mining applications focused on the supply chain inventory management but for this research there is the involvement of the rapid miner tool so that there could be the better evaluation of the companies better inventory management decisions by the companies or the managers (Auramo, Kauremaa and Tanskanen, 2005).

It is analyzed in the results that the managers can make effective decision for the aggregate sales patterns because these companies are also focused on or making better capital market theory so that they can be more powerful in the future. Thus, there are better environmental decisions in order to support the powerful communication. The managers have told in the interview that there are better environment of financial situation in the companies because the support is there for the neural network mining algorithms. Moreover, there are other several challenges that are known in the results and related to the data mining processes. There are several challenges of the inventory forecasting that can be relates to the real-world data. The marketing managers in the company think that there can be errors of the instruments or data mining process (Kohavi, Rothleder and Simoudis, 2002).

Data Mining Technique (Linear Regression)

For the data mining technique linear regression could be effective; as the linear regression is used for the common data mining technique; however, there is focus to predict the future so that the value of variable or the cost or requirement for the future inventory can be analyzed. Consequently, the linear regression is focus to find the linear relationship that is between one variable and with other variables. Linear regression could be defined as the straight line approximates the data set, the value of the future inventory costing through data mining could be forecasted through regression (Bhardwaj and Pal, 2012). The use of data mining is one independent variable, which can assess the forecast through using following formula:

Here, y is dependent variable, x is independent variable and a and b is line coefficient.

The present study is focused on the requirement for the linear regression so that there could be efficient inventory forecasting for the future (Rossel and Behrens, 2010).

Through regression analysis, there can be effective demand forecasting and it can also improve the managerial decision-making regarding the inventory forecasting for the future. Moreover, the data mining software tool is helpful to find out the extract values of variables, as the questionnaires is designed, in order to predict the future values the variables for the inventory forecasting (Kirkos, Spathis and Manolopoulos, 2007).

References;

• Auramo, J., Kauremaa, J. and Tanskanen, K. (2005) ‘Benefits of IT in supply chain management: an explorative study of progressive companies. ‘, International Journal of Physical Distribution & Logistics Management, vol. 35, no. 2, pp. 82-100.
• Bhardwaj, B.K. and Pal, S. (2012) ‘Data Mining: A prediction for performance improvement using classification.’, arXiv preprint arXiv, vol. 1, no. 1, pp. 1201-3418.
• Brandenburg, M., Govindan, K., Sarkis, J. and Seuring, S. (2014) ‘Quantitative models for sustainable supply chain management: Developments and directions.’, European Journal of Operational Research, vol. 233, no. 2, pp. 299-312.
• Elmaghraby, W. and Keskinocak., P. (2003) ‘ynamic pricing in the presence of inventory considerations: Research overview, current practices, and future directions.’, Management science , vol. 49, no. 10, pp. 1287-1309.
• Jacobs, F.R. and Bendoly, E. (2003) ‘Enterprise resource planning: developments and directions for operations management research.’, European Journal of Operational Research, vol. 146, no. 2, pp. 233-240.
• Kirkos, E., Spathis, C. and Manolopoulos, Y. (2007) ‘Data mining techniques for the detection of fraudulent financial statements’, Expert systems with applications , vol. 32, no. 4, pp. 995-1003.
• Kohavi, R., Rothleder, N.J. and Simoudis, E. (2002) ‘Emerging trends in business analytics.’, Communications of the ACM, vol. 45, no. 8, pp. 45-48.
• Koh, H.C. and Tan, G. (2011) ‘Data mining applications in healthcare.’, Journal of healthcare information management, vol. 19, no. 2, p. 65.
• Kusiak, A. and Smith, M. (2007) ‘Data mining in design of products and production systems.’, Annual Reviews in Control, vol. 3, no. 1, pp. 147-156.
• Lavrač, N., Motoda, H., Fawcett, T., Holte, R., Langley, P. and Adriaans., P. (2001) ‘Introduction: Lessons learned from data mining applications and collaborative problem solving’, Machine learning, vol. 57, no. 1, pp. 13-34.
• Liao, S.-H., Chu, P.-H. and Hsiao, P.-Y. (2012) ‘ata mining techniques and applications–A decade review from 2000 to 2011.’, Expert systems with applications, vol. 39, no. 12, pp. 11303-11311.
• Min, H. and Zhou, G. (2002) ‘Supply chain modeling: past, present and future’, Computers & industrial engineering, vol. 43, no. 1, pp. 231-249.
• Renesse, R.V., Birman, K.P. and Vogels, W. (2003) ‘Astrolabe: A robust and scalable technology for distributed system monitoring, management, and data mining.’, ACM transactions on computer systems (TOCS), vol. 21, no. 2, pp. 164-206.
• Rossel, R.A.V. and Behrens, T. (2010) ‘Using data mining to model and interpret soil diffuse reflectance spectra.’, Geoderma, vol. 158, no. 1, pp. 46-54.
• Rygielski, C., Wang, J.-C. and Yen, D.C. (2002) ‘Data mining techniques for customer relationship management.’, Technology in society, vol. 24, no. 4, pp. 483-502.
• Sánchez, A.M. and Pérez, M.P. (2005) ‘Supply chain flexibility and firm performance: a conceptual model and empirical study in the automotive industry’, International Journal of Operations & Production Management , vol. 25, no. 7, pp. 681-700.
• Sandborn, P.A., Mauro, F. and Knox, R. (2007) ‘A data mining based approach to electronic part obsolescence forecasting.’, IEEE Transactions on Components and Packaging Technologies, vol. 30, no. 3, pp. 397-401.
• Vadhavkar, K.B.S. and Gupta, A. (1998) ‘Neural networks based data mining applications for medical inventory problems’, International Journal of Agile Manufacturing, vol. 1, no. 2, pp. 187-200.
• Waller, M.A. and Fawcett, S.E. (2013) ‘ata science, predictive analytics, and big data: a revolution that will transform supply chain design and management.’, Journal of Business Logistics, vol. 34, no. 2, pp. 77-84.
• Wu, D.D. and Olson, D.L. (2013) ‘Computational simulation and risk analysis: An introduction of state of the art research.’, Elsevier, vol. 1, no. 1, pp. 1581-1587.

Appendix

Q#		Questionnaire
Use of Data Mining for Inventory Forecasting (From Marketing Managers)			Strongly Agree	Agree	Neutral	Disagree	Strongly Disagree
1	Do you believe that use of data mining tool is helpful tool for inventory forecasting in organizations?
2	Do you believe that there are some limitations of using data mining tool for inventory forecasting?
3	Do you believe that data mining tool for inventory forecasting may have some errors in the analysis?
4	Do you believe through using data mining tool for inventory forecasting, can help organizations to focus on analyze data, notice patterns and devising rules etc.?
5	Do you believe through data mining tool for inventory forecasting, organizations can do better predictions about inventory process in future?