Leveraging Supply Chain Reaction Time: The Effects of Big Data Analytics Capabilities on Organizational Resilience Enhancement in the Auto-Parts Industry
Abstract
:1. Introduction
- Q1: Do BDA capabilities play a significant role in reducing organizations’ reaction time in the face of unexpected incidents or disruptions within their supply chains?
- Q2: Furthermore, what effects does this expedited response have on enhancing organizations’ resilience?
- Q3: Does a firm’s position within the supply chain influence the effects BDA capabilities have on reaction time and organizational resilience?
- Q4: Are the effects of BDA capabilities on reaction time and organizational resilience greater for companies with longer Industry 4.0 journeys and more intense absorption of Industry 4.0 smart technologies?
- BDACs’ role in disruption response: Although the concept that BDA capabilities can reduce reaction time during supply chain disruptions may seem intuitive, there is a lack of empirical evidence demonstrating the extent to which BDACs directly influence the speed of response to unforeseen incidents. Particularly important and novel for our investigation is the description of the effects of BDACs on different and specific forms of latencies—data latency, analytical latency, and decision latency—which are fundamental components of reaction time.
- Effects on organizational resilience: The literature has yet to fully explore the systemic relationships between reaction time, facilitated by BDACs, and the enhancement of organizational resilience, particularly during supply chain disruptions.
- Position in the supply chain and BDAC impact: Knowledge is lacking regarding how a firm’s position within the supply chain (e.g., first-tier vs. indirect suppliers) influences the effect of BDACs on reaction time and organizational resilience, as many specificities can be taken into account, such as risk exposure, the quality and speed of information flow, power dynamics, and supply chain resource allocation priorities, amongst other relevant dimensions.
- Influence of Industry 4.0 journey length: There are not yet enough studies to assert whether the level of technological readiness of firms, in relation to the absorption of smart technologies from Industry 4.0, would have any influence on the potential effects of BDA capabilities on reaction times to disruptions in the supply chain and the degree of firms’ resilience in such events.
2. Theoretical Background and Research Hypotheses
2.1. BDA Capabilities
2.2. Reaction Time
2.3. Organizational Resilience
2.4. Smart Manufacturing Technologies of Industry 4.0
3. Research Methodology
3.1. Research Design and Sample Characterization
3.2. Measures
3.3. Data Analysis
4. Findings
4.1. Descriptive Data Analysis
4.2. Measurement Model Validation
4.3. Structural Model Assessment
5. Discussion
5.1. Managerial Contributions
5.2. Theoretical Contributions
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. Descriptive Statistics, Convergent Validity, and Reliability Tests
Construct | Code | Item Description | Loadings | AVE | rhoC | rhoA | Cronbach’s Alpha | Mean | Std. Dev. |
Big Data Analytics Capabilities | BDAC 1 | The organization uses cloud services to process and analyze data. | 0.559 | 0.416 | 0.875 | 0.853 | 0.843 | 0.2398 | 0.3818 |
BDAC 2 | 0.664 | 4.10 | 0.652 | ||||||
BDAC 3 | The organization has access to a large amount of unstructured data (from sources, such as social networks, websites, videos, images, among others) that can be quickly analyzed by its technicians and/or data scientists. | 0.551 | 3.48 | 1.073 | |||||
BDAC 4 | The organization can effectively integrate internal and external data from multiple sources (especially with suppliers and direct customers). | 0.587 | 3.56 | 0.897 | |||||
BDAC 5 | The organization has professionals in different areas of the company with the necessary skills and experience to analyze data, using this knowledge in the execution of their tasks/activities. | 0.658 | 3.77 | 0.717 | |||||
BDAC 6 | The organization provides training in decision-support systems (such as data mining and use of artificial intelligence for predictive analysis, among others). | 0.542 | 3.49 | 0.855 | |||||
BDAC 7 | The managers involved in data analysis in the company have a good grasp of the information requirements of different area or process managers within the organization, as well as those of its suppliers and customers. | 0.730 | 3.92 | 0.722 | |||||
BDAC 8 | The managers involved in data analysis in the company are capable of analyzing data collaboratively with both area and process managers within the organization and its suppliers and customers. | 0.699 | 3.87 | 0.727 | |||||
BDAC 9 | The managers involved in data analysis in the company are capable of anticipating and being proactive in considering the information needs of various area or process managers in the company, as well as those of the organization’s suppliers and customers. | 0.566 | 3.77 | 0.723 | |||||
BDAC10 | The organization considers data to be a valuable asset for the business and for managing its processes in the supply chain. | 0.833 | 4.16 | 0.788 | |||||
Reaction Time | REA 1 | When a disruptive or unplanned event occurs, the organization has fairly quickly access to data and information about the event. | 0.747 | 0.518 | 0.865 | 0.822 | 0.815 | 3.66 | 0.889 |
REA 2 | When a disruptive or unplanned event occurs, the organization is able to fairly quickly analyze data and gather information about the event. | 0.729 | 4.04 | 0.659 | |||||
REA 3 | When a disruption or unplanned event occurs, the organization makes decisions fairly quickly once it has access to data and analyzes data about the event. | 0.693 | 4.14 | 0.794 | |||||
REA 4 | In our company, the data are dynamically updated, allowing a real-time view of the different processes and/or areas of the organization. | 0.689 | 3.94 | 0.702 | |||||
REA 5 | There is a governance structure in place in the company to monitor and identify disruption events and put into action plans to mitigate the effects of these events. | 0.794 | 3.97 | 0.717 | |||||
REA 6 | Your value chain partners share with your company an aligned vision as to how to proceed and discuss actions to be implemented in disruption situations. | 0.657 | 4.08 | 0.768 | |||||
Organizational Resilience | RES 1 | In the face of a disruptive or unplanned event, the organization was able to respond to the disruptive situation in a way that quickly restored normal production flows. | 0.761 | 0.627 | 0.870 | 0.803 | 0.801 | 3.88 | 0.915 |
RES 2 | In the face of a disruptive or unplanned event, the organization was well-prepared to deal with potential financial effects caused by the disruption. | 0.760 | 4.11 | 0.764 | |||||
RES 3 | In the face of a disruptive or unplanned event, the organization was able to maintain a satisfactory level of connectivity with other agents in the supply chain during the period of impact of the disruption. | 0.845 | 3.79 | 0.739 | |||||
RES 4 | In the face of a disruptive or unplanned event, the organization was able to maintain a satisfactory level of functioning of its internal functions. | 0.798 | 4.13 | 0.678 |
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Dimension | Classification | Number of Responses | % |
---|---|---|---|
Working experience in the company | More than 5 years | 239 | 90.90 |
Between 3 and 5 years | 24 | 9.13 | |
Position in the organization | Director/manager of operations, logistics, and supply chain | 91 | 34.6 |
CEO, general director, superintendent | 60 | 22.8 | |
Purchasing assistant/supervisor | 41 | 15.6 | |
Operations, logistics, and supply chain assistant/supervisor | 39 | 14.8 | |
Financial assistant/supervisor | 13 | 4.94 | |
Director/manager of marketing | 11 | 4.18 | |
Director/commercial and sales manager | 8 | 3.04 | |
Company’s position | Direct supplier of parts/modules or systems to automakers | 161 | 61.2 |
Supplier of parts/modules or systems to other suppliers in the automakers’ supply chain | 102 | 38.8 | |
Number of employees | 100 to 499 | 43 | 16.3 |
500 to 999 | 131 | 49.8 | |
1000 or greater | 89 | 33.8 | |
Annual gross revenue (in US million dollars) | More than USD 18 and less than USD 60 | 54 | 20.5 |
More than USD 60 and less than or equal to USD 100 | 107 | 40.7 | |
More than USD 100 and less than USD 140 | 18 | 6.84 | |
More than USD 140 and less than USD 200 | 41 | 15.6 | |
More than USD 200 | 43 | 16.3 | |
Digital transformation journey | Has not initiated | 23 | 8.75 |
Less than 3 years | 27 | 10.27 | |
More than 3 and up to 5 years | 112 | 42.6 | |
More than 5 and up to 7 years | 14 | 5.32 | |
More than 7 and up to 10 years | 26 | 9.89 | |
More than 10 years | 61 | 23.19 | |
Area or department responsible for the digital transformation process | CEO, general director, superintendent | 49 | 31.6 |
Director/manager of operations, logistics, and supply chain | 48 | 30.9 | |
Operations, logistics, and supply chain supervisor | 33 | 21.3 | |
Financial assistant/supervisor | 13 | 8.3 | |
Commercial and sales manager | 8 | 5.1 | |
Purchasing assistant/supervisor | 4 | 2.5 | |
None | 108 | 41.1 |
Measurement Item | BDA Capabilities | Reaction Time | Organizational Resilience |
---|---|---|---|
BDAC 1 | 0.5592 | 0.2398 | 0.3818 |
BDAC 2 | 0.6639 | 0.4703 | 0.5462 |
BDAC 3 | 0.5514 | 0.3774 | 0.3848 |
BDAC 4 | 0.5868 | 0.3305 | 0.2311 |
BDAC 5 | 0.6576 | 0.2470 | 0.3620 |
BDAC 6 | 0.5422 | 0.2580 | 0.2664 |
BDAC 7 | 0.7301 | 0.4557 | 0.3726 |
BDAC 8 | 0.6986 | 0.5312 | 0.4932 |
BDAC 9 | 0.5655 | 0.4991 | 0.5160 |
BDAC10 | 0.8327 | 0.4543 | 0.4722 |
REA 1 | 0.6699 | 0.7472 | 0.5488 |
REA 2 | 0.4145 | 0.7285 | 0.6298 |
REA 3 | 0.5154 | 0.6930 | 0.5912 |
REA 4 | 0.2721 | 0.6886 | 0.3926 |
REA 5 | 0.4190 | 0.7942 | 0.5289 |
REA 6 | 0.3398 | 0.6569 | 0.4950 |
RES 1 | 0.5062 | 0.5813 | 0.7611 |
RES 2 | 0.6386 | 0.6730 | 0.7596 |
RES 3 | 0.4812 | 0.5753 | 0.8447 |
RES 4 | 0.3910 | 0.5256 | 0.7980 |
BDA Capabilities | Reaction Time | Organizational Resilience | |
---|---|---|---|
BDA capabilities | |||
Reaction time | 0.69 | ||
Organizational resilience | 0.75 | 0.90 |
Path | (R2) | (Adjusted R2) |
---|---|---|
BDA capabilities -> reaction time | 0.406 | 0.404 |
Reaction time -> organizational resilience | 0.566 | 0.564 |
Hypothesis | Relationships | Path Coefficients | t-Values | Hypothesis Supported |
---|---|---|---|---|
H1 | BDA capabilities -> reaction time | 0.637 | 12.959 | Yes |
H2 | Reaction time -> organizational resilience | 0.752 | 27.436 | Yes |
Construct Name | Reaction Time | Organizational Resilience |
---|---|---|
BDA capabilities | 0.6845 | - |
Reaction time | - | 1.3019 |
Constructs | Cluster | Size | Median | p-Value |
---|---|---|---|---|
BDA capabilities | OEM direct supplier | 160 | 3.60 | 0.475 |
OEM indirect supplier | 103 | 3.90 | ||
Reaction time | OEM direct supplier | 160 | 4.17 | <0.001 |
OEM indirect supplier | 103 | 3.83 | ||
Organizational resilience | OEM direct supplier | 160 | 4.25 | <0.001 |
OEM indirect supplier | 103 | 3.75 |
Duration of Digital Transformation Journey | Cluster 1 (n = 161) | Cluster 2 (n = 102) | ||
---|---|---|---|---|
n | % | n | % | |
Not initiated | 23 | 14.29 | 0 | 0.00 |
Less than 3 years | 19 | 11.80 | 8 | 7.84 |
More than 3 and up to 5 years | 83 | 51.55 | 29 | 28.43 |
More than 5 and up to 7 years | 8 | 4.97 | 6 | 5.88 |
More than 7 and up to 10 years | 7 | 4.35 | 19 | 18.63 |
More than 10 years | 21 | 13.04 | 40 | 39.22 |
Average Score of the Intensity of Absorption of Smart Manufacturing Technologies of I4.0 (1–5) | Cluster 1 (n = 161) (%) | Cluster 2 (n = 102) (%) |
---|---|---|
1 to 2 | 0% | 0% |
2 to 3 | 23% | 0% |
3 to 4 | 77% | 63% |
4 to 5 | 0% | 37% |
Constructs | Cluster | Size | Median | p-Value |
---|---|---|---|---|
BDA capabilities | 1 | 161 | 3.50 | <0.001 |
2 | 102 | 4.25 | ||
Reaction time | 1 | 161 | 3.83 | <0.001 |
2 | 102 | 4.17 | ||
Organizational resilience | 1 | 161 | 4.00 | <0.001 |
2 | 102 | 4.50 |
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Bronzo, M.; Barbosa, M.W.; de Sousa, P.R.; Torres Junior, N.; Valadares de Oliveira, M.P. Leveraging Supply Chain Reaction Time: The Effects of Big Data Analytics Capabilities on Organizational Resilience Enhancement in the Auto-Parts Industry. Adm. Sci. 2024, 14, 181. https://doi.org/10.3390/admsci14080181
Bronzo M, Barbosa MW, de Sousa PR, Torres Junior N, Valadares de Oliveira MP. Leveraging Supply Chain Reaction Time: The Effects of Big Data Analytics Capabilities on Organizational Resilience Enhancement in the Auto-Parts Industry. Administrative Sciences. 2024; 14(8):181. https://doi.org/10.3390/admsci14080181
Chicago/Turabian StyleBronzo, Marcelo, Marcelo Werneck Barbosa, Paulo Renato de Sousa, Noel Torres Junior, and Marcos Paulo Valadares de Oliveira. 2024. "Leveraging Supply Chain Reaction Time: The Effects of Big Data Analytics Capabilities on Organizational Resilience Enhancement in the Auto-Parts Industry" Administrative Sciences 14, no. 8: 181. https://doi.org/10.3390/admsci14080181