The Influence of the Digital Supply Chain on Operational Performance: A Study of the Food and Beverage Industry in Indonesia
Abstract
:1. Introduction
2. Literature Review
2.1. Digital Supply Chain
- Big data: By providing an integrated platform for tracking performance and customer engagement through real-time data analysis and critical decision-making scenarios, big data contributes to improved visibility. As a result, the likelihood of supply chain disturbances and delays is decreased [21].
- Cloud computing: Cloud technology allows the storage and processing of large volumes of data in real time, with information available to all supply chain participants [22,23]. In comparison to traditional information technology solutions, cloud technologies enable rapid acquisition and deployment without requiring a company to significantly extend or change its existing infrastructure [24], thus allowing the company to change as necessary.
- IoT: The real-time data generated by IoT enable the tracking of supply chain activities from product design to end user, providing reliable and timely data to assist businesses in adapting to market changes [27].
- Robotics: Autonomous robots are expected to continue to evolve in this field in the future, enabling individuals to move to more strategic, less risky, and higher-value jobs [28]. Robotics offers manufacturers greater versatility than other types of automation [29]. Robotics also answers a question of those working in the supply chain: how will the company improve efficiency and save money? [30]. In doing so, the 21st century has witnessed many companies investing much of their revenue in adopting technology in the supply chain and a massive number are considering the investment of their money in robotics and automation [31].
2.2. Operational Performance
3. Conceptual Framework and Hypotheses
3.1. Conceptual Framework
3.2. Hypotheses
3.2.1. The Digital Supply Chain and Quality Performance
3.2.2. The Digital Supply Chain and Productivity Performance
3.2.3. The Digital Supply Chain and Cost Reduction Performance
4. Research Methodology
4.1. Survey and Data Collection
4.2. Analysis Technique
4.3. Measurements
5. Research Results
5.1. Demographic Profile
5.2. Data Analysis
5.2.1. Assessment of the Measurement Model
5.2.2. Assessment of the Structural Model
6. Discussion
7. Conclusions
8. Managerial Implications
9. Limitations and Recommendations for Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. The Measurements
Main Variables | Items | Statement | References |
Digital Supply Chain | DSC_1 | Big data is used to improve our data quality. | Raman et al. [65]; Schoenherr et al. [92]; Cegielski et al. [93]; Ben-Daya et al. [94]; Merlino and Sproģe [30]. |
DSC_2 | Our company is able to monitor customer interaction through real time data analysis. | ||
DSC_3 | Our company is able to achieve information exchange with cloud computing. | ||
DSC_4 | Cloud technologies enhance process capability and local storage. | ||
DSC_5 | Blockchain improves traceability of products in the supply chain. | ||
DSC_6 | Exchange of information with customers and suppliers is easier through the application of blockchain. | ||
DSC_7 | IoT provides a link between customers and the company. | ||
DSC_8 | IoT provides the linkage for all devices to the internet associated with production processes. | ||
DSC_9 | Robotics is used to improve production capacity. | ||
DSC_10 | Our company uses or plans to use robotics on a regular basis in the future. | ||
Quality performance | QP_1 | Our company is able to produce consistent quality products with a low rate of defects. | Maani and Sluti [41]; Safizadeh et al. [95]; Tracey et al. [44]; Koufteros et al. [96]. |
QP_2 | Our company operates regular customer satisfaction surveys to monitor our product quality. | ||
QP_3 | Our company is able to maintain a low number of customer complaints concerning product quality. | ||
QP_4 | Our company is able to supply products based on conformance quality (national and international standards). | ||
Productivity performance | PP_1 | Our labor and machine productivity is performing better than in its intended function. | Ward and Duray [42]; Wong et al. [43]. |
PP_2 | Our company is able to optimize our production defect/waste to acceptable levels. | ||
PP_3 | Our company is able to provide short delivery times acceptable to our customers. | ||
PP_4 | Our company is able to increase capacity utilization in our production when demand requires it. | ||
Cost reduction performance | CP_1 | Our company is able to manufacture products at competitive prices while maintaining a profitable operational performance. | Davis and Schul [97]; Maani and Sluti [41]; Tracey et al. [44]. |
CP_2 | Our company is able to produce products from a low inventory of raw materials thereby minimizing production costs. | ||
CP_3 | Overall, our logistics costs (including distribution, transportation, and handling costs) can be reduced year on year through our supply chain management. | ||
CP_4 | The reductions in cost achieved are considerably better value than expected. |
Appendix B. Calculation of the Mean, Standard Deviation, Excess Kurtosis, and Skewness
Mean | Standard Deviation | Excess Kurtosis | Skewness | |
DSC_1 | 3.943 | 0.889 | −0.303 | −0.503 |
DSC_2 | 4.115 | 0.873 | 0.16 | −0.791 |
DSC_3 | 4.077 | 0.861 | −0.07 | −0.648 |
DSC_4 | 4.177 | 0.765 | 0.496 | −0.701 |
DSC_5 | 3.962 | 0.89 | 0.096 | −0.621 |
DSC_6 | 3.962 | 0.874 | −0.195 | −0.531 |
DSC_7 | 4.244 | 0.734 | −0.15 | −0.639 |
DSC_8 | 4.191 | 0.74 | 0.901 | −0.752 |
DSC_9 | 4.115 | 0.816 | 1.764 | −1.013 |
DSC_10 | 4.033 | 0.899 | 0.694 | −0.863 |
QP_1 | 4.167 | 0.761 | 0.991 | −0.817 |
QP_2 | 4.349 | 0.69 | 0.112 | −0.765 |
QP_3 | 4.239 | 0.726 | 0.965 | −0.78 |
QP_4 | 4.411 | 0.687 | −0.127 | −0.837 |
PP_1 | 4.206 | 0.765 | −0.239 | −0.628 |
PP_2 | 4.287 | 0.766 | 1.108 | −0.992 |
PP_3 | 4.306 | 0.665 | −0.274 | −0.539 |
PP_4 | 4.321 | 0.73 | −0.258 | −0.731 |
CP_1 | 4.182 | 0.78 | 0.979 | −0.88 |
CP_2 | 4.201 | 0.731 | 1.071 | −0.778 |
CP_3 | 4.167 | 0.822 | 0.524 | −0.841 |
CP_4 | 4.278 | 0.764 | −0.043 | −0.778 |
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N = 209 | Frequency | Percentage (%) | |
---|---|---|---|
Age of the company | 0–10 | 59 | 28.2 |
>10–20 | 65 | 31.1 | |
Over 20 | 85 | 40.7 | |
Number of employees | 0–20 person | 4 | 1.9 |
20–99 person | 59 | 28.2 | |
>=100 person | 146 | 69.9 | |
Legal entity status | Limited company (PT) | 199 | 95.2 |
Limited partnership (CV) | 4 | 1.9 | |
Private/Individual company | 6 | 2.9 | |
Educational background | High school and Diploma | 27 | 12.9 |
Undergraduate degree | 130 | 62.2 | |
Master’s degree | 50 | 23.9 | |
Doctoral degree | 2 | 1 | |
Years of experience in the company | 5 to 10 years | 170 | 81.3 |
11 to 20 years | 29 | 13.9 | |
Over 20 years | 10 | 4.8 | |
Role in the organization | Supervisor | 69 | 33 |
Department head | 11 | 5.3 | |
Assistant manager | 8 | 3.8 | |
Manager | 91 | 43.5 | |
Vice director | 5 | 2.4 | |
Director | 25 | 12 |
Main Variables | Items | Loadings | Cronbach’s Alpha | CR | AVE |
---|---|---|---|---|---|
Digital Supply Chain (DSC) | DSC_1 | 0.865 | 0.934 | 0.944 | 0.630 |
DSC_2 | 0.803 | ||||
DSC_3 | 0.803 | ||||
DSC_4 | 0.762 | ||||
DSC_5 | 0.876 | ||||
DSC_6 | 0.873 | ||||
DSC_7 | 0.723 | ||||
DSC_8 | 0.717 | ||||
DSC_9 | 0.793 | ||||
DSC_10 | 0.702 | ||||
Quality Performance (QP) | QP_1 | 0.738 | 0.769 | 0.852 | 0.591 |
QP_2 | 0.781 | ||||
QP_3 | 0.819 | ||||
QP_4 | 0.733 | ||||
Productivity Performance (PP) | PP_1 | 0.763 | 0.777 | 0.857 | 0.601 |
PP_2 | 0.841 | ||||
PP_3 | 0.777 | ||||
PP_4 | 0.714 | ||||
Cost Reduction Performance (CP) | CP_1 | 0.751 | 0.786 | 0.862 | 0.609 |
CP_2 | 0.814 | ||||
CP_3 | 0.811 | ||||
CP_4 | 0.744 |
CP | DSC | PP | QP | |
---|---|---|---|---|
CP | 0.781 | |||
DSC | 0.621 | 0.794 | ||
PP | 0.511 | 0.573 | 0.775 | |
QP | 0.553 | 0.679 | 0.564 | 0.769 |
Main Variables | DSC | QP | PP | CP |
---|---|---|---|---|
DSC_1 | 0.865 | 0.576 | 0.480 | 0.540 |
DSC_2 | 0.803 | 0.527 | 0.440 | 0.521 |
DSC_3 | 0.803 | 0.505 | 0.447 | 0.502 |
DSC_4 | 0.762 | 0.548 | 0.468 | 0.436 |
DSC_5 | 0.876 | 0.603 | 0.462 | 0.521 |
DSC_6 | 0.873 | 0.559 | 0.480 | 0.527 |
DSC_7 | 0.723 | 0.477 | 0.448 | 0.431 |
DSC_8 | 0.717 | 0.551 | 0.386 | 0.458 |
DSC_9 | 0.793 | 0.480 | 0.449 | 0.508 |
DSC_10 | 0.702 | 0.554 | 0.484 | 0.475 |
QP_1 | 0.469 | 0.738 | 0.424 | 0.437 |
QP_2 | 0.495 | 0.781 | 0.427 | 0.361 |
QP_3 | 0.610 | 0.819 | 0.389 | 0.462 |
QP_4 | 0.498 | 0.733 | 0.508 | 0.437 |
PP_1 | 0.401 | 0.446 | 0.763 | 0.355 |
PP_2 | 0.494 | 0.473 | 0.841 | 0.453 |
PP_3 | 0.466 | 0.466 | 0.777 | 0.430 |
PP_4 | 0.408 | 0.357 | 0.714 | 0.332 |
CP_1 | 0.482 | 0.402 | 0.401 | 0.751 |
CP_2 | 0.514 | 0.443 | 0.481 | 0.814 |
CP_3 | 0.500 | 0.454 | 0.335 | 0.811 |
CP_4 | 0.440 | 0.427 | 0.376 | 0.744 |
Variable | R2 |
---|---|
Quality Performance | 0.461 |
Productivity Performance | 0.329 |
Cost Reduction Performance | 0.386 |
Hypothesis | Relationship | T-Value | P-Value | Decision |
---|---|---|---|---|
Hypothesis 1 | DSC → QP | 15.099 | 0.000 | Supported |
Hypothesis 2 | DSC → PP | 10.114 | 0.000 | Supported |
Hypothesis 3 | DSC → CP | 9.928 | 0.000 | Supported |
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Saryatmo, M.A.; Sukhotu, V. The Influence of the Digital Supply Chain on Operational Performance: A Study of the Food and Beverage Industry in Indonesia. Sustainability 2021, 13, 5109. https://doi.org/10.3390/su13095109
Saryatmo MA, Sukhotu V. The Influence of the Digital Supply Chain on Operational Performance: A Study of the Food and Beverage Industry in Indonesia. Sustainability. 2021; 13(9):5109. https://doi.org/10.3390/su13095109
Chicago/Turabian StyleSaryatmo, Mohammad Agung, and Vatcharapol Sukhotu. 2021. "The Influence of the Digital Supply Chain on Operational Performance: A Study of the Food and Beverage Industry in Indonesia" Sustainability 13, no. 9: 5109. https://doi.org/10.3390/su13095109