Central European Business Review 2024, 13(5):51-69 | DOI: 10.18267/j.cebr.371

Data Analysis in Demand Forecasting: A Case Study of Poetry Book Sales in the European Area

Andrea Kolková ORCID...
VŠB – Technical University Ostrava, Faculty of Economy, Department of Business Administration, Ostrava, Czech Republic. Email: andrea.kolkova@vsb.cz

Logistics concepts are becoming less functional nowadays. The successful concept of just-in-time manufacturing seems untenable for 21st-century Europe. New ideas and new practices are emerging. One of the major trends in contemporary logistics is the demand-driven enterprise. Managing supply, fields and other processes in a company based on demand requires accurate demand forecasting at the relevant time. Relevant data are necessary for this forecasting. In the time of big data, the problem is not collecting data but evaluating them correctly. Data analytics is of great interest in business. The aim of the paper is to verify the possibilities of demand forecasting using traditional statistical methods (exponential smoothing, ARIMA), more advanced statistical methods (TBATS, etc.), methods based on artificial neural networks and a hybrid method. This is in conjunction with thorough data preparation, especially data normality testing. My research question is to provide, on data that contain a number of outliers, the results and the accuracy of the models when the data are or are not normalized. Demand forecasting for poetry books was chosen for the case study. The data were obtained from Google Trends data, i.e., searches for the topic of poetry for the period from 1 September 2013 to 31 September 2023. The results showed that the selected data contain a number of outliers that recur at regular intervals and are the result of a logical order of demand. The expected result was that data normalization increases the accuracy of the model. A method based on artificial neural networks provided significantly more accurate results. However, the resulting estimated underlying trend remained very similar. The article thus opens a discussion about the necessity of excluding outlying observations in time series where outliers exist at regular intervals.
Implications for Central European audience: The article poses fundamental scientific questions for Central Europe. Logistics concepts are developing rapidly in this area and Europe is the creator of significant innovations. Europe is currently facing great competition from foreign companies and new logistics concepts are becoming a necessity. Therefore, many European companies now feel the need to change their logistics processes. One of them may be to switch to an adaptive enterprise based on demand. The practical implications are also based on data from Europe.

Keywords: demand forecasting; demand drive adaptive enterprise; normality test; exponential smoothing; ARIMA; neural networks; hybrid model
JEL classification: C53, M15, M21

Received: January 31, 2024; Revised: March 5, 2024; Accepted: March 17, 2024; Prepublished online: July 24, 2024; Published: December 31, 2024  Show citation

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Kolková, A. (2024). Data Analysis in Demand Forecasting: A Case Study of Poetry Book Sales in the European Area. Central European Business Review, 13(5), 51-69. doi: 10.18267/j.cebr.371
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