## 3 Methodology
### Output Membership Functions  
The resulting output of the model is fuzzy number modelling, in this case, the possible change in the number of man-hours due to the influence of the considered factors. The membership functions of the output fuzzy sets were selected by analysing real data from 270 construction projects provided by a construction company. The projects were implemented in the field of constructing industrial building structures (including the erection of steel structures and specialised construction works) in the field of industrial construction, as well as works performed on industrial premises in the field of specialised technological installations. These figures refer to the differences between the planned and actual number of man-hours needed to implement a construction project. It is assumed that these differences, according to the thesis, are due to the influence of the analysed factors. The value of the recommended change in the planned number of man-hours is calculated considering yet another aspect, that is, the range of the planned man-hours into which the project under consideration falls. Four ranges were proposed and for each of them, based on actual data from the construction company, different output membership functions were developed. The reason for this division was the large number of projects and their diversity. The purpose of the division was to increase the accuracy of the results. The following numerical ranges were introduced: up to 500 man-hours; 501 to 2000 man-hours; 2001 to 10,000 man-hours; 10,001 to 50,000 man-hours. This paper will consider projects in range one (up to 500 man-hours). Data from 112 construction projects were adopted for it. Projects of up to 500 man-hours are those that can be classified as \"small\", systematically implemented and similar in terms of their organisation. To measure the degree of the planned execution time of the works in each project, the time index W was calculated, which means how long the project took in relation to the planned time. The planned implementation time of the works was determined prior to the commencement of the works using publicly available methods for estimating the execution time of construction works. The actual implementation time of the works was measured after the completion of the construction works and is measured by the total number of man-hours spent on the project. The time index W was calculated as the quotient of the actual time expressed in man-hours and the planned time expressed in man-hours, according to the following formula:  
\[W=(T\!r\!zcz/T\!plan)\times 100\% \tag{4}\]  
where: _T\(\!r\!zcz\)_--actual implementation time of the project [man-hours]; _T\(\!plan\)_--planned time of project implementation [man-hours]. Table 4 summarises the basic statistics of the projects analysed. The maximum value, minimum value and arithmetic mean, median and standard deviation were calculated. The difference between the median and the arithmetic mean is less than 2%. The value of the standard deviation, which tells us how much the results are dispersed around the arithmetic mean, is, in this case, around 35%. To characterise the different ranges of the number of man-hours due to the values of the time indicators, a division into 7 percentage ranges was adopted. The number of ranges corresponds to the number of standard values for the fuzzy sets of the model's outputs, i.e., \"reduce a lot\", \"reduce\", \"reduce a little\", \"do not change\", \"increase a little\", \"increase a lot\": a total of 7 fuzzy sets. Table 5 shows the number of cases per 7 percentage intervals in which the calculated temporal indices were contained. Based on the presented results, the largest percentage of investments (48%) were investments where the actual time was shorter than the planned time (range for the time indicator: 55% to 90%). A total of 112 projects were included. 58 of them, i.e., just over 50%,