Use of the Value Chain in the Process of Generating a Sustainable Business Strategy on the Example of Manufacturing and Industrial Enterprises in the Czech Republic
Abstract
:1. Introduction
2. Literature Review
3. Material and Methods
3.1. Dimensionality Reduction
3.2. Generalized Linear Model
- TheX matrix of the independent variables (regressors, covariates) is . Its jth column is labelled as XJ.
- is the vector of the parameters.
- The response is the column vector of the random variables, type , means .
3.3. Transformation Model
3.4. Neural Networks
- Training: 70%,
- Testing: 15%,
- Validation: 15%.
- Identity:
- Logistic function:
- Hyperbolic tangens:
- Exponential function:
- Sinus:
4. Results
- HVSoucC—1
- negative results (loss)
- HVSoucC-0
- balanced results
- HVSoucC-1
- positive results (profit)
5. Discussion
6. Conclusions
- Possibility and appropriateness of the value chain analysis based on Porter’s definition of the competitive forces proves to be a perspective analytical method in strategic management and decision-making, in particular in generating sustainable corporate strategies.
- The analyzed agreement in the outputs indicates the suitability and applicability of the tested analytical methods (dimensional reduction, regression analysis and neural networks).
- The nature and form of the results, although at the sectoral level, make it possible to express an opinion on their possible use directly in the practice, provided the methodological procedure is simplified and the relevant user software for the industry level is created.
- Neural network sensitivity analysis showed significant agreement with the outcomes of mathematical–statistical methods (dimensional reduction and regression analysis), considered to be a positive promise for similar analyzes in specific industries.
- In the current market environment of industrial enterprises, the dominant value creation activities using dimensional reduction are related to the scientific and technological development, input logistics and human resource management. These factors are defined by the authors as a sectoral golden triangle, which is not fixed and its validity is limited by the developmental stages of the economy and the interaction of the competitive factors in the competitive ring of the relevant sector and the industry with the assumption of its long-term stability in general.
- Regarding the neural networks in terms of their higher degree of sensitivity, a value-generating sectoral chain with a structure is defined (ranked from the highest degree of positive impact on the profitability): human resource management, scientific and technological development, production, input logistics, purchasing, material management, output logistics, enterprise infrastructure, marketing and sales, and repair and other activities.
- Negative parts of the value chain (intersection of the dimensional reduction and the neural networks) include purchase (price volatility in the area of raw materials resources, emergence of new economic centers, intensified market competition) and output logistics resulting from increased output control due to increasing customer demand for product quality and increasing emphasis on the environmental issues related to the production process and its sustainability.
- The underestimation of the scientific and technological development was proved, as the long-term negative situation in this area in the Czech Republic was confirmed. This finding is in line with real business practice; however it is in a complete contradiction with the declared government policy in the field of transfer of scientific knowledge to the business sphere and with the new innovation strategy of the Czech Republic 2019–2030.
- Use the same analytical method at the industry level and choose the appropriate sample of enterprises;
- Develop the detailed methodology for collecting and adjusting input information in accordance with the requirements of the analytical methods;
- Set precise assessment criteria for each part of the value chain;
- Develop a unified methodology for the presentation of the results of the value chain analysis;
- Ensure the same share of profitable and non-profitable enterprises in the sample;
- Know the parameters of the corporate environment in all its parts (micro, mezzo, and macro) to generate a sustainable strategy.
Author Contributions
Funding
Conflicts of Interest
References
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Value Chain Activities | Dir1 Manufacturing and Industry |
---|---|
Input logistics | 0.46983 |
Production | 0.12189 |
Output logistics | −0.21121 |
Marketing and sales | 0.02312 |
Repair and other services | −0.05822 |
Purchase | −0.41119 |
Scientific and technological development | 0.62247 |
Human resource management | 0.37053 |
Enterprise infrastructure | 0.14844 |
Input logistics | 0.46983 |
Estimate | Std. Error | Value | Pr(>|z|) | |
---|---|---|---|---|
(Intercept) | 0.66333 | 0.53480 | 1.240 | 0.2148 |
Input logistics | 0.78793 | 0.42570 | 1.851 | 0.0642 * |
Production | 0.34257 | 0.44925 | 0.763 | 0.4457 |
Output logistics | −0.17256 | 0.42135 | −0.410 | 0.6821 |
Marketing and sales | 0.15658 | 0.36556 | 0.428 | 0.6684 |
Repair and other services | −0.03553 | 0.39430 | −0.090 | 0.9282 |
Purchase | −0.72805 | 0.38936 | −1.870 | 0.0615 * |
Scientific and technological development | 1.62826 | 0.79296 | 2.053 | 0.0400 ** |
Human resource management | 0.62573 | 0.51448 | 1.126 | 0.2239 |
Enterprise infrastructure | 0.22014 | 0.56030 | 0.393 | 0.6944 |
Size of enterprise: micro | −0.60972 | 0.59206 | −1.030 | 0.3031 |
Size of enterprise: middle-sized | −0.36661 | 0.45623 | −0.804 | 0.4216 |
Size of enterprise: large | −0.29715 | 0.53138 | −0.559 | 0.5760 |
Value Chain Activities | Transformed VC Values | “Error Rate” of Managers |
---|---|---|
Input logistics | 0.6433694 | 0.2240146 |
Production | 0.4543599 | −0.3735971 |
Output logistics | 0.2357435 | −0.2266221 |
Marketing and sales | 0.3754306 | −0.08693502 |
Repair and other services | 0.2938995 | −0.02330477 |
Purchase | 0.0000000 | −0.3548387 |
Scientific and technological development | 1.0000000 | 0.8655914 |
Human resource management | 0.5745327 | 0.3487262 |
Enterprise infrastructure | 0.4024020 | 0.2411117 |
Overview of Active Networks (Data—Current State Calculation 2) | ||||||||
---|---|---|---|---|---|---|---|---|
Name of network | Training perform. | Test perform. | Valid. per. | Training algorithm | Error function | Activation of hidden layer | Output activation fce | |
1 | MLP 18-24-3 | 71.96970 | 74.07407 | 70.37037 | BFGS (Quasi-Newton) 6 | Entropy | Exponential | Softmax |
2 | MLP 18-16-3 | 75.75758 | 74.07407 | 74.07407 | BFGS (Quasi-Newton) 13 | Entropy | Logistic | Softmax |
3 | MLP 18-19-3 | 75.75758 | 77.77778 | 77.77778 | BFGS (Quasi-Newton) 14 | Entropy | Tang | Softmax |
4 | MLP 18-15-3 | 73.48485 | 74.07407 | 74.07407 | BFGS (Quasi-Newton) 17 | Sum.sq. | Exponential | Logistic |
5 | MLP 18-16-3 | 76.51515 | 74.07407 | 74.07407 | BFGS (Quasi-Newton) 15 | Entropy | Logistic | Softmax |
Prediction | 6HVSoucC (Substitution Matrix) (Data––Current State Calculation 2) | ||
---|---|---|---|
Category | Samples: Training, Testing, Validation | ||
HVSoucC--1 | HVSoucC-0 | HVSoucC-1 | |
1. MLP 18-24-3--1 | 0 | 0 | 0 |
1. MLP 18-24-3-0 | 0 | 4 | 5 |
1. MLP 18-24-3-1 | 9 | 38 | 130 |
2. MLP 18-16-3--1 | 0 | 0 | 0 |
2. MLP 18-16-3-0 | 0 | 10 | 5 |
2. MLP 18-16-3-1 | 9 | 32 | 130 |
3. MLP 18-19-3--1 | 0 | 0 | 1 |
3. MLP 18-19-3-0 | 1 | 13 | 5 |
3. MLP 18-19-3-1 | 8 | 29 | 129 |
4. MLP 18-15-3--1 | 0 | 0 | 0 |
4. MLP 18-15-3-0 | 0 | 10 | 8 |
4. MLP 18-15-3-1 | 9 | 32 | 127 |
5. MLP 18-16-3--1 | 0 | 0 | 0 |
5. MLP 18-16-3-0 | 0 | 11 | 5 |
5. MLP 18-16-3-1 | 9 | 31 | 130 |
HVSoucC (Summary of Classifications) (Data––Current State Calculation 2) | |||||
---|---|---|---|---|---|
Samples: Training. Testing. Validation | |||||
HVSoucC--1 | HVSoucC-0 | HVSoucC-1 | HVSoucC-Total | ||
1. MLP 18-24-3 | Total | 9.0000 | 42.00000 | 135.0000 | 186.0000 |
Correct | 0.0000 | 4.00000 | 130.0000 | 134.0000 | |
Incorrect | 9.0000 | 38.00000 | 5.0000 | 52.0000 | |
Correct (%) | 0.0000 | 9.52381 | 96.2963 | 72.0430 | |
Incorrect (%) | 100.0000 | 90.47619 | 3.7037 | 27.9570 | |
2. MLP 18-16-3 | Total | 9.0000 | 42.00000 | 135.0000 | 186.0000 |
Correct | 0.0000 | 10.00000 | 130.0000 | 140.0000 | |
Incorrect | 9.0000 | 32.00000 | 5.0000 | 46.0000 | |
Correct (%) | 0.0000 | 23.80952 | 96.2963 | 75.2688 | |
Incorrect (%) | 100.0000 | 76.19048 | 3.7037 | 24.7312 | |
3. MLP 18-19-3 | Total | 9.0000 | 42.00000 | 135.0000 | 186.0000 |
Correct | 0.0000 | 13.00000 | 129.0000 | 142.0000 | |
Incorrect | 9.0000 | 29.00000 | 6.0000 | 44.0000 | |
Correct (%) | 0.0000 | 30.95238 | 95.5556 | 76.3441 | |
Incorrect (%) | 100.0000 | 69.04762 | 4.4444 | 23.6559 | |
4. MLP 18-15-3 | Total | 9.0000 | 42.00000 | 135.0000 | 186.0000 |
Correct | 0.0000 | 10.00000 | 127.0000 | 137.0000 | |
Incorrect | 9.0000 | 32.00000 | 8.0000 | 49.0000 | |
Correct (%) | 0.0000 | 23.80952 | 94.0741 | 73.6559 | |
Incorrect (%) | 100.0000 | 76.19048 | 5.9259 | 26.3441 | |
5. MLP 18-16-3 | Total | 9.0000 | 42.00000 | 135.0000 | 186.0000 |
Correct | 0.0000 | 11.00000 | 130.0000 | 141.0000 | |
Incorrect | 9.0000 | 31.00000 | 5.0000 | 45.0000 | |
Correct (%) | 0.0000 | 26.19048 | 96.2963 | 75.8065 | |
Incorrect (%) | 100.0000 | 73.80952 | 3.7037 | 24.1935 |
Network | Sensitivity Analysis (Data––Current State Calculation 2) | ||||||
---|---|---|---|---|---|---|---|
Samples: Training. Testing. Validation | |||||||
RLZ | VTR | VYROBA | VSTUP LOG | NAKUP | VYSTUP LOG | PINF | |
1. MLP 18-24-3 | 1.049400 | 1.243114 | 1.048622 | 1.081940 | 1.029558 | 1.016740 | 1.039805 |
2. MLP 18-16-3 | 1.783906 | 1.572670 | 1.245523 | 1.351343 | 1.359443 | 1.408332 | 1.425715 |
3. MLP 18-19-3 | 1.702912 | 1.803558 | 1.396182 | 1.298427 | 1.261572 | 1.232405 | 1.321617 |
4. MLP 18-15-3 | 1.477925 | 1.406646 | 1.342680 | 1.287669 | 1.228830 | 1.150128 | 1.032551 |
5. MLP 18-16-3 | 1.589430 | 1.481737 | 1.414520 | 1.397936 | 1.402258 | 1.367124 | 1.277908 |
Average | 1.520715 | 1.501545 | 1.289505 | 1.283463 | 1.256332 | 1.234946 | 1.219519 |
Network | Sensitivity Analysis (Data––Current State Calculation 2) | |
---|---|---|
Samples: Training. Testing. Validation | ||
MARK | SERVIS | |
1. MLP 18-24-3 | 1.032285 | 1.003745 |
2. MLP 18-16-3 | 1.277958 | 1.083770 |
3. MLP 18-19-3 | 1.300687 | 1.134536 |
4. MLP 18-15-3 | 1.172191 | 1.111847 |
5. MLP 18-16-3 | 1.240417 | 1.087514 |
Average | 1.204708 | 1.084282 |
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Straková, J.; Rajiani, I.; Pártlová, P.; Váchal, J.; Dobrovič, J. Use of the Value Chain in the Process of Generating a Sustainable Business Strategy on the Example of Manufacturing and Industrial Enterprises in the Czech Republic. Sustainability 2020, 12, 1520. https://doi.org/10.3390/su12041520
Straková J, Rajiani I, Pártlová P, Váchal J, Dobrovič J. Use of the Value Chain in the Process of Generating a Sustainable Business Strategy on the Example of Manufacturing and Industrial Enterprises in the Czech Republic. Sustainability. 2020; 12(4):1520. https://doi.org/10.3390/su12041520
Chicago/Turabian StyleStraková, Jarmila, Ismi Rajiani, Petra Pártlová, Jan Váchal, and Ján Dobrovič. 2020. "Use of the Value Chain in the Process of Generating a Sustainable Business Strategy on the Example of Manufacturing and Industrial Enterprises in the Czech Republic" Sustainability 12, no. 4: 1520. https://doi.org/10.3390/su12041520