*4.1. Use of the OEE Indicator in Enterprises—Study Results*

The aim of the first stage of the research was to collect information on the use of lean maintenance methods and tools in enterprises, such as total productive maintenance (TPM), single minute exchange of die (SMED), 5S, and OEE indicator. The research, which was carried out in two stages, stage I—in 2010–2014 and stage II in 2014–2017, covered 150 production companies in the Podkarpackie Voivodship. The following criteria were taken into account when classifying the surveyed enterprises: size of the organization, production, type, industry, type of ownership, its capital, the company's condition, and the type of machines owned. Among the analyzed enterprises, there were those that carried out several types of production or operated in several industries. Among the analyzed enterprises, the biggest group were large enterprises (stage I: 46%, stage II: 52%). The next group were medium-sized enterprises (stage I: 27%, stage II: 32%). Among the surveyed enterprises, those from the metal processing industry dominated (stage I: 22.77%, stage II: 22.41%), followed by the aviation industry (stage I: 23.76%, stage II: 24.14%) and the automotive industry (stage I: 18.81%, stage II: 20.69%) (Figure 2). The majority were private enterprises (stage I: 94%, stage II: 98%) with Polish capital (stage I: 44%, stage II: 2%) or majority foreign capital (stage I: 44%, stage II: 25%) (Figure 3).

**Figure 2.** Structure of the surveyed research enterprises—type of industry.

**Figure 3.** Structure of the surveyed research enterprises—property and capital.

Unit production dominated among the surveyed enterprises (stage I: 28.44%, stage II: 28%) and low- and medium-batch production, respectively, (stage I: 24.77%, stage II: 24%) and (stage I: 21.10%, stage II: 28%) (Figure 4).

**Figure 4.** Structure of the surveyed research enterprises—type of production.

A survey method was used for the research. The designed questionnaires allowed us to obtain the data in the same form from all the respondents who did them independently. The survey included the representatives of medium and top management as well as the workers directly responsible for the supervision process of technological machines and devices and the chosen machine operators. The survey was realized in the form of conjunctive closed questions, which included a list of the prepared, provided-in-advance answers presented to a respondent with a multiple response item in which more than one option might be chosen. Additionally, other answers could be given if they were not among the provided options.

Within the conducted survey, the identification of the measures used for the effectiveness assessment of the implemented LM methods and tools as well as the benefits from their use noticed by enterprises were thoroughly analyzed. Collecting the information on the used types of measures for the effectiveness assessment of machine operation was the area of the conducted studies. The OEE indicator is one of the measures recommended in the literature. While assessing the effectiveness of the possessed machines and the implementation of the TPM method, this parameter is crucial. However, as the studies show, it is not always used [47,48]. The OEE indicator was one of the main study areas. The aim of the studies was to investigate if the OEE indicator was calculated in enterprises and how its value changed after the TPM method implementation.

Figure 5 shows that most of the analyzed enterprises still do not apply the OEE indicator (stage II: 60.38%, stage I: 73.96%). Only a few percent of the enterprises use this indicator for all machines (stage II: 7.55%, stage I: 5.21%).

**Figure 5.** Is the overall equipment effectiveness (OEE) indicator calculated?—study results.

Figure 6 shows the size of the enterprises where this indicator in not used. The second stage of the studies indicates that the indicator is the most often not used in the medium size enterprises (stage I: 7.55%, stage II: 5.21%). However, its use increased significantly in micro companies (stage I: 10.26%, stage II: 5.88%).

In addition, the fact this indicator is not most often used in the enterprises with unit production (stage I: 29.49%, stage II: 29.41%) as well as with medium-batch production (stage I: 23.08%, stage II: 29.41%) was identified. Its use increased significantly in mass production (stage I: 8.97%, stage II: 2.94%). Furthermore, the indicator is most often not used in the enterprises of aviation, metal processing, and automotive industries. However, all the enterprises of the food industry that took part in the second stage of the studies declared its application. A crucial issue during the conducted studies was to obtain the information on the rate of calculating the OEE indicator. The rate of obtaining such information is essential, because the OEE indicator values inform us on an ongoing basis about productivity of the possessed machines. If the information is collected too seldom, a prompt reaction will not be possible in cases when the use of machines decreases.

**Figure 6.** The percentage of enterprises that do not use the OEE indicator—the enterprise size.

The conducted studies show that in many enterprises the OEE indicator is calculated once a month (stage I: 26.09%, stage II: 25.00%). However, more and more often, the OEE indicator is calculated once per shift—the increase of over 17%, less often once a day—the decrease of over 16%. The obtained results show that large enterprises calculate the OEE value once per shift most often, medium enterprises once a month, small and micro enterprises once a week. The rate of calculating the indicator does not differ significantly in case of conventional and numerical machines (most often once per shift). Comparing the study results from the stages I and II, the rate of calculating changed from once a month to once a week for the machines described as "other".

Another element of the realized studies was collecting the information, which considered the value of the OEE indicator. Its value is important, because it allows us to conduct initially a general analysis of the effectiveness of the possessed machines. The value over 85% is considered as the world level value of this indicator [49]. Analyzing the obtained results, it was stated that the number of companies that declared an average value of the OEE indicator at the level of 50–70% (stage I: 18.18%, stage II: 33.33%) and at the level of 30–50% (stage I: 9.09%, stage II: 25.00%) increased significantly. In stage II of the studies, none of the analyzed companies declared the OEE values below 30%. The highest OEE indicator values of over 70% are obtained in large enterprises for numerical machines, in the aviation and automotive industry with major foreign capital. The lowest OEE indicator values, below 30%, are obtained in small enterprises for the machines described as "other", in the metal processing industry with Polish capital.

#### *4.2. Identification of Factors Influencing the E*ff*ectiveness of Lean Maintenance Implementation*

The aim of this stage was to identify the factors, which have an influence on the OEE value in analyzed enterprises. For provided analyses, the statistical chi-squared test was used. The following hypotheses were proposed zero hypotheses (H0), which means that there is not a significant difference in the solutions used in particular enterprises and alternative hypotheses H1, as there is a difference in the solutions used in particular enterprises.

These hypotheses can be written as

$$\mathbf{H}0 = \mathbf{p}\_1 = \mathbf{p}\_2 = \mathbf{p}\_3 = \dots \dots = \mathbf{p}\_n \tag{1}$$

and

$$\mathbf{P} \text{ (H1)} = \mathbf{p}\_1 \neq \mathbf{p}\_2 \neq \mathbf{p}\_3 \neq \dots \dots \neq \mathbf{p}\_n \tag{2}$$

The obtained *p*-value decided about accepting or rejecting H0, and therefore about accepting the alternative Hypothesis H1. If:


Table 2 shows the posed research hypotheses for the values of the OEE indicator as well as the obtained *p*-values.

**Table 2.** Research hypotheses—the effects obtained for the implementation of the lean maintenance methods and tools.


For the analyzed Hypotheses 3 and 4, there is a statistically validated difference in the value of the OEE indicator (*p*-value OEE = 0.041 and OEE = 0.048—Hypothesis H0 rejected, Hypothesis H1 accepted). It means that, in the studied enterprises, the value of OEE depends on the type of ownership and the enterprise industry. In case of the concerned Hypothesis 13, there is also a statistically validated difference in the value of the OEE indicator (*p*-value OEE = 0.025, *p*-value LA = 0.005—Hypothesis H0 rejected, Hypothesis H1 accepted). It means that, in the studied enterprises, the value of OEE depends on the mean time to repair. The detailed results of the obtained studies were presented in the work [50].

On this basis, the following conclusions were drawn: the factors that influence the use of lean maintenance methods and tools are for example industry and the capital owned. The presented analyses allowed us to highlight the actual activities undertaken in the management of technical infrastructure and existing problems, and, thus, the possibility of identifying factors that increase the efficiency of lean maintenance. It should be noted that the studies often showed that single factors do not have a significant impact on the studied areas, although their interaction with other factors may have a substantial impact on the analyzed area. However, the problem is that analyzing a process with many variables is very difficult. Therefore, in the second stage of the study, the concept of using artificial intelligence (AI) methods in order to assess the effectiveness of the lean maintenance concept implementation was proposed.
