Classification of Small- and Medium-Sized Enterprises Based on the Level of Industry 4.0 Implementation
Abstract
:1. Introduction
2. Theoretical Background
3. Materials and Methods
3.1. Data
3.2. Industry 4.0 Index Methodology
- The First Level of VPi4 (Digitization and Human Resources Infrastructure)—skilled people (0.61), collecting data (0.82), data storage in cloud (0.63), and data analysis (0.86).
- The Second Level of VPi4 (Automation and information system/information technology (IS/IT) Infrastructure)—information technology (IT) infrastructure (0.53), MES and ERP (0.75), linked data M2M (0.58), robots in production (0.54), mobile terminals (0.54), and sensors (0.58).
- The Third Level of VPi4 (Learning and AI Infrastructure)—learning software (0.44), sharing data with suppliers (0.67), virtual reality (VR), and simulations (0.68).
3.3. K-Means Clustering
- Specify the number of clusters (k) based on the Elbow method (graphical form) and Average Silhouette method (Equation (1)). Silhouette coefficient (SC) finds the average distance to the best-fitting cluster, compared to the average distance between a data point, x∈Ck, and other points of Ck, for determining cluster system appropriateness [62]:A value of +1 indicates a perfect clustering choice, and a value below 0 indicates a bad clustering choice. We try to find the minimum among the clusters.
- Initialize Cluster Centroids by randomly selecting k-objects from the dataset as the initial cluster centers or means.
- Form k-clusters by assigning each object (xi) to its closest centroid, based on the Euclidean distance (Equation (2)) between the object and the centroid [72]:
- Re-compute the centroid of each cluster. For each of the k-clusters, update the cluster centroid by calculating the new mean values of all the data points in the cluster.
- Repeat Steps 3 and 4, until the cluster assignments stop changing or the maximum number of iterations (usually 10) is reached. Iteratively minimize the total within sum of square (Equation (3)) of the objects to their assigned cluster centers (μk). Total within sum of square is defined as follows [72]:The smaller the value W(Ck), the better the clustering (Ck, xi). Although, finding an optimal pair (Ck, xi) is quite a computationally intensive task; finding either optimal S or optimal c is fairly easy.
- Clusters validation consists of measuring the goodness of clustering results, using one-way Analysis Of Variance (ANOVA test). The ANOVA F-test evaluates if there are any differences between group means of clusters. Further, the Tukey method of “Honest Significant Difference” is performed for multiple pairwise comparisons of the means of clusters in the analysis of variance (for more, see Reference [61]).
3.4. Statistical Analysis and Hypotheses
- H10: The VPi4 index of small- and medium-sized enterprises and VPi4 index of large enterprises are identical populations.
- H1A: The VPi4 index of small- and medium-sized enterprises and VPi4 index of large enterprises are different populations.
- H20: There is no dependency between the perception of Industry 4.0 in enterprises and theVPi4 index of small- and medium-sized enterprises.
- H2A: There is dependency between the perception of Industry 4.0 in enterprises and the VPi4 index of small- and medium-sized enterprises.
4. Results
4.1. Cluster Analysis of Small- and Medium-Sized Enterprises
4.1.1. Variables Selection and Principal Component Analysis
4.1.2. Optimal Number of Clusters Determination
4.1.3. Results of Cluster Analysis
4.1.4. Comprehensive Description of the Clusters
- Cluster 1—top I4 technological enterprises;
- Cluster 2—advances I4 enterprises;
- Cluster 3—I4 starting enterprises;
- Cluster 4—I4 noobs enterprises.
4.1.5. Description of Each Cluster
- Top Industry 4.0 Technological Enterprises (Cluster 1)
- There are 32 enterprises in the cluster. In terms of size, these are medium-sized enterprises (40.65%). Cluster 1 is characterized by 53.13% enterprises that implement the strategy of Industry 4.0. Mostly, there are the enterprises (56.25%) with high-tech and medium high-tech intensity (HTI), mostly electro and engineering (53.13%). These enterprises use a large variety of I4 technologies (Figure 5a). Technological enterprises have a very high value of variables at the first level of the VPi4, such as people, data, cloud, and analysis. These enterprises have a high value of variables as mobile platforms and IT and an average value of sensors and M2M at the second level of the VPi4. At the third level of the VPi4, these enterprises have an average value of VR, sharing data, and learning software. Some enterprises already use nanotechnologies and 3D printers. In general, the technology is the largest in these enterprises, especially at the first and second levels of the index. Due to this, they are called “I4 top technological enterprises”.
- 2.
- Industry 4.0 Advances Enterprises (Cluster 2)
- The second cluster comprises 49 enterprises, of which 65.31% are the medium-sized enterprises. A total of 46.94% of the enterprises in this cluster have an Industry 4.0 strategy. Similar to the first cluster, there are predominantly (65.31%) high-tech and medium-high intensity (HTI) enterprises in the electro and engineering sector (63.27%). The cluster is characterized by the enterprises with a high rating for some technologies (Figure 5b). These enterprises have average values of variables at first VPi4 level, such as analysis, data, and people. However, most of them still have not using cloud. At the second VPi4 level, they use a high level of IT and IS (ERP and MES). They already introduced some technologies as sharing data and learning software from the third VPi4 level. Some companies use, to a lesser extent, other Industry 4.0 applications, such as nanotechnologies, 3D printers, or auto-vehicles. Overall, enterprises in this cluster use technologies that are supported mainly by IT and infrastructure at the second level of the index. However, some higher-level technologies are used. Due to this, they are called “Advances I4 enterprises”.
- 3.
- Iindustry 4.0 Starting Enterprises (Cluster 3)
- The third cluster consists of a total of 65 enterprises. The vast majority of these enterprises is small (47.69%). However, only 21.54% of them developed an Industry 4.0 strategy. In terms of technological intensity, there are mostly the (61.54%) enterprises with low-tech and medium low-tech intensity (LTI), predominantly the electro and engineering enterprises (38.47%). This cluster includes enterprises which have started implementing Industry 4.0 technologies (Figure 6a). These enterprises have a very high value of variables at the first level of VPi4, such as in people, data, and analysis and average value of cloud. They already have IT infrastructure and, at the average level, have IS and sensors. These enterprises did not use robots, M2M, or mobile technologies. The third level value of VPi4 is very low, as compared to other applications of Industry 4.0. In general, these enterprises are characterized by the introduction of technology only at the first level of the index. Due to this, they are called “I4 starting enterprises”.
- 4.
- Industry 4.0 Noobs Enterprises (Cluster 4)
- The fourth cluster consists of 40 enterprises of a very low level of technology. Of the total, 40% are the small enterprises, and 30% are the micro-enterprises. Only 20% of them have an Industry 4.0 strategy. These are mostly (60%) the enterprises with low-tech and medium low-tech intensity (LTI). Almost 32.5% of them produce the products for domestic market, so that they probably do not require high technology use. These enterprises have very low values of almost all technologies of Industry 4.0 (Figure 6b). They are usually without new technologies or they are using only basic IT infrastructure. Second or third level of VPi4 and other applications of Industry 4.0 are not presented yet. Due to this, they are called “I4 noobs”.
4.1.6. Validation of Cluster Analysis
4.2. Comparison SMEs Index of VPi4 with Large Enterprises
5. Discussion
5.1. General Discussion of Results
5.2. Theoretical and Practional Implications
5.3. Limitations
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Group | Category of Group | Number (%) |
---|---|---|
Size | Micro Enterprises (0−9 Employees) | 16.13 |
Small Enterprise (10−49 Employees) | 40.32 | |
Medium Enterprise (50−249 Employees) | 43.55 | |
Technological Intensity | High-Tech and Medium High-Tech Intensity (HTI) | 48.92 |
of which High-Tech Sector (HTS) | 5.91 | |
and Medium High-Tech Sector (MHTS) | 43.01 | |
Low-Tech and Medium Low-Tech Intensity (LTI) | 51.08 | |
of which Low-Tech Sector (LTS) | 39.25 | |
and Medium Low-Tech Sector (MLTS) | 11.83 |
Variable * | Eigenvalue | Total Variance % | Cumulative Eigenvalue | Cumulative % |
---|---|---|---|---|
VPi4 Level 1 | 1.0954 | 36.41 | 1.0954 | 36.51 |
VPi4 Level 2 | 1.0097 | 33.66 | 2.1051 | 70.17 |
VPi4 Level 3 | 0.8949 | 29.83 | 3.0000 | 100.00 |
Variable | Cluster 1 | Cluster 2 | Cluster 3 | Cluster 4 |
---|---|---|---|---|
Within Sum of Squares | 57.6031 | 61.8557 | 73.8122 | 33.4195 |
Diameter | 3.9006 | 3.7375 | 3.5976 | 2.7862 |
Average Distance | 1.7915 | 1.4810 | 1.4024 | 1.1872 |
Separation | 0.2584 | 0.3370 | 0.2584 | 0.3583 |
Cluster Size | 32 | 49 | 65 | 40 |
Factor | Cluster 1 | Cluster 2 | Cluster 3 | Cluster 4 |
---|---|---|---|---|
VPi4 Level 1 | 0.6123 | −0.2678 | 0.7175 | −1.3276 |
VPi4 Level 2 | 1.3700 | −0.0685 | −0.7312 | 0.0931 |
VPi4 Level 3 | −0.4001 | 1.5000 | −0.2943 | −0.7022 |
Clusters | Sum Sq | Mean Sq | F-Value | Pr(>F) 1 |
---|---|---|---|---|
VPi4 Level 1 | 144.45 | 48.15 | 110.6 | 2 × 10−16 *** |
VPi4 Level 2 | 86.98 | 28.994 | 79.33 | 2 × 10−16 *** |
VPi4 Level 3 | 109.84 | 36.61 | 77.91 | 2 × 10−16 *** 1 |
Dependent Variable | Clusters Comparison | Mean Difference | Lower Bound 2 | Upper Bound 2 | p-Value 1 |
---|---|---|---|---|---|
VPi4 Level 1 | 2–1 | −0.9677 | −1.3565 | −0.5788 | 2 × 10−5 *** |
3–1 | 0.1157 | −0.2538 | 0.4851 | 0.848 | |
4–1 | −2.1330 | −2.5388 | −1.7272 | 2 × 10−5 *** | |
3–2 | −1.0833 | −1.4070 | −0.7597 | 2 × 10−5 *** | |
4–2 | 1.1654 | 0.8008 | 1.5299 | 2 × 10−5 *** | |
4–3 | −2.2487 | −2.5925 | −1.9049 | 2 × 10−5 *** | |
VPi4 Level 2 | 2–1 | −1.4048 | −1.7611 | −1.0486 | 2 × 10−4 *** |
3–1 | −2.0085 | −2.3470 | −1.6700 | 2 × 10−4 *** | |
4–1 | −1.2576 | −1.6294 | −0.8858 | 2 × 10−4 *** | |
3–2 | 0.6036 | 0.3071 | 0.9002 | 2 × 10−4 *** | |
4–2 | −0.1473 | −0.4813 | 0.1868 | 0.662 | |
4–3 | 0.7509 | 0.4359 | 1.0659 | 2 × 10−4 *** | |
VPi4 Level 3 | 2–1 | 1.6700 | 1.2660 | 2.0740 | 0.001 *** |
3–1 | 0.1087 | −0.2751 | 0.4926 | 0.8824 | |
4–1 | −0.3104 | −0.7320 | 0.1111 | 0.2261 | |
3–2 | 1.5613 | 1.2250 | 1.8976 | 0.001 *** | |
4–2 | 1.9805 | 1.6017 | 2.3592 | 0.001 *** | |
4–3 | −0.4191 | −0.7763 | −0.0619 | 0.0142 * |
Variable | Median Les 1 | Median SMEs 1 | Z | p-Value |
---|---|---|---|---|
VPi4% Total | 57.212 | 39.070 | 6.061 | 0.000 |
VPi4% Level 1 | 64.741 | 50.512 | 5.002 | 0.000 |
VPi4% Level 2 | 62.855 | 42.451 | 6.512 | 0.000 |
VPi4% Level 3 | 43.654 | 25.198 | 5.667 | 0.000 |
- | Pearson | Sperman | |||||
---|---|---|---|---|---|---|---|
Research 1 | Variables | Perception | VPi4 | p-Value | Perception | VPi4 | p-Value |
LEs | Perception | 1.000 | 0.384 | 0.000 | 1.000 | 0.383 | 0.000 |
Index VPi4 | 0.384 | 1.000 | 0.383 | 1.000 | |||
SMEs | Perception | 1.000 | 0.385 | 0.000 | 1.000 | 0.387 | 0.000 |
Index VPi4 | 0.385 | 1.000 | 0.387 | 1.000 |
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Pech, M.; Vrchota, J. Classification of Small- and Medium-Sized Enterprises Based on the Level of Industry 4.0 Implementation. Appl. Sci. 2020, 10, 5150. https://doi.org/10.3390/app10155150
Pech M, Vrchota J. Classification of Small- and Medium-Sized Enterprises Based on the Level of Industry 4.0 Implementation. Applied Sciences. 2020; 10(15):5150. https://doi.org/10.3390/app10155150
Chicago/Turabian StylePech, Martin, and Jaroslav Vrchota. 2020. "Classification of Small- and Medium-Sized Enterprises Based on the Level of Industry 4.0 Implementation" Applied Sciences 10, no. 15: 5150. https://doi.org/10.3390/app10155150
APA StylePech, M., & Vrchota, J. (2020). Classification of Small- and Medium-Sized Enterprises Based on the Level of Industry 4.0 Implementation. Applied Sciences, 10(15), 5150. https://doi.org/10.3390/app10155150