Volume 7, Issue 2, April 2019, Page: 74-81
Condition Analysis and Forecasting in the Fashion Industry
Mariusz Czekala, Wroclaw School of Banking, Finance and Management Department, Wroclaw, Poland
Agnieszka Bukietynska, Wroclaw School of Banking, Finance and Management Department, Wroclaw, Poland
Marek Gurak, MG4-Limited Company, Wroclaw, Poland
Jacek Jagodzinski, Faculty of Electronics, Wroclaw University of Science and Technology, Wroclaw, Poland
Jaroslaw Klosowski, Wroclaw School of Banking, Finance and Management Department, Wroclaw, Poland
Received: Apr. 21, 2019;       Published: Jun. 15, 2019
DOI: 10.11648/j.ijefm.20190702.14      View  191      Downloads  56
Abstract
The work considers the problem of demand for the clothing industry's goods. It shows how this problem is connected with the mathematical problem of the partition of the set. Investment decisions depend on a diagnosis based on forecasting demand in individual product groups. These groups are characterized by a number of features and even in the simplest situations (3 attributes) lead to computationally complex situations. In this situation, the recursive partitioning method can be used. This is a method related to the construction of classification trees (regression). These methods are widely used in natural, technical and economic sciences. The main direction of their applications is to support decision-making processes. The article shows how to support the construction of classification trees. The paper proposes a practical solution to the problem using the method of random partitions. The proposed method can be a complement to the recursive partitions method, or used in some situations instead. The submitted method is a practical proposal to avoid the problem of computational complexity. The numerical example shows how to replace a population of about 52 trillion by a sample of only 100. The applied method was justified by an example of a less numerous population, where the result could be verified empirically by reviewing all possibilities. Such verification is not practically possible in the case of 20 product profiles. Such a number generates a number of partitions amounting to almost 52 trillion. The article also presents the estimation of the calculation time. These results are useful from a practical point of view, although they are not optimal.
Keywords
Fashion, Clothing, Set Partition, Recursive Partitioning, Chuprov
To cite this article
Mariusz Czekala, Agnieszka Bukietynska, Marek Gurak, Jacek Jagodzinski, Jaroslaw Klosowski, Condition Analysis and Forecasting in the Fashion Industry, International Journal of Economics, Finance and Management Sciences. Vol. 7, No. 2, 2019, pp. 74-81. doi: 10.11648/j.ijefm.20190702.14
Reference
[1]
Ylan Q. Mui, Washington Post. Monday, August 31, 2009.
[2]
www.statista.com/outlook/90040000/100/underware/worldwide 10th of Apr 2019.
[3]
Wen X., Choi T.M., Chung S.H., Fashion retail supply chain management: A review of operational models, International Journal of Production Economics Volume 207, January 2019, pp. 34-55.
[4]
Gonthier J., Lajante M., Generation Y and online fashion shopping: Orientations and profiles Journal of Retailing and Consumer Services, Volume 48, May 2019, pp. 113-121.
[5]
Xia M., Wong W. K., A seasonal discrete grey forecasting model for fashion retailing. Knowledge-Based Systems, Vol. 57, Feb 2014, pp. 119-126.
[6]
Therneau T. M., Atkinson J., Mayo Foundation, An Introduction to Recursive Partitioning Using the RPART Routines, 2019.
[7]
Athey S., Imbens G., Recursive partitioning for heterogeneous causal effects, PNAS, July 5, 2016 113 (27) pp. 7353-7360
[8]
Walesiak M., Gatnar E., Statystyczna Analiza Danych, PWN, Warszawa, 2012.
[9]
Wolfram Research, Inc., Mathematica, Version 11. 3, 2019.
[10]
Djokic B., Short Note: A Fast Iterative Algorithm for Generating Set Partitions, The Computer Journal 32 (3), 1989, pp. 281-282.
[11]
Breiman L., Bagging Predictors, Machine Learning, 24, 1996, pp. 123-140.
[12]
Mugridge J., Wang Y. (2019) Applying Decision Tree in Food Industry – A Case Study. In: Wang K., Wang Y., Strandhagen J., Yu T. (eds) Advanced Manufacturing and Automation VIII. IWAMA 2018. Lecture Notes in Electrical Engineering, vol 484. Springer, Singapore.
[13]
Flachsmeyer J., Kombinatorik, VEB Berlin 1969.
[14]
Lipski W., Marek W., Analiza kombinatoryczna, PWN, Warszawa 1986.
[15]
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2019.
[16]
Yule G. U., Kendall M. G., An Introduction to the Theory of Statistics, Charles Griffin & Co. Ltd., London, 1958.
[17]
Gatnar E., Nieparametryczna metoda dyskryminacji i regresji, PWN, Warszawa, 2001.
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