Citizens are beginning to play a key role in the development of the smart city concept, providing a wealth of information for the sustainable development of cities. For this research, we planned and conducted empirical studies to examine the areas influencing the quality of life from the point of view of municipalities. A questionnaire form was developed and a survey was conducted on a sample of 84 municipal offices in Poland. We tabulated, statistically processed and analysed the results and thoroughly investigated the areas influencing the quality of life, their impact and importance. We determined which areas of the city are included in municipal research and checked whether there was a need to expand these areas. We found that the people responsible for research in municipal offices are aware that the quality of life is influenced by many areas and conditions. However, these people only choose selected areas for research and have a problem with indicating what can be extended to them. At the end of the article, the limitations of the conducted research are indicated and the directions for further research are proposed.

Objectives: An aging population and declining birth rates are among the challenges that smart cities currently face and will continue to face in the near future. In light of the above, this article seeks to answer the following question: Are older people (seniors) taken into account and described in the literature on smart cities, and if so, how? Methods: To answer this research question, a systematic literature review was conducted using the Bibliometrix package in R. In the process of systematizing the publications, the authors additionally used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method and qualitative text analysis. Findings: The research shows that relatively little attention is paid to seniors in smart cities in the literature on the subject. Among the few publications on smart aging, the technological trend dominates, in which researchers present the possibilities of using IT and ICT to improve medical and social care for seniors, and to improve their quality of life (Smart Living, Smart Mobility). In the non-technological trend, most analyses focus on the determinants of quality of life and the distinguishing features of senior-friendly cities. Implications: There is a clear lack of a “human” perspective on aging in smart cities and publications on Smart Governance and Smart People that would provide guidelines for making elderly people full and equal stakeholders in smart cities. It is also necessary to develop practical documents and procedures that define a comprehensive and long-term urban policy for elderly adults. The analyses contribute to diagnosing current and determining further directions of research on smart aging in smart cities. The results clearly imply the need to intensify social, humanistic, and governance research on the role of seniors in smart cities.

Purpose: The study aims to analyze forecast errors for various time series generated by a 3PL logistics operator across 10 distribution channels managed by the operator. Design/methodology/approach: This study examines forecasting errors across 10 distribution channels managed by a 3PL operator using Google Cloud AI forecasting. The R environment was used in the study. The research centered on analyzing forecast error series, particularly decomposition analysis of the series, to identify trends and seasonality in forecast errors. Findings: The analysis of forecast errors reveals diverse patterns and characteristics of errors across individual channels. A systematic component was observed in all analyzed household appliance channels (seasonality in all channels, and no significant trend identified only in Channel 10). In contrast, significant trends were identified in one pharmaceutical channel (Channel 02), while no systematic components were detected in the remaining channels within this group. Research limitations: Logistics operations typically depend on numerous variables, which may affect forecast accuracy. Additionally, the lack of information on the forecasting models, mechanisms (black box), and input data limits a comprehensive understanding of the sources of errors. Value of the paper: The study highlights the valuable insights that can be derived from analyzing forecast errors in the time series within the context of logistics operations. The findings underscore the need for a tailored forecasting approach for each channel, the importance of enhancing the forecasting tool, and the potential for improving forecast accuracy by focusing on trends and seasonality. The findings also emphasize that customized forecasting tools can significantly enhance operational efficiency by improving demand planning accuracy and reducing resource misallocation. This analysis makes a significant contribution to the theory and practice of demand forecasting by logistics operators in distribution networks. The research offers valuable contributions to ongoing efforts in demand forecasting by logistics operators.

In this article, we analyze the energy consumption data of business customers registered by trading companies in Poland. We focus on estimating missing data in hourly series, as forecasts of this frequency are needed to determine the volume of electricity orders on the power exchange or the contract market. Our goal is to identify an appropriate method of imputation missing data for this type of data. Trading companies expect a specific solution, so we use a procedure that allows to choose the imputation method, which will consequently improve the accuracy of forecasting energy consumption. Using this procedure, a statistical analysis of the occurrence of missing values is performed. Then, three techniques for generating missing data are selected (missing data are generated in randomly selected series without missing values). The selected imputation methods are tested and the best method is chosen based on MAE and MAPE errors.