Publications on the subject characterise Industry 4.0 by a list of its components. Listing them provides an opportunity to properly decompose and categorize specific solutions as part of Industry 4.0 approach. It allows comprehensive solutions to be created considering the importance of individual components. Furthermore, it provides an opportunity to evaluate whether the adopted solution complies with the concept of Industry 4.0 or not.

An extensive literature review (e.g. Hermann et al., 2015; Lu, 2017; Meissner et al., 2017; Oztemel & Gursev, 2018; Vogel-Heuser & Hess, 2016; Zhang et al., 2021) has shown that identifying the components of Industry 4.0 is a complex issue. The publications in this field provide a diverse set of components; some of them are highly specialized while others focus on practical applications of individual components of Industry 4.0. Therefore, the following criteria were adopted to select the components for further research: the number of citations of an article and its scope (state-of-the-art theory or practical application). Furthermore, the selection was supported by years of practical experience gained by the authors, in addition to their previous research in this area (Chadam & Kański, 2020, 2021; Kański & Pizoń, 2022; J Pizoń, Kulisz, Lipski et al., 2021; Jakub Pizoń et al., 2019, 2022; Jakub Pizoń & Gola, 2023; Stączek et al., 2021).s

It should be noted that this selection does not exhaust all the possibilities; nevertheless, in the perspective of the conducted analyses, as well as the purpose of the paper, these components can be considered representative from the viewpoint of project success. Finally, the following set of Industry 4.0 components was adopted for further research:

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  Real-time data management – tracking the system through online monitoring to prevent shortage in case of a failure.

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  Interoperability – communication between elements of cyber-physical systems with the use of industrial Internet and regular standardisation processes in order to create a smart factory.

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  Virtualisation – system monitoring, adaptation of a new system improvement by means of simulation tools or augmented reality.

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  Decentralisation – understood as a place of making decisions both at management and executive level (independent decision-making by machines, learning from previous events and actions).

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  Agility – ability to adapt to changing requirements by replacing or improving separate modules based on standardised software and hardware interfaces.

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  Service orientation – satisfaction with adapting customer's requirements to the system with the use of a perspective relying on the integration of internal and external subsystems.

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  Integrated business process – a link between physical systems and software platforms allowing communication and coordination mechanism supported by corporate data management services and connected networks (Salkin et al., 2018).

In times of dynamic growth, success of an organisation depends largely on its ability to foster new technologies, particularly the ones representing Industry 4.0. The technologies that will be particularly beneficial in production processes and improving new products contribute to gaining a competitive advantage in all sectors, particularly in manufacturing industry, as it is exceptionally innovative and competitive (Rehman et al., 2021).

Industry 4.0 is becoming strategically important in project management. It results from the fact that the implementation of Industry 4.0 solutions enables optimisation of time and costs, which streamlines critical aspects of large manufacturing companies. All of the improvements listed above are possible to be achieved by means of real-time insights. Organisations should be capable of adapting to changes and new business models (Jally et al. n.d.).

The adopted research model (Fig. 1) consisted of three stages: defining the research problem and formulating study objectives (Stage I), four indirect steps related to research process (Stage II), and discussion on the results and drawing up recommendations for application (Stage II).

Fig. 1.

Research model diagram. Source: own study.

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The first step of Stage 2 involved identification of key components ensuring project success and the most important components of Industry 4.0. The second step consisted in collecting, aggregating and validating the collected data. The results, particularly those concerning the evaluation of project success components and Industry 4.0 concept, were also subject to interpretation. The third step was the crucial stage of the study. It involved characterisation of the research sample together with justification of statistical methods used for the purpose of verifying the relations between project success and Industry 4.0 components. The last (fourth) step was the interpretation of research results, particularly the determination of dependencies between components of Industry 4.0 and components of project success, as well as project success in an aggregate form, with the use of a developed formula for determining indicators (W). The result of this step was the development of a set of application recommendations.

The fundamental reason for starting the survey was an attempt to identify the components of Industry 4.0 concept which are most commonly applied in economic practice and check how they affect the final result of works as part of a project. The analysis led to the definition of the following research problem: there is insufficient understanding of the relations between project success and Industry 4.0 components. Therefore, the main purpose of the study was to identify the conditions and factors affecting the success of projects in the context of commonly applied assumptions of Industry 4.0 concept. On the basis of the identified research problem, the following main research hypothesis was developed:

• H1

  Choosing the right configuration of Industry 4.0 components is the key condition for successful project completion.

In order to make the main hypothesis more detailed and the conducted survey more comprehensive, two supporting hypotheses were formulated:

• H1.1

  The level of implementation of Industry 4.0 solutions in companies affects project success (a relationship exists between these components).

• H1.2

  There are co-dependencies between components of Industry 4.0 and project success components.

The implementation of the adopted research methods was preceded by a review of publications concerning the conditions and factors affecting project success and other issues related to Industry 4.0 concept. An extensive analysis of reports and materials provided by organisations and associations dealing with the issues related to project management (see Project Management Institute, 2017; The Standish Group, 2020; Axelos Global Best Practice, 2019; Reputation Institute, 2019; Interbrand, 2020), Industry 4.0 (Deloitte Developments, 2020; Industry 4.0 Opportunities & Challenges of the Industrial Internet, 2014); Deloite, PWC) and the data made available by statistical offices and financial institutions was carried out. For the purposes of the authors’ own study, a survey was developed to determine critical project success factors, and identified Industry 4.0 components in particular.

The methodology adopted for the survey consisted in the development of a questionnaire, analysis and interpretation of the results, and discussion of research implications. The sample of survey respondents was selected based on non-probability sampling technique. The participants were required to have the experience in managing large-scale investment projects, as well as in creation of corporate standards on project management practice. The survey questionnaire was the basic research tool. It was composed of four sections and included 55 questions addressing 109 parameters. In the majority of questions, a five-point Likert scale was applied. It is a bipolar interval scale for measuring respondents’ attitudes and opinions. Bipolarity is understood as the occurrence of two opposite extremes – on both ends of the scale there are opposite opinions. Interval-based structure indicates that subsequent points of the scale are ranked in order, and the distance between them is the same. Likert scale was interpreted in a traditional manner (1 – I strongly disagree, 2 – I rather disagree, 3 – inconclusive, 4 – I rather agree, 5 – I strongly agree). Some of the survey questions used dedicated measurement scales depending on the purpose of the survey and on the adopted research method (nominal, dichotomous, ordinal and interval scale). Respondents completed the survey in an electronic format, following the instructions provided on the website.

The survey was carried out with the use of LimeSurvey online tool, while statistical analyses were performed with the use of IBM SPSS Statistics and StatSoft Statistica software. Inference was based on selected descriptive statistics of project success and components of Industry 4.0. Further, for the purpose of analyses, also correlations (Spearman's rank correlation analysis) between project success and components of Industry 4.0 and between project success and "clustered" components of Industry 4.0 were applied. Additionally, cross-tabulations and model-based approach, i.e. multivariate and logistic regression were used. For each data set, Cronbach's alpha coefficient (AC) was estimated, each time testing the internal consistency of the survey tool (AC>0.6). The analyses were based on the data obtained directly from survey questionnaires and converted to 0–1 form using the formula below (Formula 1). For the purposes of statistical data analysis, measurements (variables) were standardised. The purpose of standardisation was to transform measurements expressed in different units into uniform scales expressed in consistent units (standardised measurement - deviation of results expressed in the value of standard deviation from mean value being 0). Standardisation of variables allowed the unification of statistical inference, which made it possible to define the strength with which individual variables affected the analysed phenomenon and to identify the variables deviating from estimated values.

The data obtained from the survey was analysed in two phases. The analyses of source data (prior to transformation) were carried out first. In the second phase, indicators based on the data (transformed source data) were developed and used for further analyses.

The collected data were analysed with the use of selected descriptive statistics measures and statistical tests (chi-squared test, Yates's correction for continuity, Cramér's V, determination coefficient, likelihood ratio, linear relationship test). Reliability of the adopted scales was analysed with the use of Cronbach's alpha coefficient as a technique measuring homogeneity.

An original formula for determining the value of indicators (W) of meeting of specific criteria was developed for groups of questions selected in the survey (Formula 1). The indicators were used to determine the degree of achieving selected aspects of project success and to evaluate specific components of Industry 4.0, and, consequently, to determine the level of company's industrialisation in the context of Industry 4.0 and the success of the entire project. Categorical variables that took values from 1 (strongly disagree) to 5 (strongly agree) were divided into three categories: ‘0′ (strongly disagree, disagree), ‘i' (inconclusive), ‘1′ (agree, strongly agree).

Formula 1. General formula for determining the indicator (W)

n – the number of components (taken into account in development of the indicator),

i – subsequent number of analysed response (assessment),

xi – value of response (evaluation) expressed by a respondent for the ith object included in the indicator, xi∈{1,2,3,4,5}.

The resulting W indicator was interpreted as follows:

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  0 – means the value of the indicator for the sum of points below 3n – interpreted as condition unfulfilled,

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  Inconclusive – means that a value of 0 or 1 cannot be assigned to an indicator for a sum of points equal to 3n,

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  1 – means the value of the indicator for the sum of points above 3n, interpreted as condition fulfilled.

As a limit of the positive verification of each determined indicator, a middle value was adopted (3n) between the minimum (n) and maximum (5n) value of obtained score for each determined indicator. The minimum value of possible points determining a given indicator (feature) is a product of the number of responses and the value of 1 (from five-point Likert scale). The maximum value for a given indicator (feature) is the product of the number of responses and the value of 5 (from five-point Likert scale).

With the use of processed data, an attempt was made to develop a model (multiple and logistic regression analysis) of the project success's dependency on identified Industry 4.0 components.

The research was quantitative, with non-probability sampling. It was carried out amongst active large-scale project managers and people developing company project management standards (managerial staff, product managers, project managers and users). The research covered people participating in the execution of projects in the broadly-understood industrial area, as well as providing advisory and consultancy services in this domain, third party experts and contract engineers in particular. The scope of research covered the analysis of the role of a general contractor (supplier) and a subcontractor (subsuppliers) in the project. On the contractor's part, respondents included project managers, product managers and representatives of executives. On the subcontractor's part, respondents included project managers and representatives of executives. Taking the above assumptions into account, research questionnaires were addressed to industrial sector employees. The survey was carried out in enterprises having their head offices or divisions in Poland, although it covered projects executed in the international, mostly European, market. In total, 370 completed questionnaires were collected.

In the analysed cases, the role of a general contractor was represented by 71.9% of respondents, while the role of a subcontractor by 28.1% of respondents. The respondents represented micro- (21.6%), small- (25.9%), medium- (31.9%) and large-sized enterprises (20.5%). It should be noted that 67.8% of respondents were employed by the companies operating in the market for more than 5 years. The most numerous group were the employees of the electromechanical industry (22.2%) and light industry (20%). The most numerous group of all, i.e. 38.6% of respondents, were aged 36–50. In turn, 30% of respondents were senior managers. The participants of the survey were the employees with over 6 years’ work experience (62.1%), including those with over 10 years’ experience in project management (43.2% of respondents).

The analysis of the results showed that 41.6% of respondents worked in middle-volume production, 38.6% in low-volume production and 19.7% in lhigh-volume production. In addition, 37% of respondents declared that in their factories there were 51–100 machines, 28.6% reported 11–50 machines, 25.4% declared the use of fewer than 10 machines and 8.9% of respondents said their factories used more than 100 machines. The full research allows concluding that the analysis covered large-scale and very complex projects, i.e. those that involve large project teams. Table 1 presents a summary of the research sample.

First of all, the degree of incorporation of individual components of Industry 4.0, playing a crucial role in an enterprise, was analysed. The respondents were asked 24 questions concerning the degree of fulfillment of Industry 4.0 specific components. The value of Cronbach's alpha coefficient (AC=0.976) indicates thata the selection of variables adopted for the purpose of determining the application of Industry 4.0 solutions in an enterprise was adequate. The results of data analysis concerning individual parameters of Industry 4.0 are presented in Table 2.

On the basis of the results obtained, the conclusion can be drawn that the majority of respondents did not indicate the occurrence of components of Industry 4.0 in their work, which is proven by the mean value (X¯<3.0). The lowest mean value (X¯=2.51) was obtained for the use of data in response to the change (failure) of machine status. Only in four cases the mean value was above 3,0:management decisions concerning the manufacturing process are made at a higher level of organisation and cascaded to executive level (3.18), production process is carried out accordingly to the process needs (3.07), provision of services and products is adapted to individual customers’ needs (3.09) and modification of the production system accordingly to customers’ needs (3.05). It proves a positive perception of components of Industry 4.0, while the analysis of other components shows a positive trend in their perception and understanding their relevance. The conducted analysis as well as the values of the remaining indicators presented in Table 2 allow positive verification of H2 which can also be interpreted that the levels of individual components of Industry 4.0 reported in companies are similar to each other.

On the basis of components of Industry 4.0, the indicators concerning the achievement of specific levels by individual components in accordance with Formula 1 were determined. For each of the seven main components, sums of partial responses that were used to determine the indicators and applied in regression analyses, were specified. The evaluation of components of Industry 4.0 was verified with a different number of questions, depending on the analysed feature (data management – 7 questions, interoperability – 4 questions, decentralisation – 2 questions, agility – 2 questions, service orientation –2 questions, integrated business processes – 3 questions and virtualisation – 4 questions). All the questions (n=24)were developed on the basis of a 5-point Likert scale. With the use of the indicators it was determined whether, according to respondents, a specific level of individual components of Industry 4.0 was reached (indicators for each case in the surveyed sample were estimated). Next, the frequencies for three distinguished groups (negative verification, inconclusive, positive verification – Table 3) were counted. For most projects, respondents more often indicated positive verification of Industry 4.0 partial component indicator than negative.

The next step consisted in the verification of Industry 4.0 application (aggregate form). The analyses were carried out based on the responses to all questions (n=24) concerning the evaluation of objects comprising Industry 4.0 indicator – in accordance with Formula 1. The limit value indicating the application of Industry 4.0 or the absence of its application was 72. With such a criterion, frequency of occurrence of Industry 4.0 and its absence was determined. The conducted analysis shows that 52.4% of respondents indicated more frequent application of Industry 4.0 in their undertakings. The result is satisfactory, because the participation of people applying Industry 4.0 in the survey allows the actual conditions of project success to be investigated in the context of application of Industry 4.0 components.

Secondly, a similar analysis of factors considered to be of key significance to project success was carried out. The components were selected on the basis of an extensive review of publications on the subject, as described in the first part of the paper. Respondents were asked 12 questions concerning the degree of occurrence of individual project success components (5-point Likert scale). The value of Cronbach's alpha coefficient (AC=0.938) indicates proper selection of the adopted variables for the purpose of determining project success. The results of data analysis concerning individual parameters of project success were presented in Table 4.

Mean values (X¯) for nine analysed variables were between 3.09 and 3.27. In three cases, (customer satisfaction, assistance provided to colleagues, contractual penalties) the result was below 3.0. The components with the highest mean values were project team members’ satisfaction (X¯=3.26), work environment and the culture of sharing knowledge (X¯=3.27). The results of the analysis allow the conclusion that in most cases analysed the summary of project execution and fulfillment of assumed objectives is conducive to project success with a reservation that it is not tantamount to the meeting of all project objectives to the greatest extent possible. Just as in the case above, the results of analysis were presented in Table 4. This means that the frequencies of project success components reported in companies are similar.

On the basis of success components, the indicators related to the achievement of specific levels of individual project success components in accordance with Formula 1 were determined. The respondents were asked one question. With the use of the calculated indicators, it was determined whether individual project success components reached specified levels according to respondents (for each case in the research sample the indicators were estimated). Next, frequencies for three distinguished groups (negative verification, inconclusive, positive verification – Table 5) were counted. In the evaluated projects, respondents more often indicated positive verification of the indicator of success partial components than the negative one.

In the next step, the fact that the project was successful underwent verification (aggregate form). Analyses were conducted based on the responses to all survey questions (n=12) concerning the evaluation of project success indicator constituents – in accordance with Formula 1. The threshold value indicating project success or failure was 36. With this criterion, frequency of project success and failure was determined. The conducted analysis shows that 51.4% of respondents indicated more frequent success than failure in executed undertakings. The result is satisfactory, because the participation of people whose projects have been successful in the research makes allows investigation of actual conditions of project success.

Previous analyses concerned an independent approach to both components of Industry 4.0 and project success. A summary of these two areas is presented below. The research on Industry 4.0 currently carried out by researchers leaves no doubt that the implementation of solutions derived from Industry 4.0 concept is conducive to the successful completion of a project. Similar conclusions are drawn from the analysis of the authors’ own research. For the purposes of the analysis, the collected responses concerning success and failure were listed along with the information on the actual implementation of Industry 4.0 solutions.

The scatter diagram below (Fig. 2) illustrates the aggregate responses of survey participants regarding Industry 4.0 – horizontal axis 〈24;120〉 and regarding project success – vertical axis 〈12;60〉.

Fig. 2.

Scatter diagram for Industry 4.0 and project success (summary). Source: own study. N = 367.

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The figure presents two clusters marked with rectangles. The horizontal line dividing the two clusters results from the threshold value at 36 pts. This value means that it remains inconclusive whether a project was successful. The set above represents project success, whereas the set below illustrates project failure. The vertical line dividing the two sets results from the threshold value at 72 pts. This value means that it remained inconclusive whether the solutions supporting Industry 4.0 were applied. The set to the left represents the absence of Industry 4.0, whereas the set on the right illustrates the approach applying Industry 4.0. A conclusion can be drawn from the presented diagram that increased application of Industry 4.0 solutions significantly increases the chance of project success. The analyses showed that these undertakings were carried out with the use of Industry 4.0 solutions. The value of R2=0.754 coefficient proves correct adjustment of the model.

In conclusion, it has been proven that the extent of implementation of Industry 4.0 solutions in an enterprise affects project success (there is a relationship between these components), which allows positive verification of the first supporting hypothesis H1.1.

In the adopted research model, application of the solutions promoted by Industry 4.0 is the factor affecting the achievement of project success. Firstly, the analyses of correlations were conducted with the use of determined indicators of Industry 4.0 (7 components) and project success components (9 components). The analyses were carried out on standardised data. In total, 63 coefficients were obtained. The results of the analysis (Spearman's correlation) of Industry 4.0 components and project success components are presented in Table 6.

The strongest correlations were reported for: data management and project team's satisfaction (0.700); data management and ensuring benefits for the company (0.788); integration of business processes and scheduling (0.706). The lowest impact of individual components of agility were reported for contractual penalties – weak correlation in three cases [data management (0.352), interoperability (0.399), integration of business processes (0.342)].

Further, a generalisation was made and the correlation of aggregate components of Industry 4.0 with individual project success components was analysed. The analysis of results (correlation coefficients) showed a strong correlation of aggregate components of agility with all the remaining project success components apart from contractual penalties: budget (0,672), schedule (0,717), functional performance (0.691), customer satisfaction (0.568), team members’ satisfaction (0.711), achievement of company's strategic objectives (0.634), ensuring benefits to the company (0.788), work environment and the culture of sharing knowledge (0.618), and contractual penalties (0.491).

By means of another generalisation, a correlation of aggregate components of Industry 4.0 and project success was analysed. Table 7 presents (in%) the frequency of project success/failure occurrence depending on the presence or absence of Industry 4.0 solutions in an enterprise.

Absence of Industry 4.0 solutions results in project failure – as has been proven by 40.6% of respondents. The analysis conducted clearly shows that implementing Industry 4.0 solutions significantly increased the probability of a project being successful. As a conclusion of the analyses concerning the relationship between Industry 4.0 and project success, the strength of this relationship was verified. All of the obtained results are statistically significant (p<0.001). The result of the chi-squared test (χ2=188.259;df=1)indicates that there is dependence between Industry 4.0 and project success. The ϕ measure (λ(x)=208.848;df=1,φ=0.716), Cramér's V (Cramer′sV=0.716) and Spearman's rank (ρS=0.716) correlation prove considerable dependence between project result and solutions promoted by Industry 4.0.

Summing up, it has been proven that there are relationships between project success components and individual components of Industry 4.0, both individually (in various approaches) and in an aggregate form. Positive verification of the second supporting hypothesis H1.2 has confirmed that there are strong dependencies between Industry 4.0 and successful execution of tasks.

The adopted research model generally indicated Industry 4.0 as the factor affecting project success. In order to verify hypothesis H1, an attempt was made to identify the components of Industry 4.0 (from amongst its 7 main components) that showed the strongest relations with project success (independent variables). Identification was made with the use of regression analysis, referring to all the components of Industry 4.0. Two approaches to modelling were employed: multiple regression (backward elimination method) and logistic regression (backward elimination method: likelihood ratio).

In the model using multiple regression, the final project result as adopted as the independent variable (aggregate approach to project success). The obtained results lead to a conclusion that three out of seven studied components in the created model demonstrated statistical significance of less than 0.05(p<0.05). The results are shown in Table 8.

The model comprised the following components: data management (dm), decentralisation (dec) and virtualisation (vir); all of them were presented in Formula 2 below. The value of collinearity test (VIF) for variables in the model points to the occurrence of minor collinearity of predictors (1

Formula 2. Project success model I – aggregate form

The formula presented above allows a partial conclusion to be made that the increase in aggregate level of the aforementioned components affects the increase in aggregate level of project success.

Furthermore, an analysis of logistic regression was performed, as part of which both project success (dependant variable) and all independent variables assumed the binary format of 0–1. The following components were part of the model (logitP=−1.844+1.085VIR+4,628DM): virtualisation (VIR) and data management (DM) – Table 9. The detailed form of the model is presented in Formula 3.

Formula 3. Project success model II – aggregate form

Table 11 presents further calculations of the value of project success indicator depending on different values of independent variables. For the value of success to be higher than the limit value 0.5, the following components are necessary: data management and virtualisation in combination or just virtualisation (value 1).

Individual components of Industry 4.0 should be considered not only in the context of measurement or calculation, but also in terms of understanding the essence of its creation and development. The conducted analyses have demonstrated that Industry 4.0 is largely a process of sharing and stabilizing (structuring) knowledge within a company. This value cannot be managed directly, only through corporate culture that is conducive to its effective application. Effective enterprise management implies effective management of Industry 4.0 as a resource (Shaykhulova & Selivanov, 2021). Therefore, effective Industry 4.0 management shall be construed as effective management of Industry 4.0 components, leading to enhancement of profit generation, building competitive advantage and ultimately market stability. Such approach fosters innovation and the formation of atmosphere of trust which all constitute fundamental factors of providing better goods and services to consumers. By promoting market fairness, the system of intellectual property rights benefits users, consumers and society at large by fostering innovative, better products and spreading the development of knowledge that contributes to a higher quality of life around the world.

The result of the project where respondents indicated the presence or absence of Industry 4.0 solutions was presented in Fig. 3. In both cases, the data on the vertical axis was presented in an aggregate form, while the data on the horizontal axis used the values obtained after the conversion of the sum of points under approach 0 (condition unmet) and 1 (condition met).

Fig. 3.

Result for the project vs. Industry 4.0. Source: own study. N = 370.

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Outlying and extreme values have been reported for the presence of Industry 4.0. These measurements have been identified below the lower scope of non-outlying values, which means that the project failed despite the implementation of Industry 4.0 solutions, (however, these were rare cases). From the opposite perspective, the solutions of Industry 4.0 were broadly applied to projects which proved to be successful (median 97 pts, mean 90 pts). For the projects that failed, the median was merely 38 pts, while the mean was 45 pts. Outlying values have been identified for project failure. These measurements have been identified above the higher range of non-outlying values, which means that despite broad implementation of Industry 4.0 solutions, the project failed. Outlying and extreme values have been reported for project success. These measurements have been identified below the lower scope of non-outlying values, which means that despite insufficient implementation of Industry 4.0 solutions, a project succeeded. In both cases, however, these measurements were rare.

Breakdowns presented in Fig. 3 clearly show that strong dependencies exist between the application of Industry 4.0 and project success from two perspectives. It has been shown that: (1) broad application of Industry 4.0 solutions increases the chance for a project to succeed; (2) project success occurs in the enterprises where Industry 4.0 solutions have been extensively applied.