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Purpose: The main objective of this study is to conduct a time analysis on a complete PERT network in a situation where all activities in the network are critical. This analysis is more exploratory and theoretical in nature, as it assumes a very specific case of a project and the potential implications arising from it. Design/methodology/approach: The analysis was performed on the full PERT network including 6 events and the resulting number of 15 activities. The numerical procedure was carried out by: determining the number of events and parameters of the project duration, determining the (maximum) number of activities - determining the parameters of the distribution of activity durations using mathematical programming, determining the number of iterations, in each iteration: generating activity durations, determining critical paths, determining time duration of the project and analysis of the results obtained. Findings: The work draws three main conclusions: the distribution of the project duration differs significantly from the theoretical PERT time, the theoretical activity durations affect the critical importance of activities in the project implementation, the number of events in the critical path affects the project implementation deadline. Research limitations/implications: The obtained results depend on the adopted methodology, in particular the numerical procedure for generating times: optimistic, modal, pessimistic of activities and generating activity durations from a normal distribution. Further research will focus on these issues. Originality/value: the main novelty of the work is the analysis using Monte Carlo simulation on the full PERT network, where all activities are critical. [ABSTRACT FROM AUTHOR]

Purpose: The key element of effective electricity management is to improve the accuracy of forecasting its consumption. To create a forecast, data on customers' energy consumption in previous periods is required, and the accuracy of the forecasts depends on the quality and availability of data. The acquired historical data is often incomplete and contains missing values. The aim of the article is therefore to choose an appropriate method of imputation of missing values for one-dimensional time series of energy consumption. Design/methodology/approach: The aim of the article was achieved by using the Known Substring Algorithm (KSSA) to verify the imputation precision. The KSSA algorithm allowed to test of eleven imputation methods, most of which are implemented in the 'ImputeTS' package in R. Based on the RMSE error, the best imputation method was selected for the analyzed series. Findings: As a result of the analyzes carried out, it was shown that the KSSA algorithm is a good tool for selecting the appropriate imputation method in the case of one-dimensional series of electricity consumption series. Based on the RMSE error, ‘auto.arima’ turned out to be the best imputation method for the analyzed objects Research limitations/implications: Future research will concern the use of the KSSA algorithm for a larger number of energy consumption series and with greater variation. Originality/value: The article presents an important problem of the imputation of missing values in the electricity consumption series. Increasing the accuracy of electricity consumption forecasting depends on the quality of the collected data, which are often incomplete and contain missing values. Therefore, the selection of the appropriate imputation method is so important. [ABSTRACT FROM AUTHOR]

Purpose: This paper attempts to present a method for studying hourly time series of forecasting errors in electricity consumption in the context of daily and weekly seasonality. Design/methodology/approach: The proposed approach is based on MSTL (Bandara, Hyndman, Bergmeir, 2021) decomposition of hourly forecast error series. The method is presented using the example of household electricity consumption based on data (Makonin, 2019). The time series was divided into a training set and a test set. The forecast was made based on the training set for the test period. Next, the time series of differences between the actual (test set) and forecast values was examined. Calculations were performed in the R environment. Findings: Decomposition of the forecasting error time series makes it possible to isolate the seasonal (systematic) components of forecasting errors. The values of daily and weekly errors show how forecast values deviate from actual values in a systematic way. These values can be used to adjust forecasts in subsequent periods. Research limitations/implications: Identification and inclusion of seasonal components in forecasting errors may not improve forecast quality. The assumption made about daily and weekly seasonality of the error may not be met. Originality/value: This paper presents a decompositional approach to examining the time series of forecasting errors in order to identify deterministic error components for possible adjustment of future forecasts. [ABSTRACT FROM AUTHOR]

Purpose: To determine the interdependencies between Smart City areas as well as the aspects and areas between resident-oriented IT areas of the city. Design / methodology / approach: The data for the study was collected during a survey of 287 cities for Smart City. The study of interdependence was based on a correlation analysis using: Pearson's correlation coefficient, Cramér's V coefficient, and Kendall's tau. In addition, a PCA analysis was used to reduce variable dimensions. Findings: The results of the research indicate that the scope of using services within e-office services is more strongly related to functionality than to IT equipment. In turn, the economic area plays a fundamental role in the perception of the city as a Smart City. There was also a clear difference in self-evaluation regarding Smart City areas and IT aspects of the city depending on the size of the city. However, this difference does not translate into declarations regarding the readiness for evaluation in Smart City categories. Originality/value: presentation of the relationship between the areas defining the concept of Smart City dependence on the basis of an original study addressed to city representatives. The results of the study allow us to look at the Smart City concept from the perspective of the city. The results of the analysis, in addition to scientists dealing with Smart City, may be of interest to city managers in Poland. They show the way of understanding and dependencies between individual areas. They also show those dependencies that need to be strengthened in the context of sustainable development. [ABSTRACT FROM AUTHOR]

Background: The paper considers the issue of operational needs of logistics operator connected with the implementation of demand forecasting tool in his activity. The aim of this article is to present research results on the ability to meet the expectations of distribution centre managers at the operational level. To achieve the main goal, three research questions concerning general requirements and possibilities of meeting the requirements set by managers working for a logistics operator were also defined and related to operational needs. Methods: The research analysed the operational requirements of a logistics operator using a survey conducted among managers dealing with the operational work that is performed in the operator's warehouses. Then, the possibility of implementing and operating a forecasting tool based on the ARIMA algorithm in the logistics service of a confectionery manufacturer was analysed, providing the verification of usefulness of such a tool and the level of its adjustment to operational requirements. Results: The forecasting tool is especially useful in the operator's activity in order to support the resource planning process of warehouse operation. However, managers set high requirements regarding the verifiability of the operation of such a tool, which is not completely available in the current situation. The article also shows the future development paths of this tool. Conclusions: The article shows possibilities related to the use of a forecasting tool in activities related to the provision of services in contract logistics. This allows for verification of the needs and capabilities of the logistics operator who would forecast the demand to support the operations it carries out. [ABSTRACT FROM AUTHOR]