Defensive Future Studies

Defensive Future Studies

Analysis of Parts Request Portfolio Using Data Mining in order to Improve Forecasting and Supply the needs of the Defense Logistics System

Document Type : Original Article

Authors
Amir Sadeghi 1
Mohammad Karsaz 2
Mahmood Deypir 3
Mahmoud Gholami 4
1 Assistant Prof. in Shahid Sattari Aeronautical University of science and technology
2 M.A in Logistics of Shahid Sattari Aeronautical University of Science and Technology
3 Associate Prof. in Shahid Sattari Aeronautical University of Science and Technology
4 Assistant Prof. in Shahid Sattary University of Aeronautical Science and Technology
10.22034/dfsr.2021.138267.1430
Abstract
Data mining is a powerful technology that has the ability to discover knowledge hidden in a huge range of data. The purpose of this study is to analyze the portfolio of technical parts by data mining in order to improve the forecast and supply the needs of the defense logistics system. Therefore in the stage of discovering patterns and association in terms of purpose is applied, nature and method is exploratory and library information collection, and in the stage of evaluating the usefulness of patterns in terms of purpose, developmental, the nature and method is descriptive- survey and field information collection. The statistical population for data mining is the database of information technical parts requested by the units of a defense organization for related systems and repair centers over a period of 10 years. The statistical population to measure the relationship between data mining results and forecasting and supply the requirements is expert’s logistics systems. In order to analyze the data for data mining, the cross- industry standard process and SQL Server and Rapid Miner software has been used, and to analyze the data collected through a questionnaire, SPSS software has been used. The results of this study showed that method of discovering association rules can be used to analyze the request portfolio of technical parts and achieve the request pattern, and the patterns and the discovered patterns are used to improve the performance of the logistics system in the field of forecasting and supply the requirements of technical parts
Keywords
Forecasting
Request Portfolio
Cross- Industry Standard Process (CRISP)
Logistics
Data Mining

  •  

    • Abdulsalam, S. O., Adewole, K. S., Akintola, A. G., & Hambali, M. A. (2014). Data mining in market basket transaction: An association rule mining approach. International Journal of Applied Information Systems (IJAIS), 7(10), 15-20.‏
    • Adekitan, A. I., & Salau, O. (2019). The impact of engineering students' performance in the first three years on their graduation result using educational data mining. Heliyon, 5(2), e01250.‏
    • CII Institute of Logistics. (2008). Fundamentals of logistics. CII Institute of Logistics. From www.ciilogistics.com.
    • Fazlzadeh, A., Tabrizi, M. M., & Mahboobi, K. (2011). Customer relationship management in small-medium enterprises: The case of science and technology parks of Iran. African Journal of Business Management, 5(15), 6159-6167.‏
    • Ghorbani, K., KHALILI, A., Alavipanah, S. K., & NAKHAEIZADEH, G. R. (2010). Comparative study of the meteorological drought indices (SPI and SIAP) using data mining method (case study of Kermanshah Province).‏
    • Kaur, M., & Kang, S. (2016). Market Basket Analysis: Identify the changing trends of market data using association rule mining. Procedia computer science, 85, 78-85.‏
    • Mansur, K. (2012). Product inventory predictions at small medium enterprise using market basket analysis approach. Neural Networks, Procedia Economics and Finance, (4), 312- 320.
    • Mhamdi, F., and Elloumi, M. (2008). A new survey on knowledge discovery. In the second IEEE international conference on research challenges in information science, Marrakech, IEEE computer and society.
    • Venkatachari, K. (2016). Market Basket Analysis: Understanding Indian Consumer Buying Behavior of Spain Market. BVIMSR’s Journal of Management Research, 8(1), 49.‏
    • Waheed, H., Hassan, S. U., Aljohani, N. R., Hardman, J., Alelyani, S., & Nawaz, R. (2020). Predicting academic performance of students from VLE big data using deep learning models. Computers in Human Behavior, 104, 106189.‏
    • Zhao, X. (2008, November). A study of performance evaluation of HRM: based on data mining. In 2008 International Seminar on Future Information Technology and Management Engineering (pp. 45-48). IEEE.‏

    Zhu, X., Yu, Y., & Guo, X. (2009, March). Extension Data Mining and the Application in Decision Support System. In 2009 First International Workshop on Education Technology and Computer Science (Vol. 1, pp. 853-857). IEEE.