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DEMAND FORECAST WITH BUSINESS CLIMATE INDEX FOR A STEEL AND IRON INDUSTRY REPRESENTATIVE

100%
Barska M.
Metody Ilościowe w Badaniach Ekonomicznych
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2014
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vol. 15
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issue 2
27-36
EN
The steel and iron industry production is dedicated to serve other industries mainly. This makes the exercise of demand forecasting different than for consumer goods. The common sense says that demand fluctuations are influenced by general economic soundness. An attempt was made to address the question of improving forecast’s accuracy by adding a business cycle indicator as an input variable. The SARIMAX model was applied. Including a business climate indicator improved model’s performance, however no co integration is observed between the two series.
2
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Hybrid demand forecasting models: pre-pandemic and pandemic use studies

100%
Kolkova A., Rozehnal P.
Equilibrium. Quarterly Journal of Economics and Economic Policy
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2022
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vol. 17
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issue 3
699-725
EN
Research background: In business practice and academic sphere, the question of which of the prognostic models is the most accurate is constantly present. The accuracy of models based on artificial intelligence and statistical models has long been discussed. By combining the advantages of both groups, hybrid models have emerged. These models show high accuracy. Moreover, the question remains whether data in a dynamically changing economy (for example, in a pandemic period) have changed the possibilities of using these models. The changing economy will continue to be an important element in demand forecasting in the years to come. In business, where the concept of just in time already proves to be insufficient, it is necessary to open new research questions in the field of demand forecasting. Purpose of the article: The aim of the article is to apply hybrid models to bicycle sales e-shop data with a comparison of accuracy models in the pre-pandemic period and in the pandemic period. The paper examines the hypothesis that the pandemic period has changed the accuracy of hybrid models in comparison with statistical models and models based on artificial neural networks. Models: In this study, hybrid models will be used, namely the Theta model and the new forecastHybrid, compared to the statistical models ETS, ARIMA, and models based on artificial neural networks. They will be applied to the data of the e-shop with the cycle assortment in the period from 1.1. 2019 to 5.10 2021. Whereas the period will be divided into two parts, pre-pandemic, i.e. until 1 March 2020 and pandemic after that date. The accuracy evaluation will be based on the RMSE, MAE, and ACF1 indicators. Findings & value added: In this study, we have concluded that the prediction of the Hybrid model was the most accurate in both periods. The study can thus provide a scientific basis for any other dynamic changes that may occur in demand forecasting in the future. In other periods when there will be volatile demand, it is essential to choose models in which accuracy will decrease the least. Therefore, this study provides guidance for the use of methods in future periods as well. The stated results are likely to be valid even in an international comparison.
3
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Demand forecasting in a business based on experts’ opinions – an application of weibull distributions

100%
Dittmann P.
Econometrics. Ekonometria. Advances in Applied Data Analytics
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2013
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issue 1(39)
52-60
EN
Demand forecasts in a business may be constructed by various methods, e.g. by using Type-I formal models, or by using type-II formal models based on experts’ opinions. The experts can be the business’s managers or persons from outside of the studied business. The experts can not only construct forecasts, but also subjectively specify the probability of them coming true. In this article, the Weibull distribution is described, a distribution that may be applied in the process of constructing product demand forecasts for a business. The methodology for constructing a point forecast is explained, along with the methods for evaluating the chances of the forecast coming true and the methods for judging the probability connected with sales profitability.
4
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Demand forecasting in an enterprise – the forecasted variable selection problem

100%
Dittmann P., Sobolewski A. M.
Econometrics. Ekonometria. Advances in Applied Data Analytics
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2013
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issue 1(39)
61-70
EN
Forecasting process efficiency depends – to a large extent – on the correct determination of the forecasted variable. Therefore, companies should use for sales forecasting, the variables that reflect actual consumer demand. However in practice, since demand is usually not directly observable, many operational measures of demand are used. In the manufacturing and retail enterprises, the most often used variables are historical orders, shipments, and billed sales volumes. The purpose of this paper is to characterise the effects of using as the predicted variable, different operational measures of consumer demand. Theoretical discussion is illustrated by an attempt to estimate errors in demand forecasts for Avon Cosmetics’ products that are related to changes in data used for forecasting.
5
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Demand forecasting: an alternative approach based on technical indicator Pbands

88%
Kolková A., Ključnikov A.
Oeconomia Copernicana
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2021
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vol. 12
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issue 4
1063-1094
EN
Research background: Demand forecasting helps companies to anticipate purchases and plan the delivery or production. In order to face this complex problem, many statistical methods, artificial intelligence-based methods, and hybrid methods are currently being developed. However, all these methods have similar problematic issues, including the complexity, long computing time, and the need for high computing performance of the IT infrastructure. Purpose of the article: This study aims to verify and evaluate the possibility of using Google Trends data for poetry book demand forecasting and compare the results of the application of the statistical methods, neural networks, and a hybrid model versus the alternative possibility of using technical analysis methods to achieve immediate and accessible forecasting. Specifically, it aims to verify the possibility of immediate demand forecasting based on an alternative approach using Pbands technical indicator for poetry books in the European Quartet countries. Methods: The study performs the demand forecasting based on the technical analysis of the Google Trends data search in case of the keyword poetry in the European Quartet countries by several statistical methods, including the commonly used ETS statistical methods, ARIMA method, ARFIMA method, BATS method based on the combination of the Cox-Box transformation model and ARMA, artificial neural networks, the Theta model, a hybrid model, and an alternative approach of forecasting using Pbands indicator.  The study uses MAPE and RMSE approaches to measure the accuracy. Findings & value added: Although most currently available demand prediction models are either slow or complex, the entrepreneurial practice requires fast, simple, and accurate ones. The study results show that the alternative Pbands approach is easily applicable and can predict short-term demand changes. Due to its simplicity, the Pbands method is suitable and convenient to monitor short-term data describing the demand. Demand prediction methods based on technical indicators represent a new approach for demand forecasting. The application of these technical indicators could be a further forecasting models research direction. The future of theoretical research in forecasting should be devoted mainly to simplifying and speeding up. Creating an automated model based on primary data parameters and easily interpretable results is a challenge for further research.
6
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The bullwhip effect in the AR(1) demand structure

88%
Michna Z.
Mathematical Economics
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2014
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issue 10(17)
53-60
EN
In this note we derive the famous formula of F. Chen, Z. Drezner, J.K. Ryan and D. Simchi-Levi [2000a] for the bullwhip effect measure in a simple two-stage supply chain under the assumption that demands constitute autoregressive structure of order 1. Our approach is a little different than in Chen et. al [2000a] and therefore we obtain the formula as an equality unlike Chen et. al [2000a], where they have it as a lower bound. Moreover, we analyze the bullwhip effect measure formula and in some cases we have different conclusions than in Chen et. al [2000a].
7
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Residential electricity consumption in Poland

88%
ROPUSZYŃSKA-SURMA E., WĘGLARZ M.
Operations Research and Decisions
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2016
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vol. 26
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issue 3
69-82
EN
Key factors influencing electricity consumption in the residential sector in Poland have been iden-tified. A fixed-effects model was used, which includes time effects, and a set of covariates, based on the model developed by Houthakker et al. This model estimates electricity demand by using lagged values of the dependent variable along with current and lagged values of electricity prices, and other variables that affect electricity demand such as: population, economic growth, income per capita, price of related goods, etc. The model has been identified according to the research results of the authors and those obtained by Bentzen and Engsted. The set of covariates was extended to the lagged electricity price given by a tariff (taken from two years previous to the time of interest) and heating degree days index, a very important factor in European Union countries, where the climate is temperate. The authors propose four models of residential electricity demand, for which a confidence interval of 95% has been assumed. Estimation was based on Polish quarterly data for the years 2003–2013.
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Application of prediction markets phenomenon as decision support instrument in vehicle recycling sector

63%
Czwajda L., Kosacka-Olejnik M., Izabela K., Kostrzewski M., Sethanan K., Pitakaso R.
LogForum
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2019
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vol. 15
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issue 2
265-278
EN
Background: The key players in the vehicles’ recycling system are disassembling facilities, which manage flows of waste and reusable parts. The focus of the company’s business activity lies in stream of reusable parts, which is the most valuable, considering possibilities of selling (economic value) and resources saving (ecologic value). As a result of conducted research problem with demand forecasting was identified, which was affected by the specific domain of business. The major objective of the paper was to present how to support demand forecasting on parts in disassembling facility with the use of predictive markets. Methods: The problem area related to the demand forecasting in the disassembling companies was identified based on the previously conducted research and observations. The desk-research method was used to verify current knowledge on the forecasting methodology. Taking it into account, the predictive markets method was chosen in a specific research problem. Results: In the paper, the idea of predictive markets was presented. What is more, general procedure of its implementation and practical application in supporting decision in disassembling companies were described. Conclusions: Predictive markets which are based on the idea of crowdsourcing, use collective crowd intelligence, supporting many business areas, including automotive industry. The predictive market method was successfully adopted in disassembling facility in order to support decisions on demand forecasting of reusable parts. The main challenge in introducing predictive markets for enterprises application is IT support and that outlines direction for future research
PL
Wstęp: Kluczowym ogniwem w systemie recyklingu samochodów są stacje demontażu, zarządzające przepływami odpadów oraz części zamiennych. Przedsiębiorstwa te w swojej działalności skoncentrowane są na strumieniu części zamiennych jako że jest on najbardziej wartościowy, mając na uwadze możliwości sprzedaży (wartość ekonomiczna) jak również oszczędzanie zasobów naturalnych (wartość ekologiczna). Zważywszy na wartość przepływu części zamiennych, zidentyfikowano problem związany z prognozowaniem zapotrzebowania, co związane jest z charakterem prowadzonej działalności. Biorąc pod uwagę fakt, że strumień wejściowy samochodów przetwarzanych w przedsiębiorstwie, jest poza jego kontrolą, podjęto próbę wspierania prognozowania zapotrzebowania na części (strumień wyjściowy) za pomocą wykorzystania rynków predykcyjnych. Metody: Na podstawie wcześniej przeprowadzonych badań, zidentyfikowano problem związany z prognozowaniem w stacji demontażu pojazdów. Wykorzystano metodę analizy i krytyki piśmiennictwa w celu zbadania istniejących opracowań w zakresie metod prognozowania. Mając na uwadze wyniki badania literatury, wykorzystano metodę rynków predykcyjnych, którą wykorzystano w wybranym obszarze badawczym. Wyniki: W pracy przedstawiono ogólną procedurę dotyczącą wykorzystania i wdrożenia rynków predykcyjnych w procesie wspierania podejmowania decyzji w stacji demontażu pojazdów, w obszarze prognozowania. Wnioski: Rynki predykcyjne, opierające się na idei crowdsourcingu, wykorzystują tzw. „mądrość tłumu", wspierając zróżnicowane obszary działalności biznesowej, w tym również branżę motoryzacyjną. Publikacja może być traktowana jako przewodnik w zakresie użycia rynków predykcyjnych w specyficznym obszarze problemowym, w tym również tak skomplikowanym jak prognozowanie zapotrzebowania na części zamienne w stacji demontażu pojazdów.
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